extfieldsofstudy: Mathematics Medicine

text:
Near-Infrared Hyperspectral Imaging Combined with Deep Learning to Identify Cotton Seed Varieties

Cotton seed purity is a critical factor influencing the cotton yield. In this study, near-infrared hyperspectral imaging was used to identify seven varieties of cotton seeds. Score images formed by pixel-wise principal component analysis (PCA) showed that there were differences among different varieties of cotton seeds. Effective wavelengths were selected according to PCA loadings. A self-design convolution neural network (CNN) and a Residual Network (ResNet) were used to establish classification models. Partial least squares discriminant analysis (PLS-DA), logistic regression (LR) and support vector machine (SVM) were used as direct classifiers based on full spectra and effective wavelengths for comparison. Furthermore, PLS-DA, LR and SVM models were used for cotton seeds classification based on deep features extracted by self-design CNN and ResNet models. LR and PLS-DA models using deep features as input performed slightly better than those using full spectra and effective wavelengths directly. Self-design CNN based models performed slightly better than ResNet based models. Classification models using full spectra performed better than those using effective wavelengths, with classification accuracy of calibration, validation and prediction sets all over 80% for most models. The overall results illustrated that near-infrared hyperspectral imaging with deep learning was feasible to identify cotton seed varieties.

Introduction
Cotton (Gossypium spp.) is one of the most widely cultivated economic crops in the world, especially in China. What's more, cotton is the main source of natural fiber, vegetable oil and high protein meals for humans and livestock [1]. Cotton seeds of different varieties show different characteristics relating to vigor, germination, yield, quality and stress resistance, etc. Thus, cotton seed purity is an important factor for cotton yield, which is also closely related to the income of farmers.
Seed variety identification is an important issue for crop planting. Gene based techniques can be used to identify seed varieties accurately [2,3]. However, the sample preparation is complex for these methods, and workers with expert operation skill are also indispensable. Moreover, the inefficiency and high costs also make gene based techniques unable to adapt to the needs of market. This situation leads to the urgent demand of developing rapid and non-destructive techniques.
Machine vision is a rapid and non-destructive technique which could be used to identify seed varieties, with the advantages of simple sample preparation, batch detection and good adaptability to different application scenes. Moreover, it is easy to obtain the required images. Extraction of features, such as shape, texture, color and etc., is the key factor for seed variety identification. Studies have proved the feasibility to identify seed varieties using machine vision [4,5]. Figure 1 shows the average spectral reflectance curves of seven varieties of cotton seeds. Spectra of all cotton seeds of each variety were averaged to obtain average spectrum of the corresponding variety. The standard deviation (SD) of four typical wavelengths (peaks: 1119 and 1308 nm; valleys: 1204 and 1470 nm) were also presented. The waveband at 1119, 1204 and 1308 nm might related to the second overtone of C-H stretch [28]. The spectral wavelength at 1470 nm can be attributed to the O-H stretch first overtone [29]. It can be seen that similarities could be clearly observed among the change tendency of seven spectral reflectance curves. Although some differences could be found for the seven varieties of cotton seeds, the differences are distributed over several wavebands. Overlaps could also be found. Thus, further analyses should be conducted to discriminate different varieties of cotton seeds.  Different varieties of cotton seeds could be divided into different groups according to color distribution within seed kernels. As well, different division could be found in PCA score images of different PCs.
For PCA score image of PC1, the color distribution of cotton seeds could be divided into three groups (Group 1: Category 0, category 1 and category 3; Group 2: category 2 and category 4; Group 3: category 5 and category 6). Group 1 is primarily in yellow and red color, and Group 2 is mainly in green and blue colors. However, there is no clear color bias in Group 3. For PCA score image of PC2, colors of several varieties of cotton seed show obvious differences. Most parts of category 0 is dark blue, and category 3 is primarily composed of red and orange colors, while category 5 is overall in yellow tones.
Although PC3-PC10 explained less information, some differences revealed by color distribution could still be found among samples. For PCA score images of PC3, PC5, PC6 and PC7, category 3 contains more blue areas, which is the most obvious difference with the other six varieties. Thus, there are two groups (Group 1: Category 3; Group 2: the other 6 categories). Three groups (Group 1: Category 0; Group 2: category 1; Group 3: The other 5 categories) of color distribution could be found in PCA score image of PC4: Category 0 is mainly in light blue color; and category 1 is most in orange and yellow tones, while seeds of the rest categories are mixed blue with yellow. For PCA score images of PC8, there are two groups (Group 1: Category 0, category 3 and category 5; Group 2: category 1, category 2, category 4 and category 6) of color distribution. For PCA score images of PC9, there are three groups (Group 1: Category 0-category 4; Group 2: category 5; Group 3: category 6) of color distribution. Category 6 could be clearly distinguished with mainly blue color in PCA score image of PC10.
Category 1, 2 and 4 show slight differences with other varieties in the PCA score images of PC1-PC10, indicating further classification using discrimination models were needed.

Analysis of PCA Score Images
PCA was conducted for the qualitative analysis. One hyperspectral image of each variety was randomly selected. PCA was conducted on pixel-wise spectra within the seeds in the hyperspectral images of seven varieties of cotton seeds. Each pixel had score values of different PCs. Knowing the scores value of each pixel of a certain PC, it was easy to obtain the pixel-wise PCA score image ( Figure 2). Since the first ten PCs (99.667%) contain most of the information of cotton seeds, PCA score images of PC1-PC10 were obtained to show commonalities and differences among different cotton seeds. As shown in Figure 2, Figure 2b,k show the PCA score images of PC1-PC10, respectively, and the numbers in the left side represent the explained information of each PC.

Effective Wavelength Selection
The existence of uninformative variables might influence the modeling speed and accuracy. Thus, it is necessary to select effective wavelengths contributing more to the classification in order to reduce the data volume. As shown in PCA score images of the first ten PCs, differences among the seven varieties of cotton could be observed in each PC score image, and these PCs explained 99.667% of total variance. Thus, loadings of the ten PCs were used for effective wavelength selection. Figure 3 shows the detail of effective wavelengths selection. Table 1 summarized the selected effective wavelengths according to the loadings of PC1-PC10, and a total of 43 effective wavelengths were chosen for further analyses. Comparing with full spectra, the number of variables for effective wavelengths is reduced by 78.5%. As shown in Table 1 and Figure 3, some of the wavelengths were selected as effective wavelengths according to loadings of different PCs, and some successive wavelengths were selected. Different varieties of cotton seeds could be divided into different groups according to color distribution within seed kernels. As well, different division could be found in PCA score images of different PCs.
For PCA score image of PC1, the color distribution of cotton seeds could be divided into three groups (Group 1: Category 0, category 1 and category 3; Group 2: category 2 and category 4; Group 3: category 5 and category 6). Group 1 is primarily in yellow and red color, and Group 2 is mainly in green and blue colors. However, there is no clear color bias in Group 3. For PCA score image of PC2, colors of several varieties of cotton seed show obvious differences. Most parts of category 0 is dark blue, and category 3 is primarily composed of red and orange colors, while category 5 is overall in yellow tones.
Although PC3-PC10 explained less information, some differences revealed by color distribution could still be found among samples. For PCA score images of PC3, PC5, PC6 and PC7, category 3 contains more blue areas, which is the most obvious difference with the other six varieties. Thus, there are two groups (Group 1: Category 3; Group 2: the other 6 categories). Three groups (Group 1: Category 0; Group 2: category 1; Group 3: The other 5 categories) of color distribution could be found in PCA score image of PC4: Category 0 is mainly in light blue color; and category 1 is most in orange and yellow tones, while seeds of the rest categories are mixed blue with yellow. For PCA score images of PC8, there are two groups (Group 1: Category 0, category 3 and category 5; Group 2: category 1, category 2, category 4 and category 6) of color distribution. For PCA score images of PC9, there are three groups (Group 1: Category 0-category 4; Group 2: category 5; Group 3: category 6) of color distribution. Category 6 could be clearly distinguished with mainly blue color in PCA score image of PC10.
Category 1, 2 and 4 show slight differences with other varieties in the PCA score images of PC1-PC10, indicating further classification using discrimination models were needed.

