NIR reflectance spectroscopy with multivariate methods

Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy 230 (2020) 118005

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Rapid detection of adulteration of minced beef using Vis/NIR reflectance spectroscopy with multivariate methods Shizhuang Weng ⁎, Bingqing Guo, Peipei Tang, Xun Yin, Fangfang Pan, Jinling Zhao ⁎, Linsheng Huang, Dongyan Zhang National Engineering Research Center for Agro-Ecological Big Data Analysis & Application, Anhui University, 111 Jiulong Road, Hefei, China

a r t i c l e

i n f o

Article history: Received 10 September 2019 Received in revised form 8 December 2019 Accepted 27 December 2019 Available online xxxx Keywords: Visible/near-infrared reflectance spectroscopy Multivariate methods Rapid detection Adulterated minced beef

a b s t r a c t High economic returns induce the continuous occurrence of meat adulteration. In this study, visible/nearinfrared (Vis/NIR) reflectance spectroscopy with multivariate methods was used for the rapid detection of adulteration in minced beef. First, the reflectance spectra of different adulterated minced beef samples were measured at 350–2500 nm. Standardization and Savitzky–Golay (SG) smoothing were applied to reduce spectral interference and noise. Then, support vector machine (SVM), random forest (RF), partial least squares regression (PLSR), and deep convolutional neural network (DCNN) were adopted for adulteration type identification and level prediction. Moreover, principal component analysis (PCA), locally linear embedding (LLE), subwindow permutation analysis (SPA), and competitive adaptive reweighted sampling (CARS) were performed to eliminate redundant information. SG smoothing performed better on interference reduction. DCNN and PCA identified adulteration type with the accuracy above 99%. In adulteration level prediction, the RF with spectra of important wavelengths selected by CARS provided optimal performance for beef adulterated with pork, and coefficient of determination of prediction (R2P) and the root mean square error of prediction (RMSEP) were 0.973 and 2.145. The best prediction for beef adulterated with beef heart was obtained using PLSR and CARS with R2P of 0.960 and RMSEP of 2.758. Accordingly, Vis/NIR reflectance spectroscopy coupled with multivariate methods can provide the rapid and accurate detection of adulterated minced beef. © 2019 Published by Elsevier B.V.

1. Introduction Meat product quality is closely correlated to health and safety of consumers, public safety, and agricultural trade. In recent years, the consumption of meat products has been increasing in developed and developing countries. High economic benefits induce adulteration in the meat supply chain, especially in processed meat products [1]. The typical case of meat adulteration is the substitution of cheap meat or offal for expensive meat [2]. Beef is a widely consumed meat worldwide and provides protein, amino acids, and trace elements. Minced beef, the main ingredient of various processed meat products, such as hamburgers and meatballs, is frequently subjected to adulteration during production for monetary benefits [2,3]. Hence, detection of minced beef adulteration is indispensable and important in the protection of consumer rights and food safety [4]. With the removal of morphological characteristics and differences, adulterated minced meat is difficult to identify using intuitive perception alone [1,5]. Common chemical and biological analytical methods ⁎ Corresponding authors. E-mail addresses: [email protected] (S. Weng), [email protected] (J. Zhao) .

https://doi.org/10.1016/j.saa.2019.118005 1386-1425/© 2019 Published by Elsevier B.V.

that are specific and sensitive, such as chromatography, protein profiles, capillary electrophoresis, and deoxyribonucleic acid-based methods, are available to identify and quantify minced meat [6–9]. However, these methods are time-consuming, expensive, and not suitable for analysis of widespread adulteration problems. As such, simple, rapid, and economical methods are significant in the detection of adulteration [8,9]. Fast and simple technologies, such as imaging and spectroscopy, are attractive options [9,10]. Conventional imaging systems can be used to investigate the external characteristics of meat, but they cannot detect the adulteration of minced beef samples due to similar color and texture [10]. Raman spectroscopy (RS) and near infrared spectroscopy (NIRS) are common spectroscopic methods. RS can provide the specific molecular information of analytes in solid and aqueous samples with weak water interference [11]. It has been applied to the determination of beef adulteration with horsemeat and eating quality traits of beef loins [12,13]. However, for many biological samples, laser-induced fluorescence may overlap with the inherently weak Raman signal, thereby limiting its application in meat samples [14]. NIRS obtains molecular vibrational and rotational information by collecting the absorption spectra of objects. It is simple, accurate, and does not consume chemical reagents. Given these advantages, NIRS has been widely used to classify

