Malnutrition classification of children based on Z scores [20].
\\n\\n
Released this past November, the list is based on data collected from the Web of Science and highlights some of the world’s most influential scientific minds by naming the researchers whose publications over the previous decade have included a high number of Highly Cited Papers placing them among the top 1% most-cited.
\\n\\nWe wish to congratulate all of the researchers named and especially our authors on this amazing accomplishment! We are happy and proud to share in their success!
\\n"}]',published:!0,mainMedia:null},components:[{type:"htmlEditorComponent",content:'IntechOpen is proud to announce that 179 of our authors have made the Clarivate™ Highly Cited Researchers List for 2020, ranking them among the top 1% most-cited.
\n\nThroughout the years, the list has named a total of 252 IntechOpen authors as Highly Cited. Of those researchers, 69 have been featured on the list multiple times.
\n\n\n\nReleased this past November, the list is based on data collected from the Web of Science and highlights some of the world’s most influential scientific minds by naming the researchers whose publications over the previous decade have included a high number of Highly Cited Papers placing them among the top 1% most-cited.
\n\nWe wish to congratulate all of the researchers named and especially our authors on this amazing accomplishment! We are happy and proud to share in their success!
\n'}],latestNews:[{slug:"intechopen-authors-included-in-the-highly-cited-researchers-list-for-2020-20210121",title:"IntechOpen Authors Included in the Highly Cited Researchers List for 2020"},{slug:"intechopen-maintains-position-as-the-world-s-largest-oa-book-publisher-20201218",title:"IntechOpen Maintains Position as the World’s Largest OA Book Publisher"},{slug:"all-intechopen-books-available-on-perlego-20201215",title:"All IntechOpen Books Available on Perlego"},{slug:"oiv-awards-recognizes-intechopen-s-editors-20201127",title:"OIV Awards Recognizes IntechOpen's Editors"},{slug:"intechopen-joins-crossref-s-initiative-for-open-abstracts-i4oa-to-boost-the-discovery-of-research-20201005",title:"IntechOpen joins Crossref's Initiative for Open Abstracts (I4OA) to Boost the Discovery of Research"},{slug:"intechopen-hits-milestone-5-000-open-access-books-published-20200908",title:"IntechOpen hits milestone: 5,000 Open Access books published!"},{slug:"intechopen-books-hosted-on-the-mathworks-book-program-20200819",title:"IntechOpen Books Hosted on the MathWorks Book Program"},{slug:"intechopen-s-chapter-awarded-the-guenther-von-pannewitz-preis-2020-20200715",title:"IntechOpen's Chapter Awarded the Günther-von-Pannewitz-Preis 2020"}]},book:{item:{type:"book",id:"8907",leadTitle:null,fullTitle:"Advances in Quantum Communication and Information",title:"Advances in Quantum Communication and Information",subtitle:null,reviewType:"peer-reviewed",abstract:"Research in quantum communication requires knowledge of the information theory and teleportation of information in any physical form using the quantum field as media of all different processes and aspects on the managing of their information, their boson processing and their technologies. 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Development of methods for lignocellulosic hydrolysis and the use of several biomass substrates have been contributing for the advancement of second-generation biofuel production.
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Her current work is on metabolomics and proteomics of fermentation processes.",coeditorOneBiosketch:null,coeditorTwoBiosketch:null,coeditorThreeBiosketch:null,coeditorFourBiosketch:null,coeditorFiveBiosketch:null,editors:[{id:"139174",title:"Ph.D.",name:"Thalita",middleName:null,surname:"Peixoto Basso",slug:"thalita-peixoto-basso",fullName:"Thalita Peixoto Basso",profilePictureURL:"https://mts.intechopen.com/storage/users/139174/images/system/139174.jpg",biography:"Thalita Peixoto Basso received her Bachelor’s degree in Agriculture Engineering from Londrina State University (PR-Brazil). During this period, she studied the fermentation characteristics of Saccharomyces cerevisiae isolated from ethanol industrial processes.\nShe obtained her Master’s degree in Science from the Agrifood Industry, Food and Nutrition Department of the University of Sao Paulo (ESALQ/USP, SP-Brazil). During this time, she isolated and selected fungi with high cellulose activity for the enzymatic hydrolysis of sugarcane bagasse. \nShe received her Ph.D. in Science from the Soil Science Department (Agricultural Microbiology Program) at ESALQ/USP, with a period of one year as a visiting scholar at the University of California Berkeley and Energy Bioscience Institute. Meanwhile, she worked on the improvement of S. cerevisiae by hybridization for increased tolerance toward inhibitors from second-generation ethanol substrates.\nCurrently, she is Collaborating Professor of Cell Biology and Molecular Genetics at University of Sao Paulo. Additionally she is Postdoctoral Fellow working with metabolomics and proteomics of fermentation processes at the Genetics Department from ESALQ/USP.",institutionString:"University of São Paulo",position:null,outsideEditionCount:0,totalCites:0,totalAuthoredChapters:"2",totalChapterViews:"0",totalEditedBooks:"2",institution:{name:"University of Sao Paulo",institutionURL:null,country:{name:"Brazil"}}}],coeditorOne:{id:"27117",title:"Dr.",name:"Thiago Olitta",middleName:null,surname:"Basso",slug:"thiago-olitta-basso",fullName:"Thiago Olitta Basso",profilePictureURL:"https://mts.intechopen.com/storage/users/27117/images/system/27117.jpg",biography:"Thiago Olitta Basso is an Assistant Professor in the Chemical Engineering Department of the University of Sao Paulo (USP). Currently, he is involved with teaching and researching microbial physiology, microbial interactions, and strategies of metabolic and evolutionary engineering for the biotechnology sector. He worked at Novozymes as a Senior Scientist in its R&D Department, acting as a project leader in yeast physiology and fermentation optimization for traditional (1G) and advanced (2G) biofuels. He holds a B.S. degree in Pharmacy and Biochemistry by USP and a MSc. degree in Biotechnology by the University of Abertay, Scotland, UK. He obtained his PhD in Biotechnology at USP, under Andreas Gombert´s supervision. Part of his PhD was done at Delft University of Technology, in the group of Prof. Jack Pronk.",institutionString:"University of Sao Paulo",position:null,outsideEditionCount:0,totalCites:0,totalAuthoredChapters:"2",totalChapterViews:"0",totalEditedBooks:"0",institution:{name:"University of Sao Paulo",institutionURL:null,country:{name:"Brazil"}}},coeditorTwo:null,coeditorThree:null,coeditorFour:null,coeditorFive:null,topics:[{id:"12",title:"Environmental Sciences",slug:"environmental-sciences"}],chapters:[{id:"72179",title:"Production Pathways of Acetic Acid and Its Versatile Applications in the Food Industry",slug:"production-pathways-of-acetic-acid-and-its-versatile-applications-in-the-food-industry",totalDownloads:326,totalCrossrefCites:0,authors:[null]},{id:"71830",title:"Xylanase and Its Industrial Applications",slug:"xylanase-and-its-industrial-applications",totalDownloads:113,totalCrossrefCites:0,authors:[null]},{id:"74170",title:"Composting of Pig Effluent as a Proposal for the 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Venkateswarlu",coverURL:"https://cdn.intechopen.com/books/images_new/371.jpg",editedByType:"Edited by",editors:[{id:"58592",title:"Dr.",name:"Arun",surname:"Shanker",slug:"arun-shanker",fullName:"Arun Shanker"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"878",title:"Phytochemicals",subtitle:"A Global Perspective of Their Role in Nutrition and Health",isOpenForSubmission:!1,hash:"ec77671f63975ef2d16192897deb6835",slug:"phytochemicals-a-global-perspective-of-their-role-in-nutrition-and-health",bookSignature:"Venketeshwer Rao",coverURL:"https://cdn.intechopen.com/books/images_new/878.jpg",editedByType:"Edited by",editors:[{id:"82663",title:"Dr.",name:"Venketeshwer",surname:"Rao",slug:"venketeshwer-rao",fullName:"Venketeshwer Rao"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}}]},chapter:{item:{type:"chapter",id:"61311",title:"Multimode Hyperspectral Imaging for Food Quality and Safety",doi:"10.5772/intechopen.76358",slug:"multimode-hyperspectral-imaging-for-food-quality-and-safety",body:'There is a great need to assess the composition of food, quantitatively and reproducibly, in order to avoid any unintended scenarios, ranging from a product not being quite what it is stated to be (e.g., lesser quality fish or olive oil) to intentional adulteration (including by terrorist intent) to random contamination (such as by bacteria that can be lethal). These constitute the application domain of, respectively, food quality, food defense, and food safety. Given the place food occupies in society, and the possible extreme implications of any negative events, there is great interest in bringing the best testing to the task of ensuring the quality and safety of our food supply. Unfortunately, some of the currently prevalent methods (molecular/biochemical/biophysical, such as polymerase chain reaction (PCR), chromatography, mass spectrometry, etc.) are intrinsically too slow to yield results in real time, and also rely on random and very sparse sampling. We believe that the power of light as an investigational tool can be brought to the resulting challenge and focus on this possibility here.
