\r\n\tThere will be a chapter on secondary causes of sexual dysfunction disorders related to diabetes, cardiovascular disease, and obesity. A chapter on remedial measures to enhance sexual activity and maintain human relationships will be discussed. As there is a growing number of cancer survivors a chapter on cancer-related sexual dysfunction will be welcomed for including it.
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1. Introduction
The work starts with analysis of different ways of mathematical modeling of music instruments. The aim is to bridge the gap between the synthesized music note and the original note. Efforts have been taken to propose a model to preserve some of the oldest and ‘on the verge of obsolete’ music instruments. Past researchers developed various instruments’ models. One of the models included attack, decay, sustain and release (ADSR) parameter-based model. Figure 1 shows the ADSR graph. These parameters measure the time required for complete music note generation. The ADSR is also known as ‘timbre’ of the music note and is helpful for differentiating instrument families. Perception of the music note occurs with the timbre i.e. envelope and the fundamental frequency i.e. ‘pitch’ along with its harmonics. The fullness of the music notes is perceived by these harmonic frequencies. The Fast Fourier Transform (FFT) is the frequency domain representation of the any signal (here, the music notes). In the early days of modeling, the pitch and timbre set the foundation for the current development in the instrument modeling. ADSR synthesis has limitations of producing the required number of harmonics mathematically to reach that richness or fullness of the original music note.
Figure 1.
ADSR parameters. Source: https://www.a-mc.biz/.
The other method, digital waveguide (DWG), is also used for modeling the musical instruments. Figure 2 shows the schematic diagram of DWG technique. It is used to express the musical instruments in wave form guided between two fixed points. It puts forward the concept of music note as a wave traveling between two points. Bridge and Nut are the endpoints (rigid terminations) between which the music note wave is traveling. This DWG string modeling involves non-linear distortions and its post distortion gain needs to be adjusted to develop the model. DWG appears to be challenging due to more computational burden.
Figure 2.
DWG for acoustic guitar. Source: Ee.Columbia.Edu.
Then researchers also experimented with impulse response of the musical instrument. There has been competitive research to find the impulse response of musical instrument. Researchers have used hammer method for estimating the impulse response. The experimentation to find impulse response, also involved breaking the string of guitar as we discussed earlier. Well, keeping the limitations in mind, the research started with cepstral domain approach, well-known technique for speech processing.
Every instrument is unique and this uniqueness can be demonstrated with impulse response. It’s as unique as the fingerprint of a person. The experiments involved breaking the string of the guitar to record the impulse response. Certainly, this made the author to think further to improve this uniqueness of the instrument, i.e. impulse response using cepstral domain representation. Being a guitar lover, the author chose the box shaped acoustic guitar music notes for the research work.
A simple convolution operation is involved in the generation of the music note. The excitation signal, x(t) is convolved with the impulse response of the instrument to generate the music note, y(t). Figure 3 shows the block diagram for this signal processing operation. When the music note is recorded and x(t) is known, the impulse response can be estimated and recorded. This research demonstrated the extraction of the impulse response of the music note, based on adaptive cepstral domain window (ACDW) method. Figure 4 shows the outline of the cepstral domain window approach. The impulse response based synthesis is carried out and the listening tests are conducted on the guitar players to measure the mean opinion score (MoS) of synthesized notes. Further, the machine learning algorithms are used to classify the synthesized notes for playing expression.
Figure 3.
Block diagram for music note generation using convolution.
Figure 4.
Block schematic of cepstrum computation.
The outline of the chapter is as follows: the Section 2 will discuss the work by other researchers, Section 3 will discuss the methodology for the impulse response modeling while Section 4 will discuss the results in detail. The Section 5 will summarize the work and conclude the modeling work for acoustic guitar.
2. Literature review
This section reviews the modeling techniques of guitar as an instrument. The physical model of an acoustic guitar consists of three main parts: strings of guitar, the wooden sound box and the sound radiated by the soundboard. The review starts with the modeling techniques used for guitar strings, continues with the modeling techniques involving the sound box and finally the convolved signal i.e. radiated sound by the soundboard. It also covers the Neural Network (NN) and Deep Learning based classification techniques and verification of synthesized music instruments. The literature survey has been divided into: Physical or mathematical modeling and Impulse response methods.
The work by Gerald Schuller et al. in [1] has been used as reference for collection of acoustic guitar notes. They considered 5 plucking styles finger-style (FS), picked (PK), muted (MU), slap-thumb (ST), and slap-pluck (SP) and the 5 expression styles: normal (NO), vibrato (VI), bending (BE), harmonics (HA), and dead-note (DN) for feature extraction of plucking and expression styles of electric bass guitar. Anssi Klapuri et al. [2] have proposed a method for extracting the fingering configurations automatically from a recorded guitar performance. 330 different fingering configurations are considered, corresponding to different versions of the major, minor, major 7th, and minor 7th chords. Hidden Markov Model has been used.
Migneco et al. [3] proposed physical models for plucked string instruments that can produce high-quality tones using a computationally efficient implementation, but the estimation of model parameters through the analysis of audio remains challenging. Moreover, an accurate representation of the expressive aspects of a performance requires a separation of the performer’s articulation (source) from the instrument’s response (filter). This work explores a physically-inspired signal model for plucked guitar sounds. It facilitates the estimation of both string excitation and resonance parameters simultaneously. Julius O Smith [4] discussed the piano synthesis, focused on commuted synthesis. The instrument models can be treated as Linear Time Invariant systems and that’s why the commutation is possible. Commuted synthesis promotes implementation of enormous resonators inexpensively, three orders of magnitude less computation for other string instruments. The sound board and enclosure (i.e. guitar body) are commuted. It needs stored recording of their impulse responses. Otherwise it demands higher order digital filters.
Further, the work by Meng Koon Lee et al. in [5] talks about the physical modeling based on the interaction of the strings of the guitar with other parts of the guitar body. The researchers experimented with the sound generated by guitar with respect to soundboard and its relationship with the guitar body. The soundboard plays an increasingly important role compared to the sound hole, back plate, and the bridge at high frequencies. Design of bracings and their placements on the soundboard increase its structural stiffness as well as redistributing its deflection to non-braced regions and affecting its loudness as well as its response at low and high frequencies. The work is focused to increase the sound level with bracing designs and their placements. The analysis is being carried out for the archtop guitar.
The paper [6], written by Keith D Martin explains the classification technique based on physical properties. It is focused on the classification using pattern recognition. A statistical pattern-recognition technique is applied to instrument tones within a taxonomic hierarchy. The salient acoustic features related to physical properties of source excitation and resonance structure are measured from output of auditory model for 1,023 isolated tones over the full pitch ranges of 15 orchestral instruments. The data set included examples from the string (bowed and plucked), woodwind (single, double, and air reed), and brass families. Eric J. Henry et al. [7] proposed a model that can yield representations for the chords that require minimal prior knowledge to interpret. The model has been developed to address both challenges by modeling the physical constraints of a guitar to produce human-readable representations of music audio, i.e. guitar tablature via a deep convolutional network.
Jakob Abeßer et al. [8] worked on a feature-based approach for the classification of different playing techniques in bass guitar recordings. The applied audio features are chosen to capture typical instrument sounds induced by 10 different playing techniques. This work introduced a set of low-level features that allowed modeling the peculiarities of 10 different bass-related plucking and expression styles by capturing typical timbre related characteristics. The work further in [9] models the plectrum which is used for playing guitar notes. Here Francois Germain et al. proposed a model of the plectrum, a guitar pick, for use in physically inspired sound synthesis. The model is drawn from the mechanics of beams. The profile of the plectrum is computed in real time based on its interaction with the string, which depends on the movement impressed by the player and the equilibrium of dynamical forces. A condition for the release of the string is derived, which allows driving the digital waveguide simulating the string to the proper state at release time. The algorithm proposed by Henri Penttinen et al. [10] estimates the plucking point of guitar tones obtained with an under-saddle pickup. This problem is approached in the time domain by applying autocorrelation estimation. Onset detection has been improved in this proposed work. It enables a new way to control audio effect parameters in real time by simply changing the plucking point. The plucking position changes the timbre of the string’s tone, most notably the brightness. This effect is used as an expressive tool in music. By using the PPE (Plucking Point Estimation) algorithm to control an audio effect, change in the plucking position can affect the timbre even more dramatically than in the natural unprocessed case.
Gabriele Varieschi et al. [11] presented mathematical and physical models to be used in the analysis of the problem of intonation of musical instruments such as guitars, mandolins and similar instruments. The analysis is done by designing the fret’s placement on the fingerboard according to mathematical rules assuming an ideal string. The intonation of a string note gets affected when other string’s deformation and inharmonicity come into picture. To nullify the effects, the authors have designed some compensation procedures. V.E.Howle et al. [12] proposed a known tool of Eigenvalue to musical instruments. The work as its name “Eigenvalues and musical instruments” suggests is based on finding the eigenvalues of musical instruments. The instrument categories like strings, bars and drums fall under linear system’s class. It is focused on plotting the eigenvalues for different types of musical instrument giving pictorial view of change in the eigenvalues with change in different parameters like stiffness, friction or sound radiations.
Antoine Chaigne et al. [13] considered the end conditions of piano strings and proposed that it can be approximated by the input admittance at the bridge. A method of validation of admittance measurements on simple structures is proposed in this paper. High resolution signal analysis performed on string’s vibrations yields an estimate for the input admittance. This method is implemented on a simplified device composed of a piano string coupled to a thin steel beam.
A parametric modeling of string instruments is proposed by famous researchers, Matti Karjalainen et al. in [14]. Parametric modeling of musical instrument sounds again helps to re-synthesize the music sounds or morph them. This type of modeling can also be used to apply the parameters in physical and perceptual studies of acoustic instruments. It is typically based on pole-zero modeling technique applied to string instrument sounds. As proposed by Julius Smith the instruments are assumed to be linear time-invariant systems while using this parametric modeling. Our research work has been directed by the same principal as that of the work by Julius Smith.
The authors in work [15, 16] talk about sound separation. It is very important for developing the equalizers to balance the sounds of different musical instruments in music events. The method based on ‘anisotropic smoothness’ indicates that the harmonic instruments are smooth in the time domain whereas the percussive instruments are smooth in the frequency domain. The authors have worked on both the types of music notes. The spectrogram highlights this smoothness in time and the frequency domain and the method is implemented under some conditions. The work reduced the computational complexity for source separation as compared with the other methods as Monte-Carlo method and large-sized matrix multiplications. The results are discussed for the acoustic guitar and piano as the harmonic instruments and the drum as the percussive instruments. This paper again helped us to understand the spectrogram approach towards the source separation.
Further with reference to impulse response research, Nelson Lee et al. [17] proposed a method of decomposing a plucked string instrument into modular components. The model is based on parameter estimation of excitation signal, string vibration, body resonator and finally the radiated sound pressure. As the modeling progresses it becomes clear that for reaching close to body impulse responses, the order of the filter demands a hundred of poles and zeros. Inverse filtering is used to compensate for high orders of filters.