Effective Wavelength Selection
The existence of uninformative variables might influence the modeling speed and accuracy. Thus, it is necessary to select effective wavelengths contributing more to the classification in order to reduce the data volume. As shown in PCA score images of the first ten PCs, differences among the seven varieties of cotton could be observed in each PC score image, and these PCs explained 99.667% of total variance. Thus, loadings of the ten PCs were used for effective wavelength selection. Figure 3 shows the detail of effective wavelengths selection. Table 1 summarized the selected effective wavelengths according to the loadings of PC1-PC10, and a total of 43 effective wavelengths were chosen for further analyses. Comparing with full spectra, the number of variables for effective wavelengths is reduced by 78.5%. As shown in Table 1 and Figure 3, some of the wavelengths were selected as effective wavelengths according to loadings of different PCs, and some successive wavelengths were selected. (i) (j) Figure 3. Effective wavelengths selection using the first ten PCs. The letters from (a-j) represent the PCs from PC1 to PC 10.

Discriminant Models using Full Spectra and Effective Wavelengths
Two different CNN architectures were used to classify cotton seed varieties, including a selfdesign CNN architecture and a ResNet architecture. As mentioned above, the two CNN models used SoftMax function as classifier. For comparison, LR, PLS-DA and SVM models, which were widely used discriminant models in spectral data analysis, were built using full spectra or effective wavelengths. In addition, LR, PLS-DA and SVM which is widely used in traditional CNN models were also used to replace SoftMax function. They are used to handle the classification tasks based on the features extracted by the two CNN architectures for further improvement. Table 2 shows the results of different classification models using full spectra or effective wavelengths for cotton seed varieties identification. Table 2. Results of classification models using full spectra and effective wavelengths.

Discriminant Models using Full Spectra and Effective Wavelengths
Two different CNN architectures were used to classify cotton seed varieties, including a self-design CNN architecture and a ResNet architecture. As mentioned above, the two CNN models used SoftMax function as classifier. For comparison, LR, PLS-DA and SVM models, which were widely used discriminant models in spectral data analysis, were built using full spectra or effective wavelengths. In addition, LR, PLS-DA and SVM which is widely used in traditional CNN models were also used to replace SoftMax function. They are used to handle the classification tasks based on the features extracted by the two CNN architectures for further improvement. Table 2 shows the results of different classification models using full spectra or effective wavelengths for cotton seed varieties identification. For models using full spectra, classification results were decent. Most of models obtained the classification accuracy over 80%. Among eleven models using full spectra, PLS-DA model obtained the worst results, with the accuracy of calibration (81.764%), validation (79.947%) and prediction set (80.401%) all lower than other models. CNN-SVM and SVM models obtained similar results, with accuracy around 93%, 89% and 88% for calibration, validation and prediction sets, respectively, while CNN-SVM obtained slightly better performances in calibration and validation sets than SVM model. Except SVM model, pure LR and PLS-DA models performed worse than the corresponding LR and PLS-DA models combined with deep learning. For LR and PLS-DA models based on the CNN and the ResNet architecture, the accuracy was improved by about 10%, 14% for calibration set, respectively. The reason could be attributed to the strong feature learning ability of deep learning. Compared with the self-design CNN based models, ResNet based models performed better in the calibration set.
However, for the validation and prediction sets, self-design CNN based models achieved better results, with accuracy in the range of 86-89%.
For classification models using effective wavelengths, LR model obtained the worst results, with classification accuracy of calibration, validation and prediction sets all lower than 70%. Compared with LR models, CNN-SVM and SVM obtained better performances, with classification accuracy of calibration set over 89% and classification accuracy of validation and prediction sets over 84%. The self-design CNN based models achieved classification accuracy of calibration, validation and prediction all over 80%. ResNet based models showed better performances for calibration set with classification accuracy over 90%, but classification accuracy of validation and prediction was lower than 80%. In summary, the self-design CNN based models performed worse in calibration set but obtained better performance in validation and prediction sets than ResNet based models.
The classification models using full spectra performed better than those using effective wavelengths. LR model using full spectra performed significantly better than LR model using effective wavelengths. The self-design CNN based models and ResNet based models using full spectra also performed better than those using effective wavelengths. The reason was that full spectra contained more information than the selected effective wavelengths. What's more, dealing with the simple data sets by deep learning couldn't fully reflect the advantage of deep learning. The differences of performances of self-design CNN based models and ResNet based models indicating that CNN architectures had influence on classification performances. Performances of deep learning models varied due to the different classifiers, indicating the influence of classifiers on model performances. Figure 4 shows the confusion matrices of calibration, validation and prediction for CNN-SVM model using full spectra. Good classification performances could be found for category 3 (Xinjiangzaomian1) and category 5 (Xinluzhong52), for which few samples were misclassified. Category 0 (Jinxin5) were more likely to be misclassified as category 5. Category 1 (Jinxin7) were more likely to be misclassified as category 2 (Shennongmian1), category 4 (Xinluzaomian29) and category 6 (Xinluzhong42). Category 4 was more likely to be misclassified as category 1, category 2 and category 6. Category 6 was more likely to be misclassified as category 1, category 2 and category 4. Most of the cotton seeds could be accurately classified, which indicated that as a rapidly and non-destructively method, hyperspectral imaging coupled with deep learning could be used to identify cotton seed varieties.

Sample Preparation
Seven different varieties of cotton seeds were collected in 2016 from Shihezi, Xinjiang Uyghur Autonomous Region, China. These seven varieties were Jinxin5, Jinxi7, Shennongmian1, Xinjiangzaomian1, Xinluzaomian29, Xinluzhong52 and Xinluzhong42, and the corresponding number of seeds used in the study were 2353, 2497, 2242, 1031, 1122, 1804 and 2111, respectively. All these seeds were sound. The sulfuric acid solution was used for the depilation treatment of cotton seeds. The H2SO4 solution (75% mass fraction) heated to 100 °C was slowly pour into a ceramic container with cotton seeds placed inside until the cotton seeds were submerged, and then the seeds were rapidly stirred until the fluff on the seeds was completely removed. Finally, the cotton seeds were rinsed with water until the water was clear, and then the cotton seeds were air-dried before the acquisition of hyperspectral image. For further analysis, category values of the seven varieties of cotton were assigned as 0, 1, 2, 3, 4, 5, 6 (corresponding to Jinxin5, Jinxi7, Shennongmian1, Xinjiangzaomian1, Xinluzaomian29, Xinluzhong52 and Xinluzhong42, respectively). To build calibration models, the cotton seeds were randomly divided into the calibration set, the validation set and the prediction set at the ratio of 3:1:1. Accuracy = 93.695% Accuracy = 89.255% Accuracy = 88.006%

Sample Preparation
Seven different varieties of cotton seeds were collected in 2016 from Shihezi, Xinjiang Uyghur Autonomous Region, China. These seven varieties were Jinxin5, Jinxi7, Shennongmian1, Xinjiangzaomian1, Xinluzaomian29, Xinluzhong52 and Xinluzhong42, and the corresponding number of seeds used in the study were 2353, 2497, 2242, 1031, 1122, 1804 and 2111, respectively. All these seeds were sound. The sulfuric acid solution was used for the depilation treatment of cotton seeds. The H 2 SO 4 solution (75% mass fraction) heated to 100 • C was slowly pour into a ceramic container with cotton seeds placed inside until the cotton seeds were submerged, and then the seeds were rapidly stirred until the fluff on the seeds was completely removed. Finally, the cotton seeds were rinsed with water until the water was clear, and then the cotton seeds were air-dried before the acquisition of hyperspectral image. For further analysis, category values of the seven varieties of cotton were assigned as 0, 1, 2, 3, 4, 5, 6 (corresponding to Jinxin5, Jinxi7, Shennongmian1, Xinjiangzaomian1, Xinluzaomian29, Xinluzhong52 and Xinluzhong42, respectively). To build calibration models, the cotton seeds were randomly divided into the calibration set, the validation set and the prediction set at the ratio of 3:1:1.