Optical imaging is an approach rapidly growing in popularity and applications due to technological advances that have enabled the production of smaller, less expensive, more efficient, and faster light sources and detectors. These new technologies have facilitated the acquisition of more accurate optical image sets, yielding molecular, structural, and physiological information from targeted samples. There are many different optical measurement techniques used by industry and academic researchers alike, with each technology usually focusing on a specific property of light (intensity, polarization, wavelength, coherence, temporal change, etc.). We believe, however, that no single method can provide the comprehensive analysis of food that is required.
When applied to food samples, the accuracy of optical detection techniques can be limited due to factors such as low penetration depth and lack of contrast, especially for low biomarker concentrations. However, using a strategic combination of multiple optical detection technologies in an optical system that thus becomes multimode, the chemical and/or biological detection accuracy can be substantially improved. Each individual detection method can provide a specific and complementary (sometimes even synergetic) piece of information regarding the sample being examined. Thus, by combining a number of these methods, the impact of the individual limitations can be minimized, and their combined strengths may be harnessed to deliver highly specific results.
The advantages of multimode optical imaging include greatly reducing the time required for the initial detection and enumeration of contaminants, with minimal sample preparation, nondestructive evaluation, fast acquisition times, and visualization of the spatial distribution of numerous components simultaneously. These advantages are highly useful in detecting contaminants in food for assessing safety and quality, and the use of multiple modes of detection, properly combined, is essential for effectiveness and performance.
We summarize here optical technologies which are useful in food safety and quality applications, highlighting both successes and limitations, thus underscoring the usefulness of the new, multimode approach we propose.
Hyperspectral imaging (HSI) is a growing platform technology that functions by integrating conventional imaging and spectroscopy to gain spatial and spectral information from an object [1]. It is capable of capturing reflectance, transmittance, and fluorescence images in the visible and infrared regions with submillimeter spatial resolution [2] and high spectral resolution (10 nm). While HSI was originally developed for remote sensing [3], it has gained popularity in the field of food safety and analysis with new applications reported in fruits and vegetables [4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 34, 37, 42], poultry [21, 22, 23, 24, 25], and meat [26, 27, 28]. Some advantages HSI has in comparison with other techniques such as RGB imaging, NIR spectroscopy, and multicolor imaging include being able to produce spatial and spectral information, multiconstituent information, and sensitivity to minor components [1].
HSI in the near infrared (NIR) can provide chemical composition of red meat such as prediction of fat, protein, and water content of lamb meat [32]. Moreover, this method enables the detection of certain bacteria in food, such as E. coli [33]. Fungal growth on food products is of particular concern due to the potential for detrimental effects on population health ranging from allergic reactions and respiratory problems to the production of mycotoxins. HSI has been deployed to identify fungal species such as Aspergillus flavus, Aspergillus parasiticus, Aspergillus niger, and Fusarium spp. which can produce mycotoxins, which are secondary metabolites that are toxic for humans and animals [36, 37].
A common source of contamination for fresh products and other raw materials used to produce food is fecal contamination; hence it would be highly desirable to develop an automatic inspection system for use in the field and on processing lines. Multispectral detection of fecal contamination on apples using HSI imaging was demonstrated by Kim et al. [45]. A HSI system with a range of 450–851 nm was used to examine reflectance images of experimentally contaminated apples. Fecal contamination sites were evaluated using principal component analysis (PCA) with the goal of identifying two to four wavelengths that could be used in an online multispectral imaging system. As shown in Figure 1, their results showed that contamination could be identified using either of three wavelengths in the green, red, and NIR regions.
First and second principal component images obtained using 748–851 nm region of the hyperspectral reflectance image data for (A) fuji, (B) gala, (C) golden delicious, and (D) red delicious apples [45].
With the use of HSI in the spectral range of 400–1000 nm, E. coli loads in grass carp fish have been measured to evaluate microbial spoilage. In 2015, the researchers demonstrated that reflectance HSI in combination with multivariate analysis had the ability to rapidly and noninvasively quantify and visualize the E. coli loads in grass carp fish flesh during the spoilage process [35]. Distribution maps, shown in Figure 2, were created to allow for visualization of E. coli contamination. These distribution maps were vital in that they provided more detailed information of postmortem spoilage development in grass carp flesh. One of the main advantages that HSI has over conventional spectroscopy methods is its ability to visualize distribution maps of the contamination in a pixel-wise manner. By multiplying the regression coefficients of the multiple linear regression model by the spectrum of each pixel in the image, a prediction map was generated for showing the distribution of E. coli within the fish flesh. The different E. coli loads were represented by different colors from blue to red. As E. coli load increased, the color of the images shifted from blue to red, reflecting the growth of bacteria.
These are examples of distribution maps of E. coli loads in fish filets. The distribution maps showed how the level of E. coli contamination varied from one sample to the next. A shift in color intensity is seen from blue to red, reflecting the increase in E. coli contamination [35].
In 2013, Feng et al. [36] presented HSI as a nondestructive tool for direct, quantitative determination of Enterobacteriaceae loads on chicken fillets. The authors developed partial least squares regression (PLSR) models and root mean squared errors. After a simplified model was developed, the PLSR model, it was used for predicting Enterobacteriaceae loads in every pixel of the image acquired from HSI, resulting in a new image called a “prediction map.” In this prediction map, a color scale was used to describe the different microbial loads in each spot of the sample. As shown in Figure 3, when the microbial loads increase, the images shift from a blue color to a more reddish one, this reflects the growth of bacteria on the chicken fillets.
This is an image of a median-filtered prediction map for validation set using the simplified PLSR model built on three wavelengths (930, 1121, and 1345 nm). Values under each sample predict the Enterobacteriaceae counts (in log10 CFU g−1) [36].