Friedrich Türckheim et al. [18] used the ‘Novel Approach of Impulse Response measurement’ as starting point for modeling approaches or to investigate the relationship between transfer functions and the instruments’ quality. This is done usually for the experimental determination of transfer function as the complete and reliable physical models are still to be developed. The impulse responses here have been compared with the commonly used impact hammer method. The work proposed in above two references have limitations of filter orders and determination of exact transfer functions. So the author thought of different approach for the calculation of impulse response of the guitar body.
3. Methodology
Methodology adopted by the author is different than other researchers. So this section is the summary of the research work carried out for impulse response modeling of acoustic guitar. The sections 3.1 and 3.2 discuss the structure of acoustic guitar body and the collection of music notes for two different plucking styles and plucking expressions. Section 3.3 describes how the Octave Rule is used for the frequency calculation and verification of all music notes along the fretboard. Lastly, the Section 3.4 gives the details of the impulse response modeling.
3.1 Structure of acoustic guitar
Let us understand the structure of the box shaped acoustic guitar. It has a resonance cavity, shaped like a butterfly. The wings are short at nut side and are bigger at the saddle side. As shown in Figure 5, there are six strings on the acoustic guitar. The strings are tied between the saddle and the nut of guitar on the fingerboard. The fingerboard is also known as fretboard.
Figure 5.
Structure of the box shaped acoustic guitar. Source: Gabriele Umberto Varieschi.
The fingerboard of guitar consists of 19 to 21 frets. The frets are the metal marks on the fretboard, arranged in logarithmic scale. They are shown with x1, x2, x3, …x19 here. The acoustic guitar model, FAW 802 is chosen for the research work. The music notes were recorded in an acoustic studio. The acoustic guitar chosen for this research work has twenty-one frets and six strings. Music notes on twenty frets are considered for the analysis and synthesis purpose.
3.2 Collection of guitar notes and database generation
The music note is recorded for each fret of each string (except the 21st fret). The guitar notes are collected based on: plucking style and the plucking expression. The plucking style indicates the object used for plucking the string of the acoustic Guitar. The plucking expression indicates whether the note is played by a naïve (beginner) person or the expert person. The two players, one naïve and the other, expert, are recorded for two plucking styles. The Figure 6 gives overview of the collection strategy of the Guitar notes.
Figure 6.
Collection of acoustic guitar notes.
When the string is plucked by finger, it will generate a music note and it is named as ‘plucked note’ here. Similarly, if the string is plucked by plectrum (or pick), the generated music note will be named as ‘picked note’. The total number of notes generated are calculated as: the number of frets multiplied by the number of strings, i.e. (20* 6 =) 120 notes. These all are called as “fretted notes”. If no fret is pressed, then the note played will be called as ‘open string’ note. There will be a set of 120 fretted notes with 6 open string notes for single player for each style. The notes played by same player are recorded for another plucking style i.e. picked.
The Figure 7 shows the frequency values for all strings and their frets. The frets are 1F to 20F. The first column gives the string numbers along with their names: string E, string B, string G, string D, string A, string E. The column, ‘OPEN’ gives the open string frequencies with 82 Hz as the lowest frequency value and 329 Hz as the highest open string frequency value. The frequency for fretted notes varies from 87 Hz for string 6 fret 1 to 1047 Hz for string 1 fret 20. This is the maximum frequency of the guitar note. All guitar notes are therefore recorded with 16 kHz sampling frequency in ‘.wav’ format. Software for sound analysis, named, ‘Audacity’ is used for noise removal of the guitar notes.
Figure 7.
Standard frequency for guitar notes: Guitar frets and their notes versus frequency.
Figure 8 shows the scatter plot of the frequencies of all frets of all strings. The numbers 1 to 21 on the x-axis represent the fret numbers of the strings. The y-axis depicts the frequencies of guitar notes. This scatter plot helps to understand the mathematical relationship as well as the minimum and the maximum frequency values for these notes. The maximum frequency for the notes is 2 kHz so the sampling frequency of 16 kHz is selected for recording of the music notes in acoustic studio.
Figure 8.
Frequency generation for music notes on all frets and strings.
The string E with 329 Hz frequency is string 1, string B with 247 Hz frequency is string 2, string G with 196 Hz frequency is string 3, string D with 147 Hz frequency is string 4, string A with 110 Hz frequency is string 5 and the string E with 82 Hz frequency is string 6. The strings are mentioned by the numbers (like string 1, string 2 …) in further discussion of research work. Total 504 sound notes are recorded including two plucking styles and plucking expressions. Another set of 504 notes is recorded for string modeling. The dataset generated has been published on Mendeley Repository, Elsevier.
3.3 Pythagoras fractions OR rule of 18 (octave rule)
Pythagoras fractions or Pythagorean tuning system is developed to study frequency ratios of all intervals which are based on the ratio of 3:2. It is the rule, given by Eq. (1), which indicates the mathematical relationship of all the music notes and is used for checking the frequency of generated notes. Here the ‘fn’ gives the frequency of the nth fret and ‘f0’ is the frequency of open string. The ratio 1/12 is called an ‘octave’. One can use this octave relationship for analysis and tuning of instruments. The frequency analysis of recorded notes is done on the basis of octave rule. The guitar used for this work is tuned each time before starting the recording of the notes. Once the instrument is tuned, the notes generated follow the octave pattern for frequency values. Here the open string frequencies of recorded notes are verified by autocorrelation formula and then verified by Pythagoras Fractions.
fn=f0/2n12E1
This will create a table for 20 frets of each string. Table 1 shows the sample calculations of 6 frets for all the six strings from the open string frequencies. In this table, after knowing the open string frequency, all the other frequencies are calculated by using the second column which gives the octave multiplier factor. First column gives the fret number, ‘n’. The values in third to eighth columns are calculated in the way explained below.
Table 1.
Frequency calculation for pick plucked guitar notes by octave method.
Consider the sample values in cells which are highlighted in orange. The calculation for the frequency for string 1 fret 1 is done as:
e.g. 329 Hz * 1.059463 = 349.199 Hz
Thus the octave rule is used to verify the music note’s frequency. The frequency graphs are plotted using FFT algorithms. After the verification of frequencies and observation of the spectrum of the music notes, it can be stated that picked music notes are closer (sharper) to ideal frequencies than the plucked music notes.
3.4 Impulse response modeling
The frequency analysis done in above sections helped to develop better understanding of the fullness of the music notes based on their number of harmonics. This is also helpful to get better understanding of the playing style and plucking expressions. The frequency analysis is now followed by the impulse response estimation. The next five subsections deal with the synthesis part of the research work and discusses the algorithmic approach towards the impulse response modeling.
3.4.1 Introduction
The cepstral domain approach is frequently used for speech signal processing but it is not so far used for music signal processing. This method is used for separation of vocal tract response and the excitation signal in speech signal processing. Based on the same principle, this modeling work is focused on separation of: 1) impulse response or body response from 2) excitation signal of the acoustic guitar notes.
The next section discusses the algorithm for Cepstral Domain Windowing (CDW) and its application for modeling of acoustic guitar notes using the same CDW method.
3.4.2 Cepstral domain window method
Let us focus on theoretical aspects of cepstral domain. Figure 9 shows the block schematic of the cepstral domain method used for speech analysis. The input to the system is speech signal. The speech signal consists of excitation signal convolved with the impulse response of the vocal tract. On similar principle, the music note is given as input the system. It gives the representative picture of impulse response and the excitation signal, characteristically separated. The excitation signal is periodic in nature and the impulse response is the slowly varying function. The signal is passed through a smooth window function, a Hamming window function and the spectrum is plotted by calculating the FFT of the block. When the logarithm of the magnitude of FFT output is calculated, the periodic excitation signal is clearly seen as rapidly varying function and the vocal tract response appears as the slowly varying function. By using the cepstral domain window, isolation of excitation signal from body response is possible. Thus cepstral domain method can be used for modeling of music note of guitar.
Figure 9.
Block schematic of the cepstral domain method.
3.4.3 Cepstral domain windowing method for acoustic guitar notes
The block schematic of cepstral domain windowing method for analysis of acoustic guitar music notes is given in Figure 10. The algorithm for CDW method discussed in Section 3.4.2 is used for calculation of body response or impulse response of the guitar box. The input to the system is the acoustic guitar note. The FFT block gives spectrum of the music signal and then the complex logarithm of magnitude of FFT output is taken. The periodic excitation is seen as a rapidly varying function and guitar body response appears as the envelope of the spectrum. The body response is a slowly varying function. After the IFFT of the signal is calculated it enters in the cepstral domain. The cepstral domain plot indicates a cluster near the origin that represents the body response and the periodic peaks after the cluster represent the periodic excitation generated due to plucking of the string by hand or by plectrum.
Figure 10.
Cepstral domain approach to synthesize the music note.
The string1 fret 2 note is taken as sample input to the different blocks in Figure 10. Figure 11 shows the time domain representation of this acoustic guitar note used as input to the system for isolation of body response and the excitation signal for string 1 fret 2 with finger plucking style. The sampling rate for the recorded note is 16 kHz.
Figure 11.
Time domain representation of input music note, string 1 fret 1 finger plucked note.
3.4.4 Synthesis of guitar note using isolated body response and the excitation signal
The body responses and the excitation signals are calculated for 252 guitar notes including the plucking style: finger and the plectrum plucked music notes. A note is then synthesized by convolving the estimated body response and the isolated excitation signal. The results are verified using the correlation coefficients and it is observed that the constant length window poses some limitations to give highly correlated synthesized guitar note. Figure 12 shows the plots for the original guitar note and the synthesized guitar note. But the results are not satisfactory because of the low correlation coefficient values. Table 2 presents the sample values of 6 frets of string 1 music notes.
Figure 12.
Synthesized guitar note for string 1 fret 1 using CDW method.
String 1
Correlation coefficient
MoS Score based on (0–1) scale
Fret 1
0.8564
0.85
Fret 2
0.8229
0.85
Fret 3
0.8486
0.85
Fret 4
0.6842
0.8
Fret 5
0.8061
0.8
Fret 6
0.8340
0.8
Table 2.
Correlation coefficient calculations for the string 1 with all frets with fixed sized window.
1
Finger pluck
Open string
Fret 1
Fret 2
Fret 3
Fret 4
Fret 5
Fret 6
2
Number of samples in the window
70
60
60
50
50
50
50
3
Correlation coefficient
0.9512
0.9596
0.9517
0.9489
0.8196
0.8856
0.9008
4
MOS score on the scale of(0–1)
0.95
0.95
0.95
0.9
0.8
0.8
0.9
5
Pick pluck
Open string
Fret 1
Fret 2
Fret 3
Fret 4
Fret 5
Fret 6
6
Number of samples in the window
50
190
100
170
300
60
60
7
Correlation coefficient
0.9498
0.9394
0.9452
0.9288
0.9238
0.9442
0.9476
8
MOS score on the scale of (0–1)
0.9
0.9
0.9
0.9
0.9
0.9
0.9
Table 3.