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chicken breast and detect minced lamb, beef fraud, and animal meat muscles [4,15–18]. Nevertheless, it is limited to the near-infrared band, thereby possibly resulting in the loss of important information of other wavebands [10,19]. Hyperspectral imaging (HSI) is an emerging technology that integrates spectroscopy and image to simultaneously provide spectral and spatial information about the external and internal qualities of an object [20]. A hyperspectral image consists of hundreds of contiguous wavebands for each spatial position; each pixel of image is a spectroscopy on this position, which often covers the Vis/NIR range [21]. This image can be obtained using reflectance, interaction, and transmittance modes. The reflectance detection mode is widely applied to various fields, because it is less affected by factors, such as geometry and size or structures. Recently, hyperspectral reflectance imaging for assessment of meat quality and safety has attracted considerable attention and has been used to discriminate the following: mutton muscle; mutton quality attributes; water holding capacity, pH, color, and moisture content in beef; and grade of pork [4,21–25]. HSI measures spectral information with spatial distribution and generates hyperspectral cubes [23,24]. However, collecting hyperspectral cube data is timeconsuming and usually contains redundant information. Considering that minced meat samples are homogeneous, HSI is not needed. By contrast, hyperspectral non-imaging technology is more suitable for meat adulteration detection with direct and rapid acquisition of averaged Vis/NIR reflectance spectra of analyte in the perceptual region of the instrument. Multivariate methods, which are mainly used to preprocess spectra and establish qualitative and quantitative models, are indispensable when using in the automatic and intelligent analysis of Vis/NIR reflectance spectroscopy. First, standard normal variation, standardization, Savitzky–Golay (SG) smoothing, and multivariate scattering are used to remove background noise, light scattering, baseline migration, and temperature change [15,22]. As for high-dimension spectra, redundant and irrelevant information is inevitable, and methods of feature extraction and variable selection are adopted to reduce or eliminate interference. Principal component analysis (PCA), locally linear embedding (LLE), linear discriminant analysis, and isometric feature mapping have been adopted to the reduction of data dimensionality. Variable selection, such as subwindow permutation analysis (SPA), uninformative variable elimination, variable importance in projection, and competitive adaptive reweighted sampling (CARS) have been used to enhance the generalization ability of the model and to reduce overfitting [9,25–27]. The common methods of model establishment include support vector machine (SVM), random forest (RF), partial least squares regression (PLSR), and artificial neural network. Deep learning, a new branch of multivariate methods, has recently shown superior performance in image recognition and video analysis. It originates from the artificial neural network, which combines multi-layer network structures to learn the characteristics of abstract concepts for automatic selflearning from large amounts of data [28,29]. Many deep learning methods, such as recurrent neural network, deep belief network, stacked autoencoder, and deep convolutional neural network (DCNN), have been proposed. DCNN, a widely used and effective method, uses local connections to deal with spatial dependencies via sharing weights and can significantly reduce the number of network parameters [30]. Local receptive fields, weight sharing, and sub-sampling strengthens its large-scale image recognition, object detection, and video analysis [31–33]. Although DCNN obtained excellent results in the matrix form of image analysis, its application in spectral data analysis in vector form is rarely reported [34]. In exploring the use of DCNN in reflectance spectroscopy, the transformation of spectral data and design of appropriate network structures are the essential problems. Herein, we aimed to develop a fast and intelligent detection method for the determination of minced beef adulteration using reflectance spectroscopy and multivariate methods. The widely used beef, pork, beef heart, and beef tallow were selected as analysis objects. To