Changes in temperature during cold storage of meat products can lead to undesirable microbial growths, which may affect food safety. A study of the spoilage of beef was reported by Peng et al. [41]; in this work, HSI was exploited to measure biochemical changes within the fresh beef. The research demonstrated that HSI showed potential for real-time and nondestructive detection of bacterial spoilage in beef.
Work performed by Barbin et al. [43] used HSI in the near-infrared range (900–1700 nm) to determine the total viable count and psychotropic plate count in chilled pork during storage. NIR hyperspectral images in the reflectance mode were captured every 48 h from each sample. Assuming that meat spoilage is evident at a microbial load of 107 CFU per gram or cm2, the author’s defined a cutoff point of 106 CFU/g as an acceptable threshold of freshness. By examining the spectral information that was obtained from the samples, a difference was observed in the wavelength range between 1300 and 1600 nm, where fresh samples had lower absorbance than spoiled samples (see Figure 4). This spectral region is commonly assigned to N-H stretch of proteins (amines and amides) and their interactions with water, and it could suggest the occurrence of proteolytic changes, which are recognized as the main indicator for the onset of spoilage in meat products.
(a) absorbance spectra for fresh and spoiled samples (after 7 days of storage); (b) second derivative spectra for fresh and spoiled samples showing potentially relevant wavelengths [43].
In 2016, Everard et al. [51] presented fluorescence HSI coupled with multivariate image analysis techniques utilized for the detection of fecal contaminates on spinach leaves. Violet fluorescence excitation was provided at 405 nm, and light emission was recorded from 464 to 800 nm. Partial least square discriminant analysis (PLSDA) and wavelength ratio methods were compared for detection accuracy for fecal contamination. The PLSDA model had 19% false positives for nonfresh post storage leaves. A wavelength ratio technique using four wavebands (680, 688, 703, and 723 nm) was successful in identifying 100% of fecal contaminates on both fresh and nonfresh leaves.
Detection of fecal contamination on cantaloupes using HS fluorescence imagery was demonstrated by Vargas et al. [46]. HS images of cantaloupes artificially contaminated with a range of diluted bovine feces were acquired from 425 to 774 nm in response to ultraviolet-A (320–400 nm) excitation. Evaluation of images at emission peak wavelengths indicated that 675 nm exhibited the greatest contrast between contaminated and untreated surface areas. Two-band ratios compared with the single-band images enhanced the contrast between the fecal contaminated spots and untreated cantaloupe surfaces.
Yang et al. [47] examined methods to classify fecal contamination on leafy greens. They utilized HS fluorescence imaging system with ultraviolet-A excitation (320–400 nm) for detection of bovine fecal contaminants on the abaxial and adaxial surfaces of romaine lettuce and baby spinach leaves. They applied six spots of fecal contamination to each of the 40 lettuce and spinach leaves. Their results showed that for both lettuce and spinach, the detection of fecal matter was best obtained using the ratio of the signal from 666 nm divided by that from 680 nm, (R values of 0.98 for romaine lettuce and 0.96 for baby spinach).
Raman spectroscopy is a nondestructive spectroscopic technique, based on the vibrational properties of the constituent molecules, that provides molecular information about the sample under examination. The Raman signal results from molecules being excited by a small amount of incident light at a specific wavelength. The remitted light has some of its photons shifted to different wavelengths by the addition or subtraction of vibrational energy from some of the tissue intramolecular bonds [44]. Contrast is achieved when the tissue molecular constituents differ enough that the Raman signals from two tissues have different wavelength distributions. Raman spectral imaging (RSI) intertwines Raman spectroscopy and digital imaging to visualize the composition and structure of a target, thereby having great potential for food safety and analysis [29]. Historically Raman imaging systems have only been able to perform Raman measurement at a microscopic level and were unable to evaluate whole surfaces of individual foods. Recent studies have shown a benchtop point-scanning Raman chemical imaging system designed and developed for food safety research [56]. Raman imaging is a highly specific and sensitive technique as it allows for the detection of particular chemicals at low concentrations, such as detecting melamine particles in dry milk. This technique has wide applications, and due to its specificity, it may help detect contaminants in food products of different sizes.
A study aimed at the detection and differentiation of important food and waterborne bacteria (E. coli, Staphylococcus epidermidis, Listeria monocytogenes, and Enterococcus faecalis) was performed by Fan et al. [38] using surface-enhanced Raman spectroscopy (SERS) coupled with intracellular nanosilver as SERS substrates. Variations observed in the spectral patterns of bacterial pathogens are due to the different quantity and distribution of cellular components like proteins, phospholipids, nucleic acids, and carbohydrates. SERS coupled with statistical analysis has become very useful in discriminating and detecting bacterial cells, spores, and viruses.
In another study, a portable Raman sensor system was presented with an integrated 671 nm microsystem diode laser as excitation light source for the rapid in situ detection of meat spoilage and bacteria [39]. The system used in this chapter is an example of the reduction in form factor of enabled by recent advances and is comprised of three main components: a handheld measurement head with a laser driver electronics board, the Raman optical bench, and finally, a battery pack. This method was used to rapidly detect meat spoilage in specific pork cuts, musculus longissimus dorsi (LD) and musculus semimembranosus (SM). The authors were able to determine the total number of mesophilic aerobic microorganisms on the surface of the meat to show possible correlations of the bacterial growth with the measured Raman spectra. In 2007, the food industry faced substantial economic losses following the discovery of melamine, a nitrogen rich chemical, in human and pet foods [48]. In one SERS study which employed SERS-active substrates, the concentration of melamine was measured in wheat gluten, chicken feed, and processed foods such as cake and noodles [49, 50].
Spoilage and poisoning of food products by microorganisms is a major issue in food safety and human health. As these microorganisms grow and become more active, they cause deterioration of food quality and cause food intoxication. Some of the microorganisms capable of such damage are bacteria, yeast, and mold. As detailed earlier, there have been many different technologies developed to detect harmful microorganisms in food products such as hyperspectral imaging, Raman spectroscopy, and high-performance liquid chromatography. All these methods have certain intrinsic short comings. Factors such as the need for a well-equipped laboratory, high-cost equipment, complicated procedures for sample preparation and long analysis times, and trained professional operators limit their widespread application in the food processing, transportation, marketing, and preservation in various food industries.
A technology that is finding increasing favor by circumventing many of these limitations is laser speckle imaging. Laser speckle imaging has been introduced in this field of application to monitor moving particles in optically inhomogeneous media by analyzing time-varying laser speckle patterns for applications such as measuring meat quality and detecting contaminants. Unlike multiple light scattering in meat which exhibits static and deterministic speckle intensity patterns, light paths associated with the movements of living microorganisms result in time-varying changes in the speckle intensity patterns. Therefore, by detecting the decorrelation in the laser speckle intensity patterns from tissues, the living activities of microorganisms can be detected.
Another advantage of this method is the ability to examine meats sealed with transparent packaging because this method detects time-varying signals in reflected laser beams and transparent plastic does not affect these. Furthermore, the technique can provide rapid assessment as bacterial colonies can be detected within a few seconds [30]. Thus, this method provides an efficient and effective way to detect live bacteria in food products to avoid food toxicity. Speckle imaging systems have been demonstrated to indicate the presence of bacterial colonies and other contaminants in both food and water [31]. Technology such as this may be very effective in the marketplace as food producers or consumers themselves may be able to use them to assess food safety. As mentioned, there are currently several approaches available for detecting low levels of microorganisms in food; however, they require complex equipment, high costs, invasive procedures, and skilled technicians to operate which all act to restrict its widespread adoption and use in the food industry [31].