Correlation coefficients for ACDW samples: Results for plucked & picked sound notes.
The synthesis results are improved by changing the length of the window. This ‘adaptiveness’ in the length of the window is named as the ‘Adaptive Cepstral Domain Window (ACDW)’. The ACDW method gives the best estimation of the impulse response and isolates the excitation signal from the impulse response. Once it’s isolated from the excitation signal, modeling of impulse response becomes the focus.
This section covers the discussion of the ACDW method along with the results of impulse response estimation and the synthesis of guitar notes. The length of the window in cepstral domain is changed in the range of 50 samples to 300 samples. The estimation of the best impulse response is done based on correlation coefficient. The correlation coefficient is the statistical parameter to indicate the degree of similarity. A lot of experimentation is done by varying the number of samples of the cepstral domain window. It is observed that the correlation coefficient drops when the number of samples in cepstral domain window are increased further. The range is finalized after studying the impulse response and the synthesis results.
Figure 13 plots the correlation coefficients versus the number of samples in the cepstral domain for the string 1 fret 1 finger plucked Guitar note. From the figure, it’s clear that when number of samples in the chosen length of cepstral window is 70, ACDW synthesis gives highest correlation coefficient. The graph shows the decaying nature of the impulse response for the selected guitar note. So the improvement in the synthesis is achieved with the help of ACDW method. Once this is achieved, the extracted impulse responses are analyzed further to observe their relationship. The isolated impulse responses for all the frets of a single string are plotted and it’s observed that these impulses are also following the important Octave relationship from one fret to the other.
Figure 13.
Comparison of correlation coefficients for different number of samples for string 1 fret 1 finger plucked guitar note.
This triggered the thought of using the impulse response of a single fret to generate the impulse responses for the other frets. The experimentation is carried out for string 1 with all its 20 frets for impulse generation. The generated impulses were convolved with the separated excitation signals to generate all the music notes along a single fret. This gives rise to generalized acoustic guitar model where a single impulse response can be stored and used to generate all other music notes of that guitar. Figure 14 shows the time domain graph of the impulse responses showcasing their octave relationship. It demonstrates the octave relationship followed by the impulse responses and a generalized model based on impulse response is developed.
Figure 14.
Impulse responses of finger plucked guitar notes.
In summary, Figure 15 shows the model for the ‘acoustic guitar notes synthesizer’. The string number and the fret number should be passed to the model and then it will isolate the impulse or the body response from the excitation signal. The estimated impulse response can be convolved with the excitation signal to synthesize the acoustic guitar note.
Figure 15.
Block schematic of the synthesized note using impulse response modeling.
4. Discussion of results
The synthesis results for the fixed sized windows is poor, given in Table 2, as verified with MoS (Mean Opinion Score), the subjective tests, taken from 10 guitar players. The MoS is carried out and also the correlation coefficient values are calculated. The results range from the value, 0.68 to 0.9 for the correlation coefficient. That’s the reason, the length of the cepstral domain window is kept variable to achieve best correlation coefficient value. This improved the results and helped to achieve the best possible impulse response which is further used to develop the generalized guitar model. Table 3 summarized the results of the adaptive cepstral domain window method. The column gives the plucking style and the parameters of the music note, such as, the number of samples in the window, correlation coefficients and MoS score on the scale of (0–1) for the synthesized music notes.
4.1 Synthesis results of the generated impulse response and excitation signal
The separated excitation signal and the estimated impulse response are convolved together to synthesize the acoustic Guitar note. The ACDW approach provides good scope for better synthesis. The synthesis has been carried out for the two playing styles, namely, finger plucked, and pick plucked notes. The highest correlation coefficient value is 0.98 for finger plucked guitar note. The MoS (Mean Opinion Score) is also best indication for the synthesized guitar note giving highest value as 0.95.
The NN and machine learning algorithms are used to classify the plucking style and plucking expression for the original recorded notes. Once the model is trained it is further deployed for cross-validation of the synthesized music notes based on the ACDW method. The results are discussed subsection 4.1.
The contribution of this impulse response modeling work is to isolate the body response and the excitation signal of acoustic guitar notes. An innovative synthesizer for acoustic guitar notes is implemented in this research work. The work has used innovative Adaptive Cepstral Domain Windowing (ACDW) method which is not used before for musical instrument modeling. The algorithm has used the cepstral domain approach. The other contribution of the present work is to develop the generalized model for Guitar Notes using impulse response of single fret. The reason why this is possible is ‘the octave relationship’ of frequency of all the frets along a string. The other name for the music is ‘harmony’ and the music notes generated by frets on strings follow this octave relationship to be in harmony. This research proved that ‘not only in frequency domain but also in time domain the music notes live in harmony’. This harmonic relationship in the time domain is used to generate the impulse response model using single fret. After plotting the graph of impulse response of all the frets for a string, fret 20 was chosen for modeling the impulse response for other frets.
To summarize the work, impulse response modeling is implemented with good accuracy. Further, the neural network (NN) is used for classification of naïve and expert player considering the expression in the note played. The classification is also done for plucking style i.e. finger plucking and plectrum plucking. The results of the two methods of synthesis i.e. ACDW and Generalized model are cross verified by NN model. The trained model of the classification is used for verification of the synthesized notes.
4.2 Discussion of the cross-validation results
The synthesis of the acoustic guitar notes is implemented using the ACDW method and a generalized model is developed using the body response of the single fret. The validation of the synthesized guitar notes is done by the subjective (listening) tests and the correlation coefficient values. NN model is used for the classification of guitar notes with respect to plucking style and plucking expression. Further this trained model is used for testing the synthesized guitar notes to identify plucking style and plucking expression. This is named as cross validation of the models.
Table 3 summarizes the validation of the synthesized guitar notes based on: 1) Mean opinion Score i.e. MOS as the subjective tests and 2) Correlation coefficients as the statistical parameter for finding the similarity between original and synthesized music notes using ACDW approach. The NN model is used for classification of the music notes based on: 1) plucking style and 2) plucking expressions.
Table 4 summarizes the cross-validation result for synthesized music notes, only of an expert player. The last two columns are highlighted to show the result of NN modeling to predict the class. The table values confirm the model validation as the classification results are greater than 80%. Only few sample results are shown in Table below.
Plucking Style
Music Note
(0–1)Scale for the synthesized note
Correlation coefficients
Existing Class
Predicted Class
Plucked Note
String1fret19
0.8
0.8477
Expert
Expert
String1fret18
0.8
0.8144
Expert
Expert
String1fret13
0.7
0.79
Expert
Expert
String1fret12
0.7
0.71
Expert
Beginner
String1fret2
0.8
0.8909
Expert
Expert
String1fret1
0.8
0.8120
Expert
Expert
Picked Notes
String1fret19
0.85
0.8784
Expert
Expert
String1fret18
0.85
0.86
Expert
Expert
String1fret13
0.75
0.81
Expert
Expert
String1fret12
0.75
0.79
Expert
Expert
String1fret2
0.75
0.78
Expert
Beginner
String1fret1
0.8
0.71
Expert
Expert
Table 4.
Cross-validation result for the expert’s synthesized music notes.
Similarly, a cross-validation has been carried out for the plucking style where the acoustic guitar notes played by the Expert Player have been passed to the trained model and the plucking style is predicted. The results from the Impulse Response modeling method are considered for the identification of the plucking style. Table 5 summarizes the results of the trained model for identification of plucking style of synthesized notes using NN classifiers. The cells highlighted with yellow indicate the wrong classification of the plucking style. The second column gives the names of the music notes while the 3rd and the 4th columns indicate the correlation coefficients for the subjective tests and the impulse response method.
Music Note
(0–1) Scale for the synthesized note
Correlation coefficients by Impulse Response Modeling
Original Style
Predicted Style
Expert
String1fret1
0.95
0.9283
Finger
Finger
String1fret2
0.95
0.9441
Finger
Finger
String1fret3
0.9
0.9324
Finger
Finger
String1fret4
0.8
0.7897
Finger
Pick
String1fret5
0.85
0.8856
Finger
Finger
String1fret6
0.9
0.9008
Finger
Finger
Expert
String1fret1
0.95
0.9317
Pick
Pick
String1fret2
0.95
0.9452
Pick
Pick
String1fret3
0.9
0.9146
Pick
Finger
String1fret4
0.9
0.9238
Pick
Pick
String1fret5
0.9
0.9127
Pick
Pick
String1fret6
0.95
0.9434
Pick
Pick
Table 5.
Cross-validation result for the plucking style of the expert player.
5. Conclusion
The limitation of the impulse response method using the hammer method and string-breaking method are overcome with the help of cepstral domain window method. The challenges of isolation of impulse response from the excitation signal are overcome using ACDW approach and a model is developed using the body features. The main contribution of the present research work is: 1) Physical model for Guitar as an instrument using Adaptive Cepstral Domain Window (ACDW) approach, 2) Generalized Model for Impulse Response of Acoustic Guitar for All Frets using a Response of Single Fret, and 3) Classification of guitar notes based on plucking style and plucking expression. The validation of the synthesized notes is done by using subjective listening tests i.e. Mean Opinion Score (MOS) and the correlation coefficients. The classification of plucking style and plucking expression is done using NN modeling techniques. The trained model is used for testing the plucking expression of the synthesized model. This model can be used to certify if the player is becoming an expert. If the score for expert identification is greater than 95% then player can be certified as expert.