eliminate the interference of sample surface scattering and background noise on spectroscopy, standardization and SG smoothing were performed. PCA, LLE, SPA, and CARS were used to extract features and select important wavelengths in spectroscopy. Moreover, many modeling methods, such as SVM, RF, PLSR, and DCNN, were adopted to develop the models for detection of minced beef adulteration. 2. Materials and methods 2.1. Samples Beef loin, beef heart, beef tallow, and pork loin were purchased from local supermarkets. Two different batches of beef loin, beef heart, beef tallow and pork loin were separately minced using a grinder (SONBOR, TR-30, Germany). Minced beef loin and other minced meat were weighed individually, mixed thoroughly, and homogenized to obtain a total sample weight of 20 g. 2.1.1. Samples for identification of adulteration type Minced beef samples were mixed with potential adulterants, including pork, beef heart, and beef tallow, at 4%, 12%, 20%, and 30% (w/w). Minced beef loin adulteration was classified from type 0 to type 5, as follows: beef loin with pork loin; beef heart or beef tallow; beef loin with pork loin and beef heart; beef loin with pork loin and beef tallow; and beef loin with beef tallow, beef heart, and pork loin. Ten samples were prepared for each adulteration type. A total of 240 samples were obtained in this experiment. 2.1.2. Samples for prediction of adulteration level Beef loin samples were adulterated with pork loin or beef heart at a range of 0%–48% at approximately 4% increments. Adulteration rates higher than 48% were visible to the naked eye and were not considered. Six samples were prepared for each adulteration level, and 78 samples were obtained for beef loin with pork loin or beef heart. 2.2. Measurement of Vis/NIR reflectance spectra The spectra of adulterated meat samples were collected using a PSR3500® portable hyperspectral spectroradiometer (Spectral Evolutions, Lawrence, MA, USA) with DARWIN SP software (CanalStreet Lawrence, MA, USA). The instrument was equipped with three detectors, namely, a 512Si and two 256InGaAs array detectors, to measure reflectance spectra at 350–2500 nm. Spectral acquisition was performed through the use of optical fiber with an optical field angle of 8°, and the fiber optic probe is perpendicular to the sample at about 2 cm distance. Each minced sample was placed in a glass Petri dish with round pie. Fig. 1 shows the image of spectral acquisition system and spectral collection position on sample. For each individual sample, 10 reflectance spectra were measured with spectral resolution of 1 nm. Reflectance spectra can be affected by environmental factors, such as illumination and differences in the physical configuration of system. Whiteboard calibration was used to eliminate or minimize these effects. The whiteboard was calibrated according to the following equation (Eq. (1)): Re f tar ¼

DNtar  Re f white DNwhite

ð1Þ

where Reftar is the reflectance of meat samples, Refwhite is the reflectance of whiteboard, and DNtar and DNwhite are the initial radiation values of meat samples and whiteboard, respectively. 2.3. Spectral pre-treatment The optical path difference causes spectral changes. Thus, standardization is important in spectral measurements to correct negative

S. Weng et al. / Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy 230 (2020) 118005

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Fig. 1. Image of spectral acquisition system and spectral collection position on sample.

effects. SG smoothing was used to reduce random noises induced by the system's internal factors and to improve spectral signal-to-noise ratio, where SG smoothing with second-order smoothing polynomials through fifteen points was used to compute the zeroth derivative of the spectra. A large amount of redundant information is inevitable in reflectance spectroscopy of high dimension. Its removal is generally through feature extraction or variable selection, which is significant for establishing the simplified model and improving analysis robustness. PCA, LLE, SPA, and CARS were used for feature extraction or variable selection. PCA can convert multiple indices into a few comprehensive indices, namely, principal components. Each principal component can reflect most of the information in the original data, and the information contained does not repeat [9]. LLE divides the data space into n local spaces, and each local space is represented by the linear representation of the neighborhood. Then, the initial nonlinear data can be obtained by linear representation of the geometric features of the neighborhood [26]. SPA can find variables with minimal redundant information and collinearity from the spectral information. At the same time, the number of variables used in modeling can be greatly reduced [25]. CARS combines the exponential decay function and the adaptive reweighted sampling technique to optimize the variables with large absolute values of the regression coefficients in the PLS model and removes the variables with smaller weight [27].

2.4. Type identification and level prediction of adulteration 2.4.1. Machine learning methods Classification models for type identification of adulteration were developed by SVM and RF, and the regression models for level prediction of adulteration were developed by PLSR and RF. The basic idea of SVM is that through nonlinear mapping, the input vector is mapped to a high-dimensional feature space, in which the optimal separation hyperplane is constructed. The final decision function of SVM is only determined by a small number of support vectors, which determine the result [25,31,32]. RF is a non-linear ensemble method that constructs and subsequently averages a large number of randomized decision trees during training time for classification or regression [31,35,36]. The basic principle of the PLSR is to decompose many independent variables into a small number of latent variables (LVs) that represent the maximum covariance between the explanatory variables and the

response. Subsequently, these LVs are correlated with values of the target parameter [9,32,37]. 2.4.2. DCNN structures A DCNN classification model for the identification of adulteration type was established (Fig. 2A) with one input layer, four convolutional layers, three max-pooling layers, and three fully connected layers (Table S1). In the classification model, the convolutional layers were expressed using the following equation (Eq. (2)): 0 yj ¼ f @