Work performed by Yoong et al. [53] aimed to detect and quantify various levels of contamination using chicken breast meat samples. The meats contaminated with bacteria had significant decreases in the autocorrelation values over the time lag, whereas the control group (meat treated with a PBS solution) did not show any major changes. The meat treated with a high concentration of bacteria had more significant changes over the time lag compared with the meat treated with a low concentration of bacteria. Moreover, the decrease in the autocorrelation value was proportional to the concentration of the treated bacteria. The measured autocorrelation values were all statistically different from one another (p < 0.001), and the decreases in the autocorrelation were proportional to the concentration of bacteria. Thus, the authors were able to show that through various experimental validations, spontaneous bacterial activity caused strong decorrelation in laser speckle dynamics (Figure 5).
This image illustrates the groups attempt at assessing bacterial activity in meat. (A) shows representative autocorrelation amps in meat treated with various concentrations of bacteria at various time lags. (B) Averaged C(tau) values over the areas in (A) as a function of the time lag. (C) quantification of the autocorrelation values at tau = 10 s [53].
In 2014, Kim et al. [55] presented a label-free bacterial colony phenotyping technology called bacterial rapid detection using optical scattering technology (BARDOT), which can provide classification for several different types of bacteria. Recent experiments with colonies of Bacillus species using speckle imaging show a certain speckle formation that allows for the detection and identification of these bacterial species. As the center diameter of the Bacillus spp. colony grew from 500 to 900 microns, the average speckle area decreased twofold and the number of small speckles increased sevenfold. As Bacillus colonies grow, the average speckle size in the scatter pattern decreases and the number of smaller speckle increases due to the swarming growth characteristics of bacteria within the colony [40]. Singh et al. showed the real-time detection and identification of Salmonella colonies grown from inoculated peanut butter, chicken breast, and spinach or from naturally contaminated meat using BARDOT technology (90–100% accuracy) in the presence of background microbiota from naturally contaminated meat [52].
Due to the multicomponent nature of foods, their reflectance or fluorescence spectra are complex and chemometric methods using multivariate analysis are needed to extract contaminant-specific information. By varying both the excitation and detection wavelengths and measuring both reflectance and fluorescence emission properties of a food sample, we can fine-tune algorithms for specific foods and contaminants. It has been shown that for biological tissues, dual or multiple excitation fluorescence can increase the specificity and accuracy of classification and quantification of specific sources of fluorescence [54]. Rasch et al. [57] showed the combination of different spectroscopic methods (such as fluorescence and NIR spectroscopy) becomes a promising approach to circumvent such single method inherent limitations and to use optical sensing for in situ mycotoxin detection. Additional chemometric tools are essential to eliminate disturbing factors and to extract the desired biochemical information with respect to contamination with fungi and/or mycotoxins.
An example of a multimode hyperspectral imaging system operates in fluorescence and reflectance modes as well as speckle imaging is shown in Figure 6 developed by SafetySpect Inc. The system uses spectral band sequential imaging on the detection side. To ensure high signal-to-noise level, camera and spectral selection filter integration time is optimized for each spectral band from visible to the near infrared. The illumination module uses two independent light sources to provide illumination for fluorescence excitation and reflectance measurements using three computer-controlled LED illumination rings. The UVA (375 nm) and blue/violet (420 nm) LED rings provide fluorescence excitation. White LEDs will be used for reflectance illumination. The HSi-440CO hyperspectral imaging system (Gooch & Housego, UK, originally developed by ChromoDynamics, Inc.) incorporated in the proposed system can image and analyze multiple signals in fixed and living cells at video rates. Its tunable filter can switch wavelengths within microseconds. The system acquires multiwavelength, high-spatial and spectral resolution image datasets, and can compute and display quantitative signal-specific images in near real time. The spectrally controllable image capture system can record spectral images of food samples in wavelengths ranging from 450 to 800 nm. The system is configured as a tabletop platform where illumination and detection will operate above the food sample.
Configuration of the multimode HSI.
In this system, time-varying speckle signals can be quantitatively addressed with the speckle correlation time. A sample containing living microorganisms will have a correlation time way shorter than a static one, and thus contaminated food will be less time-correlated as compared to fresh food due to the spontaneous motility of microorganisms. Correlation time of scattered light from samples, the presence and activity of microorganisms can be quantitatively analyzed.
Let us consider I (x,y,t) the image of the sample at time t. The correlation coefficient between two images of the sample at different times is given by the normalized autocorrelation function:
where T is the total acquisition time, δt the time difference, and τ the time lag. In the case of food contamination assessment, the sample is expected to be static and the correlation to be close to the unity. Every decorrelation effect is due, then, to the presence of live microorganisms moving across the sample.
There is inherent risk in food (preparing, selling, and consuming it), and we need better ways of minimizing such risk. The number of people who are sickened by problematic food is staggering (it is estimated that 1/6 of the US population is thus affected yearly), and the number of people who die (~3000/year) is unacceptable. If one examines the rather extensive risk management/mitigation literature, it is evident that certain fields of human endeavor (such as air travel) are doing a better job than others in minimizing the undesirable scenarios. A particularly pragmatic take on this field was provided by Dr. J. Reason [58], who developed an approach he termed the Swiss Cheese theory (Figure 7). Basically, he posits that we all want to insert countermeasures between us and hazards, to prevent harm, but because we are human and thus imperfect, these countermeasures are like a slice of Swiss cheese. The most logical and direct improvement is to “stack” the slices of cheese, as the holes do not align, and prevention is achieved. Translated to imaging for food safety, this calls for a multimode approach, which is what we propose (see Figure 8). The number of modes needed for good performance scales, naturally, with the difficulty of the problem, and we plan to have our implementations reflect this, in the sense that a large, real-time production-level device will be based on more modes than a mid-level (e.g., restaurant) one, while a handheld, portable unit may only address 80% of the challenges, but with ~20% of the cost and size.
Dr. Reason’s Swiss cheese theory of accident causation/prevention.
Multimode imaging as a funnel of methods. The right mix (on the same instrument, in the proper sequence) optimizes performance, speed and cost simultaneously.
Malnutrition is a universal public health problem in both children and adults globally [1]. It is not only a public health concern but it is an impediment to global poverty eradication, productivity and economic growth. By eliminating malnutrition, it is estimated that 32% of the global disease burden would be removed [2]. As a widespread serious problem affecting children in developing countries, progress towards tackling the different forms of malnutrition remains relatively slow [3]. Malnutrition occurs due to an imbalance in the body, whereby the nutrients required by the body and the amount used by the body do not balance [1]. There are several forms of malnutrition and these include two broad categories namely undernutrition and over nutrition. Undernutrition manifests as wasting or low weight for height (acute malnutrition), stunting or low height for age (chronic malnutrition), underweight or low weight for age, and mineral and vitamin deficiencies or excessiveness. Over nutrition includes overweight, obesity and diet-related non-communicable diseases (NCDs) such as diabetes mellitus, heart disease, some forms of cancer and stroke [1]. Malnutrition is an important global issue currently, as it affects all people despite the geography, socio-economic status, sex and gender, overlapping households, communities and countries. Anyone can experience malnutrition but the most vulnerable groups affected are children, adolescents, women, as well as people who are immune-compromised, or facing the challenges of poverty [3].