Hyperspectral Image Acquisition and Correction
A line-scan near-infrared hyperspectral imaging system with the spectral range of 942-1646 nm was used to acquire hyperspectral images of cotton seeds. The system contains an imaging spectrograph (ImSpector N17E, Spectral Imaging Ltd., Oulu, Finland) integrated with a Xeva 992 camera (Xenics Infrared Solutions, Leuven, Belgium) which uses an OLES22 lens (Spectral Imaging Ltd., Oulu, Finland). The light source is two 150W tungsten halogen lamps (3900 Lightsource, Illumination Technologies Inc., Elbridge, NY, USA). The line scan is conducted by moving the sample plate using a stepper motor (Isuzu Optics Corp., Taiwan, China).
To acquire hyperspectral images, the system parameters were firstly adjusted to acquire clear and non-deformable images. The camera exposure time, the sample plate moving speed and the distance between sample plate and the camera were set as 3 ms, 11.5 mm/s and 14 cm. Then white reference image and dark reference image were acquired using the adjusted system. The white reference image was acquired using a piece of pure white Teflon board with nearly 100% reflectance, and the dark reference image was acquired by turning off the light source and covered the camera lens with lens cap.
To acquire hyperspectral images, single cotton seeds were placed separately. In all, the number of hyperspectral images of Jinxin5, Jinxin7, Shennongmian1, Xinjiangzaomian1, Xinluzaomian29, Xinluzhong52 and Xinluzhong42 were 19, 20, 17, 8, 9, 14 and 16. After image acquisition, the images were corrected to calibrate light intensity and reduce dark current. The image correction was conducted using the equation: where I c is the corrected image, I raw is the raw image, I white is the white reference image and I dark is the dark reference image.

Spectral Data Preprocessing and Extraction
To extract spectral data, each cotton seed was defined as the region of interest. Cotton seeds were firstly isolated from the background. A binary image was formed by the gray-scale image at 1200 nm, in which seed regions were '1 and the background regions were '0 . The binary image was then applied to the gray-scale images at each wavelength to remove the background. The outer ring of each cotton was eliminated, and pixel-wise spectra were firstly preprocessed by wavelet transform (wavelet function Daubechies 10 with decomposition level 3) followed with an area normalization. Then average spectrum was calculated from pixel-wise spectra within each cotton seed to represent the seed.

Principal Component Analysis
Principal component analysis (PCA) is a widely used qualitative analysis and data reduction method which could be used to explore data features. PCA conducts linear transformation to transform the original variables into a set of new orthogonal variables. These new variables were ranked according to the variance. The first variable with the largest variance was the first principal component (PC1), and the rest are defined in the same manner. The first few PCs contain the most useful information, and generally the first few PCs are used for qualitative analyses and data reduction. In hyperspectral images, each pixel has a spectrum. PCA can be conducted on pixel-wise spectra, and scores of each pixel at each PC can be obtained. The pixel-wise score values of each PC can be presented by color gradients, which is the PCA score images. Differences among samples can be observed intuitively from the PCA score images [30].

Partial Least Squares Discriminant Analysis
Partial least squares discriminant analysis (PLS-DA) is the most widely used supervised discrimination method. PLS-DA is performed in the same way as partial least squares regression (PLSR). PLS-DA uses a set of dummy numbers referring to the sample categories as the dependent variables, and then PLS-DA conducts the same procedures as PLSR to explore the linear relationship between the independent variables and the dependent variables [31]. Specially, the categories of the seven varieties of cottons for PLS-DA were assigned as 0000001, 0000010, 0000100, 0001000, 0010000, 0100000 and 1000000 (corresponding to Jinxin5, Jinxi7, Shennongmian1, Xinjiangzaomian1, Xinluzaomian29, Xinluzhong52 and Xinluzhong42, respectively).

Logistic Regression
Logistic regression (LR) is a linear model for classification, which can fit binary or multinomial logistic regression [32]. On the basis of linear regression algorithm, a sigmoid function is added to map the linear combination of independent variables into the value in the range [0,1]. LR algorithm can be applied with optional L1, L2 regularization. It also can be extended to multi-class classification tasks using One-vs-Rest.

Support Vector Machine
Support vector machine (SVM) is a widely used pattern recognition algorithm. SVM aims to construct a hyperplane or a set of hyperplanes to separate the samples from different classes maximally. For samples which cannot be linearly classified, kernel functions are applied to map the original data into high-dimension space. The new data in the high-dimension space may be linearly separable. Radial bias function (RBF) is a widely used kernel function, and it can deal with linear and non-linear issues effectively. In this study, SVM with RBF kernel function was used to establish discriminant models. For PLS-DA models, leave-one-out cross validation were used. For SVM models, a five-fold cross validation was used. A grid-search procedure was used to find the optimal parameters of SVM models, i.e., the penalty coefficient (C) and the kernel parameter (γ) [33].

Deep Learning Methods
Deep learning algorithms have become a kind of useful method for spectra data analyses in agriculture [21]. Convolutional neural network (CNN) is one of the well-known deep learning structures for feature extraction, classification and regression. In this study, two kinds of one-dimension CNN [26] architectures were designed and evaluated to achieve feature representation from input spectra data and realize classification based on extracted deep features.
The first architecture is shown in Figure 5a, which consists of two convolution blocks (Conv. Block), a fully connected network with three dense layers, and a SoftMax layer. The function of SoftMax layer is as follows [34]: where K is the number of elements of the input vector, z i is the ith element of the input vector and σ(z) i is the ith element of the output vector.
The training procedure for both of two mentioned CNN models was carried out to minimize the SoftMax Cross Entropy Loss using Stochastic Gradient Descent (SGD). Considering that SoftMax is not the best classifier, we used LR, PLS-DA, SVM as classifier for comparison in Figure 5. The activated output of Dense 2 layer in Figure 5a and the output of GlobalAvgPool layer in Figure 5c were defined as the input value for LR, PLS-DA and SVM models.

Effective Wavelength Selection
Spectral data extracted from the hyperspectral images contains redundant and uninformative information. Selection of informative variables contributing more to prediction is of great importance. Variable selection can reduce the number of input variables to simplify models and improve the modelling efficiency. In this study, loadings of PCA were used for effective wavelength selection [36].
Loadings are regression coefficients between original variables and the corresponding PCs, which could be obtained during the linear transformation of PCA. The absolute loading value indicates the importance of the corresponding variables. The larger the absolute loading value, the more important the variable is. Therefore, variables with larger absolute loading value of each PC can be selected as effective wavelengths.

Model Evaluation and Software
The performances of models were evaluated by the classification accuracy, which was defined as the ratio of the number of correctly classified seeds to the total number of cotton seeds. PLS-DA and SVM were conducted on Matlab R2014b (The Math Works, Natick, MA, USA). LR model was The Conv. Block is built by a one-dimension convolutional layer with a ReLU activation and a max pooling layer, which is described in Figure 5b. The numbers of kernels are 64 and 128 for Conv-1D layers in Conv. Block 1 and Conv. Block 2, respectively. The numbers of the neurons in Dense 1, Denses 2 and 3 were defined as 512, 128, 7, respectively. The second architecture is a one-dimension-convolution based ResNet (ResNet-1D) architecture shown in Figure 5c, including a 1 by 1 convolution layer, four residual blocks (RES. Block), a global average pooling (GlobalAvgPool) layer, a dense layer with 7 neurons and a SoftMax layer. The design of ResNet-1D structure refers to the well-known ResNet for two-dimension image classification [35]. The detail information of the RES. Block is shown in Figure 5d. The numbers of kernels in two Conv-1D layers are same in each RES. Block, which were defined in turn as 64, 128, 256, 512.
All convolutional layers in the two mentioned CNN models used a kernel size of 3, stride of 1. The training procedure for both of two mentioned CNN models was carried out to minimize the SoftMax Cross Entropy Loss using Stochastic Gradient Descent (SGD).
Considering that SoftMax is not the best classifier, we used LR, PLS-DA, SVM as classifier for comparison in Figure 5. The activated output of Dense 2 layer in Figure 5a and the output of GlobalAvgPool layer in Figure 5c were defined as the input value for LR, PLS-DA and SVM models.