\n',keywords:"impulse response, modeling, acoustic guitar, convolution, frets, octave rule, adaptive cepstral domain window",chapterPDFUrl:"https://cdn.intechopen.com/pdfs/78363.pdf",chapterXML:"https://mts.intechopen.com/source/xml/78363.xml",downloadPdfUrl:"/chapter/pdf-download/78363",previewPdfUrl:"/chapter/pdf-preview/78363",totalDownloads:120,totalViews:0,totalCrossrefCites:0,totalDimensionsCites:0,totalAltmetricsMentions:0,introChapter:null,impactScore:0,impactScorePercentile:44,impactScoreQuartile:2,hasAltmetrics:0,dateSubmitted:"July 23rd 2021",dateReviewed:"August 1st 2021",datePrePublished:"September 1st 2021",datePublished:"April 6th 2022",dateFinished:"September 1st 2021",readingETA:"0",abstract:"Music is the pulse of human lives and is an amazing tool to relieve and re-live. And when it comes to the signal processing, impulse is the pulse of the researchers. The work presented here is focused on impulse response modeling of noted produced by box shaped acoustic guitar. The impulse response is very fundamental behavior of any system. The music note is the convolution of the impulse response and the excitation signal of that guitar. The frequency of the generated music note follows the octave rule. The octave rule can be checked for impulse responses as well. If the excitation signal and impulse response are separated, then an impulse response of a single fret can be used to generate the impulse responses of other frets. Here the music notes are analyzed and synthesized on the basis of the plucking style and plucking expression of the guitar-player. If the impulse response of the musical instrument is known, the output music note can be synthesized in an unusual manner. Researchers have been able to estimate the impulse response by breaking the string of the guitar. Estimating the impulse response from the recorded music notes is possible using the methodology of cepstral domain window. By means of the Adaptive Cepstral Domain Window (ACDW) the author estimated the impulse response of guitar notes. The work has been further extended towards the classification of synthesized notes for plucking style and plucking expression using Neural Network and Machine Learning algorithms.",reviewType:"peer-reviewed",bibtexUrl:"/chapter/bibtex/78363",risUrl:"/chapter/ris/78363",book:{id:"10922",slug:"music-in-health-and-diseases"},signatures:"Minakshi Pradeep Atre and Shaila Apte",authors:[{id:"336498",title:"Dr.",name:"Minakshi",middleName:"Pradeep",surname:"Pradeep Atre",fullName:"Minakshi Pradeep Atre",slug:"minakshi-pradeep-atre",email:"mpa_entc@pvgcoet.ac.in",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/336498/images/14865_n.jpg",institution:null},{id:"427892",title:"Dr.",name:"Shaila",middleName:null,surname:"Apte",fullName:"Shaila Apte",slug:"shaila-apte",email:"shaila.apte@gmail.com",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",institution:null}],sections:[{id:"sec_1",title:"1. Introduction",level:"1"},{id:"sec_2",title:"2. Literature review",level:"1"},{id:"sec_3",title:"3. Methodology",level:"1"},{id:"sec_3_2",title:"3.1 Structure of acoustic guitar",level:"2"},{id:"sec_4_2",title:"3.2 Collection of guitar notes and database generation",level:"2"},{id:"sec_5_2",title:"3.3 Pythagoras fractions OR rule of 18 (octave rule)",level:"2"},{id:"sec_6_2",title:"3.4 Impulse response modeling",level:"2"},{id:"sec_6_3",title:"3.4.1 Introduction",level:"3"},{id:"sec_7_3",title:"3.4.2 Cepstral domain window method",level:"3"},{id:"sec_8_3",title:"3.4.3 Cepstral domain windowing method for acoustic guitar notes",level:"3"},{id:"sec_9_3",title:"Table 2.",level:"3"},{id:"sec_12",title:"4. Discussion of results",level:"1"},{id:"sec_12_2",title:"4.1 Synthesis results of the generated impulse response and excitation signal",level:"2"},{id:"sec_13_2",title:"4.2 Discussion of the cross-validation results",level:"2"},{id:"sec_15",title:"5. Conclusion",level:"1"}],chapterReferences:[{id:"B1",body:'Gerald Schuller, Jakob Abeßer, Christian Kehling. Parameter extraction for bass guitar sound models including playing styles”, 2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Year: 2015'},{id:"B2",body:'Ana M. Barbancho; Anssi Klapuri; Lorenzo J. Tardon; Isabel Barbancho,“Automatic Transcription of Guitar Chords and Fingering From Audio”, IEEE Transactions on Audio, Speech, and Language Processing, Year: 2012'},{id:"B3",body:'Raymond V. Migneco; Youngmoo E. Kim, “Modeling plucked guitar tones via joint source-filter estimation”, Digital Signal Processing Workshop and IEEE Signal Processing Education Workshop (DSP/SPE) 2011, 2011 IEEE.'},{id:"B4",body:'Julius O Smith III, “Piano Synthesis”, Center for Computer Research in Music and Acoustics (CCRMA) Department of Music, Stanford University, Stanford, California 94305 USA, March 2014'},{id:"B5",body:'Lee Meng Koon, Mohammad Hosseini Fouladi, and Satesh Narayana Namasivayam. “Mathematical modelling and acoustical analysis of classical guitars and their soundboards.” Advances in Acoustics and Vibration 2016'},{id:"B6",body:'Martin, Keith D., and Youngmoo E. Kim. “Musical instrument identification: A pattern-recognition approach.” The Journal of the Acoustical Society of America 104.3 (1998): 1768-1768.'},{id:"B7",body:'Eric J. Humphrey, Juan P. 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Gower, Intonation and Compensation of Fretted String Instruments, Department of Physics, Loyola Marymount University 5th September 2009'},{id:"B12",body:'V.E. Howle , Lloyd N. Trefethen, “Eigenvalues and Musical Instruments”, Center for Applied Mathematics, 657 Frank H. T. Rhodes Hall, Cornell University, Ithaca, May 2000.'},{id:"B13",body:'Kerem Ege, Antoine Caigne, “End conditions of Piano Strings”, Laboratory for Solid Mechanics, Ecole Polytechnique, UMR7649, 91128 Palaiseau Cedex, Unité de Mécanique, Ecole Nationale Supérieure de Techniques Avancées, Chemin de la Hunière.'},{id:"B14",body:'Karjalainen, Matti, and Tuomas Paatero. “High-resolution parametric modeling of string instrument sounds” Signal Processing Conference, 2005 13th European. IEEE, 2005.'},{id:"B15",body:'Tachibana, Hideyuki, et al. “Harmonic/percussive sound separation based on anisotropic smoothness of spectrograms.” IEEE/ACM Transactions on Audio, Speech, and Language Processing 22.12 (2014): 2059-2073.'},{id:"B16",body:'Tachibana, Hideyuki, et al. “Comparative evaluations of various harmonic/percussive sound separation algorithms based on anisotropic continuity of spectrogram.” 2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2012.'},{id:"B17",body:'Lee, Nelson, Julius O. Smith, and Vesa Valimaki. “Analysis and synthesis of coupled vibrating strings using a hybrid modal-waveguide synthesis model.” IEEE transactions on audio, speech, and language processing 18.4 (2009): 833-842.'},{id:"B18",body:'Friedrich Türckheim, Thorsten Smit, Carolin Hahne, and Robert Mores, “Novel Impulse Response Measurement Method for Stringed Instruments”, Proceedings of 20th International Congress on Acoustics, ICA 2010 23–27 August 2010, Sydney, Australia.'}],footnotes:[],contributors:[{corresp:"yes",contributorFullName:"Minakshi Pradeep Atre",address:"mpa_entc@pvgcoet.ac.in",affiliation:'
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1. Introduction
Tissue engineering (TE) is an interdisciplinary field whose first definition dates back to 1987. It combines the knowledge from different research areas including medicine, material science and engineering to develop engineered biological substitutes able to restore, maintain or improve tissue functions [1]. TE was introduced from the necessity of finding alternative methodologies to organ transplantations due to their increasing demand in clinical medicine. Furthermore, TE emerged as a promising approach to overcome the limitations of the conventional surgical approaches for the treatment of tissue damages caused by injuries, diseases and congenital disorders [2, 3]. These surgical procedures are based on replacing the injured tissues or organs with a healthy one harvested from the same patient (autograft), or a compatible donor (allograft). Although these approaches have been revolutionary and lifesaving, there are still some drawbacks that need to be addressed. The surgical procedures used to harvest both autografts and allografts are often invasive and painful. The risk of post-surgical limitations in the donor’s body due, for example, to infections and hematomas is, in fact, quite high. Moreover, when allografts are transplanted, the chance of inflammatory and immune responses in the patient body together with the transmission of diseases from the donor to the patient is significant [4].
TE aims at overcoming the complications associated with the conventional techniques used during organ transplantation by inducing the complete regeneration of the damaged tissues instead of replacing them [2, 3]. Several approaches to promote de novo tissue formation have been implemented in TE so far. These are mainly based on the use of biodegradable and biocompatible engineered tissues, based on the so-called ‘scaffolds’. A scaffold is a structure that provides temporary mechanical support and a guiding template to cells during the synthesis of new tissue. With the desired shape, architecture and functions. Concurrently, the scaffold biodegrades leaving space for new tissue in-growth. Notably, the biodegradability of the scaffold is what differentiates it from permanent implants. The complete biodegradation of the scaffolds prevents, the need for additional surgical interventions to remove it or, eventually, substitute it. The scaffold can be directly implanted into the injured site to induce the regeneration of the tissues in vivo. Otherwise, prior to implantation, the scaffold can be initially cellularized with cells isolated from the patient, subsequently cultured in vitro to synthesize tissues that will finally be transplanted into the defect to restore its functions (Figure 1). In this case, scaffolds can be further cultivated in bioreactors, namely, devices able to apply biophysical stimuli to cells (e.g., mechanical or chemical) to better mimic the dynamic physiological conditions. In both approaches, the scaffold can be loaded with drugs, growth factors, micro- and/or nano-particles to further facilitate the recovering capabilities of tissues [5, 6].
Figure 1.
Illustration of TE paradigm (figure created with BioRender.com).
The scaffold plays an essential role in regulating the process of new tissue formation. An ideal scaffold should be biocompatible and should degrade with kinetics compatible with the rate of tissue regeneration. It should be highly porous (< 75% [7]) with adequate pore size to promote cell migration/scaffold colonization and nutrient transfer throughout the scaffold. A scaffold should mimic the features of biological tissues in terms of topological properties (e.g., shape, size), mechanical properties (e.g., stiffness), and the biochemical processes that control and regulate the functionalities of the tissues. Moreover, it should not alter the normal functions of cells, which should adhere, migrate and proliferate within the scaffold before producing new tissue [5, 6, 8, 9]. Depending on their applications, scaffolds with different shapes, compositions and properties have been developed so far.
The biomaterial formulations used to produce the scaffold strongly affect its properties [10, 11]. Thus, the selection of the proper biomaterial formulation is pivotal for inducing the regeneration of the tissue in a controlled manner avoiding any undesired side-effects (e.g., cytotoxicity, apoptosis, carcinogenicity). The most used biomaterial formulations in TE are mainly based on synthetic biopolymers, natural biopolymers and composites [12, 13]. Synthetic biopolymers, like polycaprolactone, can be produced on a large scale under controlled conditions with predictable and reproducible physicochemical properties (e.g., mechanical properties, biodegradability) [6, 14, 15]. However, many synthetic biopolymers that have been developed so far are mainly derived from petroleum and coal, which make them not compatible with the environment [16]. Natural biopolymers include animal-derived proteins (e.g., gelatin, hyaluronic acid, collagen, silk) and animal- and vegetal-derived polysaccharides (e.g., cellulose alginate, chitosan). One of the advantages of this class of biopolymers is their biological similarity to native tissues which is beneficial for supporting cell functionalities (e.g., cell adhesion). Nonetheless, the use of animal-derived biopolymers may be associated with a high risk of transmission of diseases from animal to patient [10, 17, 18]. Therefore, the use of naturally occurring biopolymers from vegetal sources represents an attractive alternative to overcome these limitations. Moreover, they represent an ecological alternative to synthetic biopolymers in the preparation of sustainable and green scaffolds.
