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Classification of wolfberry from different geographical origins by using electronic tongue and deep learning algorithm
6th IFAC Conference on Sensing, Control and Automation for 6th IFAC 6th IFAC Conference Conference on on Sensing, Sensing, Control Control and and Automation Automation for for Agriculture Available online at www.sciencedirect.com Agriculture 6th IFAC Conference on Sensing, Control and Automation for Agriculture December 4-6, 2019. Sydney, Australia 6th IFAC Conference on Sensing, Control and Automation for December 4-6, 2019. Sydney, Australia Agriculture December 4-6, 2019. Sydney, Australia Agriculture December 4-6, 2019. Sydney, Australia December 4-6, 2019. Sydney, Australia
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IFAC PapersOnLine 52-30 (2019) 397–402
Classification of wolfberry from different geographical origins by using Classification Classification of of wolfberry wolfberry from from different different geographical geographical origins origins by by using using electronic tongue and deep learning algorithm Classification of wolfberry from different geographical origins by using tongue and deep learning algorithm Classificationelectronic of wolfberry from different geographical origins by using electronic tongue and deep learning algorithm electronic tongue and deep learning algorithm 1 1 1 1 tongue and deep learning algorithm Wang11*. Wenhao Yuan Zhengweielectronic Yang11. Zhiqiang 1.Caihong Li1. Xiaoyu Jing1. Hui Han1
Zhengwei Zhiqiang Wang Wang11*. *. Wenhao Wenhao Yuan Yuan11.Caihong .Caihong Li Li11.. Xiaoyu Xiaoyu Jing Jing11.. Hui Hui Han Han11 Zhengwei Yang Yang11.. Zhiqiang 1 . Zhiqiang Wang *. Wenhao Yuan .Caihong Li . Xiaoyu Jing . Hui Zhengwei Yang School of Computer Science and Technology, Shandong University of Technology, Zibo,255019, 1 1 1 1 1 1 1 HanChina Zhengwei Yang . Zhiqiang Wang *. Wenhao Yuan .Caihong Li . Xiaoyu Jing . Hui Han 1
School School of of Computer Computer Science Science and and Technology, Technology, Shandong Shandong University University of of Technology, Technology, Zibo,255019, Zibo,255019, China China of Computer Science and Technology, Shandong University of Technology, Zibo,255019, China School author: of Computer Science and Technology, Shandong University of Technology, Zibo,255019, China *corresponding School of Computer Science and Technology, Shandong University of Technology, Zibo,255019 *corresponding Computer Science and Shandong *corresponding author: author: School School of ofChina Computer Science and Technology, Technology, Shandong University University of of Technology, Technology, Zibo,255019 Zibo,255019 (Tel: 13953336689; e-mail: [email protected]). *corresponding author: School ofChina Computer Science and Technology, Shandong University of Technology, Zibo,255019 (Tel: Science 13953336689; e-mail: [email protected]). [email protected]). (Tel: 13953336689; e-mail: *corresponding author: School ofChina Computer and Technology, Shandong University of Technology, Zibo,255019 China (Tel: 13953336689; e-mail: [email protected]). China (Tel: 13953336689; e-mail: [email protected]). Abstract: Wolfberry is a traditional Chinese Chinese food. food. Its Its price price and and function function are are closely closely related related to to its its Abstract: Wolfberry is Abstract: Wolfberry is aa traditional traditional Chinese food. Its priceinterests and function are closely related to its geographical origin. Illegal labeling driven by commercial has brought serious food safety Abstract: Wolfberry is a traditional Chinese food. Its priceinterests and function are closely related to its geographical origin. Illegal labeling driven by commercial has brought serious food geographical origin. Illegal labeling driven by commercial interests has electronic brought serious food safety safety Abstract: Wolfberry isconsumer a traditional Chinese Its price and function are closely to its problems and and damaged confidence. In food. this study, a voltammetric voltammetric tonguerelated (VE-tongue) geographical origin. Illegal labeling driven by commercial interests has brought serious food safety problems damaged consumer confidence. In this study, a electronic tongue (VE-tongue) problems and damaged consumer confidence. Indeveloped this study,toa voltammetric tongue (VE-tongue) geographical origin. Illegal labeling driven by commercial interests recognize has electronic brought serious food safety combined with deep learning algorithm was perform of different origins of problems damaged consumer confidence. this study, electronic tongue combined with deep learning algorithm was to perform of different origins of combined and with deep Training learning algorithm wasIn developed toaa voltammetric perform recognize recognize ofNetwork, different originswas of problems and damaged consumer confidence. Indeveloped this study,(Convolutional voltammetric electronic tongue (VE-tongue) (VE-tongue) wolfberry samples. of deep learning model Neural CNN) combined with deep learning algorithm was developed to perform recognize of different origins of wolfberry samples. Training of deep learning model (Convolutional Neural Network, CNN) was wolfberry samples. Training of deep learning model (Convolutional Neural Network, CNN) was combined with deep learning algorithm was developed to perform recognize of different origins of performed samples. with 260 260 Training wolfberry of samples which were from(Convolutional different geographical geographical origins samples. samples. To wolfberry deep learning model Neural Network, CNN) performed with wolfberry samples which from 44 origins To performed with 260 Training wolfberry samples which were from 4 different different geographical origins samples. To wolfberry samples. of deep learning learningwere model (Convolutional Neuralsize Network, CNN) was was find the best performance CNN model, rate, optimizer and minibatch were modified. The geographical performed with 260 wolfberry samples which were from 44 different origins samples. To find the best performance CNN model, learning rate, optimizer and minibatch size were modified. The find the best performance CNN model, learning rate, optimizer and minibatch size were modified. The performed with 260 wolfberry samples which were from different geographical origins samples. To best classification classification accuracyCNN of CNN CNN waslearning further rate, compared with and traditional machine learning method— find the best performance model, optimizer minibatch size were modified. The best accuracy of was further compared with traditional machine learning method— best classification accuracy oftransform CNN was furtherasrate, compared with and traditional machine learning method— find thewith bestdiscrete performance CNN model, learning optimizer minibatch size were modified. The BPNN wavelet (DWT) feature extraction method. The classification accuracy best classification accuracy of CNN was further compared with traditional machine learning method— BPNN with discrete wavelet transform (DWT) as feature extraction method. The classification accuracy BPNN with discrete wavelet (DWT) featureand extraction method. The classification accuracya best classification accuracy oftransform CNN was furtheras compared with traditional machine learning method— of CNN, DWT-BPNN and BPNN are 98.27%, 88.46% 48.08% respectively. This study provides BPNN with discrete wavelet transform (DWT) as featureand extraction method. The classification accuracyaa of CNN, DWT-BPNN and are 88.46% 48.08% respectively. This provides of CNN, DWT-BPNN and BPNN BPNN are 98.27%, 98.27%, 88.46% and 48.08% respectively. This study study provides BPNN with discrete wavelet transform (DWT) as extraction method. The classification accuracy novel method for recognition recognition and classification classification offeature wolfberry from different different geographical origins, whicha of CNN, DWT-BPNN and BPNN are 98.27%, 88.46% and 48.08% respectively. This study provides novel method for and of wolfberry from geographical origins, which novel method for recognition and classification of wolfberry from traceability different geographical origins, whicha of CNN, DWT-BPNN and BPNN are 98.27%, 88.46% and origin 48.08% respectively. This study provides holds great promise for its wide applications in geographical for agricultural products. novel method for recognition and classification of wolfberry from different geographical origins, which holds great promise for its wide applications in geographical origin traceability for agricultural products. holds great promise for its wide applications in geographical origin traceability for agricultural products. novel method for recognition and classification of wolfberry from different geographical origins, which origin traceability for products. holds for wide in © 2019,great IFACpromise (International of Automatic Control) Hosting by Elsevier Ltd.Deep Allagricultural rights reserved. Keywords: Convolutional neuralapplications network, Voltammetric electronic tongue, learning, Wolfberry, holds great promise for its itsFederation wide applications in geographical geographical origin traceability for agricultural products. Keywords: Convolutional neural network, Voltammetric electronic tongue, Deep learning, Wolfberry, Keywords: Convolutional neural network, Voltammetric electronic tongue, Deep learning, Wolfberry, Classification Keywords: Convolutional Classification Classification Keywords: Convolutional neural neural network, network, Voltammetric Voltammetric electronic electronic tongue, tongue, Deep Deep learning, learning, Wolfberry, Wolfberry, Classification Classification methods are are tedious tedious and and time time wasting, wasting, which cannot cannot meet 1. INTRODUCTION methods methods aredaily tedious and time wasting, which which cannot meet meet 1. the needs of rapid detection. 1. INTRODUCTION INTRODUCTION methods are tedious and time wasting, which cannot meet the needs of daily rapid detection. the needs of daily rapid detection. 1. INTRODUCTION methods are tedious and time wasting, which cannot meet Wolfberry is one traditional Chinese food. It is widely 1. INTRODUCTION the needs of daily rapid detection. Wolfberry is one traditional Chinese food. It is widely Electronic tongue (ET) is a smart recognition electronic Wolfberry is one ittraditional Chinese food. Itcarotenoids, is widely Electronic the needs oftongue daily rapid detection. consumed because is rich in polysaccharides, (ET) is aa smart recognition electronic Wolfberry is one it food. Itcarotenoids, is widely device Electronic tongue (ET) the is theory smart recognition consumed is rich polysaccharides, aiming to imitate imitate of taste taste sense of electronic human. It It consumed because because ittraditional isfunctional rich in in Chinese polysaccharides, Wolfberry is one traditional Chinese food.and Itcarotenoids, is widely flavonoids and other components it has the Electronic tongue (ET) is a smart recognition electronic device aiming to the theory of sense of human. consumed because it is rich in polysaccharides, carotenoids, device aiming to imitate the theory of taste sense of human. It flavonoids and other functional components and it has the Electronic tongue (ET) is a smart recognition electronic is capable of employing qualitative and quantitative analysis flavonoids and other functional components and it has the consumed because it is immunity, rich in polysaccharides, carotenoids, functions of enhancing anti-tumour and anti-aging device aiming to imitate the theory of taste sense of human. is capable of employing qualitative and quantitative analysis is capable of employing qualitative and quantitative analysis flavonoids and other functional components and it has the functions of enhancing immunity, anti-tumour and anti-aging device aiming and to imitate the theoryof of taste sense ofcompound human. It It for liquid semiliquid complex functions ofand enhancing immunity, anti-tumour and aanti-aging flavonoids otheret functional components and it valuable has the for (Li et al., 2015) (He al., 2012). Wolfberry is also is capable of employing qualitative and quantitative analysis liquid and semiliquid of complex compound for liquidof employing and Its semiliquid ofof complex compound functions of enhancing immunity, anti-tumour and anti-aging (Li et al., 2015) (He et al., 2012). Wolfberry is also a valuable is capable qualitative and quantitative analysis comprehensively. merits consist fast speed of detection, (Li et al., 2015) (He et al., 2012). Wolfberry is also a valuable functions ofinenhancing immunity, anti-tumour and anti-aging tonic, used Chinese and global markets. But the quality of for liquid semiliquid complex comprehensively. Its merits consist speed ofcompound detection, comprehensively. Itsstrong meritsobjectivity, consistof of fast fast speed detection, (Li et 2015) (He al., Wolfberry is also valuable tonic, used in and global markets. But the of for liquid ofand and semiliquid ofof complex compound convenience use, and good of repeatability. tonic, used in Chinese Chinese and2012). global markets. But the aaquality quality of convenience (Li et al., al., 2015) (He et et al., 2012). Wolfberry is also valuable Chinese wolfberry