Effective Wavelength Selection
Spectral data extracted from the hyperspectral images contains redundant and uninformative information. Selection of informative variables contributing more to prediction is of great importance. Variable selection can reduce the number of input variables to simplify models and improve the modelling efficiency. In this study, loadings of PCA were used for effective wavelength selection [36].
Loadings are regression coefficients between original variables and the corresponding PCs, which could be obtained during the linear transformation of PCA. The absolute loading value indicates the importance of the corresponding variables. The larger the absolute loading value, the more important the variable is. Therefore, variables with larger absolute loading value of each PC can be selected as effective wavelengths.

Model Evaluation and Software
The performances of models were evaluated by the classification accuracy, which was defined as the ratio of the number of correctly classified seeds to the total number of cotton seeds. PLS-DA and SVM were conducted on Matlab R2014b (The Math Works, Natick, MA, USA). LR model was programmed using scikit-learn machine learning package in Python 3. Deep learning models were conducted on Python 3 and MXNET framework (Amazon, Seattle, WA, USA).

Conclusions
Hyperspectral imaging coupled with deep learning was successfully used to identify cotton seed varieties. PCA score images of the first ten PCs illustrated the differences among the seven cotton seed varieties. A total of 43 effective wavelengths were selected by loadings of the first 10 PCs. Two different CNN architectures, including a self-design CNN model and a ResNet model, using SoftMax function, PLS-DA, LR and SVM as classifiers all obtained good performances based on full spectra or effective wavelengths. Pure PLS-DA, LR and SVM models were also built using full spectra or effective wavelengths. CNN-LR and CNN-PLS-DA models performed better than pure LR and PLS-DA models. Variations on classification performances of self-design CNN and ResNet based models showed the influence of classifiers in deep learning models. The different performances between self-design CNN based models and ResNet based models illustrated the influence of CNN architectures. Classification models using full spectra performed better than those using effective wavelengths, and the differences were larger for deep learning based models. The overall result illustrated that deep learning models could be applied to identify cotton seed varieties. In future studies, sample number and different deep learning architectures should be taken into consideration to improve the accuracy and robustness of classification model.


extfieldsofstudy: Physics Mathematics

provenance: AgriculturalAndFoodSciences,2019-2020-Agricultural And Food Sciences-2019.jsonl.gz.gz

text:
Rapid Quantification Analysis of Alcohol During the Green Jujube Wine Fermentation by Electronic Nose

In order to explore the applicability of electronic nose technique for alcohol dynamic change, electronic nose was used to predict the alcohol content of the green jujube wine fermentation. The principal component regression (PCR) and multiple linear regression (MLR) were applied to establish the forecast model of the alcohol content. The results showed that the response values of sensors S1, S8, S3, S5 and S4 were the largest among the 10 sensors and changed significantly during 7 days of fermentation. The linear simulation equation was established by using the MLR to predict the alcohol content and the R2 value was 0.949, and the RMSEC was 0.974. The model shows the better predict alcohol content correlation (R2=0.946) and RMSEP (1.016). The results show that it is feasible to use the electronic nose system to analyses alcohol content dynamic chance of the green jujube wine fermentation, and electronic nose technique provides a method for rapid quantification analysis of alcohol of wine fermentation.

Introduction
In recent years, with the development of economy and the improvement of people's living quality, the requirement of food nutrition value and health efficacy is becoming higher and higher, the wine industry is turning a corner gradually. Fruit wine has the advantage of high nutritional value, strong health care effect and unique flavor, which is trusted and liked by consumers. The fruit wine is developing rapidly in the wine market, and it's future trend is pretty good. At present, it is necessary to focus on promoting innovative products such as new fruit wine, and solve the core production technology and rapid detection technology of fruit wine.
Jujube is cultivated all over our country, rich in protein, minerals and vitamins. It is a health product integrating medicine, food and tonic. However, due to the high output of Jujube in China, the products are seriously unsalable, the benefits are reduced, the losses of jujube farmers are serious, which greatly affects the sustainable development of jujube industry. In order to solve the problem of the development of jujube industry, to change the current situation of the depressed jujube industry, it is urgent to develop new products of jujube and solve the production technology of products. Popular both at home and abroad under the situation of high nutrition health food, this research focuses on the development of new elaborated wine products and to solve wine alcohol during the fermentation process of continuous change information rapid detection. The research adjusts production process conditions and ensures the high-quality product. Due to the time-consuming, cumbersome and low accuracy of traditional detection methods, it is difficult to meet the requirements of producing rapidly and timely. However, the Electronic nose is a new instrument that integrates the functions provides for analysis, identification and detection of complex volatile components, featuring fast detection speed, simple operation, high sensitivity and good reproducibility [1].
Electronic nose simulates human's sense of smell and USES pattern recognition technology to identify and process sensor array response information for detection, which meets the requirements of modern detection such as fast, real-time, Accurate and non-destructive [2]. Now, the rapid detection of electronic nose technology is used in alcohol detection of exploration and research. Buratti's team used electronic nose and electronic tongue to monitor the aroma and taste of the wine phase change [3]. XU Wan-xiu's team used electronic nose technology to detect Chinese liquor wine age [4]. ZHANG Shu-ming's team used the combination of near infrared reflectance spectrum (NIRS) and electronic nose to analyze the alcohol content in grape wine process of fermentation [5]. ZHOU Hui-min's team used electronic nose system to predict Shao Xing rice wine total sugar content [6]. The existing using electronic nose technology research mainly set in wine identification, win age detection, aroma components and sugar analysis. There are few studies on the on-line policy analysis of alcohol content of the fermentation process of fruit wine (especially jujube wine). This study elaborates electronic nose technology in the process of wine fermentation policy analysis the changes of alcohol and using linear discriminant analysis (LDA) to distinguish the company's fermentation times elaborated wine. The established model could predict the alcohol content of wine fermentation stage. The methods provide a set of efficient, accurate and rapid analytical methods for alcohol quantitative online monitoring in fruit wine fermentation process.

Sample preparation and reagents
The brewing process of jujube wine: Jujube → cleaning → nucleating → heating and softening → killing enzyme → adding sugar to adjust the sugar concentration→ adding yeast →fermentation → filtration → aging → canning →sterilization → finished product.
Taking 9 fermenting glass pots, then put the jujube juice with the sugar concentration of 20%, 24% and 26% into the fermentation tank marked with numbers 1-3, 4-6 and 7-9. Jujube juice from add yeast to the fruit wine fermentation, a total of 7 days, 26 ℃ fermentation temperature. Samples are taken at a fixed time every day from the first day to the seventh day of fermentation, and the alcohol content is measured with alcohol meter and electronic nose respectively. The yeast is active dry wine yeast of Angel, and the sugar used in the experiment was commercially available sugar.

Determination of alcohol content.
The alcohol content was determined by alcohol meter (GB 5009.225-2016) [7], the measurement result was the volume percentage of alcohol, namely the alcohol content (%) Each sample was repeated for 3 times, and the difference between the measurement results was within 0.5%.

Electronic nose data collection.
The alcohol content of jujube wine was determined by electronic nose. Before the test, 10mL of the removed sample was placed in a 20ml test tube sealed with plastic wrap and placed for 10 mins to allow volatile substances to accumulate and reach an equilibrium state before the electronic nose test. To start the test, click the "clean" icon to start cleaning, and keeping it stables for a period of time until the baseline recombination to obtain the base value of the measurement, and then carrying out the sample measurement. The volatile gas was passed through the electronic nose sensor at a flow rate of 600 mL/min, and the sampling time of the electronic nose was 80s. After each sample was measured, the recovery time of the electronic nose was 300 s, so as to ensure that the electronic nose was fully restored to the base valued state, and then carry out the measurement of the next sample [8]. A total of 63 samples were taken at a fixed time every day from the first day to the seventh day of fermentation, and 3 parallel tests were conducted for each sample.

