Examples of analytical work on various food products, involving the PCA (and/or LDA) as tools for data processing (from 1990 to 2002). Not exhaustive
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Modern analytical instruments generate large amounts of data. An infrared (IR) spectrum may include several thousands of data points (wave number). In a GC-MS (Gas Chromatography-Mass spectrometry) analysis, it is common to obtain in a single analysis 600,000 digital values whose size amounts to about 2.5 megabytes or more. There are different methods for dealing with this huge quantity of information. The simplest one is to ignore the bulk of the available data. For example, in the case of a spectroscopic analysis, the spectrum can be reduced to maxima of intensity of some characteristic bands. In the case analysed by GC-MS, the recording is, accordingly, for a special unit of mass and not the full range of units of mass. Until recently, it was indeed impossible to fully explore a large set of data, and many potentially useful pieces of information remained unrevealed. Nowadays, the systematic use of computers makes it possible to completely process huge data collections, with a minimum loss of information. By the intensive use of chemometric tools, it becomes possible to gain a deeper insight and a more complete interpretation of this data. The main objectives of multivariate methods in analytical chemistry include data reduction, grouping and the classification of observations and the modelling of relationships that may exist between variables. The predictive aspect is also an important component of some methods of multivariate analysis. It is actually important to predict whether a new observation belongs to any pre-defined qualitative groups or else to estimate some quantitative feature such as chemical concentration. This chapter presents an essential multivariate method, namely
The origin of PCA is confounded with that of linear regression. In 1870, Sir Lord Francis Galton worked on the measurement of the physical features of human populations. He assumed that many physical traits are transmitted by heredity. From theoretical assumptions, he supposed that the height of children with exceptionally tall parents will, eventually, tend to have a height close to the mean of the entire population. This assumption greatly disturbed Lord Galton, who interpreted it as a "move to mediocrity," in other words as a kind of Famous for his contributions to the foundation of modern statistics (2 test, linear regression, principal components analysis), Karl Pearson (1857-1936), English mathematician, taught mathematics at London University from 1884 to 1933 and was the editor of The Annals of Eugenics (1925 to 1936). As a good disciple of Francis Galton (1822-1911), Pearson continues the statistical work of his mentor which leads him to lay the foundation for calculating correlations (on the basis of principal component analysis) and will mark the beginning of a new science of biometrics.
Thirty-two years later, Harold Hotelling Harold Hotelling: American economist and statistician (1895-1973). Professor of Economics at Columbia University, he is responsible for significant contributions in statistics in the first half of the 20th century, such as the calculation of variation, the production functions based on profit maximization, the using the t distribution for the validation of assumptions that lead to the calculation of the confidence interval.
PCA (also known as Karhunen-Love or Hotelling transform) is a member of those descriptive methods called French statistician. Alumnus of the Ecole Normale Superieure, professor at the Institute of Statistics, University of Paris. Founder of the French school of data analysis in the years 1960-1990, Jean-Paul Benzécri developed statistical tools, such as correspondence analysis that can handle large amounts of data to visualize and prioritize the information.
Among the multivariate analysis techniques, PCA is the most frequently used because it is a starting point in the process of data mining [1, 2]. It aims at minimizing the dimensionality of the data. Indeed, it is common to deal with a lot of data in which a set of
The key idea of PCA is to represent the original data matrix
Or the non-matrix version:
With the condition
This orthogonality is a means to ensure the non-redundancy (at least at a minimum) of information “carried” by each estimated principal component.
Equation 1 can be expressed in a graphical form, as follows:
A matricized representation of the PCA decomposition principle.
Schema 2 translates this representation in a vectorized version which shows how the X matrix is decomposed in a sum of column-vectors (components) and line-vectors (eigenvectors). In a case of spectroscopic data or chromatographic data, these components and eigenvectors take a chemical signification which is, respectively, the proportion of the constituent
Schematic representation of the PCA decomposition as a sum of "components" and "eigenvectors" with their chemical significance.
The mathematical question behind this re-expression of
Equation 2 represents a
Therefore, the solution offered by PCA consists of finding the matrix
by calculating the eigenvectors of the square, symmetric covariance matrix
by calculating the eigenvectors of
by the singular value decomposition of
The dual nature of expressions
Consider a The orthogonality ensures the non-correlation of these axes and, therefore, the information carried by an axis is not even partially redundant with that carried by another.
The first component (i.e. the first axis) is calculated in order to represent the main pieces of information, and then comes the second component which represents a smaller amount of information, and so on. In other words, the
Analytical technique(s) | |||||
Cheeses | Water-soluble compounds | HPLC | PCA, LDA | Classification | 1990 [ |
Edible oils | All chemicals between 4800 et 800 cm-1 | FTIR (Mid-IR) | PCA, LDA | Authentication | 1994 [ |
Fruit puree | All chemicals between 4000 et 800 cm-1 | FTIR (Mid-IR) | PCA, LDA | Authentication | 1995 [ |
Orange juice | All chemicals between 9000 et 4000 cm-1 | FTIR (NIR) | PCA, LDA | Authentication | 1995 [ |
Green coffee | All chemicals between 4800 et 350 cm-1 | FTIR (Mid-IR) | PCA, LDA | Origin | 1996 [ |
Virgin olive oil | All chemicals between 3250 et 100 cm-1 | FT-Raman (Far- et Mid-Raman) | PCR, LDA, HCA | Authentication | 1996 [ |
General | General | FTIR (Mid-IR) | PCA, LDA + others | Classification & Authentication | 1998 [ |
Almonds | Fatty acids | GC | PCA | Origin | 1996 [ |
PCA, LDA | 1998 [ | ||||
Garlic products | Volatile sulphur compounds | GC-MS | PCA | Classification | 1998 [ |
Cider apples fruits | Physicochemical parameters | Physicochemical techniques + HPLC for sugars | LDA | Classification | 1998 [ |
Apple juice | Aromas | Capillary GC-MS + chiral GC-MS | PCA | Authentication | 1999 [ |
Meet | All chemicals between 25000 et 4000 cm-1 | IR (NIR+Visible) | PCA + others | Authentication | 2000 [ |
Coffees | Physicochemical parameters + chlorogenic acid | Physicochemical techniques + HPLC for chlorogenic acid determination | PCA | Classification according to botanical origin and other criteria | 2001 [ |
- | Data from various synthetic substances | MS + IR | PCA, HCA | Comparison of classification methods | 2001 [ |
Honeys | Sugars | GC-MS | PCA, LDA | Classification according to floral origin | 2001 [ |
Red wine | Phenolic compounds | HPLC | PCA (+PLS) | Relationship between phenolic compounds and antioxidant power | 2001 [ |
Honeys | All chemicals between 9000-4000 cm-1 | IR (NIR) | PCA, LDA | Classification | 2002 [ |
Examples of analytical work on various food products, involving the PCA (and/or LDA) as tools for data processing (from 1990 to 2002). Not exhaustive
In the case of physicochemical or sensorial data, more generally in cases where variables are not continuous like in spectroscopy or chromatography, a powerful tool is useful for interpreting the meaning of the axes: the correlation circle. On this graph, each variable is associated with a point whose coordinate on an axis factor is a measure of the correlation between the variable and the factor. In the space of dimension
The angle between two variables, measured by its cosine, is equal to the linear correlation coefficient between two variables: cos (angle) = r (V1, V2)
if the points are very close (angle close to 0): cos (angle) = r (V1, V2) = 1 then V1 and V2 are very highly positively correlated
if a is equal to 90 °, cos (angle) = r (V1, V2) = 0 then no linear correlation between X1 and X2
if the points are opposite, a is 180 °, cos (angle) = r (V1, V2) = -1: V1 and V2 are very strongly negatively correlated.
An illustration of this is given by figure 1, which presents a correlation circle obtained from a principal component analysis on physicochemical data measured on palm oil samples. In this example, different chemical parameters (such as
We speak of scores to denote the coordinates of the observations on the PC components and the corresponding graphs (objects projected in successive planes defined by two principal components) are called score-plots. Loadings denotes the contributions of original variables to the various components, and corresponding graphs called loadings-plot can be seen as the projection of unit vectors representing the variables in the successive planes of the main components. As scores are a representation of observations in the space formed by the new axes (principal components), symmetrically, loadings represent the variables in the space of principal components.
Example of score-plot and correlation circle obtained with PCA.
Observations close to each other in the space of principal components necessarily have similar characteristics. This proximity in the initial space leads to a close neighbouring in the score-plots. Similarly, the variables whose unit vectors are close to each other are said to be positively correlated, meaning that their influence on the positioning of objects is similar (again, these proximities are reflected in the projections of variables on loadings-plot). However, variables far away from each other will be defined as being negatively correlated.
When we speak of
Score-plot obtained by PCA applied on meat samples. Extracted from [
Loadings-plot obtained by PCA applied on meat samples. Extracted from [
There are different ways to achieve PCA, depending on whether one uses an iterative algorithm such as the NIPALS algorithm (Non-linear Iterative Partial Least Squares) or else a matrix factorization algorithm like SVD (Singular Value Decomposition). There are many variants of the SVD algorithm; the most well-known is probably the Golub-Reinsch algorithm [GR-SVD] [21, 22]. Both types of algorithms are well-suited for computing the eigenvalues and eigenvectors of a matrix
Let us start with the description of NIPALS. The most common version is given below.
t Initialization step: this vector is set to a column in
p Loadings for PCi
Project
Normalise loading vector p to length 1
Project
Check for convergence.
IF
With
Deflating process: remove the estimated PC component from E(i-1):
SVD is based on a theorem from linear algebra which says that a rectangular matrix
An orthogonal matrix
A diagonal matrix
The transpose of an orthogonal matrix
Usually the theorem is written as follows:
where
Presented below is the pseudo-code of the SVD algorithm:
Compute Demonstrations can be found elsewhere showing that eigenvalues of MTM and MMT are the same. The reason is that MTM and MMT respond to the same characteristic equation.
Compute eigenvalues of
Characteristic equation: its resolution gives eigenvalues of
Square root the eigenvalues of
Build a diagonal matrix
Re-use eignenvalues from step 2 in descending order and compute the eigenvectors of
Compute We know that M = USVT. Therefore, to find U knowing S, just post-multiply by S-1 to obtain AVS-1 = USS-1. Hence U = AVS-1, because SS-1 = I = 1.
The relationship between the matrices obtained by NIPALS and SVD is given by the following:
With the orthonormal
The data in Table 2 consists of the fluorescence intensities at four different wavelengths for 10 hypothetical samples 1-10.
The data processing presented here was performed with Matlab v2007b. Like all data processing software, Matlab has a number of statistical tools to perform PCA in one mouse click or a one-step command-process, but we choose to give details of the calculation and bring up the steps leading to the representation of the factorial coordinates (scores) and factor contributions (loadings). Then, we interpret the results.
Sample # | Wavelengths (nm) | |||
15,4 | 11,4 | 6,1 | 3,6 | |
16,0 | 12,1 | 6,1 | 3,4 | |
14,0 | 13,1 | 7,3 | 4,2 | |
16,7 | 10,4 | 5,9 | 3,1 | |
17,1 | 12,1 | 7,1 | 2,9 | |
81,9 | 71,3 | 42,8 | 32,9 | |
94,9 | 85,0 | 50,0 | 39,0 | |
69,9 | 75,7 | 46,3 | 50,4 | |
70,4 | 70,5 | 42,6 | 42,1 | |
61,8 | 81,9 | 50,2 | 66,2 | |
Mean | 15.75 | 61.25 | 68.92 | 29.25 |
Fluorescence intensities for four wavelengths measured on 12 samples.
This step is to subtract the intensity values of each column, the average of the said column. In other words, for each wavelength is the mean of all samples for this wavelength and subtract this value from the fluorescence intensity for each sample for the same wavelength.
The variance-covariance matrix (Table 3) is calculated according to the
1092,9 | 665,9 | 654,5 | ||
1092,9 | 731,5 | 786,6 | ||
665,9 | 731,5 | 485,5 | ||
654,5 | 786,6 | 485,5 |
Variance-covariance matrix
As mentioned above, the matrix also contains the variances of the fluorescence intensities at each wavelength on the diagonal. For example, for the fluorescence intensities at 474 nm, the variance is 1194.2.
The technique for calculating the eigenvectors of the variance-covariance matrix, and so the principal components, is called eigenvalue analysis (
Table 4 shows the eigenvalues and eigenvectors obtained by the diagonalization of the variance-covariance matrix (a process that will not be presented here, but some details on this mathematical process will be found elsewhere [23]).
Note that the sum of the eigenvalues is equal to the sum of the variances in the variance-covariance matrix, which is not surprising since the eigenvalues are calculated from the variance-covariance matrix.
Eigenvalues | |||
3163,3645 | 0,0000 | 0,0000 | 0,0000 |
0,0000 | 130,5223 | 0,0000 | 0,0000 |
0,0000 | 0,0000 | 0,5404 | 0,0000 |
0,0000 | 0,0000 | 0,0000 | 0,1007 |
Eigenvectors | |||
0,5662 | 0,6661 | 0,4719 | 0,1143 |
0,6141 | -0,0795 | -0,7072 | 0,3411 |
0,3760 | -0,0875 | -0,1056 | -0,9164 |
0,4011 | -0,7365 | 0,5157 | 0,1755 |
Eigenvalues calculated using diagonalization processing of the variance-covariance matrix.
The next step of the calculation is to determine what percentage of the variance is explained by each major component. This is done by using the fact that the sum of the eigenvalues corresponding to 100% of the explained variance, as follows:
Where λj is the jth eigenvalue. We thus obtain for the first component PC1 = 3163.4 * 1 / 3294.5 * 100 = 96.01%. PC2 is associated with 3.96% and so on, as shown in Table 5 below.
96.02 | 3.96 | 0.02 | 0.00 |
Percentage of the explained variance for each principal component.
The eigenvectors calculated above are the principal components and the values given in Table 6 are the coefficients of each principal component. Thus, component #1 is written: PC1 = 0.566*X1 + 0.614*X2 + 0.376*X3 + 0.401*X4 where X1, X2, X3 and X4 are the fluorescence intensities, 420, 474, 520 and 570 nm respectively. The factorial coordinates of each sample in the new space formed by the principal components can now be calculated directly from the equations of the PCs. For example, sample No. 1 has a coordinate on PC1 equal to: 0.566*15.4 + 0.614*11.4 + 0.376*6.1 + 0.401*3.6 = 19.453. Table 6 presents the factorial coordinates of all the samples of the initial matrix.
Centred data | ||||
19,455 | 6,128 | 0,377 | 0,678 | |
20,121 | 6,671 | 0,072 | 0,983 | |
20,420 | 4,528 | -1,256 | 0,156 | |
19,357 | 7,534 | 1,494 | 0,575 | |
20,928 | 7,635 | 0,259 | 0,051 | |
119,413 | 20,924 | 0,667 | 0,179 | |
140,394 | 23,343 | -0,522 | 0,843 | |
123,676 | -0,601 | 0,537 | 0,228 | |
116,052 | 6,511 | 0,590 | 0,397 | |
130,754 | -18,524 | 0,056 | 0,632 |
Factorial coordinates =
Therefore, we can now represent, for example, all samples in the space of two first components, as shown in Figure 4 below.
It was found that the samples are divided into two distinct groups on the initial data. Another way to visualize this division is to represent only the scores on PC1 (on PC2 or PCi) for each sample (see Figure 4). In this case, it is also obvious that the samples 1 to 5 have similar values of PC1 and, thus, form a first group (group 1), while samples 6-10 are the second group (group 2).
A complete interpretation of the results of PCA involves the graph of the loadings, i.e. the projection of the variables in the sample space. But how does one get this? Consider what has been calculated so far: the eigenvalues and eigenvectors of the matrix samples and their factorial coordinates in the space of principal components. We need to calculate the factorial coordinates of variables in the sample space. The results are called “
The graph of the loadings allows us to understand what the characteristic variables of each group of samples on the graph of the scores are. We see, in particular, that the samples of Group 1 are distinguished from the others by variables 420 and 474 while the Group 2 samples are distinguished from the others by variables 520 and 570. In other words, for these two sets of variables, the groups of samples have opposite values in quantitative terms: when a group has high values for the two pairs of variables, then the other group of samples has low values for the same pair of variables, and vice versa.
When the measured variables are of structural (mass spectrometry data) or spectral types (infrared bands or chemical shifts in NMR), the joint interpretation of scores and loadings can be extremely interesting because one can be in a position to know what distinguishes the groups of samples from a molecular point of view.
Scores & loadings on the PC1xPC2 plane.