In recent years particular attention has been paid to the adoption of methodologies to derive biopolymers from renewable sources, such as industrial by-products, such as pectin from fruit pomace produced from the fruit processing industry [19] and cellulose nanofibers obtained from paper waste [20]. The application of more ecologically viable biomaterials in TE may, in fact, strongly contribute to reduce the polluting impact of producing and using un-recyclable synthetic biopolymers. Among the renewable and natural biopolymers, pectin is gaining particular attention in TE for its advantageous properties including biocompatibility, biodegradability and non-toxicity [21, 22]. In addition, the versatility in processing pectin-based formulations allows to produce scaffolds with diverse properties and for different applications (Section 2).
This chapter aims at highlighting the applications of pectin as the building block of bidimensional (2D) and three-dimensional (3D) scaffolds for TE applications. With this aim, in Section 2 the properties of pectin as biomaterial are provided. Section 3 reports the most representative applications of pectin-based formulations for producing scaffolds for tissue regeneration in the shape of 2D films for wound healing and 3D scaffolds for tissue regeneration.
2. Properties of pectin as a biomaterial for TE applications
Pectin shows several remarkable properties as a biomaterial. It is biocompatible and biodegradable, and it is soluble in cytocompatible and non-toxic solvents (such as water). Pectin is a versatile biomaterial as its physical properties can be facilely tuned due to the presence of several functional groups (e.g., carboxylic groups) that can serve as binding sites for other functional groups, biomolecules and drugs [21, 22, 23]. It is a low-cost biomaterial due to its ubiquity in nature, and this can strongly reduce the costs associated with the development of engineered tissues.
Pectin can form hydrogel due to the ability of its macromolecules to absorb and retain large volumes of water. This unique property makes pectin a suitable candidate to produce a natural extracellular matrix, which naturally surrounds cells. Furthermore, due to the possibility to be processed under sterile and physiological conditions (i.e., the aqueous environment at 37°C), pectin enables to encapsulate cells within its matrix to produce cell-laden scaffolds [23, 24].
Pectin tends to dissolve under physiological conditions, therefore physicochemical approaches are required to stabilize pectin-based scaffolds. These are mainly based on the use of physicochemical crosslinking approaches which consist of the formation of a stable network of links among the pectin molecules. This network reduces the interactions of pectin molecules with water and prevents the disruption of pectin-based scaffolds. For example, the most employed approach to form water-insoluble scaffolds of low-methoxyl pectin is based on the use of divalent cations (e.g., Ca2+) that interact with the carboxylic groups of pectin forming the so-called ‘egg box’ structure [21]. Notably, the crosslinking treatments should also be cytocompatible (under specific conditions/concentrations), and should not interfere with the capability of pectin to encapsulate cells [25].
One of the major drawbacks that limit the application of pectin as a biomaterial for TE applications is its low cell adhesivity due to the lack of sites for cell adhesion (such as arg-gly-asp (RGD) sequences). Therefore, pectin is often combined/blended with other biopolymers or biomolecules to enhance its bioactivity [21, 26].
3. Applications of pectin in TE
Pectin-based formulations have been processed through different fabrication approaches into scaffolds with various shapes for different applications. In particular, pectin has been mainly used for the production of 2D films for wound healing, and 3D scaffolds for tissue regeneration. Figure 2 provides a graphical overview of the main applications of pectin in TE.
Figure 2.
Illustration of the application of pectin (derived from citrus fruits) for the production of scaffolds for TE applications (created with BioRender.com).
3.1 2D patches for tissue regeneration
One of the applications of pectin-based formulations is the preparation of 2D hydrogel patches for the treatment of wounds. These patches provide mechanical support to cells during the process of new tissue formation, and an antibacterial barrier preventing eventual infections. Moreover, the hydrophilic pectin molecules in the film can react with the fluids of the wound forming a soft gel. The presence of a gel allows to maintain a moist environment in the wound. This helps to remove or control secretions from the wounded tissue and in turn facilitates the healing process. The regeneration of the damaged tissue can be further promoted by the incorporation of bioactive molecules such as drugs (e.g., antibiotics) and/or growth factors within the pectin patches [21]. The controlled and prolonged release of these molecules directly in the damaged site can actively contribute to decreasing the risk of infections and accelerating the formation of new tissue. As mentioned in Section 2, pectin is often combined with other biopolymers to enhance its bioactivity and also to modulate the physical properties (e.g., tensile strength) of the final patch.
Pectin-based patches for wound healing reported in the literature so far are principally obtained in the shape of non-porous films and porous membranes, as detailed described in the following Sections 3.1.1 and 3.1.2, respectively.
3.1.1 Pectin-based films
Pectin-based films are generally 2D, non-porous and flexible substrates able to retain large volumes of water within their matrix. One of the approaches used to produce these films is the so-called ‘solvent casting’. In this approach, a pectin-based solution is initially poured into a mold, and the solvent is subsequently let to evaporate leaving a 2D non-porous film (Figure 3).
Figure 3.
Illustration of the solvent casting approach (created with BioRender.com).
Pectin-based patches produced with this approach support cell adhesion and proliferation and accelerate the processes occurring during the formation of new tissue [27, 28, 29, 30]. Moreover, films with high toughness and stretchability can be produced with solvent casting, and these can be potentially used as pectin-based patches for load-bearing tissues (e.g., cartilage, tendon) [28]. In addition, pectin-based patches for a controlled drug into the targeted tissue were also produced by incorporating drugs in the pectin matrix [30, 31].
3.1.2 Pectin nanoporous membranes
Nanoporous membranes based on pectin have been mainly obtained through electrospinning. This approach allows to produce highly porous and flexible patches starting from pectin-based/polymer solutions subjected to an external electric field. A standard electrospinning apparatus is illustrated in Figure 4. It generally consists of (i) a syringe pump containing the polymer solution, (ii) a metallic needle through which the polymer solution is ejected, (iii) a high voltage power supply (in the range of tens of kVolts), and (iv) a grounded collector (usually a metal plate). When a drop of the polymer solution is extruded through the needle, the high electric forces in the space between the needle and the collector induce its stretching and the formation of fibers from a few nanometers to microns in diameters [32]. These fibers are therefore deposited and collected on the collector forming a non-woven fibrous membrane after complete evaporation of the solvent (Figure 4).
Figure 4.
Illustration of an electrospinning setup with a magnification of the electrospun nanofibers on the collector (image obtained with scanning electron microscopy).
Pectin-based patches obtained by this approach show several advantageous properties for TE applications. The random organization of electrospun pectin fibers together with the hydrogel nature of pectin enables to mimic the nanoscale organization of the native extracellular matrix. Furthermore, the high porosity and high surface-to-volume ratio typical of electrospun patches promote cell migration and nutrient diffusion within the scaffold, which is beneficial for the process of new tissue formation [33]. Nevertheless, it is quite challenging to produce electrospun structures from pristine pectin due to some intrinsic molecular properties of pectin (such as insufficient chain entanglement) that disable the fiber formation [34]. Thus, to improve its electrospinning ability, pectin is often chemically modified [35, 36] and/or combined with other biodegradable biopolymers such as poly(ethylene oxide) [34], polyhydroxybutyrate [37] that work as carrier polymer to induce the formation of stable fibers.
Pectin-based nano-fibers find application for the preparation of films/structures that can be potentially used as patches for wound healing of soft tissues [35, 36, 37] (e.g., vascular tissue [35], retinal tissue [37]). In addition, drugs (such as antibiotics [38, 39]) and particles (such as argentum ions for antibacterial purposes [38]) can be successfully loaded in these structures obtaining patches for a local and controlled release of drugs directly into the wound.
3.2 3D pectin scaffolds
Pectin-based formulations can be further processed to obtain 3D scaffolds able to mimic the complex architecture of biological tissues. 3D pectin-based scaffolds have been principally obtained in the shape of porous 3D sponges and 3D bioprinted scaffolds.
3.2.1 Pectin-based sponges
Sponges are comparable to foams with an interconnected network of pores. This type of architecture is beneficial for cell penetration and scaffold colonization, while ensuring adequate diffusion of nutrients to cells within the scaffold. Moreover, a highly porous scaffold with open and connected pores is of critical importance as it allows for the diffusion of nutrients and waste products through the scaffold [6, 7].
Pectin-based sponges are mainly obtained by freeze-drying, also known as lyophilization. This technique consists in freezing a polymer solution followed by the evaporation of the frozen solvent by sublimation. Thus, a solid polymer matrix with numerous and interconnected pores is obtained (Figure 5). Before freezing, polymer solutions are generally poured into molds to produce porous scaffolds with the desired shape.
Figure 5.
Schematic of the process for obtaining cylindrical porous sponges was obtained by freeze-drying. Magnification of the porous sponges obtained by scanning electron microscopy (image created with BioRender.com).
Pectin-based sponges have been principally used to produce scaffolds for wound healing and tissue regeneration. For example, sponges obtained with pectin-based formulations have been used as scaffolds for different types of tissues including cartilage [40, 41], skin [42], and bone [43]. The high hydrophilicity of pectin molecules and the interconnected porosity enables these sponges to entrap a large volume of water creating a 3D hydrogel-based environment that can mimic the natural extracellular matrix [40, 41]. Furthermore, this provides and stabilizes a moist environment for wounds that strongly contributes to accelerating the healing of the wounds [44].
3.2.2 Complex shaped pectin-based scaffolds
Producing scaffolds with a customized architecture and by automated and high reproducible approaches is one of the main challenges of TE. The development of pectin-based scaffolds with patient-specific architecture may boost their clinical applications.
Pectin-based scaffolds with complex shapes have been principally obtained by extrusion-based bioprinting so far. Extrusion-based bioprinting is one of the most widely used technology in TE due to its simplicity and versatility in processing a large variety of biomaterials, cells and biomolecules. An extrusion-based bioprinter usually consists of a movable cartridge containing the biomaterial formulation (called ‘ink’) and of a movable deposition stage (Figure 6). Before bioprinting, the architecture of the scaffolds can be designed by a computer-aided design (CAD) software, or it can be derived from patient medical images acquired, for example, by computed tomography scans or magnetic resonance imaging. The 3D model of the scaffold is subsequently sliced by a computer-aided manufacturing (CAM) software in bioprinting paths and finally converted to a printable code file (called ‘G-code’) [45, 46]. During the bioprinting process, the ink is extruded onto the deposition stage following the preprogrammed paths contained in the G-code, in a layer-by-layer process.
Figure 6.
Schematic of extrusion-based bioprinting.