\nAccording to the World Health Organization (WHO), 462 million adults are underweight, while 1.9 billion adults are overweight and/or obese. In children under 5 years of age, 155 million are stunted, 52 million are wasted, 17 million are severely wasted and 41 million are overweight and/or obese [1]. The manifestation of malnutrition is multifold, but the paths to addressing prevention are key and include exclusive breastfeeding for the first 2 years of life, diverse and nutritious foods during childhood, healthy environments, access to basic services such as water, hygiene, health and sanitation, as well as pregnant and lactating women having proper maternal nutrition before, during and after the respective phases (levels and trends) [3].
\nIt is vital that malnutrition is addressed in children as malnutrition manifestations and symptoms begin to appear in the first 2 years of life [4]. Coinciding with the mental development and growth periods in children, protein energy malnutrition (PEM) is said to be a problem at ages 6 months to 2 years. Thus, this age period is considered a window period during which it is essential to prevent and/or manage acute and chronic malnutrition manifestations [4, 5, 6]. Child and maternal malnutrition together have contributed to 3.5 million annual deaths. Furthermore, children less than 5 years of age have a disease burden of 35% [7]. In 2008, 8.8 million global deaths in children less than 5 years old were due to underweight, of which 93% occurred in Africa and Asia. Approximately one in every seven children faces mortality before their fifth birthday in sub Saharan Africa (SSA) due to malnutrition [8].
\nYoung malnourished children are affected by compromised immune systems by succumbing to infectious diseases and are prone to cognitive development delays, damaging long term psychological and intellectual development effects, as well as mental and physical development that is compromised due to stunting [7, 9, 10, 11]. A malnutrition cycle exists in populations experiencing chronic undernutrition and in this cycle, the nutritional requirements are not met in pregnant women. Thus, infants born to these mothers are of low birth weight, are unable to reach their full growth potential and may therefore be stunted, susceptible to infections, illness, and mortality early in life. The cycle is aggravated when low birth weight females grow into malnourished children and adults, and are therefore more likely to give birth to infants of low birth weight as well [9]. Malnutrition is not just a health issue but also affects the global burden of malnutrition socially, economically, developmentally and medically, affecting individuals, their families and communities with serious and long lasting consequences [1].
\nStudies in Sudan, Ethiopia, Bangladesh, and Haiti have indicated that the causes of malnutrition are multi-faceted, with both environmental and dietary factors contributing to malnutrition risk in young children [12]. Diet and disease have been identified as primary immediate determinants; with household food security, access to health facilities, healthy environment, and childcare practices influenced by socio-economic conditions [13]. Mother’s antenatal visit and body mass index were also identified as risk factors for malnutrition [14]. In children under 3 years of age some of the main factors included poor nutrition, feeding practices, education and occupation of parent/caregiver, residence, household income, nutrition knowledge of mother [15]. These studies have suggested that nutrition education for the mother is important, as it is a resource that mothers can utilize for better care of their children. It can also provide the necessary skills required for childcare, improvement of her feeding practices, enable her to make choices and have preference of health facilities available, increase her nutritional needs awareness, and give her the chance of changing her beliefs regarding medicine and disease [16]. Some of the nutritional interventions that have had some success in addressing malnutrition include exclusive breastfeeding for the first 6 months of life, vitamin A supplementation, deworming, zinc treatment and rehydration salts for diarrhea, food fortification, and folic acid/iron for lactating and pregnant women, improvement of access to piped water and hygiene [17]. These interventions have positively influenced the development, growth and survival of children [18]. Malnutrition is not a uniform condition and therefore groups and areas that experience high risk of malnutrition must be identified and targeted interventions available to assist [17].
\nTo determine both over and undernutrition, assessment of the nutritional status is important. This identifies those individuals who are vulnerable and at risk, and how to guide a response [19]. In determining the nutritional status of a child, it must be referenced in comparison to a healthy child [20]. Most of the anthropometric indices are used with reference tables such as that of the National Center for Health Statistics (NCHS) and the currently widely recommended and used 2006 WHO child growth standards [21]. In expressing anthropometric indices relative to a reference population, the measurements are developed using the median and standard deviations of the reference populations, which are known as Z scores [22, 23, 24]. The Z score classification system interprets weight for age (W/A), weight for height (W/H) and height for age (H/A). Z scores describe a child’s mid upper arm circumference (MUAC)/weight/height in comparison to the median and the mid upper arm circumference (MUAC)/weight/height of the child relative to the reference population [25]. The anthropometric value is expressed by the two score system as “a number of standard deviations or Z scores below or above the reference mean or median value” [26]. Thus, the Z score is calculated as follows:
\nAs previously mentioned malnutrition consists of both over and undernutrition (Table 1).
\nUndernutrition does not only affect the health of individuals but impacts greatly on the growth of the economy and productivity, as well as the eradication of poverty. To support their growth and development, infants and young children have increased nutritional needs and therefore are most affected by undernutrition [27, 28]. Prolonged malnourished status in children can lead to the development of motor function and physical growth delays, lack of social skills, and low infection resistance, thus making them susceptible to common ailments and infections [28, 29]. Additionally, due to frequent infection, susceptible children become engaged in a negative cycle whereby infections lead to growth delays and their learning abilities are hindered, and infections in malnourished children may lead to childhood mortality [30].
\nUndernutrition is subdivided into two categories that include micronutrient malnutrition and growth failure. To differentiate between acute or chronic malnutrition, the nutritional status of an individual is assessed by using anthropometry [27]. According to Zere and McIntyre [31], anthropometry is advantageous over biochemical evaluation, as it is less invasive and cost effective; hence, in addressing child survival nutritional status anthropometry is one of the favored predictors [32]. To assess the growth status of children the most common indices used in anthropometry include low weight for height or wasting, stunting or low height for age, underweight or a low weight for age and waist/arm circumference.
\nIn PEM the condition is characterized by the individual being susceptible to infection due to long-term consumption of protein and energy that is insufficient to meet the body’s needs. While the body may first attempt to utilize the nutrients to meet the energy demands, if there is insufficient intake of energy then the consumed protein is used to meet the energy demands and does not address the functions of the protein in the body, hence leading to PEM. While PEM requires the measuring of growth parameters such as height and weight as it is not immediately obvious, in severe PEM children present with marasmus and kwashiorkor [33, 34]. Marasmus is characterized by a lack of protein and energy in the diet, while an inadequate intake of protein causes kwashiorkor. Marasmus or severe wasting (below −3SD) presents with a MUAC less than 115 mm in children under age five. Children with marasmus present with an “old man” appearance and are very thin [33]. In kwashiorkor, a child does not necessarily appear as undernourished but there is the presence of oedema. The children present with hair that is discolored and skin that is shiny and very tight. The weight for height is greater than or equal to −2SD. In marasmic-kwashiorkor bilateral oedema is present, with a weight for height less than −2SD [33, 34, 35].
\nA common presentation of PEM in children is underweight. Underweight is seen as children having a weight for age with a Z score of −2SD, with severe underweight at −3SD [36, 37]. Since proteins and/or energy are insufficient in a diet, there is weight loss or failure to gain weight. This can be accompanied by a decline in linear height [38]. While the children may present with normal body proportions such as weight to height ratios, they will be undersized and underweight [39]. Through regular monitoring of growth indices such as height and weight, underweight can be identified at an early stage [26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39]. In 2013, 99 million children less than 5 years of age were underweight. Of this figure, one third of the children were from Africa and two-thirds present in Asia. An estimated 14.6% of newborns were with low birth weight in 2015, and approximately nine out of 10 of the newborns were from low and middle income countries (LMICs). Approximately 45% of deaths in LMICs in children under age five is due to underweight. In adolescent girls the underweight prevalence increased from 5.5% in 2000 to 5.7% in 2016 [40].