Numerous chemical reactions contribute to the chemical and physical aging of oils. During heating, the oils undergo degradation and their functional and organoleptic features are significantly modified. The heating induces chemical reactions such as oxidation, polymerization, hydrolysis and cis/trans isomerization, which have an impact not only on the nutritional value of oils but which may also generate toxic compounds injurious to health [24, 25]. The most important cause of the deterioration of oils is oxidation. Among the oxidation products, our interest has focused especially on secondary oxidation products, such as aldehydes, because they are rarely present in natural unheated oil, as described by Choe et al. [26] from the study of secondary oxidation products proposed by Frankel [27]. In this work, an analytical approach was first adopted to calculate a new semi-quantitative criterion of the thermal stability of oils. This new test is based on the assumption that by focusing on a selected portion of the 1H-NMR spectra and for a relatively short time, we can model the appearance of aldehydes by a kinetic law of order 1, knowing that the mechanisms actually at work are more complex and associated with radical reactions. In the following pages is presented only that part of this work related to the application of PCA to 1H-NMR data to characterize and follow the effect of temperature and time of heating on the chemical quality of edible oils. For further information about the kinetic study and multiway treatments see [28].
Three types of edible oils were analysed. Rapeseed, sunflower and virgin olive oils were purchased at a local supermarket and used in a thermal oxidation study. Approximately 12 mL of oil was placed in 10 cm diameter glass dishes and subjected to heating in a laboratory oven with temperature control. Each of the three types of oil was heated at 170 °C, 190 °C and 210 °C, each of which is close to home-cooking temperatures. Three samples of 1 g were collected every 30 min until the end of the heating process, fixed at 180 min, resulting in a total of 189 samples to be analysed. Samples were cooled in an ice-water bath for 4 min, in order to stop thermal-oxidative reactions, and then directly analysed.
Between 0.3 and 0.5 g of oil was introduced into an NMR tube (I.D. 5 mm) with 700 µL of deuterated chloroform for the sample to reach a filling height of approximately 5 cm. The proton NMR spectrum was acquired at 300.13 MHz on a Bruker 300 Advance Ultrashield spectrometer with a 7.05 T magnetic field. A basic spin echo sequence was applied. The acquisition parameters were: spectral width 6172.8 Hz; pulse angle 90–180°; pulse delay 4.4 µs; relaxation delay 3 s; number of scans 64; plus 2 dummy scans, acquisition time 5.308 s, with a total acquisition time of about 9 min. The experiment was carried out at 25 °C. Spectra were acquired periodically throughout the thermal oxidation process. All plots of 1H NMR spectra or spectral regions were plotted with a fixed value of absolute intensity for comparison.
The initial matrix (189 samples × 1001 variables) contains 1H-NMR spectra of three oils at three heating temperatures and seven heating times. The computations were performed using the MATLAB environment, version R2007b (Mathworks, Natick, MA, USA).
The chemical changes taking place in the oils over time and at different temperatures, were monitored by 1H nuclear magnetic resonance spectroscopy. The spectral region between δ 0 and δ 7.2 was not used in this work because the changes observed were not exclusively specific to heating by-products, as were those in the spectral region between δ 9 and δ 10. Figs. 6.1–6.3 present this spectral region for rapeseed oil for different heating times at 170 °C, 190 °C and 210 °C. The increase in the aldehydic protons peaks over time and as a function of temperature is clearly visible. It is to be noted that the temperature has a similar effect in this spectral region for sunflower oil, but with a different patterns of peaks. This reflects differences in the structure of the aldehydes formed in the two oils. The effect of temperature is much smaller for olive oil due to the composition of the fatty acids and a higher concentration of natural antioxidants such as tocopherols.
PCA score plots show classical trajectories with time and temperature of the three oils, and 66% of the total variance is explained by the first 2 components (see Fig. 7). The score plots show that the thermal degradations are quite different, with that of olive oil being much less extensive, with that of rapeseed being more similar to olive oil than sunflower oil. This observation makes sense because olive oil and rapeseed oil have approximately the same range of oleic acid (respectively 55.0–83.0% and 52.0–67.0% [29]), which is one of the three major unsaturated fatty acids of edible oils, along with linoleic and linolenic acids, while sunflower oil has a lower content (14.0–38.0%). The smaller spread of scores for olive oil at a given temperature indicates that chemical changes caused by heating over time are more pronounced in rapeseed and sunflower oil than in olive oil. Olive oil is also different from the other two oils in that the orientation of its scores’ trajectory is different for each temperature. The plot of the PC1 and PC2 loadings suggests that the chemical evolution of the sunflower oil and olive oil is not characterized by the formation of the same aldehyde protons. The scores for PC1 characterize the time factor for sunflower, rapeseed and olive oils at 170 °C, while the loadings for PC1 characterize the progressive appearance of trans-2-alkenals and E,E-2,4-dialkadienals associated with peaks at δ 9.534, 9.507 and 9.554 (see Fig. 7) and the short chain alkanals associated with the peak at δ 9.766.
The scores for PC2 are related more to the time factor for rapeseed oil and olive oil at 190 and 210 °C. The loadings for PC2 are characterized by several peaks: δ 9.497 and 9.506 (possibly related to the increase of trans-2-alkenals), δ 9.763 and δ 9.786 (N-alkanals and short chain alkanals or oxo-alkanals) and δ 9.553 (possibly 4,5-epoxy trans-2-alkenals). According to Guillén et al. [30, 31], these compounds are among the secondary oxidation products. The loadings of each PC give a clear idea of which peaks increase or decrease during the heating and allow a better understanding of what differentiates the oil samples from a chemical point of view. The Euclidean distance in the multivariate space of three first principal components (i.e. those that explain the majority of the variance of the data studied (70%)) allows the proposal of a thermal stability order of studied edible oils. This order is presented in table 7.
Expanded region between δ 9 and δ 10 of the 1H NMR spectra of rapeseed oil at different times during heating (F5.1) at 170°C, (F5.2) at 190°C, (F5.3) at 210°C.
PCA scores (up) and loadings (bottom) of (63 x 1001) data matrix for 3 types of oils and 3 heating temperatures. In both cases, only PC1 and PC2 are represented.
Observed thermal stability order | ||
Temperature | ||
190 ◦C | 170 ◦C | 210 ◦C |
OO "/> SO "/>= RO | OO "/> RO "/>= SO | OO "/> SO "/>"/> RO |
Thermal stability order of the studied oils given by multivariate distances calculated from coordinates on 3 first PCs. OO: olive oil, RO: rapeseed oil, SO: sunflower oil.
The conclusion of this work indicates that olive oil is more stable than the others (rapeseed and sunflower oil), whatever the heating temperature applied. One reason for this stability is its higher concentration of natural antioxidants, such as tocopherols, and its content of oleic acid with unsaturated bonds, which allow it to participate more easily in the radical oxidation mechanisms which occur during heating and thus protect other fragile compounds in the oil for longer.
This application example of the study of the stability of heated oils shows the power of such tool as PCA in the treatment of multivariate spectroscopic data as used with the characteristic fingerprints of chemical and physical phenomena occurring in the samples over time. The PCA allows the direct use of spectra (or pieces of spectra) or analytical curves instead of the integration values typically used in NMR spectroscopy. The principle presented here is now very common in the field of spectroscopists in general and many monitoring tools or processes for monitoring chemical reactions involve PCA or more robust variants (Robust PCA, RPCA based on robust covariance estimators, RPBA based on projection pursuit, Kernel PCA, etc.).
Usually, physicochemical analysis - or else the more generally parametric monitoring of a number of physicochemical properties of a set of samples - resulted in the construction of a data matrix with samples in rows and physicochemical properties in columns. This data matrix is a mathematical representation of the characteristics of the sample set at time
Representation of a matrix and a cube of data from experimental measurements.
At this stage, a key question is raised: how should one analyse all these matrices so as to extract all the relevant information that takes into account the time factor? Several options are available to us. The first would be, for example, an averaging of each parameter per sample measured at the various dates/times. This has the immediate effect of losing the time-based information. By this operation, the effect of time on the measured parameters is no longer observable. We would obtain an average matrix resulting from all of the measured parameters of the study period. The advantage of this in relation to the question is null. Finally, we could consider using the chemometric techniques discussed previously in this book, such as principal component analysis (or others not presented here, such as the hierarchical analysis of each class of matrices stored). But then, it becomes difficult to visualize changes occurring between successive matrices which may correspond to an evolution with time (as in the case of 3D fluorescence spectra which are commonly qualified as second-order data) or to a geographical evolution of the measured parameters (e.g., when studying physicochemical a river port or lagoon in which samples are realized in various places to monitor the level of chemical pollution [32]). The overall interpretation is certainly more difficult.
The solution must be sought to tools capable of taking into account a third or Nth dimension(s) in the data while retaining the ability to account for interactions between factors. The following pages describe two of these tools; probably one of the most famous of them is the PARAllel FACtor model (PARAFAC) introduced independently by Harshman in 1970 [33] and by Carroll & Chang [34] who named the model CANDECOMP (canonical decomposition). The oldest paper we found relating the mathematical idea of PARAFAC was probably published by Hitchcock F.L. in 1927 who presents a method to decompose tensors or polyadics as a sum of products [35]. In this class of tools, the size or dimensions of the dataset are called "modes", hence the name of multi-mode techniques. The most widely used encountered term is “multiway” technique.
As discussed above, many problems involve chemical 3-ways data tables. Let us come back to the hypothetical example of a liquid chromatography coupled to a fluorescence spectrometer that produces a data set consisting of 3-way "layers" or 2-dimensional matrix sheets coming from the excitation-emission fluorescence spectra collection (Excitation-Emission Matrix: EEM) which vary as a function of elution time.
The fluorescence intensities depend on the excitation and emission wavelengths, and the elution time, these variables will be used to represent the three modes. To exploit these multiway data, the PARAFAC model is an effective possibility to extract useful information. PARAFAC is an important and useful tool for qualitative and quantitative analysis of a set of samples characterized by bilinear data such as EEM in fluorescence spectroscopy. One of the most spectacular examples of the capability of PARAFAC model is the analysis of a mixture of several pure chemical components characterized by bilinear responses. In this case, PARAFAC is able to identify the right pairs of profiles (e.g. emission and excitation profiles) of pure components as well as the right concentration proportions of the mixture. PARAFAC yields unique component solutions. The algorithm is based on a minimizing least squares method. This is to decompose the initial data table in a procedure known as "trilinear decomposition" which gives a unique solution. The trilinear decomposition comes from the model structure and sometimes data itself implies that because of its (their) natural decomposition in 3 modes. The PARAFAC model is a generalization of the PCA itself bilinear, to arrays of higher order (i.e., three or more dimensions). PCA decomposes the data into a two-mode product of a matrix called matrix
where
Note the similarities between the PARAFAC model and that of the PCA in schema 2, § “II.A
Conceptually, the Tucker3 model [37] is a generalization of two-way data decomposition methods such as PCA or singular value decomposition (SVD) to higher order arrays or tensors [8] and [9]. In such multiway methods, scores and loadings are not distinguishable and are commonly treated as numerically equivalent. Being a generalization of principal component analysis and PARAFAC to multiway data arrays, the Tucker3 model has for its objective to represent the measured data as a linear combination of a small number of optimal, orthogonal factors. For a 3-way data array, the Tucker3 model takes the following form:
Principle of the decomposition of a 3-way data cube according to the PARAFAC model.
here, xijk are the measured data, giu, hjv and ekw are the elements of the loading matrices for each the three ways (with r, s and t factors, respectively) and cuvw are the elements of the core array (of size r × s × t), while εijk are the elements of the array of the residuals. A tutorial on chemical applications of Tucker3 was proposed by Henrion [33], while Kroonenberg [34] gives a detailed mathematical description of the model and discusses advanced issues such as data preparation/scaling and core rotation. For a complete and very pedagogical comparison of Tucker3 with PARAFAC, another multiway procedure, see Jiang [35]. Nevertheless, some aspects of Tucker3 model which distinguish it from PARAFAC have to be discussed here. The first, the Tucker3 model does not impose the extraction of the same number of factors for each mode. Second, the existence of a core array,
Although PARAFAC and Tucker3 as factorial decomposition techniques come from the last century, their routine use in analytical chemistry became popular with the Rasmus Bro’s thesis in 1998. Of particular note is the remarkable PhD works published and available on the Department of Food Science website of the Faculty of Life Sciences, University of Copenhagen using the PARAFAC model and many other multivariate and multiway methods in many industrial food sectors Department of Food Science, Faculty of Life Sciences, University of Copenhagen, http://www.models.kvl.dk/theses, last visit April, 2012.
Foundations of the PARAFAC procedure: Models and conditions | ---- | 1970 [ | |
PARAFAC. Tutorial and Applications | General | 1997 [ | |
Quantification of major metabolites of acetylsalicylic acid | Fluorescence | 1998 [ | |
Fluorescence of Raw Cane Sugars | Fluorescence | 2000 [ | |
Determination of chlorophylls and pheopigments | Fluorescence | 2001 [ | |
Practical aspects of PARAFAC modelling of fluorescence excitation-emission data | Fluorescence | 2003 [ | |
Evaluation of Two-Dimensional Maps in Proteomics | 2D-PAGE imaging | 2003 [ | |
Evaluation of Processed Cheese During Storage | Front face fluorescence | 2003 [ | |
Quantification of sulphathiazoles in honeys | Fluorescence | 2004 [ | |
Evaluation of Light-Induced Oxidation in Cheese | Front face fluorescence | 2005 [ | |
Olive Oil Characterization | Front face fluorescence | 2005 [ | |
Detection of Active Photosensitizers in Butter (riboflavine, protoporphyrine, hématoporphyrine, chlorophylle a) | Fluorescence + sensory analysis | 2006 [ | |
Characterizing the pollution produced by an industrial area in the Lagoon of Venice | Physicochemical analysis | 2006 [ | |
Calibration of folic acid and methotrexate in human serum samples | Fluorescence | 2007 [ | |
Quantification of sulphaguanidines in honeys | Fluorescence | 2007 [ | |
Water distribution in smoked salmon | RMN | 2007 [ | |
Monitoring of the photodegradation process of polycyclic aromatic hydrocarbons | Fluorescence + HPLC | 2007 [ | |
Quantification of tetracycline in the presence of quenching matrix effect | Fluorescence | 2008 [ | |
Fluorescence Spectroscopy: A Rapid Tool for Analyzing Dairy Products | Fluorescence | 2008 [ | |
Determination of aflatoxin B1 in wheat | Fluorescence | 2008 [ | |
Study of pesto sauce appearance and of its relation to pigment concentration | Sensory analysis + HPLC-UV | 2008 [ | |
Determination of vinegar acidity | ATR- IR | 2008 [ | |
Multi-way models for sensory profiling data | Sensory analysis | 2008 [ | |
Noodles sensory data analysis | Physicochemical and sensory analysis | 2011 [ | |
Kinetic study for evaluating the thermal stability of edible oils | 1H-NMR | 2012 [ |
Bibliography related to PARAFAC and/or Tucker3 models. Theory and applications in areas such as chemicals and food science, medicine and process chemistry.
The interest for the use of chemometric methods to process chromatograms in order to achieve a better discrimination between authentic and adulterated honeys by linear discriminant analysis was demonstrated by our group previously [61]. An extent of this work was to quantify adulteration levels by partial least squares analysis [62]. This approach was investigated using honey samples adulterated from 10 to 40% with various industrial bee-feeding sugar syrups. Good results were obtained in the characterization of authentic and adulterated samples (96.5% of good classification) using linear discriminant analysis followed by a canonical analysis. This procedure works well but the data acquisition is a bit so long because of chromatographic time scale. A new way for honey analysis was recently investigated with interest: Front-Face Fluorescence Spectroscopy (FFFS). The autofluorescence (intrinsic fluorescence) of the intact biological samples is widely used in biological sciences due to its high sensitivity and specificity. Such an approach increases the speed of analysis considerably and facilitates non-destructive analyses. The non-destructive mode of analysis is of fundamental scientific importance, because it extends the exploratory capabilities to the measurements, allowing for more complex relationships such as the effects of the sample matrix to be assessed or the chemical equilibriums occurring in natural matrices. For a recent and complete review on the use of fluorescence spectroscopy applied on intact food systems see [63, 64]. Concerning honey area, FFFS was directly applied on honey samples for the authentication of 11 unifloral and polyfloral honey types [65] previously classified using traditional methods such as chemical, pollen, and sensory analysis. Although the proposed method requires significant work to confirm the establishment of chemometric model, the conclusions drawn by the authors are positive about the use of FFFS as a means of characterization of botanical origin of honeys samples. At our best knowledge, the previous mentioned paper is the first work having investigated the potential of 2D-front face fluorescence spectroscopy to determine the botanical origins of honey at specific excitation wavelengths. We complete this work by adopting a 3D approach of measurements. We present here below the first characterization of three clear honey varieties (Acacia, Lavender and Chestnut) by 3D-Front Face Spectroscopy.