Data analysis
Experimental results using PEN3 electronic nose system data processing software Win Muster, sorting and principal component analysis (PCA) on the test data and linear discriminant analysis (LDA); SPSS 19.0 and excel software are used to extract the characteristic values of the signal response values obtained by electronic nose measurement, analyze and process them, and establish and verify the model. The determination coefficient (R 2 ), root mean square error of calibration (RMSEC) and root mean square error of prediction (RMSEP) of the model are selected as evaluation indexes for the quantitative results of the model. The higher the R 2 is, and the smaller the RMSEC and RMSEP are, the better the prediction effect of the model is [9].

Sample alcohol content analysis
Alcohol content is an important index in the process of fruit wine brewing. The accurate detection of alcohol is an important guarantee for the production of qualified fruit wine. It is an important method for many wineries in China to monitor the alcoholic fermentation stage and also a method for determining the national standard alcoholic content. Table 1 shows the measured and predicted results of alcohol content in the fermentation of jujube wine.
The quality of the established model depends largely on the detection accuracy of sample standard values and the coverage of sample evaluation parameters. The alcohol content of 189 samples in this experiment covers high, medium and low levels, and presents normal distribution basically, indicating that the samples have good representativeness, and the quality of the model established on this basis is reliable.

Signal response of electronic nose to sample alcohol precision
The response curve of 10 metal oxide semiconductor type chemical sensor elements in the PEN3 electronic nose sensor for the detection of jujube wine samples is shown in figure 1. Each curve in the figure represents a sensor. The response signals of each sensor show a trend of increasing first and then decreasing with the change of time, and the electronic nose response signals of each sensor tend to be stable after 60s. At the beginning of injection, the signal was strengthened with the increase of injection quantity. With the completion of sampling, the signal begins to decrease until reaching a stable state, so the maximum value of the response signal is selected for date analysis. As can be seen from figure1, the response values of electronic nose sensorsS1, S3, S8, S5 and S4 are relatively large, and the response signals of each sensor gradually strengthen with time and then tend to be stable. It can be seen that the electronic nose has an obvious response to alcohol and other volatile components in the fermentation of jujube wine, and the response values of each sensor are different, indicating that it is feasible to use the PEN3 electronic nose system to determine the change of alcohol content in the fermentation of jujube wine.

Principal component analysis of jujube wine during fermentation
Principal component analysis (PCA) is a statistical method of dimension reduction, multiple correlation indicators into several comprehensive index, the strong correlation of main components of general cumulative variance greater than 85% of the total variance can represent the characteristics of the original data basically [10]. PCA is a multivariate statistical analysis method, through orthogonal transformation to multiple variables through linear transformation to choose fewer important variables. The electronic nose sensor S1, S3, S8, S5 and S4 with large response values were selected for LDA analysis, Figure 2 PCA analysis of the electronic nose sensor signal of the green jujube fermentation broth after continuous fermentation for 7 days. The circles in different areas in the figure respectively represent the collection of points of samples with different days of fermentation, and the numbers besides the circles are the days of fermentation. The distance between circles in the two-dimensional diagram indicates the level of the discrimination. With the increase of distance, the degree of differentiation between different fermentation times became better. As can be seen from figure 2, the data collection points in the figure don't overlap with different fermentation times, so the degree of differentiation of different fermentation times is relatively good. In the 40s analysis results, the first principal component (LD1) is 65.2%, the second principal component (LD2) is 21.8%, and the third principal component (LD3) is 6.8%. The cumulative contribution rate is up to 93.8%, higher than 85%. The results show that the three principal components have basically represented the main information characteristics of the samples.

Establishment of multiple linear regression model for electronic nose detection of alcohol content
The response signals of 10 sensors to the 60s of green jujube wine were taken as independent variables, while the alcohol content of green jujube wine was taken as dependent variables. Forty-two groups were randomly selected from the 63 groups of data measured by the samples for the establishment of the regression model, and the remaining 21 groups were used for the test of the established model. The prediction model was established by multiple linear regression method (MLR), and the regression model between the response value and the alcohol content of jujube wine was established as follows: . PCA analysis of e-nose sensor signals of jujube wine with different fermenting times.

Test of multiple linear regression model for electronic nose detection of alcohol content
In order to test the reliability of the MLR model, the quality of the model was tested with 21 groups of samples in the test set. As can be seen from the scatter plot of the predicted and measured alcohol values (Figure 3), there was a high correlation between the predicted and measured values of the MLR model, with a high R 2 (0.946) and a low RMSEP (1.016). Thus, the MLR model can be used to predict the alcohol content of jujube wine during fermentation.

Conclusion
The electronic nose technology is used to analyze the change of alcohol content during the fermentation of jujube wine rapidly and quantitatively, and the relationship between the response value of electronic nose gas sensor and the alcohol content during the fermentation of jujube wine is The green jujube fermentation broth is fermented continuously for seven days. Response valued larger electronic nose sensors is the S1 (aromatic benzene class), respectively the S8 (alcohol), S3 (ammonia), S5 (alkane) and S4 (hydrogen), electronic nose. PCA of sensor signal, the contribution rate of LD1, LD2 and LD3 is respectively 65.2%, 21.8% and 6.8%. The cumulative contribution rate is 93.8%. It suggests the 3 principal components can represent the main characteristics of information of the sample.
The alcohol precision electronic nose detection model established by MLR is used, with R2 of 0.949 and RMSEC of 0.974. The determination coefficient of the predicted and measured alcohol precision of the model is high (R 2 =0.946) and the predicted standard deviation is low (RMSEP= 1.016). The results show that the proposed model can be used to analyse the alcohol content in the fermentation process of jujube wine rapidly and quantitatively, and it is feasible to measure the alcohol content in real time in the fermentation process of jujube wine.


extfieldsofstudy: Chemistry Medicine

text:
Optimization of Pectin Enzymatic Extraction from Malus domestica ‘Fălticeni’ Apple Pomace with Celluclast 1.5L

Pectin was extracted from apple (Malus domestica ‘Fălticeni’) pomace with Celluclast 1.5L, at doses of 20, 40, and 60 µL/g of material. The temperature and time of extraction were varied on three levels—temperature—40, 50, and 60 °C; time—12, 18, and 24 h. For each experiment, the extraction yield (R2 = 0.8905), the galacturonic acid content (R2 = 0.9866), and the degree of esterification (R2 = 0.9520) of pectin was determined. Response surface methodology (RSM) was implemented via a Box–Behnken design, to optimize pectin extraction. In the optimum extraction conditions (temperature of 48.3 °C, extraction time of 18 h 14 min, and enzyme dose of 42.5 µL/g of pomace), the design predicted a 6.76% yield with a galacturonic acid content of 97.46 g/100 g of pectin and a degree of esterification of 96.02%. FT-IR analysis of the pectin sample obtained in these conditions showed a chemical structure similar to that of commercial apple and citrus pectin.