\nStunting is a major public health concern that begins in intrauterine life although children are only classified as stunted at approximately age 2 years. The detrimental effects of stunting include intrauterine growth retardation, as well as inadequate nutrition required for growth and development of children [41]. High frequency of infection and decreased disease resistance such as diarrhea and pneumonia are influenced by stunting. Childhood stunting may also lead to increased mortality, poor recovery from disease and is also an obesity risk factor in adulthood [41, 42]. Stunting causes growth impairment during childhood that is associated with increased cardio-metabolic disease and obesity risk and cognitive development delay in adulthood [43]. This creates both short and long term effects that indicate the importance of stunting being identified and monitored in early life [42].
\nIn children the initial 1000 days of life are an important window period for intervention implementation and tracking for the improvement of child growth and development [7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44]. Often stunting is correlated with poor socio-economic status, as well as environmental conditions surveys in South Africa (SA) have identified an increased stunting prevalence in black people compared to their Indian or white counterparts [31]. Some surveys looked at a wider age range of children (0–14 years) and higher stunting prevalence was found in children living informal settlements within urban and rural areas [36, 37, 38, 39, 40, 41, 42, 43, 44, 45].
\nIn stunting or low height for age the Z score is below 2 standard deviations [21]. It is prevalent usually in infants and children younger than 5 years [36], who are susceptible to infection and have an insufficient intake of nutrients over the long term. Low height for age is seen as the failure of an individual to reach full linear growth and if stunting occurs before age two then irreversible poor cognitive and motor developments may occur [41]. Severe stunting is indicated by a height for age that is lesser than the median by 85% to represent a standard deviation of −3SD [46]. In 2013 in children under 5 years of age, 161 million were identified as stunted globally. The trend of global decrease were evident from the period 2000–2013, during which figures declined from 199 million to 161 million (33–25%). However, one third of stunted children were still found in Africa [47]. During 2000–2018 the number and proportion of stunted children under age five rose by 6.5 million in Central and Western Africa and by 1.4 million in Southern and Eastern Africa. Thus, the stunting burden continues to escalate in Africa, creating serious human capital development complications [40].
\nIn the last five decades overweight and obesity appears to be reaching epidemic levels in both developing and developed countries [48, 49]. Eclipsing infectious disease and under-nutrition as a significant mortality and ill-health contributor, overweight and obesity have presented as the most prevalent global nutritional problem over the last two decades. Globally an estimated 1 billion adults are overweight, with 300 million of them being obese [49]. An estimated 155 million obese children contribute to this epidemic [50]. Obese children tend to become obese adults. Obesity-related health problems occur in early years of life and progress into adulthood [51]. Several chronic disease conditions in later life are associated with childhood obesity. These chronic diseases include diabetes, stroke, high blood pressure, cancers and heart disease [52]. Despite the increased prevalence of overweight and obesity in children, research evaluating treatment in these age groups is minimal. Middle-income countries such as South Africa (SA), Brazil and China have increased overweight and obesity rates across all age groups and economic levels [49]. However, over the last few years overweight has increased in every continent. It has been postulated that the number of overweight children under age five will rise from over 40 million to approximately 43 million by 2025 [53]. As of 2018, approximately half of the overweight under five children were in Asia, with a quarter in Africa. Between 2000 and 2018 in Africa, the number of overweight under five children rose by just under 44%. In children and adolescents aged 5–19 years old, the proportion of overweight in 2000 rose from one in 10 (10.3%) to just under one in five (18.4%) in 2016 [40].
\nSome developing countries such as SA are currently facing a nutrition transition with the dual burden of over and undernutrition. This nutrition transition is the replacement of traditional home cooked balanced diet meals by energy-dense foods, as well as sedentary lifestyles due to technology and urbanization. A review study highlighted the dual burden in SA in children aged 0–20 years. The prevalence of wasting and stunting was higher in younger male children and predominant in rural areas, whereas overweight/obesity prevalence was highest in females and children in urban settings. It is important for tracking of over and undernutrition in children at a district level that can also be used to prioritize, monitor and evaluate government policies regarding malnutrition [54]. More recent years have seen the double burden of malnutrition being accompanied by a triple burden of malnutrition, affecting families, communities and countries. In countries such as India and Egypt, the problem is increasing and therefore highlights the urgent need to consider child malnutrition in the greater familial and household contexts [40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55]. A study in Ghana addressed the concurrent occurrence of obesity and stunting in children aged under 5 years, providing data for the first time on such an occurrence. The study reported a stunting prevalence of 27.5%, overweight prevalence of 2.4% and an overall concurrent stunting and overweight prevalence of 1.2% [56]. A study in South Africa, with children aged 6–12 years old, reported that 9.1% were stunted, while 14.9% were overweight/obese [57]. This highlights the need for urgent targeted interventions in children to address this double burden to prevent these malnutrition issues as they transition into adulthood.
\nIn wasting or low weight for height the Z score is below 2 standard deviations [21]. Wasting is reflective of a body mass that is low in comparison to the age and may be due to disease or starvation. Weight loss and retardation of growth occur due to inadequate intake of food and long term it leads to wasting and becomes more severe with emaciation [58]. A child falls behind another child who is growing actively when his/her own growth is affected acutely [38], and the body height and weight become less than ideal for the age of the child [59]. Severe wasting occurs when the weight for height is less than the median by 70% to represent a standard deviation of −3SD [46]. According to the national Department of Health (DoH) height measurements in all children should be conducted at least every 3 months [60]. In measuring overall growth to compare growth standards, both height and weight measurements are essential. Globally, in 2013, in children less than 5 years of age, 51 million were wasted and 17 million severely wasted. Global wasting prevalence in 2013 approximated 8%, of which 3% accounted for severe wasting. A postulated third of wasted children were present in Africa and an estimate of the children severely wasted in Africa followed the same trend [61]. As of 2018–2019 52 million children are wasted, with an estimated 16.6 suffering from severe wasting in 2018 [62]. Children left untreated with severe acute malnutrition (SAM) are at least 12 times more likely to die than healthy children [63]. South Asia is the global wasting epicenter as 15.2% of children under five are wasted. Together with other hotspots such as Oceania, Southeast Asia and SSA, improvements regarding wasting are minimal [64] (Table 2).
\n\nClassification | \nZ score values | \n
---|---|
Adequately nourished | \n−2 < Z-score < +1 | \n
Moderately malnourished | \n−3 < Z-score < −2 | \n
Severely malnourished | \nZ-score < −3 | \n
Malnutrition classification of children based on Z scores [20].