X

1 xi  kij þ b j A

ð2Þ

i∈Mj

where xi is the ith input feature map, and kij is the convolution kernel used for connecting the ith input feature map and the jth output feature map, bj is the bias corresponding to the jth feature map, and f is the activation function. Here, rectified linear units (ReLU) were adopted as the activation function of convolutional layers and defined as follows Eq. (3):  f ðxÞ ¼

x; if x N0 0; if x ≤0

ð3Þ

To avoid overfitting, batch normalization was applied after each convolutional layer. The first three convolutional layers were followed by a max-pooling layer, which can reduce the dimensions of the hidden layer in the middle and the computation of the following layers. Hereafter, the feature maps in the convolutional layer were flattened to one dimension to adapt with the input of the latter fully connected layer. The three fully connected layers were employed to improve the learning ability of the model. Similarly, batch normalization was applied after the first two layers, and ReLU was used as an activation function. Dropout was used to avoid further overfitting. The last fully connected layer had six neurons (output) corresponding to six types. After the softmax function, the probability of each class was obtained, and softmax operated as a squashing function that re-normalizes the k-dimensional input vector z of real values to real values in the range [0, 1] as classification confidence scores. Classification loss was calculated by comparing the confidence scores and true labels of samples. The softmax function and loss function were expressed using the following

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Fig. 2. DCNN structures for classification (A) and regression (B) (DCNN: deep convolutional neural network).

equations (Eqs. (4) and (5)): ez j yðzÞ j ¼ PK K¼1

Loss ¼ −

X

ezk

for j ¼ 1; 2; ⋯; K

  label j log yðzÞ j

ð4Þ

ð5Þ

j

where zj represents the value of the jth neuron in the last fully connected layer, and k is the number of categories in classification. A DCNN regression model was designed (Fig. 2B) in such a way that the network structure consists of an input layer, three convolutional layers, two max-pooling layers, and three fully connected layers (Table S1). The convolutional layers are the same as those in the classification model, and ReLU was used as an activation function. The maxpooling layers followed the first two convolutional layers. Three fully connected layers were employed to connect all neurons in the previous layer to each neuron in the current layer, and ReLU was used as an activation function. The last fully connected layer only had one neuron,

which is the output regression analysis value of adulteration level. The conversion of data in vector form into data in the form of information matrix was necessary because of the special deep learning structures of DCNN. If the data vector of a sample was directly reshaped to the matrix (taking nine-dimensional data column vector as an example; Fig. 3), the correlation between the previous element (x3) and the latter element (x4) at the truncation was neglected, and the association between one particular element and the previous or latter element in same columns (x2 and x5, x5 and x8) was constructed. Such data processing cannot facilitate DCNN to learn the internal structure of original data well. In this study, the cross-product operation of vectors was performed to obtain an information matrix, which can ensure the characteristics and spatial correlation of original vector. The calculation was as follows Eq. (6): Y ¼ XX T

ð6Þ

where Y is the information matrix of the sample, and X is in the form of column vector for the same sample.

Fig. 3. Two transformation modes of original input vector suited for DCNN (taking nine-dimensional data column vector as an example) (DCNN: deep convolutional neural network).

S. Weng et al. / Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy 230 (2020) 118005

2.4.3. Performance evaluation To assess the effect of SVM, RF, PLSR, and DCNN, the obtained reflectance spectra of adulterated meat samples were divided into three sets. The calibration, validation, and prediction set were used to train the model, to adjust the model parameters, and to evaluate model performance, respectively. For type identification, 2400 spectra for six kinds of adulteration with different levels were divided into calibration (1800) and validation (300) set, and the remaining 300 spectra from the independent adulterated samples were used as prediction set. Classification performance was assessed using accuracy values of calibration (ACCc), validation (ACCv), and prediction (ACCp). Regarding the adulteration level prediction of single type, 390 and 260 spectra were selected as calibration and validation set, respectively. The other 130 spectra, which were measured from the different samples, were the prediction set. Performance was quantitatively evaluated using coefficients of calibration (R2C), validation (R2V), and prediction (R2P) and root mean square errors of calibration (RMSEC), validation (RMSEV), and prediction (RMSEP). SVM, RF, PLSR, and DCNN models were performed using Keras and Tensorflow on a workstation with GeForce RTX 2080Ti graphics processing unit. All methods were performed using scikitlearn. The computer used an Intel core i7-8700 CPU with a main frequency of 3.7 GHZ.