This work was carried out on 3 monofloral honeys (Acacia:
Fluorescence spectra were recorded using a FluoroMax-2 spectrofluorimeter (Spex-Jobin Yvon, Longjumeau, France) mounted with a variable angle front-surface accessory. The incidence angle of the excitation radiation was set at 56° to ensure that reflected light, scattered radiation and depolarisation phenomena were minimised.
The fluorescence excitation spectra were recorded from 280 nm to 550 nm (increment 4 nm; slits: 3 nm, both at excitation and emission), the fluorescence emission spectra were recorded from 280 to 600 nm, respectively. For each sample, three spectra were recorded using different aliquots.
The computations were performed using the MATLAB environment, version R2007b [Mathworks, Natick, MA, USA] and with the N-way Toolbox [67]. Each EEM corresponds to a landscape-matrix. For each succession of fluorescence spectra corresponds a collection of matrices which needs to be processed in a specific arrangement. A cube is usually used to organize landscape-matrices as depicted in the figure 10.
Representation of 3 possible types of arrangements for EEM landscapes processing.
From general point of view, processing this type of data arrangement needs two computing methods: a) 3-way methods such as PARAFAC, Tucker3 or Multiway-PCA and b) 2-way methods such as Principal Component Analysis (PCA) and any other bi-linear technique after data cube unfolding. In the first part of this work, the second approach was used: PCA after the
5.5.1.4. Results and discussion
Here are below examples of 3D-Front Face Fluorescence spectra of samples of this study. The chemical composition of honey is studied and known to a large extent for nearly 50 years. The presence of compounds beneficial to health for their antioxidant properties or for other reasons is well known, this is the case of polyphenols [68-74] or amino acids [75-83], some of them are good fluorophores like polyphenols. Works evoke the interest of using these fluorophores as tracers of the floral origin of honeys. For example, the ellagic acid was used as a tracer of heather honey Calluna and Erica species while hesperitin was used to certify citrus honeys [84, 85] and abscisic acid was considered as molecular marker of Australian Eucalyptus honeys [86]. Kaempferol was used as marker of rosemary honey as well as quercetin for sunflower honey [87]. As polyphenols, aromatic amino acids were used to characterize the botanical origin or to test the authenticity of honey, this is the case of phenylalanine and tyrosine for honey lavender [88] and the glutamic acid for honeydew honeys [81]. Therefore, recording the overall fluorescence spectrum over a large range of wavelengths allows for taking into account the fluorescence emission of the major chemical components described above. In our case, recorded spectra for Acacia, Lavender and Chestnut honeys are similar for certain spectral regions but differ in others proving the existence of different fluorophores. These chemical characteristics specific of the composition are very useful for the distinction of flower varieties. Recording of 3D fluorescence spectra containing all the emission spectra over a wide range of excitation wavelengths can take into account all the information associated with the fluorophores present in honeys.
a): At left, excitation-emission spectra of Acacia samples. At right, excitation-emission spectra of Lavender samples, (b): Excitation-Emission spectra of Chestnut samples
Figure 13 shows the PC1 vs PC2 scores-plot of the honeys dataset. PCA has played his role of data reduction technique by creating new set of axes. One can visualize all samples simultaneously on a simple xy-graph with only the two first principal components without loose of information. The first obvious conclusion is the natural classes of honeys clearly appear on the scores-plot. PC1 accounts for 58.2% of the total variance while PC2 explains 32.6% of the total variance of the initial data set. The Acacia group is separated from Chestnut and Lavender essentially on PC1, while PC2 is more characteristic of the distinction between Acacia + Lavender and Chestnut. The shape and location of fluorescence islets on 3D-spectra are assets in the distinction of samples as they relate to the chemical compounds that distinguish the groups. It is important to note here that other multivariate techniques exist that could be applied to these data successfully. Particular, the application of the technique ICA (Independent Component Analysis) which is a factorial technique similar to PCA, we would associate with each calculated component a signal having a greater chemical significance. ICA is capable from a mixture of signals to extract mutually independent components explaining more particularly the evolution of a "pure" signal [89-92]. In the case presented here, ICA should probably separate more effectively chemical source signals that are causing the observed differences between the honey samples. The chemical interpretation of this result can be assessed using the loadings on PC1 and PC2. As depicted by figure 14, some spectral regions are responsible of these separations observed on the two first axes of PCA. Two excitation/emission maxima (356/376 nm, 468/544 nm) and two excitation/emission minima (350/440 nm, 368/544 nm) are detectable on the PC1 loadings.
Ellipsoids of samples distribution drawn with interval confidence at 95%
Loadings on PC1 (at left) and PC2 (at right) computed from EEM cube of honey samples after standardization.
In the same manner, the loadings on PC2 show two excitation/emission maxima (356/376 nm, 396/470 nm; respectively zones 2 and 3) and one excitation/emission minima (350/450 nm; zone 1). These maxima and minima are not absolute and cannot be assimilate to true concentrations but represent what spectral zones have largely change in intensity and shape throughout the analysed samples. So, loadings are an overall photography of changes in the samples mainly due to their botanical origins. Figure 15 presents an overview of the main fluorophores potentially present in dairy and food products [63, 93] and some of them are present in honeys too. Here, the chemical interpretation could be made as following. Based on PC1, Acacia samples are mainly distinguished from Lavender and Chestnut honeys by fluorophores appearing in zone 1 and 2, their content in these fluorophores are greater than other samples. Symmetrically, Lavender and Chestnut honeys are more characterized by fluorophores detected in zone 3 and 4. Loadings on PC2 allow a better understanding of what is more characteristic of Chestnut honeys samples compared with others. Zone 2 and 3 are clearly associated with Chestnut samples because the loading values are positive in these regions as the scores are for these samples on the corresponding graph.
On Left - Excitation and emission maxima of fluorophores present in dairy products (from [
Let us to consider the previous example on the analysis of three varieties of monofloral honeys. The application of the PARAFAC model (with orthogonality constraint on the 3 modes) to the 3-way EEM fluorescence data can take into account the nature of these trilinear data and get factorial cards similarly to PCA. In the case of PARAFAC, it is usual to identify modes of the PARAFAC model from the dimensions of the original data cube. In our case, we built the cube of fluorescence data as HoneyCube = [151 x 91 x 32] which is [λem x λex x samples].
Therefore, according to the theoretical model presented above we are able to visualize as many factorial cards as components couples in each mode there are. It is particularly interesting in the case of mode 3 which is mode of samples. The user must specify the number of components in each mode that must be calculated in the model. We built a 3-components model by helping us with the corcondia criterion associated with the PARAFAC procedure. The corcondia criterion was created and used [94] to facilitate the choice of the optimal number of components in the calculated model. It is a number between 0% (worst model fitting) and 100% (best model fitting). The ideal case for choosing the optimal number of components should be to know the exact number of fluorescence sources in the samples, but in our case this number is unknown. We have consider three sources of fluorescence according to our knowledge of the samples (proteins and free amino acids, NADH from cellular materials and oxidation products + Maillard reaction fluorescent products from heating).The results for mode 1 (emission wavelengths), mode 2 (excitation wavelengths) and mode 3 (samples) are presented below in figures 16 and 17. An interesting parallel between the reconstructed PCA loadings and the PARAFAC loadings in each three modes is possible by forming the good figure arrangement.
On top left hand side: PCA loadings for component 1; On top right hand side: PARAFAC loadings for Mode 1 (Emission); On bottom left hand side: PARAFAC loadings for Mode 2 (Excitation); On bottom right hand side: PARAFAC loadings for Mode 3 (Samples)
A simultaneous reading of PCA and PARAFAC loadings for honeys EEM matrices gives some elements for understanding what makes distinction between honeys samples on PARAFAC loadings on mode 3. One can see, for example, samples are relatively well separated on the two first PARAFAC loadings in mode 3 (chart on the bottom right). We find good agreement between the PARAFAC profiles and loadings of PCA. Similarly with PCA loadings, those of PARAFAC show the greatest variation of concentration profiles across all samples. And negative areas in the PARAFAC profiles reflect a decrease in fluorescence and therefore a decrease in concentration of the compounds across all the samples. Complementarily, positive PARAFAC profiles indicate an increase in fluorescence and thus the concentration of the corresponding fluorescent compounds in the samples. The first PARAFAC component in mode 3 (chart on the bottom right) allows a good discrimination of the honey samples and the latter is mainly explained by the variation of the fluorescence depicted by the loading 1 (red line) both on the emission (at top and right hand side) and excitation (at bottom and left hand side) graphs. Therefore, Acacia samples have lower content in these compounds than lavender honey samples. Chestnut honeys have an intermediate level of concentration concerning these compounds. Figure 17 gives some elements to more accurately identify the chemical origin of the observed differences between samples on the 2nd PARAFAC component (in blue) on mode 3 (samples). Particularly, it is possible to associated to the 2nd PARAFAC component of mode 3 to Maillard and oxidation products (excitation wavelength: ≈360 nm; emission wavelength: ≈445 nm) depicted by the loadings 2 (blue curve) on the excitation and emission loadings graphs. This observation is explained by the method of harvesting, storage and chemical composition of honey. Indeed, lavender honey experienced a hot extraction and uncapping the honey frames also. This is because the lavender honey crystallizes faster than other honeys because of a higher concentration of sucrose while acacia honey is rich in fructose (sugar does not crystallize) and contains no sucrose. The water content and the glucose / fructose ratio of honeys are also important factors in the crystallization process [95]. The harvesting method is to cause a greater concentration of oxidation products and products of the Maillard reaction.
On top left hand side: PCA loadings for component 2; On top right hand side: PARAFAC loadings for Mode 1 (Emission); On bottom left hand side: PARAFAC loadings for Mode 2 (Excitation); On bottom right hand side: PARAFAC loadings for Mode 3 (Samples)
It is clear here that the PARAFAC loadings probably do not reflect pure signals. Indeed, in our case, the appropriate model is probably more complex because we do not know the exact number of fluorophores present in the samples and their contribution is included in each PARAFAC loadings. Thus the chemical interpretation is only partially correct, but nevertheless shows some interesting information on what produces the differences between samples. Another aspect that we have not discussed here is the type of constraint that we have imposed to the model during its construction (orthogonality, non-negativity, unimodality...). The type of constraints strongly modifies the shape of the PARAFAC loadings obtained and their comparison with PCA is not always possible. We preferred the orthogonality constraint to be in conditions similar to PCA. But this is not necessarily the best choice from spectroscopic point of view, particularly because loadings reflecting concentrations should not be any negative.
If there were one or two things to note from this application of PARAFAC, it would be, first, the simplicity of the graphical outputs and their complementarities with the results of PCA, and secondly, the possibility offered by the model to process data inherently 3-way or more (without preliminary rearrangement).
In the case of 3D fluorescence data, the loadings of PCA are not easily used directly to interpret and to construct a model of quantitative monitoring, PARAFAC modelling enables this by producing as much as possible loadings representative of changes in concentrations of fluorophores present in the samples, and a good distinction of the studied honey varieties is easily achievable with a simple PARAFAC model.
I thank Dr Dominique Bertrand (INRA Nantes, France) for proofreading and advice about the PCA and for his availability. I also thank my students who have provided much of the data used in this chapter.
Here is below a selection of the most interesting books dealing with chemometrics. This list will be convenient as well for the beginner as for the specialist.
Härdle, W.; Simar, L., Applied multivariate statistical analysis. 2nd ed.; Springer: Berlin; New York, 2007; p xii, 458 p.
Brereton, R. G., Chemometrics for pattern recognition. Wiley: Chichester, U.K., 2009; p xvii, 504 p.
Gemperline, P., Practical guide to chemometrics. 2nd ed.; CRC/Taylor & Francis: Boca Raton, 2006; p 541 p.
Miller, J. N.; Miller, J. C., Statistics and chemometrics for analytical chemistry. 5th ed.; Pearson Prentice Hall: Harlow, England; New York, 2005; p xvi, 268 p.
Beebe, K. R.; Pell, R. J.; Seasholtz, M. B., Chemometrics : a practical guide. Wiley: New York, 1998; p xi, 348 p.
Brown, S. D., Comprehensive chemometrics. Elsevier: Boston, MA, 2009
Jackson, J. E., A user\'s guide to principal components. Wiley-Interscience: Hoboken, N.J., 2003; p xvii, 569 p.
Martens, H.; Næs, T., Multivariate calibration. Wiley: Chichester [England] ; New York, 1989; p xvii, 419 p.
Massart, D. L., Handbook of chemometrics and qualimetrics. Elsevier: Amsterdam ; New York, 1997.
Sharaf, M. A.; Illman, D. L.; Kowalski, B. R., Chemometrics. Wiley: New York, 1986; p xi, 332 p.
% Size of the data matrix X
[n,p]=size(X);
% Column centering
meanX = mean(X);
X_Centred = X - ones(n,1) * meanX;
% Compute variance-covariance matrix
% for scores
CovM_scores = cov(X_Centred);
% for variables
CovM_loadings = cov(X_Centred\');
% Compute eigenvalue & eigenvectors by keeping the matrix form
[V_scores,D_scores] = eig(CovM_scores);
D_scores = fliplr(D_scores);D_scores = flipud(D_scores);
V_scores = fliplr(V_scores);
[V_loadings,D_loadings] = eig(CovM_loadings);
D_loadings = fliplr(D_loadings);D_loadings = flipud(D_loadings);
V_loadings = fliplr(V_loadings);
% Compute of total variance of the dataset: TotalVP
TotalVP = trace(D_scores);
% Compute the percentage of variance explained by each principal %component
Pourcentages = {};
for i=1:size(D_scores,1)
Perrcentages{i,1} = strcat(\'PC\',num2str(i));
Perrcentages{i,2} = num2str(D_scores(i,i)/TotalVP*100);
end;
% Compute factorial coordinates (scores) for each sample point on
% components space
Coord =[];
Coord = X_Cent*V_scores;
% Compute factorial contributions (loadings) for each sample point on
% components space
Contrib =[];
Contrib = X_Cent\'*V_loadings;
% Size of the data matrix X
[n,p]=size(X);
% Column centering
meanX = mean(X);
X_Centred = X - ones(n,1) * meanX;
% Compute scores and loadings simultaneously
[U SingularValues Loadings] = svd(X_Centred);
% Scores T
T = U * SingularValues;
CAMO (http://www.camo.no/) maker of Unscrambler software, for multivariate modelling, prediction, classification, and experimental design.
Chemometry Consultancy (http://www.chemometry.com/)
Eigenvector Research (http://www.eigenvector.com/) sells a PLS_Toolbox
Minitab (http://www.minitab.com/)
Statcon (http://www.statcon.de/)
Stat-Ease (http://www.statease.com/) maker of Design-Expert DOE software for experimental design.
Infometrix, Inc., developer of Pirouette, InStep and LineUp packages for multivariate data analysis. We specialize in integrating chemometrics into analytical instruments and process systems. (http://www.infometrix.com/)
Umetrics, maker of MODDE software for design of experiments and SIMCA multivariate data analysis. (http://www.umetrics.com/)
Thermo Electron, offers PLSplus/IQ add-on to GRAMS.
(http://www.thermo.com/com/cda/landingpage/0,,585,00.html)
Grabitech Solutions AB, offering MultiSimplex, experimental design & optimization software. (http://www.grabitech.se/)
PRS Software AS, maker of Sirius software, for data analysis and experimental design. (http://www.prs.no/)
S-Matrix Corp., maker of CARD, design of experiments software. (http://www.smatrix.com/)
Applied Chemometrics, source for chemometrics related software, training, and consulting. (http://www.chemometrics.com/)
DATAN software for multidimensional chemometric analysis. (http://www.multid.se/)
Belgian Chemometrics Society (http://chemometrie.kvcv.be/)
Département d’Analyse Pharmaceutique et Biomédicale (FABI) à la Vrije Universiteit Brussel (http://www.vub.ac.be/fabi)
McMaster University Hamilton - Department of Chemical Engineering - Prof. Dora Kourti (http://www.chemeng.mcmaster.ca/faculty/kourti/default.htm)
NAmICS (http://www.namics.nysaes.cornell.edu/)
Research NIR Centre of Excellence (NIRCE) - Prof. Paul Geladi (http://www.fieldnirce.org/)
Université d’Anvers – Groupe de Recherche pour la chimiométrie et l’analyse par rayons x - Prof. Van Espen (http://www.chemometrix.ua.ac.be/)
Université d’Anvers – Département de Mathématique, Statistique et Actuariat - Prof. Peter Goos (http://www.ua.ac.be/main.aspx?c=peter.goos)
Université royale vétérinaire et agronomique de Fredriksberg – Groupe de Recherche en Chimiométrie - Prof. Rasmus Bro (http://www.models.kvl.dk/users/rasmus/)
Université de Louvain - Research Center for Operations Research and Business Statistics - Prof. Martina Vandebroek
(http://www.econ.kuleuven.be/martina.vandebroek)
Université de Nimègue - Chemometrics Research Department - Prof. Lutgarde Buydens (http://www.cac.science.ru.nl/)
Université de Silésie – Département de chimiométrie - Prof. Beata Walczak (http://chemometria.us.edu.pl/researchBeata.html)
Johan Trygg (http://www.chemometrics.se/)
Analytical Chemistry Laboratory, AgroParisTech, France (http://www.chimiometrie.fr)
The challenge of feeding the growing world population and the necessity to provide a nutritionally balanced diet while reducing greenhouse gas emissions, as well as a transition to a diet higher in plant- rather than animal-derived proteins, require relevant increases in vegetables production. In this context, the fortification of foods and beverages has been identified as an effective, sustainable, and promising intervention capable of modulating the diet toward healthier choices, addressing environmental concerns, and meeting nutritional deficiencies and recommendations. To date, several studies investigated the nutritional value of additional ingredients to be used as wheat alternatives in cereal-based products, such as bread and pasta.