Country | \nYear of last survey | \nWasting | \nOverweight | \nStunting | \nUnderweight | \n
---|---|---|---|---|---|
Angola | \n2015–2016 | \n4.9 | \n3.4 | \n37.6 | \n19.0 | \n
Benin | \n2017–2018 | \n5.0 | \n1.9 | \n32.2 | \n16.8 | \n
Botswana | \n2007–2008 | \n7.2 | \n11.2 | \n31.4 | \n11.2 | \n
Burkina Faso | \n2017 | \n8.6 | \n1.7 | \n21.1 | \n16.2 | \n
Burundi | \n2016–2017 | \n5.1 | \n1.4 | \n55.9 | \n29.3 | \n
Cabo Verde | \n1994 | \n6.9 | \n— | \n21.4 | \n11.8 | \n
Cameroon | \n2014 | \n5.2 | \n6.7 | \n31.7 | \n14.8 | \n
Central African Republic | \n2012 | \n7.6 | \n1.9 | \n39.6 | \n24.6 | \n
Chad | \n2014–2015 | \n13.3 | \n2.8 | \n39.8 | \n29.4 | \n
Comoros | \n2012 | \n11.3 | \n10.6 | \n31.1 | \n16.9 | \n
The Congo | \n2014–2015 | \n8.2 | \n5.9 | \n21.2 | \n12.3 | \n
Cote d’Ivoire | \n2016 | \n6.1 | \n1.5 | \n21.6 | \n12.8 | \n
Democratic Republic of Congo | \n2013–2014 | \n8.1 | \n4.4 | \n42.7 | \n23.4 | \n
Djibouti | \n2012 | \n21.6 | \n8.1 | \n33.5 | \n29.9 | \n
Equatorial Guinea | \n2011 | \n3.1 | \n9.7 | \n26.2 | \n5.6 | \n
Eritrea | \n2010 | \n15.3 | \n2.0 | \n52.0 | \n39.4 | \n
Eswatini (former Swaziland) | \n2014 | \n2.0 | \n9.0 | \n25.5 | \n5.8 | \n
Ethiopia | \n2016 | \n10.0 | \n2.9 | \n38.4 | \n23.6 | \n
Gabon | \n2012 | \n3.4 | \n7.7 | \n17.0 | \n6.4 | \n
The Gambia | \n2013 | \n11.0 | \n3.2 | \n24.6 | \n16.5 | \n
Ghana | \n2014 | \n4.7 | \n2.6 | \n18.8 | \n11.2 | \n
Guinea | \n2016 | \n8.1 | \n4.0 | \n32.4 | \n18.3 | \n
Guinea—Bissau | \n2014 | \n6.0 | \n2.3 | \n27.6 | \n17.0 | \n
Kenya | \n2014 | \n4.2 | \n4.1 | \n26.2 | \n11.2 | \n
Lesotho | \n2014 | \n2.8 | \n7.5 | \n33.4 | \n10.5 | \n
Liberia | \n2013 | \n5.6 | \n3.2 | \n32.1 | \n15.3 | \n
Madagascar | \n2012–2013 | \n7.9 | \n1.1 | \n48.9 | \n32.9 | \n
Malawi | \n2015–2016 | \n2.8 | \n4.6 | \n37.4 | \n11.8 | \n
Mali | \n2015 | \n13.5 | \n1.9 | \n30.4 | \n25.0 | \n
Mauritania | \n2015 | \n14.8 | \n1.3 | \n27.9 | \n24.9 | \n
Mauritius | \n1995 | \n15.7 | \n6.5 | \n13.6 | \n13.0 | \n
Mozambique | \n2011 | \n6.1 | \n7.8 | \n42.9 | \n15.6 | \n
Namibia | \n2013 | \n7.1 | \n4.0 | \n22.7 | \n13.2 | \n
Niger | \n2016 | \n10.1 | \n1.1 | \n40.6 | \n31.4 | \n
Nigeria | \n2016–2017 | \n10.8 | \n1.5 | \n43.6 | \n31.5 | \n
Rwanda | \n2014–2015 | \n2.3 | \n7.9 | \n38.2 | \n9.6 | \n
Sao Tome and Principe | \n2014 | \n4.0 | \n2.4 | \n17.2 | \n8.8 | \n
Senegal | \n2017 | \n9.0 | \n0.9 | \n16.5 | \n14.4 | \n
Seychelles | \n2012 | \n4.3 | \n10.2 | \n7.9 | \n3.6 | \n
Sierra Leone | \n2013 | \n9.5 | \n8.8 | \n37.8 | \n18.2 | \n
Somalia | \n2009 | \n15.0 | \n3.0 | \n25.3 | \n23.0 | \n
South Africa | \n2016 | \n2.5 | \n13.3 | \n27.4 | \n5.9 | \n
South Sudan | \n2010 | \n24.3 | \n5.8 | \n31.3 | \n29.1 | \n
Togo | \n2013–2014 | \n6.6 | \n2.0 | \n27.6 | \n16.1 | \n
Uganda | \n2016 | \n3.5 | \n3.7 | \n28.9 | \n10.4 | \n
United Republic of Tanzania | \n2015–16 | \n4.5 | \n3.7 | \n34.5 | \n13.7 | \n
Zambia | \n2013–14 | \n6.2 | \n6.2 | \n40.0 | \n14.9 | \n
Zimbabwe | \n2015 | \n3.3 | \n5.6 | \n27.1 | \n8.5 | \n
Joint malnutrition country estimates of anthropometric indicators in children aged 0–59 months [65].
As a developing or middle-income country, SA is still undergoing major transitions socially, economically and in the population’s health. The country is currently facing a quadruple disease burden, with non-communicable diseases linked to diet and lifestyle; the burden of Human Immunodeficiency Virus/Acquired immunodeficiency syndrome (HIV/AIDS); infectious diseases and poverty linked to under nutrition; and deaths due to injuries [66]. As a developing country SA is in a nutrition transition where both over and undernutrition coexist [67]. The first 2 years of life are a vulnerable time frame as it is during this period that malnutrition begins. According to Faber and Wenhold [68], chronic malnutrition or stunting is more prevalent in children in SA compared to wasting. Since the post-apartheid era in 1994, SA has faced great challenges in addressing the nutritional status of infants, young children and adults [69]. However, large-scale nationwide surveys were conducted to trace the progress, failures and successes in addressing malnutrition. In 1994 the South African Vitamin A Consultative Group (SAVACG) conducted a national survey on the nutritional status of children aged 6–71 months [70]. Anthropometric results revealed that approximately 10% or 660,000 children were underweight, with one in every four children (1.5 million) affected by stunting. Severe wasting was only recorded in 0.4% of children. KwaZulu-Natal (KZN), Eastern Cape and Northern Province revealed the greatest prevalence of malnutrition [70]. In 1999 the National Food Consumption Survey (NFCS) was conducted in children aged 1–9 years [71], collecting a larger set of data in comparison to the SAVACG survey. The NFCS reported 10% underweight in children, with 20% affected by stunting and 17.1% as overweight and/or obese. The NFCS secondary analysis, focusing on children aged 1–5 years, reported underweight at 6.8%, stunting at 20.1%, overweight at 20.6% and obesity at 9.5% [69]. In 2005, the National Food Consumption Survey-Fortification Baseline (NFCS-FB) reported that of children aged 1–9 years old, 20% were affected by stunting, 9.3% were underweight, wasting was found in 4.5%, and 14% were overweight or obese [72]. The South African National Health and Nutrition Examination Survey (SANHANES) conducted in 2012 reported that in children aged 0–14 years stunting prevalence was 15.4%, with 3.8% having severe stunting. Wasting was reported at 2.9%, with severe wasting at 0.8%. Underweight was reported at 5.8%, with severe underweight at 1.1%. Regarding over nutrition, SANHANES identified 18.1% of children as overweight and 4.6% as obese [36]. The prevalence of overweight and obesity was significantly greater in females (25% and 40.1%) compared to males (19.6% and 11.6%) respectively. Underweight was significantly higher in males (13.1%) in comparison to females (4.0%) [36]. Thus, it is evident that SA is facing the malnutrition epidemic at a young age and context-specific and targeted interventions are required to prevent child malnutrition before it progresses into adulthood.