Introduction
Pectins are complex polysaccharides that are widely distributed in the primary cell walls and the middle lamella of higher plants, having important functions in their development, growth, and maturation [1,2]. The three main domains identified and characterized in the structure of pectin are homogalacturonan (HG), rhamnogalacturonan type I (RG-I), and rhamnogalacturonan type II (RG-II). HG, which is the most abundant pectic polysaccharide that makes up approximately 50%-90% of the total pectin, is a linear polymer of d-galacturonic acid units that are α(1→4) linked, partial methyl-esterified at the C-6 carboxyl, and depending on the plant source, O-acetylated at the O-2 or O-3 positions [2,3]. The second most predominant pectic polysaccharide is RG-I (20%-35% of the total pectin), which is formed of d-galacturonic acid units interspersed by α(1→2) linked, L-rhamnose units, which can be substituted with side chains of arabinans, galactans, and arabinogalactans [4]. RG-II is a branched polygalacturonate polymer that contains complex side chain sugar moieties [5]. The overall structure of pectin and its chemical composition govern its applications [6].
The main commercial sources for pectin extraction are citrus peel and apple pomace, however, the increasing industrial demand and the growing interest in valorizing side streams, to obtain pectins with diverse functional properties, have led to an extensive evaluation of alternative pectin sources [6]. Some of the new pectin sources studied in the last years were pumpkin waste, watermelon rinds [7], banana peel [8], pistachio green hull [9], cocoa pod husks [10], and sisal waste [11].
The conventional extraction methods applied to obtain pectin involve the use of water, together with chemicals such as complexing compounds, acids, or alkali. On an industrial scale, pectin is produced from apple pomace or citrus fruit peel through extraction in warm-diluted solutions of mineral acids (hydrochloric, nitric, or sulfuric acid) at a pH ranging from 1 to 3, at temperatures of 60-100 • C, and 1-12 h extraction time, depending on the source material and the desired physicochemical properties of the extracted pectin [4]. In an attempt to develop green procedures of processing fruit and vegetable by-products and waste for the extraction of pectin, milder extraction agents (e.g., citric acids and enzymes) and non-conventional extraction techniques (such as microwave-and ultrasound-assisted extraction) have been investigated. The use of enzymes for pectin extraction might bring two major advantages related to selectivity and efficiency, especially in terms of the extraction yield. Two main approaches are distinguished in enzyme-assisted extraction-(a) the use of enzymes that degrade and isolate specific pectin fragments and (b) the use of enzymes capable of deconstructing the plant cell wall and isolating the whole pectin molecule [12,13].
In this study, we took the second approach to enzyme-assisted extraction by investigating the use of enzymes that degrade the plant cell wall component to extract pectin. For this purpose, we analyzed the efficiency of the commercially available multicatalytic enzyme preparation Celluclast 1.5L in apple pectin extraction. The plant material used as a source of pectin was apple pomace obtained by locally processing apples from the autumn variety Malus domestica 'Fălticeni'. Malus domestica 'Fălticeni' apple, developed in the geographical area of Fălticeni, Suceava (Romania), is a late season variety suitable for industrial processing, which generates the premises for the capitalization of the by-product resulted from the process. The extraction of pectin was conducted on a laboratory scale, and the influence of the enzyme dose, as well as that of the extraction temperature and time, was studied.

Results and Discussion
Pectin samples were extracted and purified by means of the procedure described in Sections 3.2 and 3.3. The applied enzyme-assisted extraction with Celluclast 1.5L is a green procedure that eliminates the major disadvantages of the conventional acid extraction of pectin-the high temperatures and the low pH of the extracting solution. The lower temperatures of enzymatic extraction lead to a reduction of energy consumption and, thus, lower operational costs. By using citric acid instead of strong mineral acids (such as hydrochloric or sulfuric acid) to bring up the pH to a value of 4.5 (which is required for an optimum activity of the enzymatic preparation) the high costs incurred by the treatment of the strong acidic waste that resulted from the extraction process are avoided. Apart from these improvements, the green extraction method should provide a high-quality extract. The influence of the conditions of enzyme-assisted extraction with Celluclast 1.5L, on the pectin yield and its composition, are discussed below.

Model Fitting and Statistical Analysis
With the purpose of studying and optimizing the extraction of pectin from Malus domestica 'Fălticeni' pomace, a Box-Behnken design was used, which had three independent variables, each with three levels and three replications at the center point. For the extraction of pectin from this vegetal source, a complex process was applied that allowed the variation in the levels of the three process parameters-temperature (T, • C), time (t, h), and enzyme dose (Ed, µL/g). Table 1 presents the predicted response and the experimental values obtained for the yield (Y), the galacturonic acid content (GalA), and the degree of esterification (DE), as a result of varying the conditions of pectin extraction.
As the quadratic model showed higher values of coefficient of determination (R 2 ), adjusted R 2 , and also showed low p-values (especially for the galacturonic acid content and the degree of esterification, when compared to the linear and 2FI models), this model was selected to study the effect of the independent variables on the pectin yield and the physicochemical properties. According to the results of the analysis of variance (Table 2), the coefficients of determination obtained for the pectin yield, the galacturonic acid content, and the degree of esterification (0.8905, 0.9866, and 0.9520, respectively) indicate that the quadratic model could explain and predict most of the variation of these responses. The second-order polynomial equations describing the effect of the independent variables considered on the responses-pectin yield (Equation (1)), galacturonic acid content (Equation (2)), and degree of esterification (Equation (3)) were as follows:

Influence of the Extraction Parameters on the Yield
As seen from results presented in Table 1, the measured pectin yield varied between the lowest value of 3.40% and the maximum value of 8.08%. The conditions that led to the highest pectin recovery from the plant material were as follows-enzyme dose of 60 µL/g of apple pomace, temperature of 50 • C, and 24 h extraction time. Table 2 shows the influence of each parameter, as well as the interactions between the process parameters and their statistical significance. The evolution of the extraction yield in the function of temperature, time, and enzyme dose is presented in Figure 1A-C.
The extraction temperature is an important parameter of enzyme-assisted extraction that was previously shown to have a strong influence on the pectin yield. In the works of Pasandide et al. [14] and Geng et al. [15], the increase of temperature from 70 to 90 • C and from 80 to 100 • C, respectively, led to a maximum extraction yield. The influence of this parameter in the case of our study was completely different from the results of these two studies. As shown in Figure 1A,B, the increase of temperature did not cause a positive effect on pectin yield. Moreover, according to the data in Table 3, the extraction temperature did not significantly affect the extraction yield (p > 0.05). The best results were obtained when the temperature was 50 • C-an observation that was in accordance with the optimal value for the temperature of enzymatic pectin extraction with Celluclast 1.5L, as found by Wikiera et al. [16].  Table 3. Levels of the independent variables applied in the Box-Behnken design.

Factor
Coded Symbols Levels Extraction time (h) t 12 18 24 Enzyme dose (µL/g apple pomace) Ed 20 40 60 In this study, the influence of the extraction time on pectin yield was less pronounced; however, this working parameter was still a statistically significant factor (p < 0.05). When the temperature was fixed to 50 • C ( Figure 1C), pectin yield showed an increase with the increase of both the extraction time and the enzyme dose. The interaction between these two parameters also proved to be statistically significant (p < 0.05). An increase of the pectin yield with extraction time was also reported by Pasandide et al. [14].
Unlike time and temperature, which had little or no influence on the yield, the extraction of pectin from apple pomace was significantly influenced by the enzyme dose (p < 0.01). As the 3D graph in Figure 1C shows, together with the increase of extraction time, the increase in enzyme dose led to a substantial increase of pectin yield from 3.80% (20 µL/g of apple pomace; 12 h extraction time) to 8.08% (60 µL/g of apple pomace; 24 h extraction time). Although the increase of pectin yield was substantial, the maximum recovery reached in this study was way below the minimum value of 15.3% [17] and the level of 18.95% [16] reported for the use of Celluclast 1.5L for the apple pectin extraction. Furthermore, the maximum pectin yield was more than three times lower than the maximum yield of 21.24% that we obtained for the extraction of pectin from Malus domestica 'Fălticeni' using citric acid [18]. Although the maximum yield was obtained with the highest enzyme dose, suggesting that a further increase of this parameter might result in a higher pectin recovery from the plant material, some published studies have shown that Celluclast 1.5L should be added in small quantities because it could cleavage some of the linkages between the galacturonic acid residues [19,20]. Thus, this difference between the yield of acid extraction and that of the enzyme-assisted extraction can be attributed to some degradation in the carbohydrate structure that takes place simultaneous to the action of the multicatalytic enzymatic preparation upon the cell wall components surrounding the pectin molecule.