Legumes are considered as good source of high biological value proteins and dietary fibers. Moreover, they are rich in phenols, minerals, vitamins, and oligosaccharides. The optimal technological properties of the legume flours (e.g., high water-binding capacity and solubility) make them suitable ingredients for gluten-free foods.
Nevertheless, legumes contain part of their nutritional compounds under a nonbioavailable form and several antinutritional factors (ANFs) that may decrease digestibility of other nutrients or cause physiological discomfort or conditions. Furthermore, legumes have poor technological, rheology, and sensory attributes if compared with gluten-containing cereals. Hence, the full exploitation of such food matrices goes through the most suitable bioprocessing.
Lactic acid bacteria (LAB) are the group of microorganisms most largely used at food industrial level, having the status of Generally Recognized as Safe (GRAS). Used as natural (e.g., sourdough and spontaneous fermentation) or selected starters, LAB have the capability to conjugate desired functional activities, sensory properties, and microbiological safety.
Overall, bioprocessing including LAB fermentation is considered a safe, sustainable, and effective tool for improving the functional and nutritional features of many plant-derived matrices and to obtain suitable technological, sensory, and shelf-life characteristics of fermented foods and beverages (Figure 1). The positive effects of LAB fermentation are in part related to the acidification, although further effects can be observed, such as those related to the synthesis of metabolites and the activation of the flour endogenous enzymes. The properties of the fermented matrix are often profoundly different from the unfermented ingredients. Among the main nutritional advantages of the LAB fermentation, the increase of the protein digestibility and the decrease of the glycemic index have been largely investigated. More recently, also the degradation of the antinutritional compounds (e.g., trypsin inhibitors, phytic acid, saponins, condensed tannins, and α-galactosides) and the synthesis of bioactive compounds have been described. Starting from the conventional application of the sourdough-inspired procedures, innovative biotechnological protocols, based on the use of selected starters, automatized bioreactors, and semiliquid formulations have been recently proposed to extend to a large-scale application the use of legumes in food industry.
Main nutritional, functional, and safety properties deriving from LAB fermentation.
Indeed, fermentation (both spontaneous or guided by selected LAB) has been recognized as the most suitable and sustainable process to exploit the potential of legumes to fortify staple foods such as baked goods, pasta, extruded snacks, and plant-based fermented beverages.
In this chapter, the scientific evidence confirming the nutritional, functional, rheology, sensory, and shelf-life improvements of fermented legumes and derived food products is described.
As recommended by global organizations, due to the growing concerns related to the environmental impact of animal breeding and the health risks associated with high meat intake, the decrease in animal-derived foods consumption led to the need for more plant-based foods in diet and more energy-efficient processing [1]. Simultaneously, the large market growth of foods designed for vegetarian, vegan, and gluten-free diets generated an increased consideration in improving the nutritional quality of grains-derived ingredients to be used in food preparation [2].
Leguminosae family, belonging to the Dicotyledonae group, includes 18,000 different species. After cereals, legumes are the most important group of crops, and their consumption is widely distributed all over the world.
A large variety of legumes used for human diet are cultivated extensively or locally [3, 4]. The economic importance of the Leguminosae family is related to the low input required for their cultivation, the positive impact on the soil fertility, and the great adaptability to underrestrictive pedoclimatic conditions [4]. Moreover, the advantages of cereal-legume intercropping, also providing an efficient exploitation of natural resources, have been abundantly demonstrated [5].
Legumes are excellent sources of proteins with high biological value, providing many essential amino acids, contain carbohydrates and dietary fibers, and supply relevant levels of vitamins, minerals, oligosaccharides, and phenolic compounds [6]. The frequent consumption of legumes is effective to prevent or decrease risks of cardiovascular disease (CVD) [7], type 2 diabetes [8], some types of cancer [9], and overweight and obesity [10].
When cereals and legumes are combined in food formulations, protein efficiency improved thanks to complementary essential amino acid profiles [11]. Overall, compared with cereal, legumes contain less starch, more protein, and more fiber, whereas lipid content is either equal or higher. Starch content in wheat varies between 60 and 80%, whereas it ranges from 40 to 65% for legumes except for lupin, having a markedly lower starch content [1]. Proteins in legume flours vary between 20 and 30% and can reach up to 40% in faba and lupin flours, against the 9–18% in wheat and other cereals [1]. Fiber content is circa 2% (on dry matter) in wheat flour and semolina, while it can reach 10% in pea and faba flours, and even 20–40% in chickpea, lentil, and lupin flours [1]; however, legume flours are often obtained from whole grains (not dehulled) resulting in a higher proportion of fiber. Ultimately, lipid content varies between 1 and 3% (on dry matter) in wheat and legume flours except for chickpea and lupin flours in which it can reach 10–13% [1].
Besides nutritional composition, the main proteins contained in cereals and legumes also present several differences in terms of type and functionality. In wheat, for example, gluten proteins (gliadins and glutenins) are the most abundant, accounting for 80% of total protein fraction [12]. In legumes, globulins are the dominant group, accounting for 50–70% of total proteins [13]. Wheat gliadins and glutenins contain higher concentration of sulfur amino acids compared with legume globulins, meaning they have more reactive cysteine residues [13, 14]. Moreover, low-molecular-mass albumins are present in both cereal and legume grains, reaching, respectively, 15 and 15–40% of the total proteins content [13]. Just as for proteins, starch granules in wheat and legumes show differences. They both contain linear amylose and branched amylopectin organized in semicrystalline and amorphous structures; however, they differ in shape and amylose/amylopectin ratio [15]. Legume starches have a higher proportion of amylose than wheat starch, ranging from 24/76 to 40/60 for pea and lentil starches and from 23/77 to 35/65 for chickpea starch [16].
Legumes contain several ANFs, such as raffinose, phytic acid, condensed tannins, saponins, alkaloids, lectins, pyrimidine glycosides, and protease inhibitors [17]. Overall, ANFs decrease the bioaccessibility and bioavailability of other nutrients, and, in some cases, are responsible for adverse reactions to the ingestion.
The content of raffinose-family oligosaccharides (RFOs, raffinose, verbascose, and stachyose) in legumes ranges from 1 to 6% with stachyose as the most abundant compound [18]. While in cereals, it is commonly lower than 1.5%, with raffinose as the sole or the most abundant compound [19, 20]. RFOs are nondigestible oligosaccharides that may result in adverse digestive symptoms when about 15 g/person per day are exceeded [21], a threshold that is readily reached in legume-based diets. Raffinose and RFO are indeed fermented by the intestinal microbiota with abundant gas production, causing discomfort and flatulence.
Phytic acid is the main storage compound for phosphorous and minerals in cereal and legume seeds. In legumes, its concentration can reach 20 g/kg [22, 23]. Phytic acid and divalent minerals (e.g., Ca2+, Zn2+ and iron) form stable complexes (phytates) that are insoluble and not hydrolyzed in the gastrointestinal tract, thus reducing the bioavailability of minerals for the monogastrics. Ca2+ and Zn2+ deficiencies are commonly observed in developing countries, and complexation of dietary minerals by phytates in plant-derived foods contributes to the mineral deficiency [17]. Iron uptake from plant-derived foods is impeded not only by complexation with phytate but also by complexation with condensed tannins [24, 25].
Proanthocyanidins, gallotannins. and ellagitannins, commonly referred to as tannins, are phenolic compounds that occur in a wide variety of plant foods. Their presence in cereals and legumes is dependent on the plant species and the cultivar [26]. Tannins impart bitter taste, reduce protein and starch digestibility by inhibition of pancreatic enzymes, and reduce iron uptake [26, 27]. The presence of tannins reduces the caloric content and the glycemic index of foods [28], but the abundance in diet reduces the supply of macro- and micro-nutrients.
Lectins and specific inhibitors of digestive enzymes (proteases and amylases) further reduce the digestibility of starch and proteins in legumes [26, 29].
Some ANFs are heat-labile (e.g., protease inhibitors and lectins) and easily removed by thermal treatments. Nevertheless, phytic acid, raffinose, tannins, and saponins are rather thermostable. Dehulling, soaking, air classification, extrusion, steaming, and pregelatinization are the main technological options for decreasing the negative impact of ANF on legume consumption [30, 31, 32]. Nevertheless, biological methods such as germination, enzyme treatments, and especially, fermentation seem to be more efficient [30, 31, 33, 34].
Proteolysis, enzyme inhibition due to acidification, acid activation of flour endogenous enzymes (e.g., phytases) and/or microbial enzyme activities (e.g., α-galactosidase, β-glucosidase, phytases, tannases) are responsible for the inactivation of most ANFs.
Raffinose family oligosaccharides are hydrolyzed through the activity of α-galactosidases, levansucrase, and sucrose-phosphorylase activities of lactic acid bacteria [35, 36] or corresponding enzymes of fungal cultures; their removal in legume fermentations has been amply reported [37].
In cereal matrices [22], the phytase activity is often sufficient to degrade phytates, especially in acidic conditions [18, 38]. Therefore, phytate degradation in LAB-fermented matrices spontaneously occurs without microbial enzymes involvement [18]. The optimal pH for the activity of the cereal phytases corresponds to 5.5; nevertheless, phytases are still active at pH levels lower than those commonly reached by sourdough (3.8–4.2) [18]. Sourdough fermentation and other types of traditional bioprocesses involving LAB (e.g., fermentations for production of cereal porridges or beverages) allow the increase of the mineral bioavailability [39]. Compared with that found in cereals, the phytase activity in legumes is poor [22, 40]. Nevertheless, pretreatments and processing conditions including fractionation, germination, soaking, thermal treatments, and fermentation drastically decrease phytate levels in legumes [41]. In many spontaneously fermented legume products, substrate-derived phytases are inactivated, and phytate degradation is achieved by fermentation with bacilli or fungal cultures, for example,
Metabolism of tannins or other polyphenols by LAB was deeply characterized only in a few fermented plant-derived matrices [44, 45].
The lactic fermentation of grass pea (
Besides the abovementioned ANFs, faba bean is rich in two glucosidic aminopyrimidine derivatives, vicine and convicine, which, upon hydrolysis of the β-glucosidic bond, generate the aglycones divicine (2,6-diamino-4,5-dihydroxypyrimidine) and isouramil (6-amino-2,4,5-trihydroxypyrimidine), respectively [55]. Divicine and isouramil trigger favism disease in susceptible individuals. Technological processes (air classification, roasting, and boiling) and selection of cultivars with low content of such compounds seemed to be only in part effective [55, 56]. On the contrary, β-glucosidase from LAB effectively degraded the pyrimidine glycosides from faba bean suspension and flour [30]. When used as starter to ferment fava bean flour,
Different legume proteins act in susceptible individuals as allergens. Their complex structures are difficult to degrade. The selection of legumes’ natural variants or the use of specific biotechnological processes has been exploited to solve this issue. However, some side effects such as an increase in the protein synthesis pathways of the seed and the synthesis of other proteins that might be allergenic have been also reported [59, 60, 61, 62]. Overall, plant proteins exhibit low digestibility compared with animal proteins. Poor protein digestibility can cause gastrointestinal disorder, and the increase in protein digestibility could reduce the level of immunoreactive proteins in their active forms, thus reducing the risk of food allergies symptoms [63]. Several studies showed that LAB fermentation increases the digestibility of plant proteins through the combined activity of microbial and endogenous proteases and peptidases [64, 65]. The use of fermentation to reduce or eliminate allergenicity of soy products represents an interesting opportunity to produce hypoallergenic food products from legumes [66, 67]. It was indeed shown that fermentation of soybean meal with
Besides allergens, many undesirable substances, contaminating foods and feeds, are harmful to human and animal health. These include mycotoxins, which are widely present in food and feeds commodities. The role of different microorganisms including fungi, yeasts, and bacteria in mycotoxins degradation has been investigated. Several studies extensively reported that mycotoxin degradation mechanisms are different and include cell wall binding, enzyme degrading, or structure modification. However, the degradative mechanisms are strain-dependent [68, 69, 70, 71, 72, 73].
For example, patulin is a mycotoxin synthesized by different fungi, such as
Fermented foods often contain biogenic amines, derived from microbial metabolisms, and characterized by a dose-dependent toxicity. Biogenic amines (BAs) are produced not only by Gram-positive and Gram-negative bacteria, but also by yeasts and molds [80]. Also LAB are considered as BAs producers in fermented foods and
Many intrinsic and extrinsic parameters affect the BAs production (e.g., pH, temperature, and water activity); nevertheless, their control is often difficult during food processes. The BAs production is strain-dependent; therefore, the starter selection is an efficient tool to decrease their accumulation in fermented foods. Another effective strategy includes the use of amine oxidizing selected starters [84].
Through their oxidases, such microorganisms catalyze the oxidative deamination of BAs and their conversion to aldehydes, hydrogen peroxide, and ammonia [85]. Kim et al. [86] isolated strains of
Another growing concern for the consumer is represented by the potential presence of chemicals and pesticides in foods, especially if correlated to the global recommendation to increase the dietary uptake of fruit and vegetables. It has been reported, for example, that the cumulative intake of pesticides by high consumers of fruits and vegetables in Brasil exceeds the Acute Reference Dose [90]. There is a consensus that the level of residual pesticides in foods needs to be decreased. However, the replacement of conventional pesticides in agriculture is a slow and difficult process. Therefore, the possibility to degrade pesticides through fermentation has been investigated. Several chemicals can be converted by microorganisms, but many of the most effective species characterize the environmental microbiota and are not easily usable in food processing.
The conversion of pesticides during food fermentation has been investigated in correlation, for example, to the large diffusion of contaminated soy (genetically resistant to the herbicide glyphosate). The degradation of organophosphorus insecticides was observed during the fermentation of Kimchi by
Besides decreasing antinutritional factors and allergy, LAB can fulfill a task of biopreservation [96]. This word can be defined as the extension of shelf-life and food safety by means of natural or controlled microbiota and/or their antimicrobial compounds [97]. Overall, LAB fermentation is one of the most common methods of food biopreservation.
In South-East Asia, specific biopreservation strategies to limit pathogens and spoilage microorganisms contamination in foods have been proposed. Overall, the most common contamination of legumes in the field is represented by sporulating bacteria; then, fungi can develop and produce mycotoxins. Finally, different pathogens can occasionally derive from cross-contamination with other foods.
Phan et al. [98] studied LAB strains isolated from fermented products from Vietnam, including dua gia (bean sprouts), identifying
The biopreservation mechanisms by which LAB inhibit spoilage organisms include the destabilization of cell membrane and subsequent interference with the proton gradient, inhibition enzyme activity, and creation of reactive oxygen species [96]. Moreover, LAB strains are able to produce antimicrobial compounds such as low-molecular-weight metabolites (reuterin, reutericyclin, diacetyl, fatty acids), hydrogen peroxide, antifungal compounds (propionate, phenyl-lactate, hydroxyphenyl-lactate, and 3-hydroxy fatty acids), and bacteriocins that may be exploited in the biopreservation of foods [99]. There is a wide number of bacteriocins produced by LAB that are classified into three classes: Class I (Lantibiotics), class II (Non Lantibiotics), and class III (Big peptides) depending on their chemical and genetic characteristics. The antibacterial activity of nisin, the most studied lantibiotics, has been demonstrated against
Fungi are the most common spoilage microorganisms of baked goods and represent a huge economic problem in bakery sector. The use of chemical preservatives is currently the only effective tool to prolong the microbial shelf-life of baked goods [103, 104]. Nevertheless, the European directive on preservatives has recently decreased the allowed concentrations of preservatives, and consumers require clean label and preservative-free baked goods. Therefore, the scientific and industrial research is now oriented toward the search for new preservatives, derived from natural sources. Overall, plants produce proteins and peptides involved in fungal resistance mechanisms, and seeds of many different species of leguminous plants are rich in such active compounds [105]. It was reported that the water-soluble extract of
More recently, a LAB-fermented chickpea flour was proposed as fresh pasta ingredients aiming at prolonging the shelf-life of the product, moreover, achieving different nutritional advantages [109].