5

valley at around 1900 nm is observed for the absorption bands of proteins due to N\\H overtones [16,38,39]. In the Vis-NIR region, the behavior of beef samples adulterated with pork loin and beef heart is distinguishable at 400–550, 750–920, and 1000–1250 nm. The minced beef loin adulterated with beef tallow has shown a clear distinct reflectance trend at 480–530 and 750–1900 nm regions. The reflectance values of beef loin, pork loin, and beef heart are between those of beef loin with pork loin or beef heart. The reflectance trends of the other two adulterated types are similar to those of beef loin adulterated with pork loin and beef heart. The spectra of beef loin adulterated with beef heart in different proportions (0%–48%) are shown in Fig. 4B. The differences that emerged in the fingerprint region are proportional to adulteration degree. With increasing adulteration ratio, the holistic reflectivity curve increased gradually. As for the spectra of minced beef loin adulterated with pork loin (Fig. 4C), the behavior of beef samples adulterated with pork loin was similar to that of minced beef loin adulterated with beef heart but with a relatively smaller change. These results preliminarily proved that Vis/NIR reflectance spectroscopy is a feasible method for detecting adulteration in minced beef.

3.2. Discrimination of minced beef adulteration 3. Results and discussion 3.1. Spectral properties of pure and adulterated samples The spectra of minced beef loin with and without adulterants were measured, as shown in Fig. 4A. As seen from the figure, the overall trends of spectra are similar. The main difference is spectral reflectance, and some regions differ in spectral shape. As for the visible region, the absorption band at 450 nm is associated with the Soret absorption band of erythrocyte hemoglobin [38], and the bands at 540 and 560 nm are attributed to respiratory pigments, principally myoglobin or deoxymyoglobin [39]. Meanwhile, the absorption peaks at 525 and 570 nm are associated with metmyoglobin and oxymyoglobin, respectively, whereas the peak around 650 nm is due to O\\H second stretching overtone [16]. In the NIR region, the absorption peak at 800 nm is associated with O\\H first stretching overtone. The peaks at 856 and 1098 nm are assigned to the C\\H third overtone [16]. The peaks around 1225 and 1440 nm are attributed to O\\H first stretching overtone and water absorption, respectively. And the peak at 980 nm is related to the second overtone of O\\H bending [39]. The O\\H sensitive regions have high absorbance values in NIR signals because of the relatively high moisture content in meat samples. Moreover, a small

The spectra of pure beef loin, beef loin with pork loin, beef loin with beef heart, beef loin with beef tallow, beef loin with pork loin and beef heart, beef loin with pork loin and beef tallow, and beef loin with beef tallow and beef heart were measured, and each kind was labeled as one type (type 0 to type 5). RF and SVM were first used to develop the classification models for discrimination of adulteration types. Standardization and SG smoothing were applied to reduce noise and background interference. From the spectra in Fig. 5, spectral reflectance is compressed after standardization of the original spectra (Fig. 5A). The spectra became smoother after SG smoothing (Fig. 5B). The experiment's results are shown in Table 1, and the settings for model parameter are shown in Table S2. The RF model with raw spectra has classified the adulteration samples with ACCc, ACCv, and ACCp of 97.89%, 97.67%, and 95.33%. Standardization and SG smoothing can improve the accuracy of RF models. Best performances were obtained using SG smoothingpretreated spectra as evidenced by ACCc, ACCv, and ACCp of 98.46%, 95.67%, and 96.67%, respectively. A similar trend was observed in SVM models. The optimal SVM model was developed with spectra pre-treated by SG smoothing, which provided ACCc, ACCv, and ACCp of 96.67%, 95.33% and 95.13%, respectively. SG smoothing

Fig. 4. Spectra of various minced beef loin (A) and spectra of beef loin adulterated with beef heart (B) and pork loin (C) in the range of 0%–48%.

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Fig. 5. Spectra of minced beef loin and mined beef loin with different adulterants after standardization (A) and SG smoothing (B) (SG smoothing: Savitzky–Golay smoothing).