differs from place to place. dishonest comprehensively. Its merits consist of fast speed of detection, of use, strong objectivity, and good repeatability. convenience of use, strong objectivity, and good repeatability. tonic, used in Chinese and global markets. But the quality of Chinese wolfberry differs from place to place. dishonest comprehensively. Its merits consist of fast speed of detection, Nowadays, ET has strong been objectivity, employed in in the field of food, food, Chinese wolfberry differs from markets. place to But place. dishonest tonic, used in Chinese and global theadulterated quality of Nowadays, traders driven by commercial interests has convenience of use, and good repeatability. ET has been employed the field of Nowadays, ET has been employed in the field of food, Chinese wolfberry differs from place to place. dishonest traders driven by commercial interests has adulterated convenience of use, strong objectivity, and good repeatability. environment and biomedical analysis, etc. Krantz-Rülcker traders driven by commercial interests has adulterated Chinese wolfberry differs from place to place. dishonest wolfberries, which brought serious food safety problems and Nowadays, ET has been in the field environment and biomedical analysis, Krantz-Rülcker environment andto biomedical analysis, etc. Krantz-Rülcker traders driven by commercial interests has adulterated wolfberries, which serious food and Nowadays, ET has been employed employed in etc. the(Krantz-Rülcker field of of food, food, has applied ET monitor the environment wolfberries, which brought serious fooditsafety safety problems and traders driven by brought commercial interests hasproblems adulterated damaged consumer confidence. Hence, is very important to environment and biomedical analysis, etc. Krantz-Rülcker has applied to monitor the environment (Krantz-Rülcker wolfberries, which brought serious food safety problems and has applied ET to monitor the environment (Krantz-Rülcker damaged consumer confidence. Hence, it is very important to environment and biomedical analysis, etc. Krantz-Rülcker et al., 2001). Tian et al. and Gao et al. gained their own damaged consumer confidence. Hence, itsafety is veryproblems important to et wolfberries, which brought serious food and distinguish the quality of Chinese wolfberry that of applied ETTian to monitor the environment (Krantz-Rülcker al., 2001). et and Gao et al. gained their own damaged consumer confidence. Hence, it is veryfrom important to has et al., 2001). Tian et al. al.through and Gao et ET al. (Tian gained their own distinguish the quality of Chinese wolfberry from that of has applied ET to monitor the environment (Krantz-Rülcker success in field of food using et al. 2013) distinguish the quality of Chinese wolfberry from that of damaged consumer areas. confidence. Hence, it is very important to success different producing et al., 2001). Tian et al. and Gao et al. gained their in field of food through using ET (Tian et al. 2013) success in field of food through using ET (Tian et al. 2013) distinguish the quality of Chinese wolfberry from that of different producing areas. et al.,et2001). Tian et al. and Gao et al. gained their own own (Gao al., 2012). different producing areas.of Chinese wolfberry from that of (Gao distinguish the quality success in field of food through using ET (Tian et al. 2013) et al., 2012). (Gao et al., 2012). different producing areas. success in field of food through using ET (Tian et al. 2013) In the food industry, the appearance of sensory panels can different producing areas. (Gao et In the industry, the appearance of sensory panels The recognition recognition pattern system system is is much much vital vital for for the the VEIn the food food industry, the appearance ofsensory sensoryattributes panels can can (Gao et al., al., 2012). 2012).pattern describe quality, and the analysis of is The The recognition pattern system is much vital for the VEVEIn the food industry, the appearance of sensory panels can describe quality, and the analysis of sensory attributes is tongue system, which contains feature extraction and describe quality, and the analysis of sensory attributes is In the food industry, the appearance of sensory panels can helpful for quality assessment (Bleibaum et al., 2002). The recognition pattern system is much vital for the VEtongue system, which contains feature extraction and tongue system, which contains feature extraction and describe quality, and the analysis of sensory attributes is helpful for quality assessment (Bleibaum et al., 2002). The recognition pattern system is much vital for the VErecognition algorithm (Wei et al., 2018). Feature extraction is helpful for quality assessment (Bleibaum et al., 2002). describe quality, and the analysis of sensory attributes is Traditional wolfberry detection methods discriminate tongue system, which contains feature extraction and recognition algorithm (Wei et al., 2018). Feature extraction is recognition algorithm (Wei et al., 2018). Feature extraction is helpful for quality assessment (Bleibaum et al., 2002). Traditional wolfberry detection methods discriminate tongue system, which contains feature extraction and employed to extract useful information of VE-tongue because Traditional wolfberry detection methods discriminate helpful for quality assessment (Bleibaum et al., 2002). wolfberry of difference of colour, shape, odour, taste, etc. recognition algorithm (Wei et al., 2018). Feature extraction is employed to extract useful information of VE-tongue because employed to extract useful information of VE-tongue because Traditional wolfberry detection methods discriminate wolfberry of difference of colour, shape, odour, taste, etc. recognition algorithm (Wei et al., 2018). Feature extraction is there is much random noise in VE-tongue signals. Yin et al. wolfberry ofthis difference ofdetection colour, shape, odour, taste,some etc. there Traditional wolfberry methods discriminate However, subjective detection method has employed to extract useful information of VE-tongue because is much random noise in VE-tongue signals. Yin et al. wolfberry of difference of colour, shape, odour, taste, etc. there is much random noise in VE-tongue signals. Yin et al. However, this subjective detection method has some employed to extract useful information of VE-tongue because and Shi et al. employed discrete wavelet transform (DWT) to However, subjective detection method has wolfberry ofthis difference of colour, shape, odour, taste,some etc. thereShi problems, such being by emotions, nonis much random noise in VE-tongue signals. (DWT) Yin et al. et al. employed discrete wavelet transform to However, this as detection method has some and Shi et employed discrete wavelet (DWT) to problems, such assubjective being affected affected by personal personal emotions, non- and there is much random of noise VE-tongue Yin(Shi et al. achieve theal. extraction ET in signals (Yintransform etsignals. al.,2018) et problems, such as being affected by personal emotions, nonHowever, this subjective detection method has are some repeatability and so on. Other methods of analysis to and Shi et al. employed discrete wavelet transform (DWT) to achieve the extraction of ET signals (Yin et al.,2018) (Shi et achieve the extraction of ET signals (Yin et al.,2018) (Shi et problems, such as being affected by personal emotions, nonrepeatability and so on. Other methods of analysis are to and Shi et al. employed discrete wavelet transform (DWT) to al., 2018). Wang et al. employed “area method” to extract the repeatability and so on. Other methods of analysis are to problems, suchcontent as being affected by personal emotions, non- al., determine the of polysaccharides (Gong et al., 2017), achieve the extraction of ET signals (Yin et al.,2018) (Shi 2018). Wang et al. employed “area method” to extract the al., 2018). Wang et al. employed “area method” to extract the repeatability and so on. Other methods of analysis are to determine the content of polysaccharides (Gong et al., 2017), achieve the extraction of ET signals (Yin et al.,2018) (Shi et et area feature data et and Fast Fourier transform (FFT) to extract extract determine thelyceum content of polysaccharides (Gong et al., 2017), repeatability and soclassified on. Otherbymethods ofsecond analysis are to area the fruits of using the derivative al., 2018). Wang al. employed “area method” to extract the feature data and Fast Fourier transform (FFT) to area feature data feature and Fourier transform (FFT) to extract determine the content of polysaccharides (Gong et al., 2017), the fruits of lyceum classified by using the second derivative al., 2018). Wang et al.Fast employed “area method” to2019). extract the the significant data (Wang et al., The the fruits of lyceum classified by using the second derivative determine the content of polysaccharides (Gong et al., 2017), infrared spectrum and the by two-dimensional correlation area feature data and Fourier transform (FFT) to the significant data (Wang et 2019). The the significant feature data (Wang et al., al., 2019). The the fruits of lyceum classified using the derivative infrared spectrum and two-dimensional area feature data feature and Fast Fast Fourier transform (FFT) to extract extract recognition algorithm can be used to quantitatively determine infrared spectrum and etthe the two-dimensional correlation the fruitsspectrum of lyceum(Yao classified by usinggeographical the second secondcorrelation derivative infrared al.2010), origin of the significant feature data (Wang et al., 2019). The recognition algorithm can be used to quantitatively determine recognition algorithm can be used to quantitatively determine spectrum and the two-dimensional correlation infrared spectrum (Yao et al.2010), geographical origin of the significant feature data (Wang et al., 2019). The contentalgorithm of components components and realise classification classification infrared spectrum (Yao al.2010), geographical origin of the spectrum (goji) and et the two-dimensional correlation Chinese wolfberry discriminated through stable carbon recognition can be used to quantitatively determine content of and realise the content of components and realise classification infrared spectrum (Yao et al.2010), geographical origin of Chinese wolfberry (goji) discriminated through stable carbon recognition algorithm can be used to quantitatively determine (recognition, identification, discrimination). Several kinds of Chinese wolfberry (goji) discriminated throughetstable carbon infrared spectrum (Yao et al.2010), geographical origin of (recognition, isotopic ratios of wolfberry volatile (Meng al., 2019). the content of components and realise classification identification, discrimination). Several kinds of Chinese wolfberry (goji) discriminated through stable carbon (recognition, identification, discrimination). Several kinds of isotopic ratios of wolfberry volatile (Meng et al., 2019). the content of components and realise classification recognition algorithms have been employed for the isotopic ratios of (goji) wolfberry volatile (Meng al., carbon 2019). (recognition, identification, Chinese wolfberry discriminated throughetstable However, all these discrimination). Several for kindsthe of algorithms have been isotopic ratios of wolfberry volatile (Meng et al., 2019). recognition recognition algorithms have been employed employed for the However, all these (recognition, identification, discrimination). Several kinds of classification mission of VE-tongue such as principal However, all these isotopic ratios of wolfberry volatile (Meng et al., 2019). classification recognition algorithms have been employed for the mission of VE-tongue such as principal classification mission ofhave VE-tongue such as principal However, all these recognition algorithms been employed for the However, all these classification classification mission mission of of VE-tongue VE-tongue such such as as principal principal 2405-8963 © Copyright © 2019, 2019 IFAC IFAC (International Federation of Automatic Control) 397Hosting by Elsevier Ltd. All rights reserved. 1 1School
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component analysis (PCA), partial least squares discriminant analysis (PLS-DA), linear discriminant analysis (LDA), cluster algorithm (CA) and support vector machine (SVM). Novakowski employed PCA to discriminate wines and whiskies (Novakowski et al., 2011). Pigani employed PLSDA to discriminate white wines (Pigani, L et al., 2008). Tiwari employed LDA to identify miofloral honey (Tiwari, K et al., 2013). Haddi employed cluster algorithm (CA) to classifiy Moroccan virgin olive oil (Haddi, et al., 2013). Bougrini employed SVM to realise aging time and brand determination of pasteurized milk (Bougrini et al., 2014), and Hu employed ANN to classify different varieties of rice (Hu et al., 2016). Methods mentioned above need artificial feature extraction which are time-consuming and elaborate. There are many agriculture problems managed through classic algorithm of machine learning as well. Al-Hiary employed machine learning to realise fast and accurate detection and classification of plant diseases while it needed artificial feature extraction (Al-Hiary et al., 2011). In deep learning, it becomes unnecessary (Long et al., 2018).