Legumes are used as food ingredients worldwide, but only in few geographical areas they are commonly used for the production of fermented foods (Table 1), such as Japanese natto, Nigerian dawadawa or iru, Nepalese kinema, and Thai thua nao. Fermented legumes are consumed directly or used as ingredients or flavoring agents [124]. Yukiwari-natto and hama-natto spontaneous microbiota are dominated by molds, while
Product | Main ingredients | Microorganisms | Area | Reference |
---|---|---|---|---|
Adai | Legume seeds and cereal grains | Lactic acid bacteria ( | South India | [110] |
Afiyo (okpehe or kpaye) | Mesquite bean ( | Nigeria | [111] | |
Aisa | Bacilli ( | Nigeria | [112] | |
Amriti | Black gram dal ( | Aerobic mesophilic bacteria | India | [113] |
Bedvin roti | Black gram dal, opium seed or walnut flour | India | [114] | |
Chungkokjang (cheonggukjang or jeonkukjang) | Soybean ( | Bacilli ( | Korea | [115, 116] |
Dawadawa (soydawadawa) | Soybean | Nigeria | [117] | |
Dawadawa, kinda, iru, soumbala | Locust bean ( | West and Central Africa | [117] | |
Dhokla | Rice grains and bengal gram dal ( | India | [118] | |
Dosa | Black gram dal and rice grains | India | [118] | |
Douchi | Black soybean | China | [117] | |
Gochujang/Kochujang | Soybean, red pepper, rice, barley malt powder | Bacilli ( | Korea | [115] |
Idli | Black gram dal and rice grains | India, Sri Lanka | [118] | |
Kinema, hawaijar, tungrymbai, aakhone, bekang, peruyyan | Soybean | Darjeeling hills and North East of India, Bhutan, Nepal | [117] | |
Maseura (masyaura) | Black gram dal/ricebean ( | Lactic acid bacteria, bacilli, and yeast | India, Nepal | [119] |
Meitauza | Okara (soybean press cake) | China, Taiwan | [117] | |
Meju | Soybean | Fungi and bacilli ( | Korea | [115] |
Natto | Soybean | Japan, Korea | [117] | |
Oncom: Hitam (black) and merah (red) | Peanut ( | Indonesia | [118] | |
Oso | Nigeria | [117] | ||
Otiru | African yam bean ( | Nigeria | [117] | |
Owoh | African yam bean | Nigeria | [117] | |
Papad or papadam | Black gram, bengal gram, lentil ( | India | [118] | |
Pitha (chakuli, enduri, munha, chhuchipatra, podo) | Black gram dal and rice grain | Lactic acid bacteria | India | [110] |
Sepubari | Black gram dal | India | [120] | |
Soybean paste: Doenjang or jang, miso, tauco, tao chieo | Soybean, wheat or rice grains | China, Indonesia, Japan, Korea, Thailand | [117] | |
Soy sauce: Jiang you, shoyu or tamari shoyu, kanjang, kicap, kecap, taosi, ketjap, inyu | Soybean/black soybean and wheat grains | China, Japan, Korea, Malaysia, Indonesia, Philippines, Indonesia, Taiwan, Hong Kong | [117] | |
Sufu or furu | Soybean | China, Taiwan | [118, 121] | |
Tempeh | Soybean | East Java, Indonesia | [118, 122] | |
Thua nao | Soybean | Thailand | [117] | |
Tuong | Soybean | Bacilli ( | North and central Vietnam | [123] |
Vada | Legume and cereal | Lactic acid bacteria ( | India | [110] |
Ugba/ukpaka | African oil bean ( | West and Central Africa | [117] | |
Wadi | Black gram dal | Northern India | [118] | |
Yandou | Soybean | China | [117] |
Main traditional food products containing fermented legumes.
Fermentation has an important impact on the nutritional and sensory profile of legumes [2, 96]. However, production of traditional fermentation products is often managed empirically, with rudimentary equipment, and based on the activity of endogenous microorganisms [96]. The quality of raw materials as well as the biotechnologies is not standardized [96]. These products are characterized by the local cultural identity. Despite their important sensorial role in Asian food, bringing, for instance, the umami taste to the meals [126], the necessity to improve overall quality and to minimize food safety hazards has been recently highlighted [127].
LAB have an important role in some of the traditional fermented legume products (such as in vietnamese tuong and cambodian sieng), but many other microorganisms (bacteria, yeasts, and molds) are involved in spontaneous fermentation processes. Nevertheless, the advantages of legumes fermentation with LAB are gaining interest from the scientific and food industry community [2].
Besides the direct consumption as conventional dishes, legumes have a great potential as ingredients in various baked goods and pasta. Their use as fortifiers should increase their consumption as strongly recommended in many dietary guidelines. With this goal in mind, in the past decades, many researchers focused on using legume flours (also sprouted), fermented or not, as part of food formulations. Fermentation of legumes mainly determines improvement of the protein digestibility and related nutritional values and the biological availability of fibers and total phenols (Table 2). However, unlike cereal flour sourdoughs, very little is known about the microbiota of sourdough-type propagation, when only legume flour is used. Coda et al. [136] explored this topic investigating, through 16S rRNA gene pyrosequencing and culture-dependent analysis, the microbial ecology of faba bean sourdoughs obtained from an Italian and a Finnish cultivar, belonging respectively to
Legume | Fermentation type | Effects | References |
---|---|---|---|
Bean (Adzuki bean) | Increase of GABA concentration | [128] | |
Bean | Spontaneous fermentation | Decrease of α-galactosides, phytic acid, trypsin inhibitors and condensed tannins concentrations | [33] |
Bean (Kidney beans) | Spontaneous fermentation; inoculum with | Increase of GABA concentration | [129] |
Bean, chickpea, grass pea, lentil, pea (local cultivars) | Increase of phytase and antioxidant activity; increase of free amino acids, γ-aminobutyric acid (GABA), soluble fibers, and total phenols concentrations. Decrease of raffinose and condensed tannins concentrations | [34] | |
Bean, chickpea, grass pea, lentil, pea (local cultivars) | Release of lunasin-like polypeptides; inhibition of the proliferation of human adenocarcinoma Caco2 cells | [130] | |
Chickpea | Increase of free amino acid and GABA concentrations; decrease of the starch hydrolysis index (HI); increase of antioxidant activity; increased palatability and overall acceptability of bread | [131] | |
Chickpea | Synthesis of linear dextran from sucrose | [132] | |
Chickpea (black chickpea) | Increase of free amino acids, resistant starch, and protein digestibility; release of bound phenolic compounds; decrease of raffinose, condensed tannins, trypsin inhibitors, and saponins. Decrease of HI, increase of antioxidant potential and overall acceptability of fortified pasta | [133] | |
Chickpea, lentil | Increase in the concentrations of peptides, free amino acids and GABA, increase of protein digestibility and decrease of starch availability. Decrease of phytic acid, condensed tannins, raffinose concentrations and trypsin inhibitory activity | [54] | |
Cowpea | Spontaneous fermentation | Increase of lysine concentration and essential amino acids concentration | [53] |
Cowpea, mottled cowpea, speckled kidney bean, small rice bean | Spontaneous fermentation; inoculum with | Increase of antioxidant activity | [134, 135] |
Faba bean | Decrease of vicine and convicine concentration, trypsin inhibitor activity, starch hydrolysis index. Increase of protein digestibility, and free amino acids and GABA concentrations | [57] | |
Faba bean ( | Type I sourdough | Increase of free amino acid content and antioxidant activity. Decrease of α-galactosides and condensed tannins concentrations | [136] |
Faba bean (Mediterranean accessions) | Increase in the concentrations of peptides, free amino acids, and GABA, increase of protein digestibility; decrease of α-galactosides, trypsin inhibitors, condensed tannins, and vicine concentrations | [58] | |
Faba bean (high protein content) | Improved amino acid profile, increased nitrogen utilization rate and PER of bread; decrease of anti-nutritional compounds and increase antioxidant potential in bread | [137] | |
Faba bean | Synthesis of vitamin B12 | [138] | |
Faba bean | Increase of protein concentration. Increase of viscoelastic behavior, specific volume of bread. Decrease of crumb hardness of bread | [139] | |
Faba bean | Increase of protein digestibility and protein biological indexes. Increase of volume and hardness of bread. Decrease of glycemic index | [140] | |
Faba bean | Increase of protein digestibility, nutritional indexes, and resistant starch. No detrimental effect on pasta texture and cooking loss | [141] | |
Grass pea | Decrease of phytic acid concentration and trypsin inhibitory activity | [52] | |
Lentil | Release of potentially bioactive peptides having antioxidant and angiotensin I-converting enzyme (ACE) inhibitory activities | [142] | |
Lentil | Release of bioactive peptides showing ACE-inhibitory properties | [143] | |
Lentil (native and sprouted) | Synthesis of dextran from sucrose. Increase of total and soluble fiber content, specific volume and decrease of crumb hardness and staling rate in wheat bread supplemented with 30% of lentil sourdough | [144] | |
Lentil, bean, chickpea, and pea flours, raw and gelatinized | Increase of free amino acids and protein digestibility; degradation of phytic acid, condensed tannins and raffinose; decrease of trypsin inhibitory activity and starch hydrolysis index | [31] | |
Lupin | KTU05-7 and | Increase of protein bioavailability and digestibility | [145] |
Lupin | Increase of protein content; degradation of anti-nutritional factors (α-galactosides, phitic acid and alkaloids) | [146] | |
Mixture of soybean and African breadfruit | Spontaneous fermentation | Increase of protein digestibility and improvement of the sensory properties | [147] |
Mixture of chickpea/lentil/bean | Type I sourdough | Increase of free amino acid concentration; increase of antioxidant and phytase activities | [32] |
Mixture of chickpea and pseudo-cereals | Increase of free amino acid and GABA concentrations; decrease of the starch hydrolysis index (HI); increase of antioxidant activity; increased palatability and overall acceptability of bread | [131] |
Main advantages of the LAB fermentation on legume flours and legume-fortified bread.
Traditional varieties and biotypes, often replaced by modern cultivars selected for improved agronomic and commercial traits, can also be rediscovered and valorized through fermentation [34, 58, 130, 133]. Nineteen Italian legume flours, fermented with selected strains of
Nevertheless, either considering gluten-free products or wheat-based baked goods, the lack of gluten is one of the challenges deriving from the use of legumes. The addition of wheat-legume flours increases water absorption providing more water for dough starch gelatinization during baking and preventing stretching and tearing of gluten strands [53]. Substitution of wheat flour with legumes at levels higher than 20–30% causes detrimental effects on dough and bread properties, which results in sticky and excessively compact [53, 140]. Hence, maintaining good technological properties is a key factor in the success of products that go beyond laboratory-scale levels. Sourdough fermentation of legume flours, mainly interfering with starch gelatinization, and fibers hydration lead to the improvement of the structural characteristics of the fortified bread [32, 128, 148].
Fermentation can further contribute to improving the structural properties of fortified baked goods if exopolysaccharides-producing LAB are selectively employed. Indeed, the replacement of wheat flour (up to 43%) with a faba bean sourdough fermented with
The increase of the antioxidant activity during fermentation was largely documented in legume flours most likely associated with the biotransformation between soluble phenols and the release of bound phenols [31, 34, 132, 133, 134, 135, 143]. The bioconversion of phenolic compounds into more available and biologically active forms mainly relies upon acidification and microbial enzymes. In LAB phenolic compounds metabolism comes from the need to detoxify such compounds but also have a role in preserving the cellular energy balance [149, 150, 151]. Fermentation of black chickpea with
Fermentation can also be used to enhance the content of compounds lacking in vegetable matrices such as vitamin B12. Species of the former
The release of bioactive peptides showing
As an ancient practice, germination of legumes is becoming an emerging process because of the significant enhancement in bioactive components (e.g., vitamins, dietary fibers, peptides and amino acids, and phenols) and palatability. The fortification of baked goods with flours from sprouted legumes has been proposed recently [154]. During germination, reserves within the storage tissues of the seed undergo hydrolysis in low-molecular-weight compounds and mobilize to support seedling growth [155]. Parameters such as temperature, humidity, steeping (soaking), and length of germination determine the degree of these changes [156]. Nevertheless, the combination of germination and sourdough fermentation seems to better exploit the nutritional modification of grains in terms of protein and starch hydrolysis and mineral solubility [157]. Sprouting and sourdough fermentation with
Fermented sprouted flours were used to make breads with high protein digestibility and low starch availability and appreciable sensory attributes [54]. Germination followed by sourdough fermentation improved the IVPD and enhanced the sensory properties of soybean and African breadfruit seeds [147]. The same occurred for the germinated and fermented cowpea flour, which fortified the bread formula with high lysine content and optimal essential amino acid balance [53]. While more recently, sprouted lentil sourdough, added with 25% sucrose, and fermented with
Just like bread, pasta is considered a staple food worldwide with the potential to modulate the diet, and the addition of fermented legumes accounts for a further step toward this goal. Regardless, the biotechnology used for the production, higher content of proteins and fibers, and lower starch content characterize legume-containing pasta. Nonetheless, fermentation contributes to improving not only the nutritional profile, but also the technological features of fortified pasta [158].
Faba bean flour, either raw or fermented (spontaneously or with selected starters), used as dough or freeze-dried material, is among the most reported legume flours in pasta-making [141, 159, 160, 161]. The percentage of semolina replacement mostly ranges from 10 to 50% [141, 160, 161], reaching up to 100%, as in the case of gluten-free faba bean pasta described by Rosa-Sibakov and colleagues [159].
Besides the increase in proteins and dietary fibers content, which is directly proportional to the percentage of semolina replacement with both raw and fermented faba bean, as consequence of the proteolysis occurred during fermentation, a higher content of peptides and FAA was observed in pasta containing faba bean fermented by
Experimental pasta was also produced using exclusively fermented faba bean flour [159]. Whereas protein and starch content were similar between fermented and unfermented faba bean pasta (circa 35% and 43%, respectively), RS was found progressively higher in fermented fava bean pasta suggesting the possibility to use fermentation as a mean to decrease GI of commercial gluten-free products [164], usually higher than that of conventional foods [165].
Similar effects to those obtained in pasta fortified with fermented faba bean were obtained when spontaneously fermented pigeon pea (
A Mediterranean black chickpea flour was fermented with
Despite all the nutritional advantages deriving from the use of fermented legumes in pasta making, good sensory and textural properties remain a necessary foundation to achieve products approved by consumers. Differences in sensorial attributes and textural properties between pasta fortified with prefermented ingredients and the conventional one are often perceived unpleasant by trained assessors especially when semolina replacement exceeds 50% [169]. Increased chewiness, sourness, flavor, and off-flavor intensity were observed when fermented faba bean was added to pasta [159], as well as the onset of the red color, as the consequence of Maillard reaction [170]. However, fermentation also showed an important role in the improvement of sensory and textural characteristics of legume flours since it allowed the elimination of beany flavor [171]. Since the balance between flavors and off-flavors often lies in the amount of fortifier added [167], the right compromise between higher nutritional and functional properties and acceptable sensory and rheological ones should be addressed.
The rising demand for healthier plant-based food lies in the increasing awareness of the adverse risks associated with the consumption of animal proteins as well as the environmental impact animal farming entails. In this evolving agricultural system, legumes play a fundamental role in regard to both the support of good and sustainable agronomical practices and the maintenance of healthier diets.
Apart from their consumption as they are, legumes are the main ingredient of many traditional food products. Nevertheless, their consumption is often limited by antinutritional compounds and poor sensory and technological properties. Recently, the effectiveness of sourdough fermentation-inspired biotechnologies has proved to be pivotal in improving legumes and legume-based foods acceptability and safety. Through the release of bioactive peptides, phenolic compounds, and soluble fibers or the degradation of antinutritional compounds, fermentation with selected starters proved to be able to improve the nutritional and functional properties of legumes. By synthesizing exopolysaccharides, better rheological properties can be obtained while microbiological safety can be achieved through the degradation of biogenic ammines, mycotoxins, or activity toward spoilage or pathogenic microorganisms.
Fermentation allows overcoming the issues that hold back legumes’ potential and intensifies their use as ingredients in innovative formulations of staple foods, such as baked goods and pasta with a more balanced nutritional and functional profile.
The underlining idea behind functional foods is to reduce the prevalence of diet-related diseases by modulating the consumption of commonly eaten foods fortified with high-value ingredients. Fermented legumes fit the profile of such ingredients, but educating consumers on their health benefits, so that they can make an informed choice, is of paramount importance. It is necessary to get rid of the stigma of legumes as “poor man’s meat” and recognize their value not only in agricultural practices but also their pivotal role in healthy and sustainable diets. Furthermore, there is growing recognition that changes in nutrition are critical to achieve several of the Sustainable Development Goals developed by the United Nations to promote prosperity while protecting the planet. In order to meet the global food demands, focus should be put into promoting the cultivation and utilization of local or underutilized legume crops often neglected and underexploited, which yet have a great impact on the biodiversity as well as in enhancing food and nutrition security. Whereas, from an academia point of view, those mechanisms, which are still unclear or need more exploiting, behind the advantages of fermentation in terms of biopreservation and safety in general, should be pursued as research topics, since they can further unleash legumes’ potential.