obtains better results. Thus, the spectra were pre-treated by SG smoothing in the follow-up analysis. Redundant information is inevitable when the spectra are of high dimension. The input of DCNN comes from the transposition and point multiplication of spectra, and high-dimension spectra contribute to the difficulty of calculating the abovementioned process. Thus, PCA and LLE were used to extract the main information and reduce the dimension of spectra. The spectral dimension decreased from 2151 to 56 with PCA by retaining 99% of the cumulative contribution rate. It has also decreased from 2151 to 56 with LLE. Then, the processed spectra were used to develop the classification models of adulteration using RF, SVM, and DCNN. The results are shown in Table 2, and the settings for each model parameter are shown in Table S3. As seen from the table, with PCA, the accuracy values of RF and SVM models degraded slightly to 95.33% and 96.33% (ACCc), 94.33% and 94.65% (ACCv), and 91.67% and 94.48% (ACCp). LLE improved the performance of SVM model by about by 1.5% and had a negative effect on the RF model. The best discrimination results were obtained using DCNN and PCA, as given by ACCc of 99.50%, ACCv of 99.17%, and ACCp of 99.00%, which were significantly better than RF and SVM because of DCNN's powerful extraction ability. However, DCNN and LLE obtained inferior results, which could be due to the fact that the features extracted by LLE were unfit for the DCNN structure. The accuracy-epoch and loss-epoch curves for DCNN with PCA are shown in Fig. 6A. After 23 epochs of training, the performance of DCNN model stabilized, thereby suggesting that the designed DCNN has been trained completely. The best classification result of DCNN model is demonstrated in Fig. 6B, wherein the black circle represents the actual label of sample, and the red circle represents the

3.3. Prediction of minced beef adulteration level After obtaining the adulteration types, further analysis of the level of minced beef adulteration was needed. The spectra of beef loin with pork loin and beef loin with beef heart were measured at adulteration ratios of 0%, 4%, 8%, 12%, 16%, 20%, 24%, 28%, 32%, 36%, 40%, 44%, and 48%. PCA, LLE, SPA, and CARS were used for feature extraction or variable selection. For beef loin adulterated with pork loin, the spectral dimension decreased from 2151 to 60 with PCA and LLE, from 2151 to 82 with SPA, and from 2151 to 166 with CARS. The important wavelengths selected by SPA and CARS are shown in Table S4. For beef loin adulterated with beef heart, spectral dimension decreased from 2151 to 42 with PCA and LLE, from 2151 to 32 with SPA, and from 2151 to 81 with CARS. The important wavelengths selected by SPA and CARS are shown in Table S5. Then, PLSR, RF, and DCNN were used to develop the regression models for the quantitative analysis of adulteration level (Tables 3 and

Table 2 Classification results of RF, SVM, and DCNN models using PCA and LLE.

Table 1 Classification results of RF and SVM models using different pre-treatments. Model

Pre-treatment

ACCc (%)

ACCv (%)

ACCp (%)

RF

RAW Standardization SG smoothing RAW Standardization SG smoothing

97.89 98.01 98.46 95.67 91.82 96.67

97.67 97.82 95.67 92.67 90.18 95.33

95.33 95.44 96.67 92.00 88.06 95.13

SVM

predicted label of sample. Sample classification was correct if the black and red circles coincide and was wrong if the circles did not coincide. The figure shows that a sample in type 2 was misclassified into type 3, and two samples in type 4 were misclassified into type 1. Thus, Vis/NIR reflectance spectroscopy and DCNN with PCA provided accurate discrimination of minced beef adulteration. More recently, Maestresalas et al. classified minced beef adulterated with pork meat by NIRS, best results were achieved with 80% correctly classified samples [17]. The classification effect was lower than the result of our experiment.

ACCc: accuracy values of calibration; ACCv: accuracy values of validation; ACCp: accuracy values of prediction; RF: random forest; SVM: support vector machine; SG smoothing: Savitzky–Golay smoothing.

Model

Pre-treatment

ACCc (%)

ACCv (%)

ACCp (%)

RF

PCA LLE PCA LLE PCA LLE

95.33 97.33 96.33 98.12 99.50 95.82

94.33 95.33 94.65 97.00 99.17 89.76

91.67 95.18 94.48 96.67 99.00 89.33

SVM DCNN

ACCc: accuracy values of calibration; ACCv: accuracy values of validation; ACCp: accuracy values of prediction; RF: random forest; SVM: support vector machine; DCNN: deep convolutional neural network; PCA: principal component analysis; LLE: locally linear embedding.

S. Weng et al. / Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy 230 (2020) 118005

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Fig. 6. Accuracy-epoch and loss-epoch curves for DCNN with PCA (A) and the best classification result of DCNN model (B) (DCNN: deep convolutional neural network; PCA: principal component analysis).