electrode served as the auxiliary electrode. Yu et al. and Winquist et al. modified the excitation signal of the large amplitude pulse voltammetry (LAPV) (Yu et al., 2019) (Winquist et al., 1997). LAPV was added between the auxiliary and working electrodes, which generates through LabVIEW software with a scanning frequency of 10 Hz. The excitation signal range of each working electrode fluctuates from 1 V to -1 V with a potential step of 0.2V, which was shown in Fig2. The image of VE-tongue signal is shown in Fig.3.
Fig.1. VE-tongue self-developed system: (1). Reference electrode. (2) Auxiliary electrode. (3) Working electrode.
Deep learning is an emerging image processing and data analysis technology in recent years, which has broad application prospects and development potential (Kamilaris et al., 2018). Nowadays, many fields are beginning to employ deep learning to address pattern recognition, object detection and image understanding. Deep learning is about training neural network architectures consisting of several nonlinear processing layers. It gains large success because of the appearance of new model regularization techniques (Witten et al., 2016), improved design of nonlinearities (Dahl et al., 2013), the development of hardware and the progress of big data. Ferentinos developed a convolutional neural network models to perform detection and diagnosis of plant disease (Ferentinos et al., 2018). Yu has employed deep learning to achieve the weed detection in turfgrass (Yu et al., 2019). Nodera employed deep learning to identify the resting needle electromyography signals (Nodera et al., 2019). Zhao employed CNN to realise waveform classification and seismic recognition (Zhao et al., 2019). However, as far as we know, VE-tongue combined with deep learning methodology has not been used in the literature to classify wolfberry from different geographical sources.
幅值/V
1
0.2
0
-1
0
时间/S
1
Fig.2. The excitation signals generated by the LabVIEW software.
Fig.3. The curve of collected response signals 2.2 Sample preparation and dataset The signal of the VE-tongue of goji samples was gained through the method of Yin (Yin et al.,2018). There are 260 wolfberry samples from 4 different geographical origins. It contains 70 signals of Ningxia wolfberry, 60 signals of Xinjiang wolfberry, 70 signals of Gansu wolfberry and 60 signals of Qinghai wolfberry. In this study, the dataset was divided into training set (80% of dataset) and testing set (20% of dataset) respectively and all the models in this study were trained and tested by using training set and testing set respectively.
In this study, we come up with employing deep learning to realize the recognition of ET signals so that it becomes possible to avert the employment of feature extraction and the deep learning which was used to recognize the signals of ET has gotten good performance. 2. MATERIALS AND METHOD 2.1 Voltammetric electronic tongue As shown in Fig.1, The Voltammetric electronic tongue (VEtongue) consist of signal-conditioning circuit, sensor array, NI DAQ card (NI-6002) and LabVIEW software. The sensor array was a standard three-electrode system involving eight electrodes, which were platinum, gold, titanium, palladium, silver, tungsten, nickel, and glassy carbon. The Ag/AgCl electrode was used for reference electrode and the platinum
2.3 Data pre-processing Every point of the signals of the VE tongue fluctuate from -3 voltage to 4 voltage. It is hard for the pattern recognition system to process these signals. In order to eliminate the dimension effects between indicators, data normalization is 398
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Deep learning relies deeply on Graphics Processing Unit (GPU) with Compute Unified Device Architecture (CUDA) core enabled. In this study, one deep learning framework called Keras was employed. It performed on one computer Intel Core i7 with 3.7GHz, GTX 1080Ti GPU and 24G Random Access Memory (RAM).
needed to solve the comparability between data indicators. In this study, the formula of method of normalization employed is as follows: Xscale
=
x − xmin xmax − xmin
399
(1)
3. RESULT
The training set and the test set employ the normalization respectively. After data normalization, the original data are in the same order of magnitude, which is suitable for comprehensive comparative evaluation.
In this study, BPNN using normalization pre-processing and BPNN using DWT feature extraction were employed to compare with CNN. The best classification method of Yin was compared with the CNN model of this study as well (Yin et al.,2018).
2.4 Back propagation neural network
3.1 CNN classification result
The Artificial neural network (ANN) is mathematical model imitating the behaviour of characteristics of animal’s nervous system to process distributed parallel information. ANN processes superior non-linear mapping capabilities, fine fault tolerance, adaptive capabilities, and distributed storage. Back propagation neural network (BPNN) is one of the multiple layers forward neural networks. The BPNN generally consist of three kinds of layers: one input layer, one or more hidden layers and one output layer. To realize precise learning, the raw data are divided into two parts which are training set and testing set used to realize construction and quality check of obtained numerical models, respectively (Wei et al., 2011).
For getting the best performance, CNN performance was evaluated by modifying the hyperparameter and optimizer of output layer. Minibatch size was set as 22 and the number of epochs was set as 10. Minibatch divides training data into smaller batches, and updates model coefficients by gradient descent method. Three mainstream activation functions —— Adam, Stochastic Gradient Descent (SGD), Root Mean Square Prop (RMSprop) were used to compare the performance of CNN. The accuracy of classification gained from CNN using different optimizer was evaluated. Each optimizer was employed different value of learning rate. The minimum value of learning rate was set as 10-4 and the maximum learning rate value was set as 10-2. The value of learning rate was set as 0.0001, 0.0005, 0.001, 0.005, 0.01 in each trial. The accuracy of classification for different value of learning rate in testing set is plotted and shown in Fig.5. It shows that the Adam optimizer with 0.0005 learning rate has the better performance compared to RMSprop optimizer and SGD optimizer. Its classification accuracy is up to 98.27%. The best classification accuracy of RMSprop optimizer whose learning rate is set as 0.001 is 90.83%. The classification accuracy of SGD optimizer is unsatisfying whose best classification accuracy is 62.25%, which shows that SGD optimizer is not suitable for this study.
2.5 Convolutional neural network For achieving the classification of VE-tongue signals, the classical structure of CNN—LeNet5 which has gained great success on handwritten digital classification is employed. As shown in Fig.4, CNN structure modified consist of 4 convolutional layers, 3 max pooling layers, 2 fully connected layers and 1 output layers, which employ softmax activation function. The function of convolutional layer is aimed for extracting features and the pooling layer filters out some features by down sampling. Different from the twodimensional convolution of handwritten digital image classification, the VE tongue signals adopts one-dimensional convolution. The convolutional kernel of every convolutional layer is 5. The number of channels of Conv_1 and Conv_2 are 6 and the number of channels of Conv_3 and Conv_4 are 16. The fully connected layer’s structure is 120-34-4 and the activation function is tanh.
Fig.4. The structure of CNN
Fig.5. Accuracy of classification of testing set using Adam, RMSprop, SGD with different values of learning rate in testing set
2.6 System configuration
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In the next scenario, minibatch size was modified to analyse its importance in the performance of the classification accuracy. As shown in Fig.6, when minibatch size was set to 22, CNN gained its best performance. While the classification of minibatch size that was set to 2 was 90.38%, which was the lowest classification accuracy. The classification accuracy of minibatch size which was set to 12 and 32 was 96.15% and 94.23% respectively. The poor accuracy of the above categories may be due to the difficulty in distinguishing VE-tongue signals from other categories.
Xinjiang goji sample was misclassified, and other goji samples were classified correctly. Confusion Matrix 18 Ningxia
18
0
0
0
Xinjiang
1
16
0
0
Gansu
0
0
10
0
Qinghai
0
0
0
7
Ningxia
Xinjiang
Gansu
16
True Label
14 12 10 8 6 4 2 0 Qinghai
Fig.7. Confusion matrix for CNN in testing set using Adam optimizer with 0.0005 learning rate Table 1. Indexes of models
Model Fig.6. Classification accuracy of different number of minibatch size in testing set
CNN
The evaluation indexes of classification model adopted in this study are Precision, Recall, F1-Score. Precision is the proportion of the number of cases classified correctly to the number of cases classified correctly. Recall is ratio of the number of positive cases correctly classified to the actual number of positive cases. F1-Score is weighted harmonic average of recall and precision. The specific formula is as follows:
Pprec =
Tp Tp + Fp
Precall = F1 =
Tp Tp + FN
DWTBPNN
BPNN
(2)
Geographical Origins of Wolfberry Ningxia goji Xinjiang goji Gansu goji Qinghai goji Ningxia goji Xinjiang goji Gansu goji Qinghai goji Ningxia goji Xinjiang goji Gansu goji Qinghai goji
Pprec (%)
Precall (%)
F1-Score (%)
95 100 100 100 100 83 77 100 100 100 45 64
100 94 100 100 94 88 100 57 94 12 100 100
97 97 100 100 97 86 87 73 97 21 62 78
3.2 BPNN classification results (3)
2 * Pprec * Precall Pprec + Precall
(4)
In Which, TP is the number of positive samples and correct classified wolfberry, TN is the number of positive samples misclassified, FN is the number of negative samples correctly classified, FP is the number of negative samples misclassified. The results of classification of goji samples employing CNN are shown in Table 1, in which the precision of classification of Ningxia goji got 95% precision. The precision of classification of Xinjiang goji, Gansu goji and Qinghai goji all got 100%. The recall of classification of Ningxia goji, Gansu goji and Qinghai goji all got 100% while Xinjiang goji got 94%. F1-Score of classification of Ningxia goji and Xinjiang goji got 97% while Gansu goji and Qinghai goji got 100% respectively.