Effect of the Extraction Conditions on the Content of Galacturonic Acid of Pectin
As shown in Table 1, the content of galacturonic acid determined in the pectin extracted from Malus domestica 'Fălticeni' pomace varied between 68.21 g/100 g pectin (temperature, 40 • C; time, 24 h; and enzyme dose, 40 µL/g pomace) and 99.45 g/100 g pectin (temperature, 50 • C; time, 18 h; and enzyme dose, 40 µL/g pomace). Table 2 presents the influence of the extraction conditions on this chemical parameter, as well as the interactions between parameters and their statistical significance. The changes in the content of galacturonic acid determined by the extraction conditions are visualized in the 3D graphics of Figure 1D-F.
The extraction temperature was the first parameter that showed a significant influence (p < 0.001) on the galacturonic acid content. As shown in Figure 1D,E, the increase of temperature led to a higher galacturonic acid content in the extracted pectin. A similar effect of the extraction temperature on this chemical parameter was also reported by Priyangini et al. [10], when studying the extraction of pectin from cocoa pod husks. Regarding the effect of the interactions between temperature and other extraction parameters on the galacturonic acid content (Table 3), a strong influence (p < 0.01) of the interaction of temperature-enzyme dose and temperature-extraction time was noted. This last extraction parameter manifested an opposite effect on the galacturonic acid content, when compared to the influence of temperature. Figure 1D,F show that the increase in extraction time resulted in a decrease of the galacturonic acid content of pectin. The same effect of a prolonged extraction time was also observed by Colodel et al. [4] in a study on the extraction of pectin from ponkan peel. A different conclusion with respect to the effect of this parameter was stated in the study of Vriesmann et al. [3], who found that the extraction of pectin from cacao pod was not significantly influenced by the pH and the extraction time.
Similar to the influence of time, the increase of enzyme dose caused a decrease in the galacturonic acid content of apple pectin ( Figure 1E,F). This effect was opposite to that manifested by enzyme dose on the extraction yield. As presented in Table 2, the interaction between the two parameters whose increase led to a decrease of the content of galacturonic acid (namely time and enzyme dose) was statistically significant (p < 0.001). The maximum value for the galacturonic acid content of pectin extracted with Celluclast 1.5L was higher (99.45 g/100 g versus 93.90 g/100 g) than that determined for pectin extracted from Malus domestica 'Fălticeni' using citric acid [18]. A slightly higher galacturonic acid content of the pectin extracted with this enzyme preparation, in comparison to that extracted with sulfuric acid, was also reported by Wikiera et al. [16].

Influence of the Extraction Conditions on the Degree of Esterification of Pectin
The data presented in Table 1 shows that the various levels of the extraction parameters determined variations in the degree of esterification of apple pectin, between a minimum value of 90.55% (temperature, 60 • C; time, 18 h; and enzyme dose, 20 µL/g pomace) and a maximum of 97.21% (temperature, 40 • C; time, 24 h; and enzyme dose, 40 µL/g pomace). Considering the classification of pectin according to the degree of esterification [21], all samples of apple pectin obtained in this study were high-esterified. Although no great variation was recorded for the degree of esterification, the influence of the extraction conditions on this parameter was a subject of interest for the research, particularly from the point of view of comparing the effect of the extraction parameters on the degree of esterification to that manifested on the pectin yield and its galacturonic acid content. The evolution of the degree of esterification in the function of temperature, time, and enzyme dose is presented in Figure 1G-I.
As presented in Table 2, the extraction temperature was the most important parameter that greatly influenced the degree of esterification of the extracted pectin (p < 0.001). Figure 1G shows an increase of the degree of esterification when the extraction temperature decreased and the extraction time increased (the enzyme dose was fixed to 40 µL/g pomace). The interaction between these two parameters was statistically significant (p < 0.05). A similar influence of temperature on the degree of esterification was reported by Pasandide et al. [14], for Citrus medica peel pectin. As mentioned before, the increase in extraction time had a positive influence on the degree of esterification (p < 0.05); however, this observation was not corroborated with previous studies that concluded in the finding that harsh extraction conditions such as high temperature and pH, and prolonged extraction, could determine increased de-esterification of the polygalacturonic chain [22].
Regarding the influence of enzyme dose, Table 2 shows that this working parameter did not significantly affect the degree of esterification of the extracted apple pectin (p > 0.05). However, the interactions of enzyme dose-temperature and enzyme dose-time were statistically significant (p < 0.01). Figure 1H,I indicate that the degree of esterification determined for the pectin samples was higher when lower enzyme doses were used for the extraction. The difference was more pronounced in the case of the variation of the degree of esterification determined by the temperature and enzyme dose interaction (time was fixed to 18 h).

Optimization of Extraction Parameters
In this study, the extraction conditions (temperature, time, and enzyme dose) were optimized, such that the pectin yield and its galacturonic acid content and degree of esterification was maximized. By using the response surface methodology (RSM) for optimization, the software used in this study predicted that a temperature of 48.3 • C, extraction time of 18 h 14 min, and enzyme dose of 42.5 µL/g pomace were the optimal conditions to obtain a 6.76% pectin yield, with a content of galacturonic acid of 97.46 g/100 g pectin and a degree of esterification of 96.02%. Figure 2 shows the infrared spectra of pectin extracted from the Malus domestica 'Fălticeni' apple, using the multicatalytic enzyme preparation Celluclast 1.5L under the optimal conditions presented in Section 2.3, together with the spectra obtained for commercial apple and citrus pectin. By comparison with previous studies on the FT-IR spectra of pectin [23,24], for the three samples that were analyzed in this study, no major peaks were found in the region of 3600-3000 cm −1 . The peak at 1743 cm −1 indicated ester carbonyl (C=O) stretching, while 1606 cm −1 was attributed to carboxylate ion stretching [25]; the ratio between these peaks could be used for the quantification of the degree of esterification. Based on the absorption spectra, the apple pectin sample extracted with Celluclast 1.5L was high-esterified and was found to be more similar to the commercial citrus pectin than commercial apple pectin, in terms of the degree of esterification. Besides the strong asymmetrical stretching at 1606 cm −1 , the carbohydrate groups showed another weak symmetric stretching band around 1439 cm −1 . This was followed by intense absorption patterns between 1300 and 800 cm −1 . These are collectively referred to as the 'finger print' region for carbohydrates because it is unique to a compound and, therefore, allows the identification of major chemical groups in polysaccharides [26]. In this region, the bands between 1120-990 cm −1 were identified as the range for the spectral identification of galacturonic acid in pectin molecules [27]. The high intensity peak at 1013 cm −1 indicated that the apple pectin sample extracted with Celluclast 1.5L contains pyranose, and the peaks at 920 cm −1 and 830 cm −1 referred to the absorption of d-glucopyranosyl and α-d-mannopyranose, respectively; similar findings were reported in previous studies by Wang et al. [28] and Zhang et al. [29].

Thermal Properties
With the purpose of investigating the thermal behavior of pectin extracted from Malus domestica 'Fălticeni' apple pomace with the multicatalytic enzyme preparation Celluclast 1.5L (under the optimal conditions) and to compare it with that of commercial apple and citrus pectin, differential scanning calorimetry (DSC) analysis was conducted. As shown in the thermograms presented in Figure 3, for the samples of commercial pectin, no endothermic peaks (melting temperature) were observed, while exothermic peaks (degradation temperature) were recorded at 243 • C for citrus pectin and at 255 • C for apple pectin. According to previous studies, endothermic peaks indicated the presence of water in the analyzed sample, hydrogen bonding among the galacturonic acid units, and also conformational changes of the galacturonan ring [28,30]. In the case of this study, the lack of an endothermic peak could indicate that no breakage of hydrogen bonds occurred in the structure of these pectin samples. Conversely, the exothermic peaks of commercial apple and citrus pectin were close to those reported in the works of Wang et al. [23,31], indicating a similar profile of chemical constituents.
Regarding the thermal behavior of the pectin sample extracted from Malus domestica 'Fălticeni' apple pomace, Figure 3 shows a very small endothermic peak at 120 • C and an exothermic peak at 230 • C, which were in accordance with the melting and degradation temperatures recorded by Wang et al. [23]. When compared to the commercial apple pectin sample, pectin extracted with Celluclast 1.5L had a lower degradation temperature that might be explained by some differences in its chemical structure, which were also observed in the FT-IR analysis. However, considering the intensity of the flow of heat, thermal analysis showed that the sample obtained from apple pomace by enzymatic extraction possessed a higher thermal stability than commercial apple and citrus pectins, which showed more significant changes during heating. From the point of view of the use of pectin in food industry, this might suggest good behavior during thermal processing.