4), and the settings of each model parameter are shown in Tables S6 and S7. For beef loin adulterated with pork loin, the PLSR model with raw spectra revealed RMSEV, R2V, RMSEP, and R2P of 4.348, 0.918, 4.246, and 0.928, respectively. After PCA, LLE, and CARS, the performances of PLSR models for the validation and prediction sets improved. The best result was obtained by PLSR and CARS, which had RMSEV, R2V, RMSEP, and R2P of 3.012, 0.962, 2.847, and 0.965, respectively. For RF models, the use of PCA, LLE, SPA, and CARS reduced prediction error. The best RF model, which had RMSEV = 2.947, R2V = 0.963, RMSEP = 2.145, and R2P = 0.973, was developed with CARS (Fig. 7A). The predicted error was relatively higher in the DCNN models. DCNN with PCA provided better analysis performance as evidenced by RMSEV = 3.435, R2V = 0.950, RMSEP = 3.548, and R2P = 0.942 (Fig. 7B). RF and CARS can predict the adulteration level of beef loin with pork loin with the lowest error. In beef loin adulterated with beef heart, PLSR with raw spectra obtained RMSEV = 4.367, R2V = 0.915, RMSEP = 4.504, and R2P = 0.912.

RF obtained RMSEV = 4.035, R2V = 0.937, RMSEP = 4.638, and R2P = 0.896. The application effects of PCA, LLE, SPA, and CARS on PLSR, RF, and DCNN were similar to those of the abovementioned experiments. The best PLSR model, which had RMSEV = 2.546, R2V = 0.978, RMSEP = 2.758, and R2P = 0.960, was constructed with CARS of Fig. 7C. For the RF model, the best performance was obtained using LLE as evidenced by RMSEV = 3.464, R2V = 0.939, RMSEP = 2.976, and R2P = 0.957. For the DCNN model, the optimal prediction result was obtained using PCA, which had RMSEV = 3.127, R2V = 0.955, RMSEP = 3.628, and R2P = 0.936 (Fig. 7D). Overall, PLSR and CARS provided the best analysis effect for adulteration of beef loin with beef heart. Likewise, Rady and Adedeji studied the detection of pork adulteration in minced beef of 1–50% using Vis/NIR. And a PLSR model was performed. Authors reported good prediction results with a R2P of 0.85 and a RMSEP of 0.40 [38]. The prediction effect was lower than the result of our experiment. Although the prediction performance of DCNN models was generally worse than those of the RF and PLSR models, the prediction performance gap was small (Fig. 7). The phenomenon was attributed to the

Table 3 Prediction results of beef loin adulterated with pork loin using different pre-treatments (Unit of adulteration level: %).

Table 4 Prediction results of beef loin adulterated with beef heart (Unit of adulteration level: %).

Model

PLSR

RF

DCNN

Pre-treatment

RAW PCA LLE SPA CARS RAW PCA LLE SPA CARS RAW PCA LLE SPA CARS

Calibration set

Validation set

Prediction set

R2C

RMSEC

R2V

RMSEV

R2P

RMSEP

0.993 0.957 0.969 0.955 0.982 0.988 0.987 0.993 0.983 0.988 – 0.957 0.821 0.901 0.912

1.216 3.625 2.741 3.243 2.017 1.644 1.705 1.244 1.937 1.642 – 3.306 6.318 4.436 4.276

0.918 0.953 0.976 0.926 0.962 0.941 0.924 0.943 0.893 0.963 – 0.950 0.795 0.898 0.907

4.348 3.449 2.282 3.944 3.012 3.758 4.136 3.535 4.763 2.947 – 3.435 6.846 4.636 4.343

0.928 0.940 0.961 0.896 0.965 0.937 0.944 0.952 0.959 0.973 – 0.942 0.772 0.876 0.901

4.246 3.618 3.049 4.658 2.847 3.723 3.528 3.142 3.129 2.145 – 3.548 7.156 4.926 4.425

R2C: coefficient of determination of calibration; R2V: coefficient of determination of validation; R2P: coefficient of determination of prediction; RMSEC: the root mean square error of calibration; RMSEV: the root mean square error of validation; RMSEP: the root mean square error of prediction; PLSR: partial least squares regression; RF: random forest; DCNN: deep convolutional neural network; PCA: principal component analysis; LLE: locally linear embedding; SPA: subwindow permutation analysis; CARS: competitive adaptive reweighted sampling.