In this section, BPNN without feature extraction and BPNN with discrete wavelet transform (DWT) are compared. Referring to the method to finding the most ideal BPNN model in Shi et al., we choose the final BPNN model is structured with a 120-17-4 topology and the activation function of BPNN is tanh (Shi et al., 2018). The activation function of output layer is sigmoid function. RMSprop is chose to be the optimizer. In this trail, all parameters of optimizer are default. The loss function of both models is categorical cross entropy. As shown in Fig.8, The performance of the BPNN model with DWT pre-processing is much better than the BPNN model with normalization pre-processing. The classification accuracy of the BPNN model with DWT pre-processing is 88.46%, while the classification accuracy of the BPNN model with normalization pre-processing is 48.08%. Fig.9 illustrated the confusion matrix of classification consequence of two models. It showed that most of wolfberry samples were classified correctly through the BPNN model with DWT feature extraction, however, the BPNN with normalization pre-processing cannot identify the wolfberry samples accurately.
The confusion matrix of testing set of classification of goji samples employing CNN is shown in Fig.7. There only one 400
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The indexes of BPNN with DWT feature extraction and BPNN with normalization pre-processing are shown in Table 1 respectively. Table 1 shows that the BPNN using DWT feature extraction is able to classify Ningxia goji and Qinghai goji accurately while the classification of Xinjiang goji and Gansu goji have a deviation and the BPNN with normalization pre-processing can classify Ningxia goji and Xinjiang goji correctly, however, Gansu goji and Qinghai goji are misclassified.
let alone the performance of BPNN without feature extraction. Its classification accuracy of 98.27% is much closer to the best performance in Yin (Yin et al., 2018). It employed SVM model with PSO algorithm as feature extraction method which gained 100% accuracy. The performance of the CNN model in this study has been evaluated through modifying its optimizer and learning rate. Actually, the optimizer and learning rate both influence the performance of the model. The Maximum accuracy which is 98.27% is gained from the model whose optimizer is set as Adam optimizer and its learning rate is set as 0.0005. We also referred to the method of Shi. This method employed BPNN and used DWT as feature extraction. This method was used to classify wolfberry samples with different geographical origins in this study (Shi et al., 2018). Its classification accuracy was 88.46% that much better than the BPNN only with normalization pre-processing whose classification accuracy is 48.08%. Qinghai and Xinjiang goji samples cannot classify accurately through BPNN with DWT feature extraction. It may be caused by the resemblance between VE-tongue signals of Qinghai goji and Xinjiang goji. This study shows CNN is much potential performance for addressing VEtongue signals. The most challenging thing is that our dataset is not sufficient. Therefore, our next mission is that we employ transfer learning to deal with this issue.
Fig.8. The performance comparison from testing set between BPNN with feature extraction—DWT and BPNN with normalization pre-processing
True Label
Confusion Matrix Ningxia
18
0
0
0
Xinjiang
0
15
2
0
Gansu
0
1
9
0
Qinghai
0
3
0
4
Gansu
Qinghai
18 16
ACKNOWLEDGMENTS
14
Ningxia Xinjiang
This work was supported by Shandong Provincial Natural Science Foundation, China (ZR2019MF024), Innovation Fund for Industry, University and Research of Science and Technology Development Centre of Ministry of Education (2018A02010) and the CERNET next generation Internet technology innovation project (NGII20170314)
12 10 8 6 4 2 0
REFERENCES
(a)
He, N., Yang, X., Jiao, Y., Tian, L., & Zhao, Y. (2012). Characterisation of antioxidant and antiproliferative acidic polysaccharides from Chinese wolfberry fruits. Food Chemistry, 133(3), 978-989. Li, H. Y., Chan, H. H., Chu, P. H., Chang, R. C. C., & So, K. F. (2015). Secondary degeneration after partial optic nerve injury and possible neuroprotective effects of Lycium barbarum (Wolfberry). In Lycium Barbarum and Human Health (pp. 135-151). Springer, Dordrecht. Bleibaum, R. N., Stone, H., Tan, T., Labreche, S., SaintMartin, E., & Isz, S. (2002). Comparison of sensory and consumer results with electronic nose and tongue sensors for apple juices. Food Quality and Preference, 13(6), 409-422. Gong, G., Dang, T., Deng, Y., Han, J., Zou, Z., Jing, S., ... & Wang, Z. (2018). Physicochemical properties and biological activities of polysaccharides from Lycium barbarum prepared by fractional precipitation. International journal of biological macromolecules, 109, 611-618. Yao, X., Peng, Y., Zhou, Q., Xiao, P., & Sun, S. (2010). Distinction of eight Lycium species by Fourier-transform infrared spectroscopy and two-dimensional correlation
True Label
Confusion Matrix Ningxia
18
0
0
0
Xinjiang
7
0
7
3
Gansu
0
0
0
10
Qinghai
0
0
0
7
Gansu
Qinghai
18 16 14
Ningxia Xinjiang
401
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(b)
Fig.9. (a) Confusion matrix for BPNN in testing set with DWT feature extraction; (b)Confusion matrix for BPNN in testing set with normalization pre-processing 4. CONCLUSIONS In this study, The CNN we employed performed ideally even though the VE-tongue signal of wolfberry samples did not employ the feature extraction methods. Its performance is much better than that of BPNN with DWT feature extraction, 401
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IR spectroscopy. Journal of Molecular Structure, 974(13), 161-164. Meng, J., Liu, Z., Gou, C. L., Rogers, K. M., Yu, W. J., Zhang, S. S., ... & Zhang, L. (2019). Geographical origin of Chinese wolfberry (goji) determined by carbon isotope analysis of specific volatile compounds. Journal of Chromatography B, 1105, 104-112. Kamilaris, A., & Prenafeta-Boldú, F. X. (2018). Deep learning in agriculture: A survey. Computers and Electronics in Agriculture, 147, 70-90. Witten, I. H., Frank, E., Hall, M. A., & Pal, C. J. (2016). Data Mining: Practical machine learning tools and techniques. Morgan Kaufmann.Dahl, G. E., Sainath, T. N., & Hinton, G. E. (2013, May). Improving deep neural networks for LVCSR using rectified linear units and dropout. In 2013 IEEE international conference on acoustics, speech and signal processing (pp. 8609-8613). IEEE. Krantz-Rülcker, C., Stenberg, M., Winquist, F., & Lundström, I. (2001). Electronic tongues for environmental monitoring based on sensor arrays and pattern recognition: a review. Analytica chimica acta, 426(2), 217-226. Tian, X., Wang, J., & Cui, S. (2013). Fast discriminating of purity on minced mutton using electronic tongue. Transactions of the Chinese Society of Agricultural Engineering, 29(20), 255-262. Gao, L., Wang, J., & Cui, S. (2012). Evaluation of fresh juice of strawberries at different degrees of ripeness using electronic nose and electronic tongue. Journal of Zhejiang University (Agriculture and Life Sciences), 38(6), 715-724. Yin, T., Guo, T., Ma, Z., Wang, Z., Sun, X., & Li, C. (2018). Classification of wolfberry with different geographical origins by using voltammetric electronic tongue. IFACPapersOnLine, 51(17), 654-659. Shi, Q., Guo, T., Yin, T., Wang, Z., Li, C., Sun, X., ... & Yuan, W. (2018). Classification of Pericarpium Citri Reticulatae of Different Ages by Using a Voltammetric Electronic Tongue System. INTERNATIONAL JOURNAL OF ELECTROCHEMICAL SCIENCE, 13(12), 11359-11374. Wang, J., Zhu, L., Zhang, W., & Wei, Z. (2019). Application of the voltammetric electronic tongue based on nanocomposite modified electrodes for identifying rice wines of different geographical origins. Analytica chimica acta, 1050, 60-70. Ferentinos, K. P. (2018). Deep learning models for plant disease detection and diagnosis. Computers and Electronics in Agriculture, 145, 311-318. Yu, J., Sharpe, S. M., Schumann, A. W., & Boyd, N. S. (2019). Deep learning for image-based weed detection in turfgrass. European Journal of Agronomy, 104, 78-84. Winquist, F., Wide, P., & Lundström, I. (1997). An electronic tongue based on voltammetry. Analytica chimica acta, 357(1-2), 21-31. Wei, Z., Wang, J., & Ye, L. (2011). Classification and prediction of rice wines with different marked ages by using a voltammetric electronic tongue. Biosensors and Bioelectronics, 26(12), 4767-4773.
402
Wei, Z., Yang, Y., Wang, J., Zhang, W., & Ren, Q. (2018). The measurement principles, working parameters and configurations of voltammetric electronic tongues and its applications for foodstuff analysis. Journal of food engineering, 217, 75-92. Novakowski, W., Bertotti, M., & Paixão, T. R. (2011). Use of copper and gold electrodes as sensitive elements for fabrication of an electronic tongue: Discrimination of wines and whiskies. Microchemical Journal, 99(1), 145151. Pigani, L., Foca, G., Ionescu, K., Martina, V., Ulrici, A., Terzi, F., ... & Seeber, R. (2008). Amperometric sensors based on poly (3, 4-ethylenedioxythiophene)-modified electrodes: Discrimination of white wines. analytica chimica acta, 614(2), 213-222. Tiwari, K., Tudu, B., Bandyopadhyay, R., & Chatterjee, A. (2013). Identification of monofloral honey using voltammetric electronic tongue. Journal of Food Engineering, 117(2), 205-210. Hu, H., Niu, Q., Pan, Y., & Wang, L. (2016). Voltammetric Electronic Tongue for Different Varieties of Rice Classification Based on Square Wave Voltammetry. In Mechanical Engineering and Control Systems: Proceedings of 2015 International Conference on Mechanical Engineering and Control Systems (MECS2015) (pp. 354-358). Haddi, Z., Alami, H., El Bari, N., Tounsi, M., Barhoumi, H., Maaref, A., ... & Bouchikhi, B. E. N. A. C. H. I. R. (2013). Electronic nose and tongue combination for improved classification of Moroccan virgin olive oil profiles. Food Research International, 54(2), 1488-1498. Bougrini, M., Tahri, K., Haddi, Z., El Bari, N., Llobet, E., Jaffrezic-Renault, N., & Bouchikhi, B. (2014). Aging time and brand determination of pasteurized milk using a multisensor e-nose combined with a voltammetric etongue. Materials Science and Engineering: C, 45, 348358. Nodera, H., Osaki, Y., Yamazaki, H., Mori, A., Izumi, Y., & Kaji, R. (2019). Deep learning for waveform identification of resting needle electromyography signals. Clinical Neurophysiology, 130(5), 617-623. Zhao M, Chen S, Dave Yuen (2019). Waveform classification and seismic recognition by convolution neural network. Chinese J. Geophys. (in Chinese), 62(1), 374-382. Al-Hiary, H., Bani-Ahmad, S., Reyalat, M., Braik, M., & ALRahamneh, Z. (2011). Fast and accurate detection and classification of plant diseases. International Journal of Computer Applications, 17(1), 31-38. Long, M., Ouyang, C., Liu, H., & Fu, Q. (2018). Image recognition of Camellia oleifera diseases based on convolutional neural network & transfer learning. Transactions of the Chinese Society of Agricultural Engineering (Transactions of the CSAE), 2018, 34(18): 194 － 201.