The application of pectin-based inks in extrusion-based bioprinting is relatively recent compared to the other fabrication approaches described in the previous sections. Pectin solutions are often not suitable to be processed through extrusion-based bioprinting and structures with poor shape fidelity are often obtained. The first application of pectin as ink for extrusion-based bioprinting dates back to 2017. In this case, pectin was combined with another biopolymer (Pluronic F-127), and complex-shaped scaffolds were bioprinted [47, 48]. Cells were successfully loaded within this formulation and 3D bioprinted to produce living 3D constructs [24]. From that moment, other pectin-based inks have been developed and optimized to produce 3D scaffolds with high shape fidelity [49, 50, 51]. For example, pectin-based scaffolds with more complex shapes such as a human ear and nose shape for cartilage tissue regeneration were successfully obtained (Figure 6) [41].
4. Conclusions
TE represents an alternative approach to conventional surgical techniques used to treat damaged, injured or diseased tissues or organs. This approach is based on the use of tissue-mimicking and biodegradable constructs, based on the so-called ‘scaffolds’, able to restore, maintain or improve tissue functions. The physicochemical properties of the final scaffold play a key role in the process of new tissue formation. The selection of the proper biomaterial formulation is therefore essential. Recently, renewable biomaterials derived from industrial by-products are finding increasing application in TE as an alternative to petroleum-derived and unrecyclable polymers. In this regard, pectin, a polysaccharide commercially derived from citrus peel and apple pomace (both by-products of the food processing industry), is gaining attention in TE due to its biocompatibility, biodegradability and non-cytotoxicity. Diverse pectin-based formulations have been developed and employed for the fabrication of functional scaffolds for TE applications.
This chapter presented the most representative applications of pectin-based formulations for the fabrication of scaffolds for TE applications. In particular, by properly processing these formulations through specific fabrication techniques is possible to produce pectin-based scaffolds with different features: from 2D non-porous films (obtained by solvent casting) to 3D scaffolds with patient-specific shape (obtained by extrusion-based bioprinting). Although pectin shows diverse advantageous properties as biomaterial, its application in clinical practice is still under investigation. The increasing number of studies on the preparation of biocompatible pectin-based formulations may strongly boost the employment of this polysaccharide in the fabrication of sustainable scaffolds for future TE applications.
Acknowledgments
The authors wish to acknowledge the Crosslab Additive Manufacturing of the Department of Information Engineering of the University of Pisa.
Conflict of interest
The authors declare no conflict of interest.
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These provide structural support to cells and regulate the process of new tissue formation. The properties of the scaffold are essentials, and they can be controlled by varying the biomaterial formulation and the fabrication technology used to its production. Pectin is emerging as an alternative biomaterial to non-degradable and high-cost petroleum-based biopolymers commonly used in this field. It shows several promising properties including biocompatibility, biodegradability, non-toxicity and gelling capability. Pectin-based formulations can be processed through different fabrication approaches into bidimensional and three-dimensional scaffolds. This chapter aims at highlighting the potentiality in using pectin as biomaterial in the field of tissue engineering. The most representative applications of pectin in preparing scaffolds for wound healing and tissue regeneration are discussed.",reviewType:"peer-reviewed",bibtexUrl:"/chapter/bibtex/79710",risUrl:"/chapter/ris/79710",signatures:"Anna Lapomarda, Aurora De Acutis, Carmelo De Maria and Giovanni Vozzi",book:{id:"10742",type:"book",title:"Pectins",subtitle:"The New-Old Polysaccharides",fullTitle:"Pectins - The New-Old Polysaccharides",slug:"pectins-the-new-old-polysaccharides",publishedDate:"July 6th 2022",bookSignature:"Martin Alberto Masuelli",coverURL:"https://cdn.intechopen.com/books/images_new/10742.jpg",licenceType:"CC BY 3.0",editedByType:"Edited by",isbn:"978-1-83969-597-1",printIsbn:"978-1-83969-596-4",pdfIsbn:"978-1-83969-598-8",isAvailableForWebshopOrdering:!0,editors:[{id:"99994",title:"Dr.",name:"Martin",middleName:"Alberto",surname:"Alberto Masuelli",slug:"martin-alberto-masuelli",fullName:"Martin Alberto Masuelli"}],productType:{id:"1",title:"Edited Volume",chapterContentType:"chapter",authoredCaption:"Edited by"}},authors:[{id:"286832",title:"Dr.",name:"Aurora",middleName:null,surname:"De Acutis",fullName:"Aurora De Acutis",slug:"aurora-de-acutis",email:"deacutis.aurora@gmail.com",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",institution:{name:"University of Pisa",institutionURL:null,country:{name:"Italy"}}},{id:"422858",title:"Prof.",name:"Giovanni",middleName:null,surname:"Vozzi",fullName:"Giovanni Vozzi",slug:"giovanni-vozzi",email:"giovanni.vozzi@unipi.it",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",institution:{name:"University of Pisa",institutionURL:null,country:{name:"Italy"}}},{id:"425351",title:"Dr.",name:"Anna",middleName:null,surname:"Lapomarda",fullName:"Anna Lapomarda",slug:"anna-lapomarda",email:"anna.lapomarda@ing.unipi.it",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",institution:null},{id:"442631",title:"Dr.",name:"Carmelo",middleName:null,surname:"De Maria",fullName:"Carmelo De Maria",slug:"carmelo-de-maria",email:"carmelo.demaria@unipi.it",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",institution:{name:"University of Pisa",institutionURL:null,country:{name:"Italy"}}}],sections:[{id:"sec_1",title:"1. Introduction",level:"1"},{id:"sec_2",title:"2. Properties of pectin as a biomaterial for TE applications",level:"1"},{id:"sec_3",title:"3. Applications of pectin in TE",level:"1"},{id:"sec_3_2",title:"3.1 2D patches for tissue regeneration",level:"2"},{id:"sec_3_3",title:"3.1.1 Pectin-based films",level:"3"},{id:"sec_4_3",title:"3.1.2 Pectin nanoporous membranes",level:"3"},{id:"sec_6_2",title:"3.2 3D pectin scaffolds",level:"2"},{id:"sec_6_3",title:"3.2.1 Pectin-based sponges",level:"3"},{id:"sec_7_3",title:"3.2.2 Complex shaped pectin-based scaffolds",level:"3"},{id:"sec_10",title:"4. Conclusions",level:"1"},{id:"sec_11",title:"Acknowledgments",level:"1"},{id:"sec_14",title:"Conflict of interest",level:"1"}],chapterReferences:[{id:"B1",body:'Meyer U. The history of tissue engineering and regenerative medicine in perspective. In: Fundamentals of Tissue Engineering and Regenerative Medicine. Berlin, Heidelberg: Springer; 2009. pp. 5-12. 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Pectin methacrylate (PEMA) and gelatin-based hydrogels for cell delivery: Converting waste materials into biomaterials. ACS Applied Materials & Interfaces. 2019;11(13):12283-12297. DOI: 10.1021/acsami.9b00154'},{id:"B26",body:'Chen F, Ni Y, Liu B, Zhou T, Yu C, Su Y, et al. Self-crosslinking and injectable hyaluronic acid/RGD-functionalized pectin hydrogel for cartilage tissue engineering. Carbohydrate Polymers. 2017;166:31-44. DOI: 10.1016/j.carbpol.2017.02.059'},{id:"B27",body:'Chlapanidas T, Tosca MC, Faragò S, Perteghella S, Galuzzi M, Lucconi G, et al. Formulation and characterization of silk fibroin films as a scaffold for adipose-derived stem cells in skin tissue engineering. International Journal of Immunopathology and Pharmacology. 2013;26(I):43-49. DOI: 10.1177/03946320130260S106'},{id:"B28",body:'Wu X, Sun H, Qin Z, Che P, Yi X, Yu Q , et al. Fully physically crosslinked pectin-based hydrogel with high stretchability and toughness for biomedical application. International Journal of Biological Macromolecules. 2020;149:707-716. DOI: 10.1016/j.ijbiomac.2020.01.297'},{id:"B29",body:'Kraskouski A, Hileuskaya K, Kulikouskaya V, Kabanava V, Agabekov V, Pinchuk S, et al. Polyvinyl alcohol and pectin blended films: Preparation, characterization, and mesenchymal stem cells attachment. Journal of Biomedical Materials Research—Part A. 2021;109(8):1379-1392. DOI: 10.1002/jbm.a.37130'},{id:"B30",body:'Zulema K, Valle M, Acuña RAS, Arana JVR, Lobo N, Rodriguez C, et al. Natural film based on pectin and allantoin for wound healing: Obtaining, characterization, and rat model. BioMed Research International. 2020;2020:1-7. DOI: 10.1155/2020/6897497'},{id:"B31",body:'Prezotti FG, Siedle I, Boni FI, Chorilli M, Müller I, Cury BSF. Mucoadhesive films based on gellan gum/pectin blends as potential platform for buccal drug delivery. Pharmaceutical Development and Technology. 2020;25(2):159-167. DOI: 10.1080/10837450.2019.1682608'},{id:"B32",body:'Bhardwaj N, Kundu SC. Electrospinning: A fascinating fiber fabrication technique. Biotechnology Advances. 2010;28(3):325-347. DOI: 10.1016/j.biotechadv.2010.01.004'},{id:"B33",body:'Afsharian YP, Rahimnejad M. Bioactive electrospun scaffolds for wound healing applications: A comprehensive review. Polymer Testing. 2021;93:106952. DOI: 10.1016/j.polymertesting.2020.106952'},{id:"B34",body:'Akinalan BB, Sanem A. Role of rheology on the formation of Nanofibers from pectin and polyethylene oxide blends. Journal of Applied Polymer Science. 2020;137(3):48294. DOI: 10.1002/app.48294'},{id:"B35",body:'Li N, Xue F, Zhang H, Sanyour HJ, Rickel AP, Uttecht A, et al. Fabrication and characterization of pectin hydrogel nanofiber scaffolds for differentiation of mesenchymal stem cells into vascular cells. ACS Biomaterials Science & Engineering. 2019;5(12):6511-6519. 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Porous nano-minerals substituted apatite/chitin/pectin nanocomposites scaffolds for bone tissue engineering. Arabian Journal of Chemistry. 2020;13(10):7418-7429. DOI: 10.1016/j.arabjc.2020.08.018'},{id:"B44",body:'Yu N, Wang X, Ning F, Jiang C, Li Y, Peng H, et al. Development of antibacterial pectin from Akebia trifoliata var. australis waste for accelerated wound healing. Carbohydrate Polymers. 2019;217(235):58-68. DOI: 10.1016/j.carbpol.2019.03.071'},{id:"B45",body:'Pati F, Jang J, Lee JW, Cho DW. Extrusion bioprinting. In: Essentials of 3D Biofabrication and Translation. Amsterdam, Netherlands: Elsevier; 2015. pp. 123-152. DOI: 10.1016/B978-0-12-800972-7.00007-4'},{id:"B46",body:'Ning L, Chen X. A brief review of extrusion-based tissue scaffold bio-printing. Biotechnology Journal. 2017;12(8):1600671. DOI: 10.1002/biot.201600671'},{id:"B47",body:'Banks A, Guo X, Chen J, Kumpaty S, Zhang W. Novel bioprinting method using a pectin based bioink. Technology and Health Care. 2017;25(4):651-655. DOI: 10.3233/THC-160764'},{id:"B48",body:'Stealey S, Guo X, Ren L, Bryant E, Kaltchev M, Chen J, et al. Stability improvement and characterization of bioprinted pectin-based scaffold. Journal of Applied Biomaterials and Functional Materials. 2019;17(1):1-5. DOI: 10.1177/2280800018807108'},{id:"B49",body:'Lapomarda A, Pulidori E, Cerqueni G, Chiesa I, Blasi MD, Geven MA, et al. Pectin as rheology modifier of a gelatin-based biomaterial ink. Materials. 2021;14(11):3109. DOI: 10.3390/ma14113109'},{id:"B50",body:'Lapomarda A, Cerqueni G, Geven MA, Chiesa I, De Acutis A, De Blasi M, et al. Physicochemical characterization of pectin-gelatin biomaterial formulations for 3D bioprinting. Macromolecular Bioscience. 2021;21(9):2100168. DOI: 10.1002/mabi.202100168'},{id:"B51",body:'Pereira RF, Sousa A, Barrias CC, Bártolo PJ, Granja PL. A single-component hydrogel bioink for bioprinting of bioengineered 3D constructs for dermal tissue engineering. Materials Horizons. 2018;5(6):1100-1111. DOI: 10.1039/C8MH00525G'}],footnotes:[],contributors:[{corresp:null,contributorFullName:"Anna Lapomarda",address:null,affiliation:'
Research Center ‘E. Piaggio’, University of Pisa, Italy
Department of Ingegneria dell’Informazione, University of Pisa, Italy
'},{corresp:null,contributorFullName:"Aurora De Acutis",address:null,affiliation:'
Research Center ‘E. Piaggio’, University of Pisa, Italy
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Research Center ‘E. Piaggio’, University of Pisa, Italy
Department of Ingegneria dell’Informazione, University of Pisa, Italy
Research Center ‘E. Piaggio’, University of Pisa, Italy
Department of Ingegneria dell’Informazione, University of Pisa, Italy
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He was a research associate at the University College of Wales in Aberystwyth (UK) and EURATOM Fellow at the CEA Centre de Recherche Nucleaires de Saclay (F). His research field has been computational methods in condensed matter physics and complexity science. He is currently the research director at ENEA Casaccia Research Centre, at the Laboratory of Analysis and Protection of Critical Infrastructures, and at the Italian Node of the European Infrastructure Simulation and Analysis Centre (EISAC.it). 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We believe financial barriers should not prevent researchers from publishing their findings. With the need to make scientific research more publicly available and support the benefits of Open Access, more and more institutions and funders are dedicating resources to assist faculty members and researchers cover Open Access Publishing Fees (OAPFs). In addition, IntechOpen provides several further options presented below, all of which are available to researchers, and could secure the financing of your Open Access publication.