The authors declare no conflict of interest.
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\\n"}]'},components:[{type:"htmlEditorComponent",content:'Copyright is the term used to describe the rights related to the publication and distribution of original Works. Most importantly from a publisher's perspective, copyright governs how Authors, publishers and the general public can use, publish, and distribute publications.
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The CC BY 3.0 and CC BY 4.0 license permits Works to be freely shared in any medium or format, as well as the reuse and adaptation of the original contents of Works (e.g. figures and tables created by the Authors), as long as the source Work is cited and its Authors are acknowledged in the following manner:
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However, some of these heavy metals in high doses can be harmful to the body while others such as cadmium, mercury, lead, chromium, silver, and arsenic in minute quantities have delirious effects in the body causing acute and chronic toxicities in humans. The focus of this chapter is to describe the various mechanism of intoxication of some selected heavy metals in humans along with their health effects. Therefore it aims to highlight on biochemical mechanisms of heavy metal intoxication which involves binding to proteins and enzymes, altering their activity and causing damage. More so, the mechanism by which heavy metals cause neurotoxicity, generate free radical which promotes oxidative stress damaging lipids, proteins and DNA molecules and how these free radicals propagate carcinogenesis are discussed. 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The aim of this chapter is to guide the researcher interested in this subject to select the appropriate assay for their study.",book:{id:"6310",slug:"genotoxicity-a-predictable-risk-to-our-actual-world",title:"Genotoxicity",fullTitle:"Genotoxicity - A Predictable Risk to Our Actual World"},signatures:"Özlem Sultan Aslantürk",authors:[{id:"211212",title:"Dr.",name:"Özlem Sultan",middleName:null,surname:"Aslantürk",slug:"ozlem-sultan-aslanturk",fullName:"Özlem Sultan Aslantürk"}]},{id:"66259",doi:"10.5772/intechopen.85270",title:"Antioxidant Compounds and Their Antioxidant Mechanism",slug:"antioxidant-compounds-and-their-antioxidant-mechanism",totalDownloads:7576,totalCrossrefCites:58,totalDimensionsCites:152,abstract:"An antioxidant is a substance that at low concentrations delays or prevents oxidation of a substrate. Antioxidant compounds act through several chemical mechanisms: hydrogen atom transfer (HAT), single electron transfer (SET), and the ability to chelate transition metals. The importance of antioxidant mechanisms is to understand the biological meaning of antioxidants, their possible uses, their production by organic synthesis or biotechnological methods, or for the standardization of the determination of antioxidant activity. In general, antioxidant molecules can react either by multiple mechanisms or by a predominant mechanism. The chemical structure of the antioxidant substance allows understanding of the antioxidant reaction mechanism. This chapter reviews the in vitro antioxidant reaction mechanisms of organic compounds polyphenols, carotenoids, and vitamins C against free radicals (FR) and prooxidant compounds under diverse conditions, as well as the most commonly used methods to evaluate the antioxidant activity of these compounds according to the mechanism involved in the reaction with free radicals and the methods of in vitro antioxidant evaluation that are used frequently depending on the reaction mechanism of the antioxidant.",book:{id:"8008",slug:"antioxidants",title:"Antioxidants",fullTitle:"Antioxidants"},signatures:"Norma Francenia Santos-Sánchez, Raúl Salas-Coronado, Claudia Villanueva-Cañongo and Beatriz Hernández-Carlos",authors:[{id:"143354",title:"Dr.",name:"Raúl",middleName:null,surname:"Salas-Coronado",slug:"raul-salas-coronado",fullName:"Raúl Salas-Coronado"},{id:"148546",title:"Dr.",name:"Norma Francenia",middleName:null,surname:"Santos-Sánchez",slug:"norma-francenia-santos-sanchez",fullName:"Norma Francenia Santos-Sánchez"},{id:"193718",title:"Dr.",name:"Beatriz",middleName:null,surname:"Hernández-Carlos",slug:"beatriz-hernandez-carlos",fullName:"Beatriz Hernández-Carlos"},{id:"278133",title:"Dr.",name:"Claudia",middleName:null,surname:"Villanueva-Cañongo",slug:"claudia-villanueva-canongo",fullName:"Claudia Villanueva-Cañongo"}]},{id:"40253",doi:"10.5772/50486",title:"Lipid Nanoparticulate Drug Delivery Systems: A Revolution in Dosage Form Design and Development",slug:"lipid-nanoparticulate-drug-delivery-systems-a-revolution-in-dosage-form-design-and-development",totalDownloads:11291,totalCrossrefCites:22,totalDimensionsCites:105,abstract:null,book:{id:"2509",slug:"recent-advances-in-novel-drug-carrier-systems",title:"Recent Advances in Novel Drug Carrier Systems",fullTitle:"Recent Advances in Novel Drug Carrier Systems"},signatures:"Anthony A. 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Among these heavy metals, a few have direct or indirect impact on the human body. Some of these heavy metals such as copper, cobalt, iron, nickel, magnesium, molybdenum, chromium, selenium, manganese and zinc have functional roles which are essential for various diverse physiological and biochemical activities in the body. However, some of these heavy metals in high doses can be harmful to the body while others such as cadmium, mercury, lead, chromium, silver, and arsenic in minute quantities have delirious effects in the body causing acute and chronic toxicities in humans. The focus of this chapter is to describe the various mechanism of intoxication of some selected heavy metals in humans along with their health effects. Therefore it aims to highlight on biochemical mechanisms of heavy metal intoxication which involves binding to proteins and enzymes, altering their activity and causing damage. More so, the mechanism by which heavy metals cause neurotoxicity, generate free radical which promotes oxidative stress damaging lipids, proteins and DNA molecules and how these free radicals propagate carcinogenesis are discussed. Alongside these mechanisms, the noxious health effects of these heavy metals are discussed.",book:{id:"7111",slug:"poisoning-in-the-modern-world-new-tricks-for-an-old-dog-",title:"Poisoning in the Modern World",fullTitle:"Poisoning in the Modern World - New Tricks for an Old Dog?"},signatures:"Godwill Azeh Engwa, Paschaline Udoka Ferdinand, Friday Nweke Nwalo and Marian N. Unachukwu",authors:[{id:"241837",title:"Mr.",name:"Godwill Azeh",middleName:null,surname:"Engwa",slug:"godwill-azeh-engwa",fullName:"Godwill Azeh Engwa"},{id:"274194",title:"BSc.",name:"Paschaline Ferdinand",middleName:null,surname:"Okeke",slug:"paschaline-ferdinand-okeke",fullName:"Paschaline Ferdinand Okeke"},{id:"286975",title:"Dr.",name:"Friday",middleName:null,surname:"Nweke Nwalo",slug:"friday-nweke-nwalo",fullName:"Friday Nweke Nwalo"},{id:"286976",title:"Dr.",name:"Marian",middleName:null,surname:"Unachukwu",slug:"marian-unachukwu",fullName:"Marian Unachukwu"}]},{id:"49459",title:"Pharmacokinetics of Drugs Following IV Bolus, IV Infusion, and Oral Administration",slug:"pharmacokinetics-of-drugs-following-iv-bolus-iv-infusion-and-oral-administration",totalDownloads:15464,totalCrossrefCites:16,totalDimensionsCites:22,abstract:null,book:{id:"4491",slug:"basic-pharmacokinetic-concepts-and-some-clinical-applications",title:"Basic Pharmacokinetic Concepts and Some Clinical Applications",fullTitle:"Basic Pharmacokinetic Concepts and Some Clinical Applications"},signatures:"Tarek A. 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Antioxidant compounds act through several chemical mechanisms: hydrogen atom transfer (HAT), single electron transfer (SET), and the ability to chelate transition metals. The importance of antioxidant mechanisms is to understand the biological meaning of antioxidants, their possible uses, their production by organic synthesis or biotechnological methods, or for the standardization of the determination of antioxidant activity. In general, antioxidant molecules can react either by multiple mechanisms or by a predominant mechanism. The chemical structure of the antioxidant substance allows understanding of the antioxidant reaction mechanism. This chapter reviews the in vitro antioxidant reaction mechanisms of organic compounds polyphenols, carotenoids, and vitamins C against free radicals (FR) and prooxidant compounds under diverse conditions, as well as the most commonly used methods to evaluate the antioxidant activity of these compounds according to the mechanism involved in the reaction with free radicals and the methods of in vitro antioxidant evaluation that are used frequently depending on the reaction mechanism of the antioxidant.",book:{id:"8008",slug:"antioxidants",title:"Antioxidants",fullTitle:"Antioxidants"},signatures:"Norma Francenia Santos-Sánchez, Raúl Salas-Coronado, Claudia Villanueva-Cañongo and Beatriz Hernández-Carlos",authors:[{id:"143354",title:"Dr.",name:"Raúl",middleName:null,surname:"Salas-Coronado",slug:"raul-salas-coronado",fullName:"Raúl Salas-Coronado"},{id:"148546",title:"Dr.",name:"Norma Francenia",middleName:null,surname:"Santos-Sánchez",slug:"norma-francenia-santos-sanchez",fullName:"Norma Francenia Santos-Sánchez"},{id:"193718",title:"Dr.",name:"Beatriz",middleName:null,surname:"Hernández-Carlos",slug:"beatriz-hernandez-carlos",fullName:"Beatriz Hernández-Carlos"},{id:"278133",title:"Dr.",name:"Claudia",middleName:null,surname:"Villanueva-Cañongo",slug:"claudia-villanueva-canongo",fullName:"Claudia Villanueva-Cañongo"}]},{id:"66742",title:"Introductory Chapter: Alkaloids - Their Importance in Nature and for Human Life",slug:"introductory-chapter-alkaloids-their-importance-in-nature-and-for-human-life",totalDownloads:4113,totalCrossrefCites:16,totalDimensionsCites:32,abstract:null,book:{id:"6828",slug:"alkaloids-their-importance-in-nature-and-human-life",title:"Alkaloids",fullTitle:"Alkaloids - Their Importance in Nature and Human Life"},signatures:"Joanna Kurek",authors:[{id:"214632",title:"Dr.",name:"Joanna",middleName:null,surname:"Kurek",slug:"joanna-kurek",fullName:"Joanna Kurek"}]}],onlineFirstChaptersFilter:{topicId:"19",limit:6,offset:0},onlineFirstChaptersCollection:[{id:"82962",title:"Pluralism Medical Treatment, Prevention, and Control of COVID-19 Infection and Its Long-Sufferings among the Older Adults in the Northeast of Thailand from 2019 to 2022",slug:"pluralism-medical-treatment-prevention-and-control-of-covid-19-infection-and-its-long-sufferings-amo",totalDownloads:11,totalDimensionsCites:0,doi:"10.5772/intechopen.106339",abstract:"COVID-19 in 2019 has brought both changes and challenges to the world. This global pandemic has an impact on people of all age levels, especially older adults. In Thailand, older persons are at high risk of COVID-19 infection. They are included in the so-called 608 groups. The objective of this review article was to synthesize and present medical pluralism, the development of drugs from herbs, and projects conducted to treat, prevent, and control the infection and long sufferings of COVID-19. The review covers 10 studies, three projects produced at Mahasarakham University, Chaiyaphum Rajabhat University, and Khon Kaen University that were reviewed, synthesized, and analyzed. The results of the synthesis indicate that modern and Thai traditional medicine can help reduce the severity of the infection and long sufferings of COVID-19. The medical pluralism between modern and Thai traditional medicine is needed to remedy COVID-19 cases among the older adults in the Northeast of Thailand.",book:{id:"11690",title:"COVID-19 Drug Development - Recent Advances, New Perspectives, and Applications",coverURL:"https://cdn.intechopen.com/books/images_new/11690.jpg"},signatures:"Pissamai Homchampa, Khemika Napattaradechanon, Parichat Yatniyom, Thawalrat Ratanasiri, Piyaporn Sansila, Thanawan Sirisuk, Thawalwong Ratanasiri and Amornrat Ratanasiri"},{id:"82353",title:"Pharmacovigilance of Biological Drugs",slug:"pharmacovigilance-of-biological-drugs",totalDownloads:4,totalDimensionsCites:0,doi:"10.5772/intechopen.105520",abstract:"The use of biological drugs has significantly increased over the past decades and has allowed for the treatment of many life-threatening and chronic diseases. The patent expiration of biological innovative medicines enables copies of these drugs called biosimilars. The availability of biosimilars enhances competition, with the potential to improve patient access to biological medications and contribute to the financial sustainability of the healthcare systems. Unlike equivalent drugs, biosimilars are not identical but similar to their innovator products because of the differences in the manufacturing process, which is a biological process. However, they are considered comparable to their originators in safety, quality characteristics, biological activity, and efficacy. The regulatory procedures used for generic drugs cannot be applied for biosimilars, so they are subjected to rigorous characterization as well as comparative clinical studies. Since they are highly complex molecules produced from living cells, even small change in the production process can have major implications on their safety and effectiveness profile, causing a potential risk of immune-based adverse reactions. For all these reasons, for biological drugs, a robust long-term pharmacovigilance system is necessary. It is desirable that in the future, there are further guidance and resolution of the ongoing discussions on biosimilar labeling, naming, pharmacovigilance and interchangeability/substitution, to ensure the appropriate use of these drugs in clinical practice.",book:{id:"11679",title:"Pharmacovigilance and Regulations",coverURL:"https://cdn.intechopen.com/books/images_new/11679.jpg"},signatures:"Simona Guerzoni, Flavia Lo Castro, Carlo Baraldi, Giuliana Colella and Luca Pani"},{id:"82868",title:"Recent Strategies for Ocular Drug Delivery: Promises and Challenges",slug:"recent-strategies-for-ocular-drug-delivery-promises-and-challenges",totalDownloads:9,totalDimensionsCites:0,doi:"10.5772/intechopen.106335",abstract:"Ocular diseases include various anterior and posterior segment diseases. Due to the unique anatomy and physiology of the eye, efficient ocular drug delivery is a great challenge to researchers. The emerging nanoscience is playing an important role in the development of novel strategies for ocular disease management. Various active molecules have been designed to associate with nanocarriers to overcome ocular barriers and interact with certain ocular tissues. In this chapter, highlights will be made on barrier to intraocular delivery, general pathways for ocular absorption, and factors affecting intraocular bioavailability. The recent attempts of nanotechnology for treating anterior and posterior ocular diseases will be explored. This will include nanomicelles, nanoparticles, nanosuspensions, vesicular systems, in situ gel, dendrimers, contact lenses, implants, microneedles, and cell-based delivery systems. In addition, gene-based ocular delivery systems will be discussed. In this chapter, we will also provide a comprehensive overview of drug-device combinations used for ocular diseases such as glaucoma, dry eye disease, infections, and inflammations. Furthermore, drug delivery devices for ocular surgeries are discussed. Finally, challenges and future prospective of ocular delivery systems will be explored.",book:{id:"11688",title:"Advances in Drug Delivery Methods",coverURL:"https://cdn.intechopen.com/books/images_new/11688.jpg"},signatures:"Amal H. El-Kamel and Asmaa A. Ashour"},{id:"82727",title:"Mesoporous Silica Based Cancer Theranostic: A Modern Approach in Upcoming Medicine",slug:"mesoporous-silica-based-cancer-theranostic-a-modern-approach-in-upcoming-medicine",totalDownloads:13,totalDimensionsCites:0,doi:"10.5772/intechopen.105447",abstract:"In case cancers are located deep inside the body and are very tough to diagnose, diagnostic tools like MRI/CT scans can be employed to detect these cancers. The major challenge in such cases is the delivery of MRI active agents or visualizing agents to the target site. In this context we will discuss different mesoporous nanoparticles that can be employed to target the tissue at a specific location, its functionalization to reach the target site (Folic acid), different simple dyes as well as specific dyes which offer theranostic functionality. The nanoparticles like mesoporous silica nanoparticles offer the possibility to load therapeutic and diagnostic agents. Its surface allow multiple functionalization and conjugations which offer target specific delivery of these agents. Moreover we will also overview different modern drug delivery inventions for offering theranostic application.",book:{id:"11688",title:"Advances in Drug Delivery Methods",coverURL:"https://cdn.intechopen.com/books/images_new/11688.jpg"},signatures:"Ajinkya Pote, Vikas Ahirrao and Vishal Pande"},{id:"82680",title:"Recent Pharmaceutical Developments in the Treatment of Cancer Using Nanosponges",slug:"recent-pharmaceutical-developments-in-the-treatment-of-cancer-using-nanosponges",totalDownloads:12,totalDimensionsCites:0,doi:"10.5772/intechopen.105817",abstract:"Nanosponges are a class of nanoparticles characterized by their sponge-like surface that ensures high loading capacity. Cancer causes high mortality and requires precise treatment without harming the body. Hence, nanoparticles are required to target medications to tumor. Nanosponges may be synthesized from various polymers and metals, giving them distinct properties. The majority of polymer synthesis entails crosslinking, while metal synthesis entails the isolation of metal nanoparticles accompanied by their assembly into sponges. Nanosponges must be functionalized to precisely attack