Materials
Apple pomace was obtained in a small scale plant by processing apples (Malus domestica 'Fălticeni') from the 2016 harvest, in the Fălticeni area of Suceava, Romania. The pomace was dried in an oven with air circulation at 60 • C, until constant weight, and then powdered in a food processor. The resulting powder was passed through an analytical sieve shaker Retsch AS 200 (Retsch GmbH, Haan, Germany) and the apple pomace with particle sizes of 125-200 µm was used for the pectin extraction. Celluclast 1.5L, citric acid, ethyl alcohol, sulfamic acid, potassium hydroxide, sulfuric acid, sodium tetraborate, sodium hydroxide, d-galacturonic acid, m-hydroxydiphenyl, and commercial apple and citrus pectin were purchased from Merck KGaA (Darmstadt, Germany).

Enzyme-Assisted Extraction of Apple Pectin
The procedure followed to obtain the pectin from apple (Malus domestica 'Fălticeni') pomace with Celluclast 1.5L is presented in Figure 4. In the first step, the water used for the extraction was brought to a pH of 4.5 with citric acid. Then, the extraction mixture was prepared by adding 4.5 of apple pomace to 45 mL of water, keeping a solid-to-liquid ratio of 1:10. Enzyme doses of 20, 40, and 60 µL/g of apple pomace, respectively, were added to the water-pomace mixture, according to the Box-Behnken design presented in Table 3. The extraction process was conducted at different temperatures (40, 50, and 60 • C) and times (12,18, and 24 h) with constant shaking (200 rpm).

Pectin Precipitation and Purification
After extraction, the enzyme was inactivated at 121 • C for 5 min and the samples were cooled to the room temperature, prior to precipitation and purification. First, a centrifugation at 4000 rpm for 40 min was performed to separate pectin from the remaining solid material. The supernatant was filtered through a clean cheesecloth folded 6 times, transferred in a laboratory glass bottle, and mixed with cold (4 • C) ethyl alcohol (>96%, v/v), with the volume added being calculated to reach a final concentration of alcohol of 70%. The mixture was kept at 4-6 • C for 12 h to complete the precipitation. Then, the precipitated pectin was separated from the mixture, by centrifugation, under the same conditions as mentioned above. The wet pectin was washed three times, with 96% ethyl alcohol, and finally dried in a hot air oven, at 50 • C, to a constant weight.
The extraction efficiency was calculated using Equation (4): where, m 0 -weight of dried pectin (g) and m-weight of dried apple pomace powder (g).

Galacturonic Acid Assay
The content of galacturonic acid was measured using the sulfamate/m-hydroxydiphenyl method developed by Filisetti-Cozzi et al. [32]. The preparation of the samples was made according to Miceli-Garcia [33], as follows-20 mg of pectin were mixed with 50 mL of distilled water (at 40 • C) under constant shaking (400 rpm), until the sample was completely dispersed, then the volume was adjusted to 100 mL with warm distilled water. From the pectin solution, aliquots of 400 µL were placed in glass tubes, to which 40 µL of 4 M sulfamic acid solution followed by 2.4 mL of sulfuric acid containing 75 mM of sodium tetraborate were added, while vigorously vortexing the content of the tubes for at least 5 s, in between the additions. The samples were boiled for 20 min in a water bath, and then cooled for 10 min in an ice bath. A total of 80 µL of m-hydroxydiphenyl solution in 0.5% (w/v) sodium hydroxide were added to each test tube and the content was vortex mixed, and after 10-30 min, the absorbance was read at 525 nm, against the reagent control (prepared following the same methodology as described above, but replacing the m-hydroxydiphenyl solution with 0.5% (w/v) sodium hydroxide) with a UV-Vis-NIR spectrophotometer (Shimadzu Corporation, Kyoto, Japan). A standard curve of d-galacturonic acid was made for each batch of samples that was analyzed.

Determination of the Degree of Esterification of Pectin
For the determination of the degree of esterification of the extracted pectin, the titrimetric method previously described by Franchi et al. [34] was used. In brief, 50 mg of pectin were dissolved in 10 mL of boiled distilled water and the resulting solution was titrated with 0.1 N NaOH, using phenolphthalein as an indicator; the volume of used sodium hydroxide solution was recorded as V 1 . Subsequently, 20 mL of 0.5 M NaOH were added and the solution was allowed to stand for 30 min. A total of 20 mL of 0.5 M HCl were subsequently added to neutralize the solution under vigorous stirring, and a final titration with 0.1 N NaOH was made, recording the sodium hydroxide volume as V 2 . The degree of esterification was calculated using Equation (5):

Fourier Transform Infrared (FT-IR) Spectroscopy Analysis
The pectin sample extracted with Celluclast 1.5L in optimal conditions and the commercial apple and citrus pectin samples were subjected to FT-IR spectroscopy, using a Fourier-Transform Infrared Spectrophotometer (Spectrum Two; Perkin Elmer, Waltham, Massachusetts, MA, USA). The characteristic spectra of the pectin samples were recorded in the range 4000-400 cm −1 at a resolution of 4 cm −1 [28]. Spekwin32-optical spectroscopy software (Version 1.72.2, Dr. Friedrich Menges Softwareentwicklung, Oberstdorf, Germany) was used to display the spectra.

Thermal Analysis
Differential scanning calorimetry (DSC 8500; Perkin Elmer) was used in this study to investigate the thermal properties of the pectin sample extracted with Celluclast 1.5L (under optimal conditions) and compare them with those of the commercial apple and citrus pectin samples. From each sample, 5 mg of dried powder were added to the aluminum pans that were immediately sealed and placed in the DSC instrument, together with an empty aluminum pan that was used as a reference. The change in heat flow was analyzed from 0 • C to 300 • C (heating rate of 10 • C/min) in dynamic inert nitrogen atmosphere, at a flow rate of 20 mL/min.

Experimental Design
The effect of the independent variables (temperature (T), time (t), and enzyme dose (Ed)) on the extraction yield, galacturonic acid content, and degree of esterification was monitored using a Box-Behnken response surface experimental design. The coded levels of the design variables are shown in Table 3. All computation and graphics were made using Design Expert v11 (trial version, Stat-Ease, Minneapolis, Minnesota, MN, USA).

Conclusions
The pomace obtained by processing apples from the autumn variety Malus domestica 'Fălticeni', in the geographical area of Fălticeni, Suceava, Romania, was found to be a good source material for pectin extraction. For the extraction, the commercially available multicatalytic enzyme preparation, Celluclast 1.5L, was used, and the following process parameters were varied-temperature, time, and enzyme dose. The effect of these parameters was studied by recording the changes in pectin yield and its galacturonic acid content and the degree of esterification. Pectin yield was found to be strongly influenced by enzyme dose, while temperature and extraction time had little or no effect on the extraction. Regarding the effect of process parameters on the galacturonic acid content of pectin, the increase in temperature was found to have a positive influence, but that of time and enzyme dose had a negative impact on this parameter. Although all extracted samples were classified as high-esterified, those that had higher degree of esterification were obtained at a low temperature and a prolonged extraction time. FT-IR spectra showed that the pectin sample extracted under optimal conditions had a chemical structure similar to that of commercial citrus and apple pectin, while DSC analysis revealed a high thermal stability of this sample.
As a final remark, it is important to address the prospects for the industrial-scale application of enzyme-assisted extraction of pectin from Malus domestica 'Fălticeni' apple pomace. This application successfully meets the important conditions for industrialization, which are the production of high-quality pectin and the use of a sustainable extraction process that involves the addition of a reduced quantity of cheap multicatalytic preparation Celluclast 1.5L, and eliminates the requirement of a low pH and high extraction temperatures. In this way, the possibility of corrosion, as well as rapid wearing out of the equipment would no longer present as a problem. From this point of view, pectin production with Celluclast 1.5L fits the trend of environment-friendly extraction technologies.