",metaTitle:"Waiver Policy",metaDescription:"We feel that financial barriers should never prevent researchers from publishing their research. With the need to make scientific research more publically available and support the benefits of Open Access, more institutions and funders have dedicated funds to assist their faculty members and researchers cover the APCs associated with publishing in Open Access. Below we have outlined several options available to secure financing for your Open Access publication.",metaKeywords:null,canonicalURL:"/page/waiver-policy",contentRaw:'[{"type":"htmlEditorComponent","content":"
Paying the OAPF
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At IntechOpen, the majority of OAPFs are paid by an Author’s institution or funding agency - Institutions (73%) vs. Authors (23%).
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The first step in obtaining funds for your Open Access publication begins with your institution or library. IntechOpen’s publishing standards align with most institutional funding programs. Our advice is to petition your institution for help in financing your Open Access publication.
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However, as Open Access becomes a more commonly used publishing option for the dissemination of scientific and scholarly content, in addition to institutions, there are a growing number of funders who allow the use of grants for covering OA publication costs, or have established separate funds for the same purpose.
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Please consult our Open Access Funding page to explore some of these funding opportunities and learn more about how you could finance your IntechOpen publication. Keep in mind that this list is not definitive, and while we are constantly updating and informing our Authors of new funding opportunities, we recommend that you always check with your institution first.
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IntechOpen Waivers in Action
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For Authors who are unable to obtain funding from their institution or research funding bodies and still need help in covering publication costs, IntechOpen offers the possibility of applying for a Waiver.
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Our mission is to support Authors in publishing their research and making an impact within the scientific community. Currently, 14% of Authors receive full waivers and 6% receive partial waivers.
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While providing support and advice to all our international Authors, waiver priority will be given to those Authors who reside in countries that are classified by the World Bank as low-income economies. In this way, we can help ensure that the scientific work being carried out can make an impact within the worldwide scientific community, no matter where an Author might live.
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How to Apply for a Waiver
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The application process is open after your submitted manuscript has been accepted for publication. To apply, please fill out a Waiver Request Form and send it to your Author Service Manager. If you have an official letter from your university or institution showing that funds for your OA publication are unavailable, please attach that as well. The Waiver Request will normally be addressed within one week from the application date. All chapters that receive waivers or partial waivers will be designated as such online.
Feel free to contact us at funders@intechopen.com if you have any questions about Funding options or our Waiver program. If you have already begun the process and require further assistance, please contact your Author Service Manager, who is there to assist you!
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Note: All data represented above was collected by IntechOpen from 2013 to 2017.
At IntechOpen, the majority of OAPFs are paid by an Author’s institution or funding agency - Institutions (73%) vs. Authors (23%).
\n\n
The first step in obtaining funds for your Open Access publication begins with your institution or library. IntechOpen’s publishing standards align with most institutional funding programs. Our advice is to petition your institution for help in financing your Open Access publication.
\n\n
However, as Open Access becomes a more commonly used publishing option for the dissemination of scientific and scholarly content, in addition to institutions, there are a growing number of funders who allow the use of grants for covering OA publication costs, or have established separate funds for the same purpose.
\n\n
Please consult our Open Access Funding page to explore some of these funding opportunities and learn more about how you could finance your IntechOpen publication. Keep in mind that this list is not definitive, and while we are constantly updating and informing our Authors of new funding opportunities, we recommend that you always check with your institution first.
\n\n
IntechOpen Waivers in Action
\n\n
For Authors who are unable to obtain funding from their institution or research funding bodies and still need help in covering publication costs, IntechOpen offers the possibility of applying for a Waiver.
\n\n
Our mission is to support Authors in publishing their research and making an impact within the scientific community. Currently, 14% of Authors receive full waivers and 6% receive partial waivers.
\n\n
While providing support and advice to all our international Authors, waiver priority will be given to those Authors who reside in countries that are classified by the World Bank as low-income economies. In this way, we can help ensure that the scientific work being carried out can make an impact within the worldwide scientific community, no matter where an Author might live.
\n\n
How to Apply for a Waiver
\n\n
The application process is open after your submitted manuscript has been accepted for publication. To apply, please fill out a Waiver Request Form and send it to your Author Service Manager. If you have an official letter from your university or institution showing that funds for your OA publication are unavailable, please attach that as well. The Waiver Request will normally be addressed within one week from the application date. All chapters that receive waivers or partial waivers will be designated as such online.
Feel free to contact us at funders@intechopen.com if you have any questions about Funding options or our Waiver program. If you have already begun the process and require further assistance, please contact your Author Service Manager, who is there to assist you!
\n\n
Note: All data represented above was collected by IntechOpen from 2013 to 2017.