tumors. There are several patents on nanosponges synthesis and their use. Future trends in the usage of nanosponges include simultaneous distribution of several molecules and expanding the spectrum of use from medicinal delivery to substance encapsulation for a multitude of applications. As their usage in the pharmaceutical industry grows, more emphasis should be put on toxicity-related aspects induced by the near association of cell membrane and nanosponge resulting in intracellular dissolution or reactive oxygen species (ROS) generation, which in turn damages various cellular components. Many techniques have been created to reduce toxicity, including functionalization with various materials such as antioxidants, polymers and altering nanosponges composition. As the application of nanosponges increases in many industries, the phenomenon related to toxicity must be further explored through research.",book:{id:"11688",title:"Advances in Drug Delivery Methods",coverURL:"https://cdn.intechopen.com/books/images_new/11688.jpg"},signatures:"Kapil Gore, Sankha Bhattacharya and Bhupendra G. Prajapati"},{id:"82523",title:"Trypan Blue Exclusion Assay, Neutral Red, Acridine Orange and Propidium Lodide",slug:"trypan-blue-exclusion-assay-neutral-red-acridine-orange-and-propidium-lodide",totalDownloads:12,totalDimensionsCites:0,doi:"10.5772/intechopen.105699",abstract:"Cytotoxicity and cell viability assessments are very important parameters that are widely used in fundamental research and drug development to determine the safety profile of toxic compounds. These assays measure the degree to which a substance can cause toxic damage to cells or cell death. There are different assays that have been employed to determine the cytotoxicity of substances. These assays either determine enzymatic function, cell viability, mitochondrial activity, lipid metabolism, cell proliferation and/or cell death. These assays entail use of different kinds of dyes such as trypan blue exclusion dye, neutral red, acridine orange and propidium iodide to stain the cells. Trypan blue dye permeates compromised cell membrane to stain necrotic cells. However, this can lead to false positive and false negative results as it does not provide information on sub-lethal injury. As a result, neutral red and acridine orange can be used as counterstains for trypan blue to stain the lysosome of live cells. Acridine orange can also be used to stain nucleic acids in living cells and is usually co-stained with propidium iodide or ethidium bromide. This is because propidium iodide permeates only compromised plasma membrane thus co-staining cells with these dyes can provide vital information that can be used to differentiate between live and dead cells.",book:{id:"11678",title:"Cytotoxicity",coverURL:"https://cdn.intechopen.com/books/images_new/11678.jpg"},signatures:"Arinzechukwu Ude, Kaiyven Afi-Leslie, Kelechi Okeke and Emmanuel Ogbodo"}],onlineFirstChaptersTotal:55},preDownload:{success:null,errors:{}},subscriptionForm:{success:null,errors:{}},aboutIntechopen:{},privacyPolicy:{},peerReviewing:{},howOpenAccessPublishingWithIntechopenWorks:{},sponsorshipBooks:{sponsorshipBooks:[],offset:8,limit:8,total:0},allSeries:{pteSeriesList:[{id:"14",title:"Artificial Intelligence",numberOfPublishedBooks:9,numberOfPublishedChapters:90,numberOfOpenTopics:6,numberOfUpcomingTopics:0,issn:"2633-1403",doi:"10.5772/intechopen.79920",isOpenForSubmission:!0},{id:"7",title:"Biomedical Engineering",numberOfPublishedBooks:12,numberOfPublishedChapters:107,numberOfOpenTopics:3,numberOfUpcomingTopics:0,issn:"2631-5343",doi:"10.5772/intechopen.71985",isOpenForSubmission:!0}],lsSeriesList:[{id:"11",title:"Biochemistry",numberOfPublishedBooks:33,numberOfPublishedChapters:330,numberOfOpenTopics:4,numberOfUpcomingTopics:0,issn:"2632-0983",doi:"10.5772/intechopen.72877",isOpenForSubmission:!0},{id:"25",title:"Environmental Sciences",numberOfPublishedBooks:1,numberOfPublishedChapters:19,numberOfOpenTopics:4,numberOfUpcomingTopics:0,issn:"2754-6713",doi:"10.5772/intechopen.100362",isOpenForSubmission:!0},{id:"10",title:"Physiology",numberOfPublishedBooks:14,numberOfPublishedChapters:145,numberOfOpenTopics:4,numberOfUpcomingTopics:0,issn:"2631-8261",doi:"10.5772/intechopen.72796",isOpenForSubmission:!0}],hsSeriesList:[{id:"3",title:"Dentistry",numberOfPublishedBooks:9,numberOfPublishedChapters:139,numberOfOpenTopics:2,numberOfUpcomingTopics:0,issn:"2631-6218",doi:"10.5772/intechopen.71199",isOpenForSubmission:!0},{id:"6",title:"Infectious Diseases",numberOfPublishedBooks:13,numberOfPublishedChapters:122,numberOfOpenTopics:4,numberOfUpcomingTopics:0,issn:"2631-6188",doi:"10.5772/intechopen.71852",isOpenForSubmission:!0},{id:"13",title:"Veterinary Medicine and Science",numberOfPublishedBooks:11,numberOfPublishedChapters:112,numberOfOpenTopics:3,numberOfUpcomingTopics:0,issn:"2632-0517",doi:"10.5772/intechopen.73681",isOpenForSubmission:!0}],sshSeriesList:[{id:"22",title:"Business, Management and Economics",numberOfPublishedBooks:1,numberOfPublishedChapters:21,numberOfOpenTopics:3,numberOfUpcomingTopics:0,issn:"2753-894X",doi:"10.5772/intechopen.100359",isOpenForSubmission:!0},{id:"23",title:"Education and Human Development",numberOfPublishedBooks:0,numberOfPublishedChapters:10,numberOfOpenTopics:1,numberOfUpcomingTopics:1,issn:null,doi:"10.5772/intechopen.100360",isOpenForSubmission:!0},{id:"24",title:"Sustainable Development",numberOfPublishedBooks:1,numberOfPublishedChapters:19,numberOfOpenTopics:5,numberOfUpcomingTopics:0,issn:"2753-6580",doi:"10.5772/intechopen.100361",isOpenForSubmission:!0}],testimonialsList:[{id:"6",text:"It is great to work with the IntechOpen to produce a worthwhile collection of research that also becomes a great educational resource and guide for future research endeavors.",author:{id:"259298",name:"Edward",surname:"Narayan",institutionString:null,profilePictureURL:"https://mts.intechopen.com/storage/users/259298/images/system/259298.jpeg",slug:"edward-narayan",institution:{id:"3",name:"University of Queensland",country:{id:null,name:"Australia"}}}},{id:"13",text:"The collaboration with and support of the technical staff of IntechOpen is fantastic. 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His research interests and specialties include financial econometrics, financial economics, international economics and finance, housing markets, financial markets, among others.",institutionString:null,institution:{name:"University of Southampton",institutionURL:null,country:{name:"United Kingdom"}}},editorTwo:null,editorThree:null},subseries:{paginationCount:3,paginationItems:[{id:"86",title:"Business and Management",coverUrl:"https://cdn.intechopen.com/series_topics/covers/86.jpg",isOpenForSubmission:!0,editor:{id:"128342",title:"Prof.",name:"Vito",middleName:null,surname:"Bobek",slug:"vito-bobek",fullName:"Vito Bobek",profilePictureURL:"https://mts.intechopen.com/storage/users/128342/images/system/128342.jpg",biography:"Dr. Vito Bobek works as an international management professor at the University of Applied Sciences FH Joanneum, Graz, Austria. He has published more than 400 works in his academic career and visited twenty-two universities worldwide as a visiting professor. Dr. Bobek is a member of the editorial boards of six international journals and a member of the Strategic Council of the Minister of Foreign Affairs of the Republic of Slovenia. He has a long history in academia, consulting, and entrepreneurship. His own consulting firm, Palemid, has managed twenty significant projects, such as Cooperation Program Interreg V-A (Slovenia-Austria) and Capacity Building for the Serbian Chamber of Enforcement Agents. He has also participated in many international projects in Italy, Germany, Great Britain, the United States, Spain, Turkey, France, Romania, Croatia, Montenegro, Malaysia, and China. Dr. Bobek is also a co-founder of the Academy of Regional Management in Slovenia.",institutionString:"Universities of Applied Sciences FH Joanneum, Austria",institution:{name:"Universities of Applied Sciences Joanneum",institutionURL:null,country:{name:"Austria"}}},editorTwo:{id:"293992",title:"Dr.",name:"Tatjana",middleName:null,surname:"Horvat",slug:"tatjana-horvat",fullName:"Tatjana Horvat",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002hXb0hQAC/Profile_Picture_1642419002203",biography:"Tatjana Horvat works as a professor for accountant and auditing at the University of Primorska, Slovenia. She is a Certified State Internal Auditor (licensed by Ministry of Finance RS) and Certified Internal Auditor for Business Sector and Certified accountant (licensed by Slovenian Institute of Auditors). 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He has edited more than 50 international books, presented more than 500 lectures/posters in congresses/meetings, and published more than 1,100 scientific papers in international journals.",institutionString:"Tehran University of Medical Sciences",institution:{name:"Tehran University of Medical Sciences",country:{name:"Iran"}}},{id:"180733",title:"Dr.",name:"Jean",middleName:null,surname:"Engohang-Ndong",slug:"jean-engohang-ndong",fullName:"Jean Engohang-Ndong",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/180733/images/system/180733.png",biography:"Dr. Jean Engohang-Ndong was born and raised in Gabon. After obtaining his Associate Degree of Science at the University of Science and Technology of Masuku, Gabon, he continued his education in France where he obtained his BS, MS, and Ph.D. in Medical Microbiology. He worked as a post-doctoral fellow at the Public Health Research Institute (PHRI), Newark, NJ for four years before accepting a three-year faculty position at Brigham Young University-Hawaii. Dr. Engohang-Ndong is a tenured faculty member with the academic rank of Full Professor at Kent State University, Ohio, where he teaches a wide range of biological science courses and pursues his research in medical and environmental microbiology. Recently, he expanded his research interest to epidemiology and biostatistics of chronic diseases in Gabon.",institutionString:"Kent State University",institution:{name:"Kent State University",country:{name:"United States of America"}}},{id:"188773",title:"Prof.",name:"Emmanuel",middleName:null,surname:"Drouet",slug:"emmanuel-drouet",fullName:"Emmanuel Drouet",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/188773/images/system/188773.png",biography:"Emmanuel Drouet, PharmD, is a Professor of Virology at the Faculty of Pharmacy, the University Grenoble-Alpes, France. As a head scientist at the Institute of Structural Biology in Grenoble, Dr. Drouet’s research investigates persisting viruses in humans (RNA and DNA viruses) and the balance with our host immune system. He focuses on these viruses’ effects on humans (both their impact on pathology and their symbiotic relationships in humans). He has an excellent track record in the herpesvirus field, and his group is engaged in clinical research in the field of Epstein-Barr virus diseases. He is the editor of the online Encyclopedia of Environment and he coordinates the Universal Health Coverage education program for the BioHealth Computing Schools of the European Institute of Science.",institutionString:null,institution:{name:"Grenoble Alpes University",country:{name:"France"}}},{id:"131400",title:"Prof.",name:"Alfonso J.",middleName:null,surname:"Rodriguez-Morales",slug:"alfonso-j.-rodriguez-morales",fullName:"Alfonso J. Rodriguez-Morales",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/131400/images/system/131400.png",biography:"Dr. Rodriguez-Morales is an expert in tropical and emerging diseases, particularly zoonotic and vector-borne diseases (especially arboviral diseases). He is the president of the Travel Medicine Committee of the Pan-American Infectious Diseases Association (API), as well as the president of the Colombian Association of Infectious Diseases (ACIN). He is a member of the Committee on Tropical Medicine, Zoonoses, and Travel Medicine of ACIN. He is a vice-president of the Latin American Society for Travel Medicine (SLAMVI) and a Member of the Council of the International Society for Infectious Diseases (ISID). Since 2014, he has been recognized as a Senior Researcher, at the Ministry of Science of Colombia. He is a professor at the Faculty of Medicine of the Fundacion Universitaria Autonoma de las Americas, in Pereira, Risaralda, Colombia. He is an External Professor, Master in Research on Tropical Medicine and International Health, Universitat de Barcelona, Spain. He is also a professor at the Master in Clinical Epidemiology and Biostatistics, Universidad Científica del Sur, Lima, Peru. In 2021 he has been awarded the “Raul Isturiz Award” Medal of the API. Also, in 2021, he was awarded with the “Jose Felix Patiño” Asclepius Staff Medal of the Colombian Medical College, due to his scientific contributions to COVID-19 during the pandemic. He is currently the Editor in Chief of the journal Travel Medicine and Infectious Diseases. His Scopus H index is 47 (Google Scholar H index, 68).",institutionString:"Institución Universitaria Visión de las Américas, Colombia",institution:null},{id:"332819",title:"Dr.",name:"Chukwudi Michael",middleName:"Michael",surname:"Egbuche",slug:"chukwudi-michael-egbuche",fullName:"Chukwudi Michael Egbuche",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/332819/images/14624_n.jpg",biography:"I an Dr. Chukwudi Michael Egbuche. I am a Senior Lecturer in the Department of Parasitology and Entomology, Nnamdi Azikiwe University, Awka.",institutionString:null,institution:{name:"Nnamdi Azikiwe University",country:{name:"Nigeria"}}},{id:"284232",title:"Mr.",name:"Nikunj",middleName:"U",surname:"Tandel",slug:"nikunj-tandel",fullName:"Nikunj Tandel",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/284232/images/8275_n.jpg",biography:'Mr. Nikunj Tandel has completed his Master\'s degree in Biotechnology from VIT University, India in the year of 2012. He is having 8 years of research experience especially in the field of malaria epidemiology, immunology, and nanoparticle-based drug delivery system against the infectious diseases, autoimmune disorders and cancer. He has worked for the NIH funded-International Center of Excellence in Malaria Research project "Center for the study of complex malaria in India (CSCMi)" in collaboration with New York University. The preliminary objectives of the study are to understand and develop the evidence-based tools and interventions for the control and prevention of malaria in different sites of the INDIA. Alongside, with the help of next-generation genomics study, the team has studied the antimalarial drug resistance in India. Further, he has extended his research in the development of Humanized mice for the study of liver-stage malaria and identification of molecular marker(s) for the Artemisinin resistance. At present, his research focuses on understanding the role of B cells in the activation of CD8+ T cells in malaria. Received the CSIR-SRF (Senior Research Fellow) award-2018, FIMSA (Federation of Immunological Societies of Asia-Oceania) Travel Bursary award to attend the IUIS-IIS-FIMSA Immunology course-2019',institutionString:"Nirma University",institution:{name:"Nirma University",country:{name:"India"}}},{id:"334383",title:"Ph.D.",name:"Simone",middleName:"Ulrich",surname:"Ulrich Picoli",slug:"simone-ulrich-picoli",fullName:"Simone Ulrich Picoli",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/334383/images/15919_n.jpg",biography:"Graduated in Pharmacy from Universidade Luterana do Brasil (1999), Master in Agricultural and Environmental Microbiology from Federal University of Rio Grande do Sul (2002), Specialization in Clinical Microbiology from Universidade de São Paulo, USP (2007) and PhD in Sciences in Gastroenterology and Hepatology (2012). She is currently an Adjunct Professor at Feevale University in Medicine and Biomedicine courses and a permanent professor of the Academic Master\\'s Degree in Virology. She has experience in the field of Microbiology, with an emphasis on Bacteriology, working mainly on the following topics: bacteriophages, bacterial resistance, clinical microbiology and food microbiology.",institutionString:null,institution:{name:"Universidade Feevale",country:{name:"Brazil"}}},{id:"229220",title:"Dr.",name:"Amjad",middleName:"Islam",surname:"Aqib",slug:"amjad-aqib",fullName:"Amjad Aqib",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/229220/images/system/229220.png",biography:"Dr. Amjad Islam Aqib obtained a DVM and MSc (Hons) from University of Agriculture Faisalabad (UAF), Pakistan, and a PhD from the University of Veterinary and Animal Sciences Lahore, Pakistan. Dr. Aqib joined the Department of Clinical Medicine and Surgery at UAF for one year as an assistant professor where he developed a research laboratory designated for pathogenic bacteria. Since 2018, he has been Assistant Professor/Officer in-charge, Department of Medicine, Manager Research Operations and Development-ORIC, and President One Health Club at Cholistan University of Veterinary and Animal Sciences, Bahawalpur, Pakistan. He has nearly 100 publications to his credit. His research interests include epidemiological patterns and molecular analysis of antimicrobial resistance and modulation and vaccine development against animal pathogens of public health concern.",institutionString:"Cholistan University of Veterinary and Animal Sciences",institution:{name:"University of Agriculture Faisalabad",country:{name:"Pakistan"}}},{id:"333753",title:"Dr.",name:"Rais",middleName:null,surname:"Ahmed",slug:"rais-ahmed",fullName:"Rais Ahmed",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/333753/images/20168_n.jpg",biography:null,institutionString:null,institution:{name:"University of Agriculture Faisalabad",country:{name:"Pakistan"}}},{id:"62900",title:"Prof.",name:"Fethi",middleName:null,surname:"Derbel",slug:"fethi-derbel",fullName:"Fethi Derbel",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/62900/images/system/62900.jpeg",biography:"Professor Fethi Derbel was born in 1960 in Tunisia. He received his medical degree from the Sousse Faculty of Medicine at Sousse, University of Sousse, Tunisia. He completed his surgical residency in General Surgery at the University Hospital Farhat Hached of Sousse and was a member of the Unit of Liver Transplantation in the University of Rennes, France. He then worked in the Department of Surgery at the Sahloul University Hospital in Sousse. Professor Derbel is presently working at the Clinique les Oliviers, Sousse, Tunisia. His hospital activities are mostly concerned with laparoscopic, colorectal, pancreatic, hepatobiliary, and gastric surgery. He is also very interested in hernia surgery and performs ventral hernia repairs and inguinal hernia repairs. He has been a member of the GREPA and Tunisian Hernia Society (THS). During his residency, he managed patients suffering from diabetic foot, and he was very interested in this pathology. For this reason, he decided to coordinate a book project dealing with the diabetic foot. Professor Derbel has published many articles in journals and collaborates intensively with IntechOpen Access Publisher as an editor.",institutionString:"Clinique les Oliviers",institution:null},{id:"300144",title:"Dr.",name:"Meriem",middleName:null,surname:"Braiki",slug:"meriem-braiki",fullName:"Meriem Braiki",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/300144/images/system/300144.jpg",biography:"Dr. Meriem Braiki is a specialist in pediatric surgeon from Tunisia. She was born in 1985. She received her medical degree from the University of Medicine at Sousse, Tunisia. She achieved her surgical residency training periods in Pediatric Surgery departments at University Hospitals in Monastir, Tunis and France.