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Classification of wolfberry from different geographical origins by using Classification Classification of of wolfberry wolfberry from from different different geographical geographical origins origins by by using using electronic tongue and deep learning algorithm Classification of wolfberry from different geographical origins by using tongue and deep learning algorithm Classificationelectronic of wolfberry from different geographical origins by using electronic tongue and deep learning algorithm electronic tongue and deep learning algorithm 1 1 1 1 tongue and deep learning algorithm Wang11*. Wenhao Yuan Zhengweielectronic Yang11. Zhiqiang 1.Caihong Li1. Xiaoyu Jing1. Hui Han1
Zhengwei Zhiqiang Wang Wang11*. *. Wenhao Wenhao Yuan Yuan11.Caihong .Caihong Li Li11.. Xiaoyu Xiaoyu Jing Jing11.. Hui Hui Han Han11 Zhengwei Yang Yang11.. Zhiqiang 1 . Zhiqiang Wang *. Wenhao Yuan .Caihong Li . Xiaoyu Jing . Hui Zhengwei Yang School of Computer Science and Technology, Shandong University of Technology, Zibo,255019, 1 1 1 1 1 1 1 HanChina Zhengwei Yang . Zhiqiang Wang *. Wenhao Yuan .Caihong Li . Xiaoyu Jing . Hui Han 1
School School of of Computer Computer Science Science and and Technology, Technology, Shandong Shandong University University of of Technology, Technology, Zibo,255019, Zibo,255019, China China of Computer Science and Technology, Shandong University of Technology, Zibo,255019, China School author: of Computer Science and Technology, Shandong University of Technology, Zibo,255019, China *corresponding School of Computer Science and Technology, Shandong University of Technology, Zibo,255019 *corresponding Computer Science and Shandong *corresponding author: author: School School of ofChina Computer Science and Technology, Technology, Shandong University University of of Technology, Technology, Zibo,255019 Zibo,255019 (Tel: 13953336689; e-mail: [email protected]). *corresponding author: School ofChina Computer Science and Technology, Shandong University of Technology, Zibo,255019 (Tel: Science 13953336689; e-mail: [email protected]). [email protected]). (Tel: 13953336689; e-mail: *corresponding author: School ofChina Computer and Technology, Shandong University of Technology, Zibo,255019 China (Tel: 13953336689; e-mail: [email protected]). China (Tel: 13953336689; e-mail: [email protected]). Abstract: Wolfberry is a traditional Chinese Chinese food. food. Its Its price price and and function function are are closely closely related related to to its its Abstract: Wolfberry is Abstract: Wolfberry is aa traditional traditional Chinese food. Its priceinterests and function are closely related to its geographical origin. Illegal labeling driven by commercial has brought serious food safety Abstract: Wolfberry is a traditional Chinese food. Its priceinterests and function are closely related to its geographical origin. Illegal labeling driven by commercial has brought serious food geographical origin. Illegal labeling driven by commercial interests has electronic brought serious food safety safety Abstract: Wolfberry isconsumer a traditional Chinese Its price and function are closely to its problems and and damaged confidence. In food. this study, a voltammetric voltammetric tonguerelated (VE-tongue) geographical origin. Illegal labeling driven by commercial interests has brought serious food safety problems damaged consumer confidence. In this study, a electronic tongue (VE-tongue) problems and damaged consumer confidence. Indeveloped this study,toa voltammetric tongue (VE-tongue) geographical origin. Illegal labeling driven by commercial interests recognize has electronic brought serious food safety combined with deep learning algorithm was perform of different origins of problems damaged consumer confidence. this study, electronic tongue combined with deep learning algorithm was to perform of different origins of combined and with deep Training learning algorithm wasIn developed toaa voltammetric perform recognize recognize ofNetwork, different originswas of problems and damaged consumer confidence. Indeveloped this study,(Convolutional voltammetric electronic tongue (VE-tongue) (VE-tongue) wolfberry samples. of deep learning model Neural CNN) combined with deep learning algorithm was developed to perform recognize of different origins of wolfberry samples. Training of deep learning model (Convolutional Neural Network, CNN) was wolfberry samples. Training of deep learning model (Convolutional Neural Network, CNN) was combined with deep learning algorithm was developed to perform recognize of different origins of performed samples. with 260 260 Training wolfberry of samples which were from(Convolutional different geographical geographical origins samples. samples. To wolfberry deep learning model Neural Network, CNN) performed with wolfberry samples which from 44 origins To performed with 260 Training wolfberry samples which were from 4 different different geographical origins samples. To wolfberry samples. of deep learning learningwere model (Convolutional Neuralsize Network, CNN) was was find the best performance CNN model, rate, optimizer and minibatch were modified. The geographical performed with 260 wolfberry samples which were from 44 different origins samples. To find the best performance CNN model, learning rate, optimizer and minibatch size were modified. The find the best performance CNN model, learning rate, optimizer and minibatch size were modified. The performed with 260 wolfberry samples which were from different geographical origins samples. To best classification classification accuracyCNN of CNN CNN waslearning further rate, compared with and traditional machine learning method— find the best performance model, optimizer minibatch size were modified. The best accuracy of was further compared with traditional machine learning method— best classification accuracy oftransform CNN was furtherasrate, compared with and traditional machine learning method— find thewith bestdiscrete performance CNN model, learning optimizer minibatch size were modified. The BPNN wavelet (DWT) feature extraction method. The classification accuracy best classification accuracy of CNN was further compared with traditional machine learning method— BPNN with discrete wavelet transform (DWT) as feature extraction method. The classification accuracy BPNN with discrete wavelet (DWT) featureand extraction method. The classification accuracya best classification accuracy oftransform CNN was furtheras compared with traditional machine learning method— of CNN, DWT-BPNN and BPNN are 98.27%, 88.46% 48.08% respectively. This study provides BPNN with discrete wavelet transform (DWT) as featureand extraction method. The classification accuracyaa of CNN, DWT-BPNN and are 88.46% 48.08% respectively. This provides of CNN, DWT-BPNN and BPNN BPNN are 98.27%, 98.27%, 88.46% and 48.08% respectively. This study study provides BPNN with discrete wavelet transform (DWT) as extraction method. The classification accuracy novel method for recognition recognition and classification classification offeature wolfberry from different different geographical origins, whicha of CNN, DWT-BPNN and BPNN are 98.27%, 88.46% and 48.08% respectively. This study provides novel method for and of wolfberry from geographical origins, which novel method for recognition and classification of wolfberry from traceability different geographical origins, whicha of CNN, DWT-BPNN and BPNN are 98.27%, 88.46% and origin 48.08% respectively. This study provides holds great promise for its wide applications in geographical for agricultural products. novel method for recognition and classification of wolfberry from different geographical origins, which holds great promise for its wide applications in geographical origin traceability for agricultural products. holds great promise for its wide applications in geographical origin traceability for agricultural products. novel method for recognition and classification of wolfberry from different geographical origins, which origin traceability for products. holds for wide in © 2019,great IFACpromise (International of Automatic Control) Hosting by Elsevier Ltd.Deep Allagricultural rights reserved. Keywords: Convolutional neuralapplications network, Voltammetric electronic tongue, learning, Wolfberry, holds great promise for its itsFederation wide applications in geographical geographical origin traceability for agricultural products. Keywords: Convolutional neural network, Voltammetric electronic tongue, Deep learning, Wolfberry, Keywords: Convolutional neural network, Voltammetric electronic tongue, Deep learning, Wolfberry, Classification Keywords: Convolutional Classification Classification Keywords: Convolutional neural neural network, network, Voltammetric Voltammetric electronic electronic tongue, tongue, Deep Deep learning, learning, Wolfberry, Wolfberry, Classification Classification methods are are tedious tedious and and time time wasting, wasting, which cannot cannot meet 1. INTRODUCTION methods methods aredaily tedious and time wasting, which which cannot meet meet 1. the needs of rapid detection. 1. INTRODUCTION INTRODUCTION methods are tedious and time wasting, which cannot meet the needs of daily rapid detection. the needs of daily rapid detection. 1. INTRODUCTION methods are tedious and time wasting, which cannot meet Wolfberry is one traditional Chinese food. It is widely 1. INTRODUCTION the needs of daily rapid detection. Wolfberry is one traditional Chinese food. It is widely Electronic tongue (ET) is a smart recognition electronic Wolfberry is one ittraditional Chinese food. Itcarotenoids, is widely Electronic the needs oftongue daily rapid detection. consumed because is rich in polysaccharides, (ET) is aa smart recognition electronic Wolfberry is one it food. Itcarotenoids, is widely device Electronic tongue (ET) the is theory smart recognition consumed is rich polysaccharides, aiming to imitate imitate of taste taste sense of electronic human. It It consumed because because ittraditional isfunctional rich in in Chinese polysaccharides, Wolfberry is one traditional Chinese food.and Itcarotenoids, is widely flavonoids and other components it has the Electronic tongue (ET) is a smart recognition electronic device aiming to the theory of sense of human. consumed because it is rich in polysaccharides, carotenoids, device aiming to imitate the theory of taste sense of human. It flavonoids and other functional components and it has the Electronic tongue (ET) is a smart recognition electronic is capable of employing qualitative and quantitative analysis flavonoids and other functional components and it has the consumed because it is immunity, rich in polysaccharides, carotenoids, functions of enhancing anti-tumour and anti-aging device aiming to imitate the theory of taste sense of human. is capable of employing qualitative and quantitative analysis is capable of employing qualitative and quantitative analysis flavonoids and other functional components and it has the functions of enhancing immunity, anti-tumour and anti-aging device aiming and to imitate the theoryof of taste sense ofcompound human. It It for liquid semiliquid complex functions ofand enhancing immunity, anti-tumour and aanti-aging flavonoids otheret functional components and it valuable has the for (Li et al., 2015) (He al., 2012). Wolfberry is also is capable of employing qualitative and quantitative analysis liquid and semiliquid of complex compound for liquidof employing and Its semiliquid ofof complex compound functions of enhancing immunity, anti-tumour and anti-aging (Li et al., 2015) (He et al., 2012). Wolfberry is also a valuable is capable qualitative and quantitative analysis comprehensively. merits consist fast speed of detection, (Li et al., 2015) (He et al., 2012). Wolfberry is also a valuable functions ofinenhancing immunity, anti-tumour and anti-aging tonic, used Chinese and global markets. But the quality of for liquid semiliquid complex comprehensively. Its merits consist speed ofcompound detection, comprehensively. Itsstrong meritsobjectivity, consistof of fast fast speed detection, (Li et 2015) (He al., Wolfberry is also valuable tonic, used in and global markets. But the of for liquid ofand and semiliquid ofof complex compound convenience use, and good of repeatability. tonic, used in Chinese Chinese and2012). global markets. But the aaquality quality of convenience (Li et al., al., 2015) (He et et al., 2012). Wolfberry is also valuable Chinese wolfberry differs from place to place. dishonest comprehensively. Its merits consist of fast speed of detection, of use, strong objectivity, and good repeatability. convenience of use, strong objectivity, and good repeatability. tonic, used in Chinese and global markets. But the quality of Chinese wolfberry differs from place to place. dishonest comprehensively. Its merits consist of fast speed of detection, Nowadays, ET has strong been objectivity, employed in in the field of food, food, Chinese wolfberry differs from markets. place to But place. dishonest tonic, used in Chinese and global theadulterated quality of Nowadays, traders driven by commercial interests has convenience of use, and good repeatability. ET has been employed the field of Nowadays, ET has been