\nDuring 2012–2013, WHO member states recognized the seriousness of malnutrition and its effect on global health [3]. Thus, at the United Nation’s General Assembly in 2016, the United Nations Decade of Action on Nutrition 2016–2025 was announced. This set a time frame for all forms of malnutrition to be addressed and for diet-related and nutrition targets to be met by 2025. This also set the time frame for the Sustainable Development Goals (SGDs) to be achieved before 2030, particularly SDG 2 that aims to improve nutrition, achieve food security and end hunger, as well as SDG 3 that aims to ensure healthy living and promote well-being for all [1]. To tackle the malnutrition epidemic food fortification is important to ensure that children with good weight do not risk becoming overweight or obese [73]. All malnutrition indicators must be included in interventions, and more importantly treated together rather than stand-alone issues [74]. As part of the health system strengthening and with the goal of combatting malnutrition, existing policies on child malnutrition must be evaluated. The coexistence of stunting and overweight/obesity remains a challenge in LMICs that requires multi-sectoral action. During infancy and early childhood optimal nutrition is vital to ensure that, development and rapid growth demands are met. In the efforts to tackle the nutrition disparities, the first 1000 days of life are an important window period, presenting the opportunity to prevent both stunting and overweight/obesity [75]. Interventions must be inclusive of both linear growth and appropriate weight, beginning in early life and preferably during this important window period. To further tackle the double and triple burdens of malnutrition, early screening and identification of at risk children, including those already with malnutrition, is essential at healthcare facilities [76]. Thus, a more holistic, context-specific approach is required, whereby interventions not only take into consideration the risk factors, but also consider the inclusion of nutritionists and educating mothers on self and childcare regarding nutrition [77]. Furthermore, child malnutrition research and interventions must be up-scaled from community level to provincial and national levels so that it informs policy on the intervention strategies that can address the burden of child malnutrition. This is vital as children left untreated transition into malnourished adulthood, increasing the healthcare costs and needs, weakening the healthcare systems, and perpetuating the vicious malnutrition cycle.
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I am also a member of the team in charge for the supervision of Ph.D. students in the fields of development of silicon based planar waveguide sensor devices, study of inelastic electron tunnelling in planar tunnelling nanostructures for sensing applications and development of organotellurium(IV) compounds for semiconductor applications. I am a specialist in data analysis techniques and nanosurface structure. 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After obtaining a Master's degree in Mechanical Engineering, he continued his PhD studies in Robotics at the Vienna University of Technology. Here he worked as a robotic researcher with the university's Intelligent Manufacturing Systems Group as well as a guest researcher at various European universities, including the Swiss Federal Institute of Technology Lausanne (EPFL). During this time he published more than 20 scientific papers, gave presentations, served as a reviewer for major robotic journals and conferences and most importantly he co-founded and built the International Journal of Advanced Robotic Systems- world's first Open Access journal in the field of robotics. Starting this journal was a pivotal point in his career, since it was a pathway to founding IntechOpen - Open Access publisher focused on addressing academic researchers needs. Alex is a personification of IntechOpen key values being trusted, open and entrepreneurial. Today his focus is on defining the growth and development strategy for the company.",institutionString:null,institution:{name:"TU Wien",country:{name:"Austria"}}},{id:"19816",title:"Prof.",name:"Alexander",middleName:null,surname:"Kokorin",slug:"alexander-kokorin",fullName:"Alexander Kokorin",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/19816/images/1607_n.jpg",biography:"Alexander I. Kokorin: born: 1947, Moscow; DSc., PhD; Principal Research Fellow (Research Professor) of Department of Kinetics and Catalysis, N. Semenov Institute of Chemical Physics, Russian Academy of Sciences, Moscow.\r\nArea of research interests: physical chemistry of complex-organized molecular and nanosized systems, including polymer-metal complexes; the surface of doped oxide semiconductors. He is an expert in structural, absorptive, catalytic and photocatalytic properties, in structural organization and dynamic features of ionic liquids, in magnetic interactions between paramagnetic centers. The author or co-author of 3 books, over 200 articles and reviews in scientific journals and books. He is an actual member of the International EPR/ESR Society, European Society on Quantum Solar Energy Conversion, Moscow House of Scientists, of the Board of Moscow Physical Society.",institutionString:null,institution:{name:"Semenov Institute of Chemical Physics",country:{name:"Russia"}}},{id:"62389",title:"PhD.",name:"Ali Demir",middleName:null,surname:"Sezer",slug:"ali-demir-sezer",fullName:"Ali Demir Sezer",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/62389/images/3413_n.jpg",biography:"Dr. Ali Demir Sezer has a Ph.D. from Pharmaceutical Biotechnology at the Faculty of Pharmacy, University of Marmara (Turkey). He is the member of many Pharmaceutical Associations and acts as a reviewer of scientific journals and European projects under different research areas such as: drug delivery systems, nanotechnology and pharmaceutical biotechnology. Dr. Sezer is the author of many scientific publications in peer-reviewed journals and poster communications. Focus of his research activity is drug delivery, physico-chemical characterization and biological evaluation of biopolymers micro and nanoparticles as modified drug delivery system, and colloidal drug carriers (liposomes, nanoparticles etc.).",institutionString:null,institution:{name:"Marmara University",country:{name:"Turkey"}}},{id:"61051",title:"Prof.",name:"Andrea",middleName:null,surname:"Natale",slug:"andrea-natale",fullName:"Andrea Natale",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:null},{id:"100762",title:"Prof.",name:"Andrea",middleName:null,surname:"Natale",slug:"andrea-natale",fullName:"Andrea Natale",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"St David's Medical Center",country:{name:"United States of America"}}},{id:"107416",title:"Dr.",name:"Andrea",middleName:null,surname:"Natale",slug:"andrea-natale",fullName:"Andrea Natale",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Texas Cardiac Arrhythmia",country:{name:"United States of America"}}},{id:"64434",title:"Dr.",name:"Angkoon",middleName:null,surname:"Phinyomark",slug:"angkoon-phinyomark",fullName:"Angkoon Phinyomark",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/64434/images/2619_n.jpg",biography:"My name is Angkoon Phinyomark. I received a B.Eng. degree in Computer Engineering with First Class Honors in 2008 from Prince of Songkla University, Songkhla, Thailand, where I received a Ph.D. degree in Electrical Engineering. My research interests are primarily in the area of biomedical signal processing and classification notably EMG (electromyography signal), EOG (electrooculography signal), and EEG (electroencephalography signal), image analysis notably breast cancer analysis and optical coherence tomography, and rehabilitation engineering. I became a student member of IEEE in 2008. During October 2011-March 2012, I had worked at School of Computer Science and Electronic Engineering, University of Essex, Colchester, Essex, United Kingdom. In addition, during a B.Eng. I had been a visiting research student at Faculty of Computer Science, University of Murcia, Murcia, Spain for three months.\n\nI have published over 40 papers during 5 years in refereed journals, books, and conference proceedings in the areas of electro-physiological signals processing and classification, notably EMG and EOG signals, fractal analysis, wavelet analysis, texture analysis, feature extraction and machine learning algorithms, and assistive and rehabilitative devices. I have several computer programming language certificates, i.e. Sun Certified Programmer for the Java 2 Platform 1.4 (SCJP), Microsoft Certified Professional Developer, Web Developer (MCPD), Microsoft Certified Technology Specialist, .NET Framework 2.0 Web (MCTS). 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