\r\nShe is currently working at the Pediatric surgery department, Sidi Bouzid Hospital, Tunisia. Her hospital activities are mostly concerned with laparoscopic, parietal, urological and digestive surgery. She has published several articles in diffrent journals.",institutionString:"Sidi Bouzid Regional Hospital",institution:null},{id:"229481",title:"Dr.",name:"Erika M.",middleName:"Martins",surname:"de Carvalho",slug:"erika-m.-de-carvalho",fullName:"Erika M. de Carvalho",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/229481/images/6397_n.jpg",biography:null,institutionString:null,institution:{name:"Oswaldo Cruz Foundation",country:{name:"Brazil"}}},{id:"186537",title:"Prof.",name:"Tonay",middleName:null,surname:"Inceboz",slug:"tonay-inceboz",fullName:"Tonay Inceboz",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/186537/images/system/186537.jfif",biography:"I was graduated from Ege University of Medical Faculty (Turkey) in 1988 and completed his Med. PhD degree in Medical Parasitology at the same university. I became an Associate Professor in 2008 and Professor in 2014. I am currently working as a Professor at the Department of Medical Parasitology at Dokuz Eylul University, Izmir, Turkey.\n\nI have given many lectures, presentations in different academic meetings. I have more than 60 articles in peer-reviewed journals, 18 book chapters, 1 book editorship.\n\nMy research interests are Echinococcus granulosus, Echinococcus multilocularis (diagnosis, life cycle, in vitro and in vivo cultivation), and Trichomonas vaginalis (diagnosis, PCR, and in vitro cultivation).",institutionString:"Dokuz Eylül University",institution:{name:"Dokuz Eylül University",country:{name:"Turkey"}}},{id:"71812",title:"Prof.",name:"Hanem Fathy",middleName:"Fathy",surname:"Khater",slug:"hanem-fathy-khater",fullName:"Hanem Fathy Khater",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/71812/images/1167_n.jpg",biography:"Prof. Khater is a Professor of Parasitology at Benha University, Egypt. She studied for her doctoral degree, at the Department of Entomology, College of Agriculture, Food and Natural Resources, University of Missouri, Columbia, USA. She has completed her Ph.D. degrees in Parasitology in Egypt, from where she got the award for “the best scientific Ph.D. dissertation”. She worked at the School of Biological Sciences, Bristol, England, the UK in controlling insects of medical and veterinary importance as a grant from Newton Mosharafa, the British Council. Her research is focused on searching of pesticides against mosquitoes, house flies, lice, green bottle fly, camel nasal botfly, soft and hard ticks, mites, and the diamondback moth as well as control of several parasites using safe and natural materials to avoid drug resistances and environmental contamination.",institutionString:null,institution:{name:"Banha University",country:{name:"Egypt"}}},{id:"99780",title:"Prof.",name:"Omolade",middleName:"Olayinka",surname:"Okwa",slug:"omolade-okwa",fullName:"Omolade Okwa",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/99780/images/system/99780.jpg",biography:"Omolade Olayinka Okwa is presently a Professor of Parasitology at Lagos State University, Nigeria. She has a PhD in Parasitology (1997), an MSc in Cellular Parasitology (1992), and a BSc (Hons) Zoology (1990) all from the University of Ibadan, Nigeria. She teaches parasitology at the undergraduate and postgraduate levels. She was a recipient of a Commonwealth fellowship supported by British Council tenable at the Centre for Entomology and Parasitology (CAEP), Keele University, United Kingdom between 2004 and 2005. She was awarded an Honorary Visiting Research Fellow at the same university from 2005 to 2007. \nShe has been an external examiner to the Department of Veterinary Microbiology and Parasitology, University of Ibadan, MSc programme between 2010 and 2012. She is a member of the Nigerian Society of Experimental Biology (NISEB), Parasitology and Public Health Society of Nigeria (PPSN), Science Association of Nigeria (SAN), Zoological Society of Nigeria (ZSN), and is Vice Chairperson of the Organisation of Women in Science (OWSG), LASU chapter. She served as Head of Department of Zoology and Environmental Biology, Lagos State University from 2007 to 2010 and 2014 to 2016. She is a reviewer for several local and international journals such as Unilag Journal of Science, Libyan Journal of Medicine, Journal of Medicine and Medical Sciences, and Annual Research and Review in Science. \nShe has authored 45 scientific research publications in local and international journals, 8 scientific reviews, 4 books, and 3 book chapters, which includes the books “Malaria Parasites” and “Malaria” which are IntechOpen access publications.",institutionString:"Lagos State University",institution:{name:"Lagos State University",country:{name:"Nigeria"}}},{id:"273100",title:"Dr.",name:"Vijay",middleName:null,surname:"Gayam",slug:"vijay-gayam",fullName:"Vijay Gayam",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/273100/images/system/273100.jpeg",biography:"Dr. Vijay Bhaskar Reddy Gayam is currently practicing as an internist at Interfaith Medical Center in Brooklyn, New York, USA. He is also a Clinical Assistant Professor at the SUNY Downstate University Hospital and Adjunct Professor of Medicine at the American University of Antigua. He is a holder of an M.B.B.S. degree bestowed to him by Osmania Medical College and received his M.D. at Interfaith Medical Center. His career goals thus far have heavily focused on direct patient care, medical education, and clinical research. He currently serves in two leadership capacities; Assistant Program Director of Medicine at Interfaith Medical Center and as a Councilor for the American\r\nFederation for Medical Research. As a true academician and researcher, he has more than 50 papers indexed in international peer-reviewed journals. He has also presented numerous papers in multiple national and international scientific conferences. His areas of research interest include general internal medicine, gastroenterology and hepatology. He serves as an editor, editorial board member and reviewer for multiple international journals. His research on Hepatitis C has been very successful and has led to multiple research awards, including the 'Equity in Prevention and Treatment Award” from the New York Department of Health Viral Hepatitis Symposium (2018) and the 'Presidential Poster Award” awarded to him by the American College of Gastroenterology (2018). He was also awarded 'Outstanding Clinician in General Medicine” by Venus International Foundation for his extensive research expertise and services, perform over and above the standard expected in the advancement of healthcare, patient safety and quality of care.",institutionString:"Interfaith Medical Center",institution:{name:"Interfaith Medical Center",country:{name:"United States of America"}}},{id:"93517",title:"Dr.",name:"Clement",middleName:"Adebajo",surname:"Meseko",slug:"clement-meseko",fullName:"Clement Meseko",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/93517/images/system/93517.jpg",biography:"Dr. Clement Meseko obtained DVM and PhD degree in Veterinary Medicine and Virology respectively. He has worked for over 20 years in both private and public sectors including the academia, contributing to knowledge and control of infectious disease. Through the application of epidemiological skill, classical and molecular virological skills, he investigates viruses of economic and public health importance for the mitigation of the negative impact on people, animal and the environment in the context of Onehealth. \r\nDr. Meseko’s field experience on animal and zoonotic diseases and pathogen dynamics at the human-animal interface over the years shaped his carrier in research and scientific inquiries. He has been part of the investigation of Highly Pathogenic Avian Influenza incursions in sub Saharan Africa and monitors swine Influenza (Pandemic influenza Virus) agro-ecology and potential for interspecies transmission. He has authored and reviewed a number of journal articles and book chapters.",institutionString:"National Veterinary Research Institute",institution:{name:"National Veterinary Research Institute",country:{name:"Nigeria"}}},{id:"158026",title:"Prof.",name:"Shailendra K.",middleName:null,surname:"Saxena",slug:"shailendra-k.-saxena",fullName:"Shailendra K. Saxena",position:null,profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRET3QAO/Profile_Picture_2022-05-10T10:10:26.jpeg",biography:"Professor Dr. Shailendra K. Saxena is a vice dean and professor at King George's Medical University, Lucknow, India. His research interests involve understanding the molecular mechanisms of host defense during human viral infections and developing new predictive, preventive, and therapeutic strategies for them using Japanese encephalitis virus (JEV), HIV, and emerging viruses as a model via stem cell and cell culture technologies. His research work has been published in various high-impact factor journals (Science, PNAS, Nature Medicine) with a high number of citations. He has received many awards and honors in India and abroad including various Young Scientist Awards, BBSRC India Partnering Award, and Dr. JC Bose National Award of Department of Biotechnology, Min. of Science and Technology, Govt. of India. Dr. Saxena is a fellow of various international societies/academies including the Royal College of Pathologists, United Kingdom; Royal Society of Medicine, London; Royal Society of Biology, United Kingdom; Royal Society of Chemistry, London; and Academy of Translational Medicine Professionals, Austria. He was named a Global Leader in Science by The Scientist. He is also an international opinion leader/expert in vaccination for Japanese encephalitis by IPIC (UK).",institutionString:"King George's Medical University",institution:{name:"King George's Medical University",country:{name:"India"}}},{id:"94928",title:"Dr.",name:"Takuo",middleName:null,surname:"Mizukami",slug:"takuo-mizukami",fullName:"Takuo Mizukami",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/94928/images/6402_n.jpg",biography:null,institutionString:null,institution:{name:"National Institute of Infectious Diseases",country:{name:"Japan"}}},{id:"233433",title:"Dr.",name:"Yulia",middleName:null,surname:"Desheva",slug:"yulia-desheva",fullName:"Yulia Desheva",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/233433/images/system/233433.png",biography:"Dr. Yulia Desheva is a leading researcher at the Institute of Experimental Medicine, St. Petersburg, Russia. She is a professor in the Stomatology Faculty, St. Petersburg State University. She has expertise in the development and evaluation of a wide range of live mucosal vaccines against influenza and bacterial complications. Her research interests include immunity against influenza and COVID-19 and the development of immunization schemes for high-risk individuals.",institutionString:'Federal State Budgetary Scientific Institution "Institute of Experimental Medicine"',institution:null},{id:"238958",title:"Mr.",name:"Atamjit",middleName:null,surname:"Singh",slug:"atamjit-singh",fullName:"Atamjit Singh",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/238958/images/6575_n.jpg",biography:null,institutionString:null,institution:null},{id:"252058",title:"M.Sc.",name:"Juan",middleName:null,surname:"Sulca",slug:"juan-sulca",fullName:"Juan Sulca",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/252058/images/12834_n.jpg",biography:null,institutionString:null,institution:null},{id:"191392",title:"Dr.",name:"Marimuthu",middleName:null,surname:"Govindarajan",slug:"marimuthu-govindarajan",fullName:"Marimuthu Govindarajan",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/191392/images/5828_n.jpg",biography:"Dr. M. Govindarajan completed his BSc degree in Zoology at Government Arts College (Autonomous), Kumbakonam, and MSc, MPhil, and PhD degrees at Annamalai University, Annamalai Nagar, Tamil Nadu, India. He is serving as an assistant professor at the Department of Zoology, Annamalai University. His research interests include isolation, identification, and characterization of biologically active molecules from plants and microbes. He has identified more than 20 pure compounds with high mosquitocidal activity and also conducted high-quality research on photochemistry and nanosynthesis. He has published more than 150 studies in journals with impact factor and 2 books in Lambert Academic Publishing, Germany. He serves as an editorial board member in various national and international scientific journals.",institutionString:null,institution:null},{id:"274660",title:"Dr.",name:"Damodar",middleName:null,surname:"Paudel",slug:"damodar-paudel",fullName:"Damodar Paudel",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/274660/images/8176_n.jpg",biography:"I am DrDamodar Paudel,currently working as consultant Physician in Nepal police Hospital.",institutionString:null,institution:null},{id:"241562",title:"Dr.",name:"Melvin",middleName:null,surname:"Sanicas",slug:"melvin-sanicas",fullName:"Melvin Sanicas",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/241562/images/6699_n.jpg",biography:null,institutionString:null,institution:null},{id:"322007",title:"Dr.",name:"Maria Elizbeth",middleName:null,surname:"Alvarez-Sánchez",slug:"maria-elizbeth-alvarez-sanchez",fullName:"Maria Elizbeth Alvarez-Sánchez",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Universidad Autónoma de la Ciudad de México",country:{name:"Mexico"}}},{id:"337443",title:"Dr.",name:"Juan",middleName:null,surname:"A. Gonzalez-Sanchez",slug:"juan-a.-gonzalez-sanchez",fullName:"Juan A. Gonzalez-Sanchez",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"University of Puerto Rico System",country:{name:"United States of America"}}},{id:"337446",title:"Dr.",name:"Maria",middleName:null,surname:"Zavala-Colon",slug:"maria-zavala-colon",fullName:"Maria Zavala-Colon",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"University of Puerto Rico, Medical Sciences Campus",country:{name:"United States of America"}}},{id:"338856",title:"Mrs.",name:"Nur Alvira",middleName:null,surname:"Pascawati",slug:"nur-alvira-pascawati",fullName:"Nur Alvira Pascawati",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Universitas Respati Yogyakarta",country:{name:"Indonesia"}}}]}},subseries:{item:{id:"94",type:"subseries",title:"Climate Change and Environmental Sustainability",keywords:"Environmental Protection, Socio-economic Development, Resource Exploitation, Environmental Degradation, Climate Change, Degraded Ecosystems, Biodiversity Loss",scope:"\r\n\tSustainable development focuses on linking economic development with environmental protection and social development to ensure future prosperity for people and the planet. To tackle global challenges of development and environment, the United Nations General Assembly in 2015 adopted the 17 Sustainable Development Goals. SDGs emphasize that environmental sustainability should be strongly linked to socio-economic development, which should be decoupled from escalating resource use and environmental degradation for the purpose of reducing environmental stress, enhancing human welfare, and improving regional equity. Moreover, sustainable development seeks a balance between human development and decrease in ecological/environmental marginal benefits. Under the increasing stress of climate change, many environmental problems have emerged causing severe impacts at both global and local scales, driving ecosystem service reduction and biodiversity loss. Humanity’s relationship with resource exploitation and environment protection is a major global concern, as new threats to human and environmental security emerge in the Anthropocene. Currently, the world is facing significant challenges in environmental sustainability to protect global environments and to restore degraded ecosystems, while maintaining human development with regional equality. Thus, environmental sustainability with healthy natural ecosystems is critical to maintaining human prosperity in our warming planet.
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