employed in the field of food, Chinese wolfberry differs from place to place. dishonest traders driven by commercial interests has adulterated convenience of use, strong objectivity, and good repeatability. environment and biomedical analysis, etc. Krantz-Rülcker traders driven by commercial interests has adulterated Chinese wolfberry differs from place to place. dishonest wolfberries, which brought serious food safety problems and Nowadays, ET has been in the field environment and biomedical analysis, Krantz-Rülcker environment andto biomedical analysis, etc. Krantz-Rülcker traders driven by commercial interests has adulterated wolfberries, which serious food and Nowadays, ET has been employed employed in etc. the(Krantz-Rülcker field of of food, food, has applied ET monitor the environment wolfberries, which brought serious fooditsafety safety problems and traders driven by brought commercial interests hasproblems adulterated damaged consumer confidence. Hence, is very important to environment and biomedical analysis, etc. Krantz-Rülcker has applied to monitor the environment (Krantz-Rülcker wolfberries, which brought serious food safety problems and has applied ET to monitor the environment (Krantz-Rülcker damaged consumer confidence. Hence, it is very important to environment and biomedical analysis, etc. Krantz-Rülcker et al., 2001). Tian et al. and Gao et al. gained their own damaged consumer confidence. Hence, itsafety is veryproblems important to et wolfberries, which brought serious food and distinguish the quality of Chinese wolfberry that of applied ETTian to monitor the environment (Krantz-Rülcker al., 2001). et and Gao et al. gained their own damaged consumer confidence. Hence, it is veryfrom important to has et al., 2001). Tian et al. al.through and Gao et ET al. (Tian gained their own distinguish the quality of Chinese wolfberry from that of has applied ET to monitor the environment (Krantz-Rülcker success in field of food using et al. 2013) distinguish the quality of Chinese wolfberry from that of damaged consumer areas. confidence. Hence, it is very important to success different producing et al., 2001). Tian et al. and Gao et al. gained their in field of food through using ET (Tian et al. 2013) success in field of food through using ET (Tian et al. 2013) distinguish the quality of Chinese wolfberry from that of different producing areas. et al.,et2001). Tian et al. and Gao et al. gained their own own (Gao al., 2012). different producing areas.of Chinese wolfberry from that of (Gao distinguish the quality success in field of food through using ET (Tian et al. 2013) et al., 2012). (Gao et al., 2012). different producing areas. success in field of food through using ET (Tian et al. 2013) In the food industry, the appearance of sensory panels can different producing areas. (Gao et In the industry, the appearance of sensory panels The recognition recognition pattern system system is is much much vital vital for for the the VEIn the food food industry, the appearance ofsensory sensoryattributes panels can can (Gao et al., al., 2012). 2012).pattern describe quality, and the analysis of is The The recognition pattern system is much vital for the VEVEIn the food industry, the appearance of sensory panels can describe quality, and the analysis of sensory attributes is tongue system, which contains feature extraction and describe quality, and the analysis of sensory attributes is In the food industry, the appearance of sensory panels can helpful for quality assessment (Bleibaum et al., 2002). The recognition pattern system is much vital for the VEtongue system, which contains feature extraction and tongue system, which contains feature extraction and describe quality, and the analysis of sensory attributes is helpful for quality assessment (Bleibaum et al., 2002). The recognition pattern system is much vital for the VErecognition algorithm (Wei et al., 2018). Feature extraction is helpful for quality assessment (Bleibaum et al., 2002). describe quality, and the analysis of sensory attributes is Traditional wolfberry detection methods discriminate tongue system, which contains feature extraction and recognition algorithm (Wei et al., 2018). Feature extraction is recognition algorithm (Wei et al., 2018). Feature extraction is helpful for quality assessment (Bleibaum et al., 2002). Traditional wolfberry detection methods discriminate tongue system, which contains feature extraction and employed to extract useful information of VE-tongue because Traditional wolfberry detection methods discriminate helpful for quality assessment (Bleibaum et al., 2002). wolfberry of difference of colour, shape, odour, taste, etc. recognition algorithm (Wei et al., 2018). Feature extraction is employed to extract useful information of VE-tongue because employed to extract useful information of VE-tongue because Traditional wolfberry detection methods discriminate wolfberry of difference of colour, shape, odour, taste, etc. recognition algorithm (Wei et al., 2018). Feature extraction is there is much random noise in VE-tongue signals. Yin et al. wolfberry ofthis difference ofdetection colour, shape, odour, taste,some etc. there Traditional wolfberry methods discriminate However, subjective detection method has employed to extract useful information of VE-tongue because is much random noise in VE-tongue signals. Yin et al. wolfberry of difference of colour, shape, odour, taste, etc. there is much random noise in VE-tongue signals. Yin et al. However, this subjective detection method has some employed to extract useful information of VE-tongue because and Shi et al. employed discrete wavelet transform (DWT) to However, subjective detection method has wolfberry ofthis difference of colour, shape, odour, taste,some etc. thereShi problems, such being by emotions, nonis much random noise in VE-tongue signals. (DWT) Yin et al. et al. employed discrete wavelet transform to However, this as detection method has some and Shi et employed discrete wavelet (DWT) to problems, such assubjective being affected affected by personal personal emotions, non- and there is much random of noise VE-tongue Yin(Shi et al. achieve theal. extraction ET in signals (Yintransform etsignals. al.,2018) et problems, such as being affected by personal emotions, nonHowever, this subjective detection method has are some repeatability and so on. Other methods of analysis to and Shi et al. employed discrete wavelet transform (DWT) to achieve the extraction of ET signals (Yin et al.,2018) (Shi et achieve the extraction of ET signals (Yin et al.,2018) (Shi et problems, such as being affected by personal emotions, nonrepeatability and so on. Other methods of analysis are to and Shi et al. employed discrete wavelet transform (DWT) to al., 2018). Wang et al. employed “area method” to extract the repeatability and so on. Other methods of analysis are to problems, suchcontent as being affected by personal emotions, non- al., determine the of polysaccharides (Gong et al., 2017), achieve the extraction of ET signals (Yin et al.,2018) (Shi 2018). Wang et al. employed “area method” to extract the al., 2018). Wang et al. employed “area method” to extract the repeatability and so on. Other methods of analysis are to determine the content of polysaccharides (Gong et al., 2017), achieve the extraction of ET signals (Yin et al.,2018) (Shi et et area feature data et and Fast Fourier transform (FFT) to extract extract determine thelyceum content of polysaccharides (Gong et al., 2017), repeatability and soclassified on. Otherbymethods ofsecond analysis are to area the fruits of using the derivative al., 2018). Wang al. employed “area method” to extract the feature data and Fast Fourier transform (FFT) to area feature data feature and Fourier transform (FFT) to extract determine the content of polysaccharides (Gong et al., 2017), the fruits of lyceum classified by using the second derivative al., 2018). Wang et al.Fast employed “area method” to2019). extract the the significant data (Wang et al., The the fruits of lyceum classified by using the second derivative determine the content of polysaccharides (Gong et al., 2017), infrared spectrum and the by two-dimensional correlation area feature data and Fourier transform (FFT) to the significant data (Wang et 2019). The the significant feature data (Wang et al., al., 2019). The the fruits of lyceum classified using the derivative infrared spectrum and two-dimensional area feature data feature and Fast Fast Fourier transform (FFT) to extract extract recognition algorithm can be used to quantitatively determine infrared spectrum and etthe the two-dimensional correlation the fruitsspectrum of lyceum(Yao classified by usinggeographical the second secondcorrelation derivative infrared al.2010), origin of the significant feature data (Wang et al., 2019). The recognition algorithm can be used to quantitatively determine recognition algorithm can be used to quantitatively determine spectrum and the two-dimensional correlation infrared spectrum (Yao et al.2010), geographical origin of the significant feature data (Wang et al., 2019). The contentalgorithm of components components and realise classification classification infrared spectrum (Yao al.2010), geographical origin of the spectrum (goji) and et the two-dimensional correlation Chinese wolfberry discriminated through stable carbon recognition can be used to quantitatively determine content of and realise the content of components and realise classification infrared spectrum (Yao et al.2010), geographical origin of Chinese wolfberry (goji) discriminated through stable carbon recognition algorithm can be used to quantitatively determine (recognition, identification, discrimination). Several kinds of Chinese wolfberry (goji) discriminated throughetstable carbon infrared spectrum (Yao et al.2010), geographical origin of (recognition, isotopic ratios of wolfberry volatile (Meng al., 2019). the content of components and realise classification identification, discrimination). Several kinds of Chinese wolfberry (goji) discriminated through stable carbon (recognition, identification, discrimination). Several kinds of isotopic ratios of wolfberry volatile (Meng et al., 2019). the content of components and realise classification recognition algorithms have been employed for the isotopic ratios of (goji) wolfberry volatile (Meng al., carbon 2019). (recognition, identification, Chinese wolfberry discriminated throughetstable However, all these discrimination). Several for kindsthe of algorithms have been isotopic ratios of wolfberry volatile (Meng et al., 2019). recognition recognition algorithms have been employed employed for the However, all these (recognition, identification, discrimination). Several kinds of classification mission of VE-tongue such as principal However, all these isotopic ratios of wolfberry volatile (Meng et al., 2019). classification recognition algorithms have been employed for the mission of VE-tongue such as principal classification mission ofhave VE-tongue such as principal However, all these recognition algorithms been employed for the However, all these classification classification mission mission of of VE-tongue VE-tongue such such as as principal principal 2405-8963 © Copyright © 2019, 2019 IFAC IFAC (International Federation of Automatic Control) 397Hosting by Elsevier Ltd. All rights reserved. 1 1School
Copyright 2019 IFAC 397 Peer review© of International Federation of Automatic Copyright ©under 2019 responsibility IFAC 397Control. 10.1016/j.ifacol.2019.12.592 Copyright © 2019 IFAC 397 Copyright © 2019 IFAC 397
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component analysis (PCA), partial least squares discriminant analysis (PLS-DA), linear discriminant analysis (LDA), cluster algorithm (CA) and support vector machine (SVM). Novakowski employed PCA to discriminate wines and whiskies (Novakowski et al., 2011). Pigani employed PLSDA to discriminate white wines (Pigani, L et al., 2008). Tiwari employed LDA to identify miofloral honey (Tiwari, K et al., 2013). Haddi employed cluster algorithm (CA) to classifiy Moroccan virgin olive oil (Haddi, et al., 2013). Bougrini employed SVM to realise aging time and brand determination of pasteurized milk (Bougrini et al., 2014), and Hu employed ANN to classify different varieties of rice (Hu et al., 2016). Methods mentioned above need artificial feature extraction which are time-consuming and elaborate. There are many agriculture problems managed through classic algorithm of machine learning as well. Al-Hiary employed machine learning to realise fast and accurate detection and classification of plant diseases while it needed artificial feature extraction (Al-Hiary et al., 2011). In deep learning, it becomes unnecessary (Long et al., 2018).
electrode served as the auxiliary electrode. Yu et al. and Winquist et al. modified the excitation signal of the large amplitude pulse voltammetry (LAPV) (Yu et al., 2019) (Winquist et al., 1997). LAPV was added between the auxiliary and working electrodes, which generates through LabVIEW software with a scanning frequency of 10 Hz. The excitation signal range of each working electrode fluctuates from 1 V to -1 V with a potential step of 0.2V, which was shown in Fig2. The image of VE-tongue signal is shown in Fig.3.