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Fragmentation schemes are followed by the simplified spectra, which help the understanding of such complex phenomena. At the end of the chapter, acquisition of mass spectrum is discussed. 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Analytical method validation, thinking about the maximum relevant processes for checking the best parameters of analytical methods, using numerous relevant overall performance indicators inclusive of selectivity, specificity, accuracy, precision, linearity, range, limit of detection (LOD), limit of quantification (LOQ), ruggedness, and robustness are severely discussed in an effort to prevent their misguided utilization and ensure scientific correctness and consistency among publications.",book:{id:"6379",slug:"calibration-and-validation-of-analytical-methods-a-sampling-of-current-approaches",title:"Calibration and Validation of Analytical Methods",fullTitle:"Calibration and Validation of Analytical Methods - A Sampling of Current Approaches"},signatures:"Tentu Nageswara Rao",authors:[{id:"220824",title:"Dr.",name:"Tentu",middleName:null,surname:"Nageswara Rao",slug:"tentu-nageswara-rao",fullName:"Tentu Nageswara Rao"}]},{id:"58596",title:"Linearity of Calibration Curves for Analytical Methods: A Review of Criteria for Assessment of Method Reliability",slug:"linearity-of-calibration-curves-for-analytical-methods-a-review-of-criteria-for-assessment-of-method",totalDownloads:8095,totalCrossrefCites:19,totalDimensionsCites:44,abstract:"Calibration curve is a regression model used to predict the unknown concentrations of analytes of interest based on the response of the instrument to the known standards. 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The derivatization is typically done to change the analyte properties for a better separation and also for enhancing the method sensitivity. In GC/MS, derivatization may improve the capability of compound identification. Examples illustrating such improvements are included. The second part describes several types of derivatization that are more frequently used in analytical practice. These include alkylation (e.g., methylation), formation of aryl derivatives, silylation (e.g., formation of trimethylsilyl derivatives), acylation (e.g., reactions with acyl chlorides or with chloroformates), and several other types of derivatizations. 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Such mixtures are much too complicated to be separated for analytical duties in a reasonable period of time using only a single-dimensional chromatographic method. However, if a complex mixture is separated by an initial dimension using multi-dimensional liquid chromatography, a simpler portion of that separation is collected and goes to the second dimension. Each of these fractions will be analyzed separately, allowing exceedingly complex mixtures to be resolved in a short period of time. This chapter explains the fundamental principles, theoretical discussions as well as various applications with typical examples of multi-dimensional liquid chromatography in different fields.",book:{id:"11204",title:"Analytical Liquid Chromatography - New Perspectives",coverURL:"https://cdn.intechopen.com/books/images_new/11204.jpg"},signatures:"Esayas Tesfaye, Tadele Eticha, Ariaya Hymete and Ayenew Ashenef"},{id:"81761",title:"Progress in Technology of the Chromatographic Columns in HPLC",slug:"progress-in-technology-of-the-chromatographic-columns-in-hplc",totalDownloads:15,totalDimensionsCites:0,doi:"10.5772/intechopen.104123",abstract:"Chromatographic column is an essential part of a any HPLC separation, and significant progress has been made in developing columns with better performance to provide better separation, a shorter separation time, resilience to a wider pH range of the mobile phase, longer lifetime, use of lower volumes of mobile phase, etc. All these characteristics were achieved by the introduction of novel technologies and improvements of the older ones. These include smaller particle used to fill the column, more homogeneous spherical particles, core-shell particles, monolithic columns, more pure silica as a stationary phase support, use of ethylene bridge silica, a wider variety of active phases, use of mixed mode stationary phases, use of polymers as stationary phase, use of various endcapping techniques, etc. Miniaturization and progress in the instrumentation played an important role for the chromatographic column development. All these aspects are summarized in the present chapter.",book:{id:"11204",title:"Analytical Liquid Chromatography - New Perspectives",coverURL:"https://cdn.intechopen.com/books/images_new/11204.jpg"},signatures:"Serban C. Moldoveanu and Victor David"},{id:"81479",title:"Perspective Chapter: Mixed-Mode Chromatography",slug:"perspective-chapter-mixed-mode-chromatography",totalDownloads:29,totalDimensionsCites:0,doi:"10.5772/intechopen.104545",abstract:"In this chapter, we present mixed-mode stationary phases and their applications in the determination of nonpolar, polar, and charged compounds, as well as larger molecules such as peptides or proteins using a single column. Mixed-mode chromatography (MMC) has been growing rapidly in recent years, owing to the new generation of mixed-mode stationary phases and a better understanding of multimode interactions. Mixed-mode chromatography provides a wide range of selectivities and adequate retention of a variety of compounds, especially polar and charged molecules. In summary, this technique is particularly useful in the pharmaceutical analysis of drugs, impurities, biopharmaceuticals, and polar compounds in natural products.",book:{id:"11204",title:"Analytical Liquid Chromatography - New Perspectives",coverURL:"https://cdn.intechopen.com/books/images_new/11204.jpg"},signatures:"Ngoc-Van Thi Nguyen"},{id:"81368",title:"Ionic Liquids in Liquid Chromatography",slug:"ionic-liquids-in-liquid-chromatography",totalDownloads:39,totalDimensionsCites:0,doi:"10.5772/intechopen.104122",abstract:"Ionic liquids (ILs) are salts of organic cations that are present in liquid state. They can be used as alternative to organic solvents for various analytical processes such as extracting solvents in sample preparation, or as mobile phase or components of the mobile phase in high performance liquid chromatography (HPLC). Also they can be used as stationary phase in gas chromatography (GC), or attached to a solid support as stationary phase in HPLC. Ils are typically more environmentally-friendly solvents than the classic organic solvents having low volatility, flammability and toxicity. The chapter presents various applications of ILs in liquid chromatography.",book:{id:"11204",title:"Analytical Liquid Chromatography - New Perspectives",coverURL:"https://cdn.intechopen.com/books/images_new/11204.jpg"},signatures:"Victor David and Serban C. Moldoveanu"},{id:"80336",title:"Perspective Chapter: Advantages of Ion Mobility Coupled with HPLC/UPLC",slug:"perspective-chapter-advantages-of-ion-mobility-coupled-with-hplc-uplc",totalDownloads:58,totalDimensionsCites:0,doi:"10.5772/intechopen.102380",abstract:"Ion mobility is a new separation technique that can be coupled with high performance liquid chromatography (HPLC) or ultra-performance liquid chromatography (UPLC). Variances in cross-sectional ionic areas of different molecules create differential speeds through a gas allowing for millisecond separations. Combining ion mobility with both liquid chromatography and mass spectrometry with fragmentation, separations can be achieved on the second (HPLC), millisecond (ion mobility), and microsecond (mass spectrometry) timescales. This orthogonal separation greatly cleans up mass spectral data of co-eluting peaks from the liquid chromatography and adds to the descriptive data of each ion. With descriptive data such as retention time, cross-sectional area, m/z ratio, and mass spectral fragmentation, many options become available for analytical analysis. Options ranging from descriptive data collation into instrument libraries to sensitivity enhancement for trace analysis will be explored in this chapter along with the description of different forms of ion mobility.",book:{id:"11204",title:"Analytical Liquid Chromatography - New Perspectives",coverURL:"https://cdn.intechopen.com/books/images_new/11204.jpg"},signatures:"Robert Owen Bussey III"}],onlineFirstChaptersTotal:5},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:140,numberOfOpenTopics:2,numberOfUpcomingTopics:0,issn:"2631-6218",doi:"10.5772/intechopen.71199",isOpenForSubmission:!0},{id:"6",title:"Infectious Diseases",numberOfPublishedBooks:13,numberOfPublishedChapters:123,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:22,numberOfOpenTopics:3,numberOfUpcomingTopics:0,issn:"2753-894X",doi:"10.5772/intechopen.100359",isOpenForSubmission:!0},{id:"23",title:"Education and Human Development",numberOfPublishedBooks:0,numberOfPublishedChapters:11,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:"13",text:"The collaboration with and support of the technical staff of IntechOpen is fantastic. The whole process of submitting an article and editing of the submitted article goes extremely smooth and fast, the number of reads and downloads of chapters is high, and the contributions are also frequently cited.",author:{id:"55578",name:"Antonio",surname:"Jurado-Navas",institutionString:null,profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRisIQAS/Profile_Picture_1626166543950",slug:"antonio-jurado-navas",institution:{id:"720",name:"University of Malaga",country:{id:null,name:"Spain"}}}},{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"}}}}]},series:{item:{id:"24",title:"Sustainable Development",doi:"10.5772/intechopen.100361",issn:"2753-6580",scope:"
\r\n\tTransforming our World: the 2030 Agenda for Sustainable Development endorsed by United Nations and 193 Member States, came into effect on Jan 1, 2016, to guide decision making and actions to the year 2030 and beyond. Central to this Agenda are 17 Goals, 169 associated targets and over 230 indicators that are reviewed annually. The vision envisaged in the implementation of the SDGs is centered on the five Ps: People, Planet, Prosperity, Peace and Partnership. This call for renewed focused efforts ensure we have a safe and healthy planet for current and future generations.
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\r\n\tThis Series focuses on covering research and applied research involving the five Ps through the following topics:
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\r\n\t1. Sustainable Economy and Fair Society that relates to SDG 1 on No Poverty, SDG 2 on Zero Hunger, SDG 8 on Decent Work and Economic Growth, SDG 10 on Reduced Inequalities, SDG 12 on Responsible Consumption and Production, and SDG 17 Partnership for the Goals
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\r\n\t2. Health and Wellbeing focusing on SDG 3 on Good Health and Wellbeing and SDG 6 on Clean Water and Sanitation
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\r\n\t3. Inclusivity and Social Equality involving SDG 4 on Quality Education, SDG 5 on Gender Equality, and SDG 16 on Peace, Justice and Strong Institutions
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\r\n\t4. Climate Change and Environmental Sustainability comprising SDG 13 on Climate Action, SDG 14 on Life Below Water, and SDG 15 on Life on Land
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\r\n\t5. Urban Planning and Environmental Management embracing SDG 7 on Affordable Clean Energy, SDG 9 on Industry, Innovation and Infrastructure, and SDG 11 on Sustainable Cities and Communities.
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\r\n\tThe series also seeks to support the use of cross cutting SDGs, as many of the goals listed above, targets and indicators are all interconnected to impact our lives and the decisions we make on a daily basis, making them impossible to tie to a single topic.
",coverUrl:"https://cdn.intechopen.com/series/covers/24.jpg",latestPublicationDate:"August 2nd, 2022",hasOnlineFirst:!0,numberOfPublishedBooks:1,editor:{id:"262440",title:"Prof.",name:"Usha",middleName:null,surname:"Iyer-Raniga",slug:"usha-iyer-raniga",fullName:"Usha Iyer-Raniga",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRYSXQA4/Profile_Picture_2022-02-28T13:55:36.jpeg",biography:"Usha Iyer-Raniga is a professor in the School of Property and Construction Management at RMIT University. Usha co-leads the One Planet Network’s Sustainable Buildings and Construction Programme (SBC), a United Nations 10 Year Framework of Programmes on Sustainable Consumption and Production (UN 10FYP SCP) aligned with Sustainable Development Goal 12. The work also directly impacts SDG 11 on Sustainable Cities and Communities. She completed her undergraduate degree as an architect before obtaining her Masters degree from Canada and her Doctorate in Australia. Usha has been a keynote speaker as well as an invited speaker at national and international conferences, seminars and workshops. Her teaching experience includes teaching in Asian countries. She has advised Austrade, APEC, national, state and local governments. She serves as a reviewer and a member of the scientific committee for national and international refereed journals and refereed conferences. She is on the editorial board for refereed journals and has worked on Special Issues. Usha has served and continues to serve on the Boards of several not-for-profit organisations and she has also served as panel judge for a number of awards including the Premiers Sustainability Award in Victoria and the International Green Gown Awards. Usha has published over 100 publications, including research and consulting reports. Her publications cover a wide range of scientific and technical research publications that include edited books, book chapters, refereed journals, refereed conference papers and reports for local, state and federal government clients. She has also produced podcasts for various organisations and participated in media interviews. She has received state, national and international funding worth over USD $25 million. Usha has been awarded the Quarterly Franklin Membership by London Journals Press (UK). Her biography has been included in the Marquis Who's Who in the World® 2018, 2016 (33rd Edition), along with approximately 55,000 of the most accomplished men and women from around the world, including luminaries as U.N. Secretary-General Ban Ki-moon. 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Her focus is on quality, innovation, leadership, and personalised learning. She works primarily at the strategic and policy levels, both nationally and internationally, and with key international organisations. She is committed to promoting and improving OFDL in the context of SDG4 and the future of education. Ossiannilsson has more than 20 years of experience in her current field, but more than 40 years in the education sector. She works as a reviewer and expert for the European Commission and collaborates with the Joint Research Centre for Quality in Open Education. Ossiannilsson also collaborates with ITCILO and ICoBC (International Council on Badges and Credentials). She is a member of the ICDE Board of Directors and has previously served on the boards of EDEN and EUCEN. Ossiannilsson is a quality expert and reviewer for ICDE, EDEN and the EADTU. She chairs the ICDE OER Advocacy Committee and is a member of the ICDE Quality Network. She is regularly invited as a keynote speaker at conferences. She is a guest editor for several special issues and a member of the editorial board of several scientific journals. She has published more than 200 articles and is currently working on book projects in the field of OFDL. Ossiannilsson is a visiting professor at several international universities and was recently appointed Professor and Research Fellow at Victoria University of Wellington, NZ. Ossiannilsson has been awarded the following fellowships: EDEN Fellows, EDEN Council of Fellows, and Open Education Europe. She is a ICDE OER Ambassador, Open Education Europe Ambassador, GIZ Ambassador for Quality in Digital Learning, and part of the Globe-Community of Digital Learning and Champion of SPARC Europe. On a national level, she is a quality developer at the Swedish Institute for Standards (SIS) and for ISO. 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