Model

PLSR

RF

DCNN

Pre-treatment

RAW PCA LLE SPA CARS RAW PCA LLE SPA CARS RAW PCA LLE SPA CARS

Calibration set

Validation set

Prediction set

R2C

RMSEC

R2V

RMSEV

R2P

RMSEP

0.995 0.952 0.942 0.929 0.981 0.984 0.988 0.988 0.979 0.986 – 0.978 0.821 0.945 0.956

1.046 3.328 3.346 4.147 2.036 1.958 1.776 1.584 1.963 1.864 – 2.816 6.373 3.547 3.364

0.915 0.940 0.927 0.904 0.978 0.937 0.954 0.939 0.917 0.926 – 0.955 0.795 0.923 0.947

4.367 3.614 3.735 4.358 2.546 4.035 3.362 3.464 2.335 3.863 – 3.127 6.859 3.847 3.524

0.912 0.928 0.925 0.848 0.960 0.896 0.948 0.957 0.839 0.900 – 0.936 0.772 0.911 0.935

4.504 4.046 3.713 4.939 2.758 4.638 3.354 2.976 3.947 4.526 – 3.628 7.115 4.227 3.736

R2C: coefficient of determination of calibration; R2V: coefficient of determination of validation; R2P: coefficient of determination of prediction; RMSEC: the root mean square error of calibration; RMSEV: the root mean square error of validation; RMSEP: the root mean square error of prediction; PLSR: partial least squares regression; RF: random forest; DCNN: deep convolutional neural network; PCA: principal component analysis; LLE: locally linear embedding; SPA: subwindow permutation analysis; CARS: competitive adaptive reweighted sampling.

8

S. Weng et al. / Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy 230 (2020) 118005

Fig. 7. Predicted results of beef loin adulterated with pork loin by RF and CARS (A) and DCNN and PCA (B) in the prediction set; predicted results of beef loin adulterated with beef heart using PLSR and CARS (C) and DCNN and PCA (D) in the prediction set (R2P: coefficient of determination of prediction; RMSEP: the root mean square error of prediction; RF: random forest; CARS: competitive adaptive reweighted sampling; DCNN: deep convolutional neural network; PCA: principal component analysis; PLSR: partial least squares regression).

limited sample size, because DCNN was more suitable for big scale data. With increasing sample size, DCNN was still a good choice.

may influence results. Thus, future research should be performed to investigate the influence of the abovementioned factors to improve robustness.

4. Conclusions Author contributions In this study, a simple method using Vis/NIR reflectance spectroscopy with multivariate methods was developed for the rapid detection of minced beef adulteration. For type identification, DCNN with PCA obtained the best discrimination results with ACCc of 99.50%, ACCv of 99.17%, and ACCp of 99.00%. In adulteration level prediction, RF and CARS obtained the optimal performance as evidenced by RMSEV = 2.947, R2V = 0.963, RMSEP = 2.145, and R2P = 0.973 for beef loin adulterated with pork loin. PLSR and CARS achieved the best analysis effect for adulteration of beef loin with beef heart, as shown by RMSEV, R2V, RMSEP, and R2P of 2.546, 0.978, 2.758, and 0.960, respectively. In addition, DCNN can accurately identify adulterants contained in minced beef, but its performance in predicting the adulteration level in minced beef is lower than those of RF and PLSR models. Accordingly, Vis/NIR reflectance spectroscopy with multivariate methods led to the accurate and rapid detection of minced beef adulteration. It can be expected to discriminate other agricultural adulteration and poor quality of products. However, some factors, such as meat species and temperature,

Conceptualization, S.W. and B.G.; Methodology, X.Y. and B.G.; Software, F.P. and P.T.; Validation, S.W. and B.G.; Formal analysis, B.G.; Investigation, B.G.; Data curation, B.G.; Writing—original draft preparation, B.G.; Writing—review and editing, S.W. and B.G.; Visualization, B.G.; Supervision, J.Z. and D.Z; Project administration, B.G.; Funding acquisition, S.W. and L.H. Declaration of competing interest All authors declare that there is no conflict of interest. Acknowledgements This study is supported by Natural Science Foundation of Anhui Province (No. 1708085QF134), Anhui Provincial Major Scientific and Technological Special Project (No. 17030701062), National Natural

S. Weng et al. / Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy 230 (2020) 118005

Science Foundation of China (Nos. 3170123 and 61672032), Natural Science Research Project of Anhui Provincial Education Department (KJ2018A0009), Innovation and Entrepreneurship Program for Oversea Returnee in Hefei, and National Key Research and Development Program of China (2016YFD0800904). Appendix A. Supplementary data Supplementary data to this article can be found online at https://doi. org/10.1016/j.saa.2019.118005.

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