Fig.1. VE-tongue self-developed system: (1). Reference electrode. (2) Auxiliary electrode. (3) Working electrode.
Deep learning is an emerging image processing and data analysis technology in recent years, which has broad application prospects and development potential (Kamilaris et al., 2018). Nowadays, many fields are beginning to employ deep learning to address pattern recognition, object detection and image understanding. Deep learning is about training neural network architectures consisting of several nonlinear processing layers. It gains large success because of the appearance of new model regularization techniques (Witten et al., 2016), improved design of nonlinearities (Dahl et al., 2013), the development of hardware and the progress of big data. Ferentinos developed a convolutional neural network models to perform detection and diagnosis of plant disease (Ferentinos et al., 2018). Yu has employed deep learning to achieve the weed detection in turfgrass (Yu et al., 2019). Nodera employed deep learning to identify the resting needle electromyography signals (Nodera et al., 2019). Zhao employed CNN to realise waveform classification and seismic recognition (Zhao et al., 2019). However, as far as we know, VE-tongue combined with deep learning methodology has not been used in the literature to classify wolfberry from different geographical sources.
幅值/V
1
0.2
0
-1
0
时间/S
1
Fig.2. The excitation signals generated by the LabVIEW software.
Fig.3. The curve of collected response signals 2.2 Sample preparation and dataset The signal of the VE-tongue of goji samples was gained through the method of Yin (Yin et al.,2018). There are 260 wolfberry samples from 4 different geographical origins. It contains 70 signals of Ningxia wolfberry, 60 signals of Xinjiang wolfberry, 70 signals of Gansu wolfberry and 60 signals of Qinghai wolfberry. In this study, the dataset was divided into training set (80% of dataset) and testing set (20% of dataset) respectively and all the models in this study were trained and tested by using training set and testing set respectively.
In this study, we come up with employing deep learning to realize the recognition of ET signals so that it becomes possible to avert the employment of feature extraction and the deep learning which was used to recognize the signals of ET has gotten good performance. 2. MATERIALS AND METHOD 2.1 Voltammetric electronic tongue As shown in Fig.1, The Voltammetric electronic tongue (VEtongue) consist of signal-conditioning circuit, sensor array, NI DAQ card (NI-6002) and LabVIEW software. The sensor array was a standard three-electrode system involving eight electrodes, which were platinum, gold, titanium, palladium, silver, tungsten, nickel, and glassy carbon. The Ag/AgCl electrode was used for reference electrode and the platinum
2.3 Data pre-processing Every point of the signals of the VE tongue fluctuate from -3 voltage to 4 voltage. It is hard for the pattern recognition system to process these signals. In order to eliminate the dimension effects between indicators, data normalization is 398
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Deep learning relies deeply on Graphics Processing Unit (GPU) with Compute Unified Device Architecture (CUDA) core enabled. In this study, one deep learning framework called Keras was employed. It performed on one computer Intel Core i7 with 3.7GHz, GTX 1080Ti GPU and 24G Random Access Memory (RAM).
needed to solve the comparability between data indicators. In this study, the formula of method of normalization employed is as follows: Xscale
=
x − xmin xmax − xmin
399
(1)
3. RESULT
The training set and the test set employ the normalization respectively. After data normalization, the original data are in the same order of magnitude, which is suitable for comprehensive comparative evaluation.
In this study, BPNN using normalization pre-processing and BPNN using DWT feature extraction were employed to compare with CNN. The best classification method of Yin was compared with the CNN model of this study as well (Yin et al.,2018).
2.4 Back propagation neural network
3.1 CNN classification result
The Artificial neural network (ANN) is mathematical model imitating the behaviour of characteristics of animal’s nervous system to process distributed parallel information. ANN processes superior non-linear mapping capabilities, fine fault tolerance, adaptive capabilities, and distributed storage. Back propagation neural network (BPNN) is one of the multiple layers forward neural networks. The BPNN generally consist of three kinds of layers: one input layer, one or more hidden layers and one output layer. To realize precise learning, the raw data are divided into two parts which are training set and testing set used to realize construction and quality check of obtained numerical models, respectively (Wei et al., 2011).
For getting the best performance, CNN performance was evaluated by modifying the hyperparameter and optimizer of output layer. Minibatch size was set as 22 and the number of epochs was set as 10. Minibatch divides training data into smaller batches, and updates model coefficients by gradient descent method. Three mainstream activation functions —— Adam, Stochastic Gradient Descent (SGD), Root Mean Square Prop (RMSprop) were used to compare the performance of CNN. The accuracy of classification gained from CNN using different optimizer was evaluated. Each optimizer was employed different value of learning rate. The minimum value of learning rate was set as 10-4 and the maximum learning rate value was set as 10-2. The value of learning rate was set as 0.0001, 0.0005, 0.001, 0.005, 0.01 in each trial. The accuracy of classification for different value of learning rate in testing set is plotted and shown in Fig.5. It shows that the Adam optimizer with 0.0005 learning rate has the better performance compared to RMSprop optimizer and SGD optimizer. Its classification accuracy is up to 98.27%. The best classification accuracy of RMSprop optimizer whose learning rate is set as 0.001 is 90.83%. The classification accuracy of SGD optimizer is unsatisfying whose best classification accuracy is 62.25%, which shows that SGD optimizer is not suitable for this study.
2.5 Convolutional neural network For achieving the classification of VE-tongue signals, the classical structure of CNN—LeNet5 which has gained great success on handwritten digital classification is employed. As shown in Fig.4, CNN structure modified consist of 4 convolutional layers, 3 max pooling layers, 2 fully connected layers and 1 output layers, which employ softmax activation function. The function of convolutional layer is aimed for extracting features and the pooling layer filters out some features by down sampling. Different from the twodimensional convolution of handwritten digital image classification, the VE tongue signals adopts one-dimensional convolution. The convolutional kernel of every convolutional layer is 5. The number of channels of Conv_1 and Conv_2 are 6 and the number of channels of Conv_3 and Conv_4 are 16. The fully connected layer’s structure is 120-34-4 and the activation function is tanh.
Fig.4. The structure of CNN
Fig.5. Accuracy of classification of testing set using Adam, RMSprop, SGD with different values of learning rate in testing set
2.6 System configuration
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In the next scenario, minibatch size was modified to analyse its importance in the performance of the classification accuracy. As shown in Fig.6, when minibatch size was set to 22, CNN gained its best performance. While the classification of minibatch size that was set to 2 was 90.38%, which was the lowest classification accuracy. The classification accuracy of minibatch size which was set to 12 and 32 was 96.15% and 94.23% respectively. The poor accuracy of the above categories may be due to the difficulty in distinguishing VE-tongue signals from other categories.
Xinjiang goji sample was misclassified, and other goji samples were classified correctly. Confusion Matrix 18 Ningxia
18
0
0
0
Xinjiang
1
16
0
0
Gansu
0
0
10
0
Qinghai
0
0
0
7
Ningxia
Xinjiang
Gansu
16
True Label
14 12 10 8 6 4 2 0 Qinghai
Fig.7. Confusion matrix for CNN in testing set using Adam optimizer with 0.0005 learning rate Table 1. Indexes of models
Model Fig.6. Classification accuracy of different number of minibatch size in testing set
CNN
The evaluation indexes of classification model adopted in this study are Precision, Recall, F1-Score. Precision is the proportion of the number of cases classified correctly to the number of cases classified correctly. Recall is ratio of the number of positive cases correctly classified to the actual number of positive cases. F1-Score is weighted harmonic average of recall and precision. The specific formula is as follows:
Pprec =
Tp Tp + Fp
Precall = F1 =
Tp Tp + FN
DWTBPNN
BPNN
(2)
Geographical Origins of Wolfberry Ningxia goji Xinjiang goji Gansu goji Qinghai goji Ningxia goji Xinjiang goji Gansu goji Qinghai goji Ningxia goji Xinjiang goji Gansu goji Qinghai goji
Pprec (%)
Precall (%)
F1-Score (%)
95 100 100 100 100 83 77 100 100 100 45 64
100 94 100 100 94 88 100 57 94 12 100 100
97 97 100 100 97 86 87 73 97 21 62 78
3.2 BPNN classification results (3)
2 * Pprec * Precall Pprec + Precall
(4)
In Which, TP is the number of positive samples and correct classified wolfberry, TN is the number of positive samples misclassified, FN is the number of negative samples correctly classified, FP is the number of negative samples misclassified. The results of classification of goji samples employing CNN are shown in Table 1, in which the precision of classification of Ningxia goji got 95% precision. The precision of classification of Xinjiang goji, Gansu goji and Qinghai goji all got 100%. The recall of classification of Ningxia goji, Gansu goji and Qinghai goji all got 100% while Xinjiang goji got 94%. F1-Score of classification of Ningxia goji and Xinjiang goji got 97% while Gansu goji and Qinghai goji got 100% respectively.
In this section, BPNN without feature extraction and BPNN with discrete wavelet transform (DWT) are compared. Referring to the method to finding the most ideal BPNN model in Shi et al., we choose the final BPNN model is structured with a 120-17-4 topology and the activation function of BPNN is tanh (Shi et al., 2018). The activation function of output layer is sigmoid function. RMSprop is chose to be the optimizer. In this trail, all parameters of optimizer are default. The loss function of both models is categorical cross entropy. As shown in Fig.8, The performance of the BPNN model with DWT pre-processing is much better than the BPNN model with normalization pre-processing. The classification accuracy of the BPNN model with DWT pre-processing is 88.46%, while the classification accuracy of the BPNN model with normalization pre-processing is 48.08%. Fig.9 illustrated the confusion matrix of classification consequence of two models. It showed that most of wolfberry samples were classified correctly through the BPNN model with DWT feature extraction, however, the BPNN with normalization pre-processing cannot identify the wolfberry samples accurately.
The confusion matrix of testing set of classification of goji samples employing CNN is shown in Fig.7. There only one 400
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The indexes of BPNN with DWT feature extraction and BPNN with normalization pre-processing are shown in Table 1 respectively. Table 1 shows that the BPNN using DWT feature extraction is able to classify Ningxia goji and Qinghai goji accurately while the classification of Xinjiang goji and Gansu goji have a deviation and the BPNN with normalization pre-processing can classify Ningxia goji and Xinjiang goji correctly, however, Gansu goji and Qinghai goji are misclassified.
let alone the performance of BPNN without feature extraction. Its classification accuracy of 98.27% is much closer to the best performance in Yin (Yin et al., 2018). It employed SVM model with PSO algorithm as feature extraction method which gained 100% accuracy. The performance of the CNN model in this study has been evaluated through modifying its optimizer and learning rate. Actually, the optimizer and learning rate both influence the performance of the model. The Maximum accuracy which is 98.27% is gained from the model whose optimizer is set as Adam optimizer and its learning rate is set as 0.0005. We also referred to the method of Shi. This method employed BPNN and used DWT as feature extraction. This method was used to classify wolfberry samples with different geographical origins in this study (Shi et al., 2018). Its classification accuracy was 88.46% that much better than the BPNN only with normalization pre-processing whose classification accuracy is 48.08%. Qinghai and Xinjiang goji samples cannot classify accurately through BPNN with DWT feature extraction. It may be caused by the resemblance between VE-tongue signals of Qinghai goji and Xinjiang goji. This study shows CNN is much potential performance for addressing VEtongue signals. The most challenging thing is that our dataset is not sufficient. Therefore, our next mission is that we employ transfer learning to deal with this issue.
Fig.8. The performance comparison from testing set between BPNN with feature extraction—DWT and BPNN with normalization pre-processing
True Label
Confusion Matrix Ningxia
18
0
0
0
Xinjiang
0
15
2
0
Gansu
0
1
9
0
Qinghai
0
3
0
4
Gansu
Qinghai
18 16
ACKNOWLEDGMENTS
14
Ningxia Xinjiang
This work was supported by Shandong Provincial Natural Science Foundation, China (ZR2019MF024), Innovation Fund for Industry, University and Research of Science and Technology Development Centre of Ministry of Education (2018A02010) and the CERNET next generation Internet technology innovation project (NGII20170314)
12 10 8 6 4 2 0
REFERENCES
(a)
He, N., Yang, X., Jiao, Y., Tian, L., & Zhao, Y. (2012). Characterisation of antioxidant and antiproliferative acidic polysaccharides from Chinese wolfberry fruits. Food Chemistry, 133(3), 978-989. Li, H. Y., Chan, H. H., Chu, P. H., Chang, R. C. C., & So, K. F. (2015). Secondary degeneration after partial optic nerve injury and possible neuroprotective effects of Lycium barbarum (Wolfberry). In Lycium Barbarum and Human Health (pp. 135-151). Springer, Dordrecht. Bleibaum, R. N., Stone, H., Tan, T., Labreche, S., SaintMartin, E., & Isz, S. (2002). Comparison of sensory and consumer results with electronic nose and tongue sensors for apple juices. Food Quality and Preference, 13(6), 409-422. Gong, G., Dang, T., Deng, Y., Han, J., Zou, Z., Jing, S., ... & Wang, Z. (2018). Physicochemical properties and biological activities of polysaccharides from Lycium barbarum prepared by fractional precipitation. International journal of biological macromolecules, 109, 611-618. Yao, X., Peng, Y., Zhou, Q., Xiao, P., & Sun, S. (2010). Distinction of eight Lycium species by Fourier-transform infrared spectroscopy and two-dimensional correlation
True Label
Confusion Matrix Ningxia
18
0
0
0
Xinjiang
7
0
7
3
Gansu
0
0
0
10
Qinghai
0
0
0
7
Gansu
Qinghai
18 16 14
Ningxia Xinjiang
401
12 10 8 6 4 2 0
(b)
Fig.9. (a) Confusion matrix for BPNN in testing set with DWT feature extraction; (b)Confusion matrix for BPNN in testing set with normalization pre-processing 4. CONCLUSIONS In this study, The CNN we employed performed ideally even though the VE-tongue signal of wolfberry samples did not employ the feature extraction methods. Its performance is much better than that of BPNN with DWT feature extraction, 401
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IR spectroscopy. Journal of Molecular Structure, 974(13), 161-164. Meng, J., Liu, Z., Gou, C. L., Rogers, K. M., Yu, W. J., Zhang, S. S., ... & Zhang, L. (2019). Geographical origin of Chinese wolfberry (goji) determined by carbon isotope analysis of specific volatile compounds. Journal of Chromatography B, 1105, 104-112. Kamilaris, A., & Prenafeta-Boldú, F. X. (2018). Deep learning in agriculture: A survey. Computers and Electronics in Agriculture, 147, 70-90. Witten, I. H., Frank, E., Hall, M. A., & Pal, C. J. (2016). Data Mining: Practical machine learning tools and techniques. Morgan Kaufmann.Dahl, G. E., Sainath, T. N., & Hinton, G. E. (2013, May). Improving deep neural networks for LVCSR using rectified linear units and dropout. In 2013 IEEE international conference on acoustics, speech and signal processing (pp. 8609-8613). IEEE. Krantz-Rülcker, C., Stenberg, M., Winquist, F., & Lundström, I. (2001). Electronic tongues for environmental monitoring based on sensor arrays and pattern recognition: a review. Analytica chimica acta, 426(2), 217-226. Tian, X., Wang, J., & Cui, S. (2013). Fast discriminating of purity on minced mutton using electronic tongue. Transactions of the Chinese Society of Agricultural Engineering, 29(20), 255-262. Gao, L., Wang, J., & Cui, S. (2012). Evaluation of fresh juice of strawberries at different degrees of ripeness using electronic nose and electronic tongue. Journal of Zhejiang University (Agriculture and Life Sciences), 38(6), 715-724. Yin, T., Guo, T., Ma, Z., Wang, Z., Sun, X., & Li, C. (2018). Classification of wolfberry with different geographical origins by using voltammetric electronic tongue. IFACPapersOnLine, 51(17), 654-659. Shi, Q., Guo, T., Yin, T., Wang, Z., Li, C., Sun, X., ... & Yuan, W. (2018). Classification of Pericarpium Citri Reticulatae of Different Ages by Using a Voltammetric Electronic Tongue System. INTERNATIONAL JOURNAL OF ELECTROCHEMICAL SCIENCE, 13(12), 11359-11374. Wang, J., Zhu, L., Zhang, W., & Wei, Z. (2019). Application of the voltammetric electronic tongue based on nanocomposite modified electrodes for identifying rice wines of different geographical origins. Analytica chimica acta, 1050, 60-70. Ferentinos, K. P. (2018). Deep learning models for plant disease detection and diagnosis. Computers and Electronics in Agriculture, 145, 311-318. Yu, J., Sharpe, S. M., Schumann, A. W., & Boyd, N. S. (2019). Deep learning for image-based weed detection in turfgrass. European Journal of Agronomy, 104, 78-84. Winquist, F., Wide, P., & Lundström, I. (1997). An electronic tongue based on voltammetry. Analytica chimica acta, 357(1-2), 21-31. Wei, Z., Wang, J., & Ye, L. (2011). Classification and prediction of rice wines with different marked ages by using a voltammetric electronic tongue. Biosensors and Bioelectronics, 26(12), 4767-4773.
402
Wei, Z., Yang, Y., Wang, J., Zhang, W., & Ren, Q. (2018). The measurement principles, working parameters and configurations of voltammetric electronic tongues and its applications for foodstuff analysis. Journal of food engineering, 217, 75-92. Novakowski, W., Bertotti, M., & Paixão, T. R. (2011). Use of copper and gold electrodes as sensitive elements for fabrication of an electronic tongue: Discrimination of wines and whiskies. Microchemical Journal, 99(1), 145151. Pigani, L., Foca, G., Ionescu, K., Martina, V., Ulrici, A., Terzi, F., ... & Seeber, R. (2008). Amperometric sensors based on poly (3, 4-ethylenedioxythiophene)-modified electrodes: Discrimination of white wines. analytica chimica acta, 614(2), 213-222. Tiwari, K., Tudu, B., Bandyopadhyay, R., & Chatterjee, A. (2013). Identification of monofloral honey using voltammetric electronic tongue. Journal of Food Engineering, 117(2), 205-210. Hu, H., Niu, Q., Pan, Y., & Wang, L. (2016). Voltammetric Electronic Tongue for Different Varieties of Rice Classification Based on Square Wave Voltammetry. In Mechanical Engineering and Control Systems: Proceedings of 2015 International Conference on Mechanical Engineering and Control Systems (MECS2015) (pp. 354-358). Haddi, Z., Alami, H., El Bari, N., Tounsi, M., Barhoumi, H., Maaref, A., ... & Bouchikhi, B. E. N. A. C. H. I. R. (2013). Electronic nose and tongue combination for improved classification of Moroccan virgin olive oil profiles. Food Research International, 54(2), 1488-1498. Bougrini, M., Tahri, K., Haddi, Z., El Bari, N., Llobet, E., Jaffrezic-Renault, N., & Bouchikhi, B. (2014). Aging time and brand determination of pasteurized milk using a multisensor e-nose combined with a voltammetric etongue. Materials Science and Engineering: C, 45, 348358. Nodera, H., Osaki, Y., Yamazaki, H., Mori, A., Izumi, Y., & Kaji, R. (2019). Deep learning for waveform identification of resting needle electromyography signals. Clinical Neurophysiology, 130(5), 617-623. Zhao M, Chen S, Dave Yuen (2019). Waveform classification and seismic recognition by convolution neural network. Chinese J. Geophys. (in Chinese), 62(1), 374-382. Al-Hiary, H., Bani-Ahmad, S., Reyalat, M., Braik, M., & ALRahamneh, Z. (2011). Fast and accurate detection and classification of plant diseases. International Journal of Computer Applications, 17(1), 31-38. Long, M., Ouyang, C., Liu, H., & Fu, Q. (2018). Image recognition of Camellia oleifera diseases based on convolutional neural network & transfer learning. Transactions of the Chinese Society of Agricultural Engineering (Transactions of the CSAE), 2018, 34(18): 194 － 201.