Nondestructive detection of frying times for soybean oil by NIR-spectroscopy technology with Adaboost-SVM (RBF)

Journal Pre-proof Nondestructive detection of frying times for soybean oil by NIR-spectroscopy technology with Adaboost-SVM (RBF) Jinlong Li, Laijun Sun, Ruonan Li

PII:

S0030-4026(20)30082-6

DOI:

https://doi.org/10.1016/j.ijleo.2020.164248

Reference:

IJLEO 164248

To appear in:

Optik

Received Date:

4 December 2019

Accepted Date:

16 January 2020

Please cite this article as: Li J, Sun L, Li R, Nondestructive detection of frying times for soybean oil by NIR-spectroscopy technology with Adaboost-SVM (RBF), Optik (2020), doi: https://doi.org/10.1016/j.ijleo.2020.164248

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Nondestructive detection of frying times for soybean oil by NIR-spectroscopy technology with Adaboost-SVM (RBF) Jinlong Lia, Laijun Suna*, Ruonan Lib a College b School

of Electronic Engineering, Heilongjiang University, Harbin, 150080

of Computer Science and Technology, Heilongjiang University, Harbin, 150080

Abstract: Soybean oil with fried repeatedly at high temperature produces harmfully substances to threaten human health. In this paper, Adaboost-SVM (RBF) classification model combined with near infrared spectroscopy (NIRS) was proposed to detect frying times of soybean oil based on the optimal selection of crucial parameters and the combination of Adaboost, support vector machine (SVM) of radial basis function (RBF) as the kernel function. Herein, four modes were designed to divide 15 fryings into primary and secondary stages, then the created classification models were compared. Specially, after classifying as I, II, III class by mode 3, the accuracy of

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primary model established by Adaboost reached 98%. Afterwards, spectra with pre-processed by various methods were analyzed. As expected that the performance of secondary models were significantly reinforced, especially the

accuracy of SVM (RBF) model increased from 76% to 88.89%. Additionally, successive projections algorithm (SPA) was applied to obtain relevant wavelengths. When the dimensions of I, II, III class were severally reduced to

14, 6, 8, the accuracy of SVM (RBF) model was the best average value of 93.33%. Finally, the results of external

Keywords: Soybean oil; Frying times; Adaboost; SVM; SPA

1 Introduction

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validation indicated that the accuracy of primary and secondary models reached 95.55%, 91.11%, respectively.

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As a basic way of food processing, frying has been widely used in home cooking and industrial production. In general, the oil temperature of deep-fried food is over 180℃ [1], which might bring about a series of chemical reactions and produce some volatile substances [2]. Driven by profits, some vendors used frying oil repeatedly, which resulted in a sharply increasing of acid value and total polar compound [3], and seriously affected the quality of food. At present, most of studies are mainly centered on the influence of frying on the change of substance. For instance, after analyzing the quality of fried edible oil, Solomon Karimi et al. [4] confirmed that the change of thiobarbituric acid reactive substance could reflect the overall degree of peroxidatic reaction of grease. J. Chung et al. [5] indicated that when dough was fried at 160℃, the content of conjugated dienoic acid in soybean oil was went up as time went, but the content of conjugated dienoic acid and p-anisole was added sequentially. Although the quality of frying oil can be detected by chemical methods, they are restricted due to the high-cost and severe pollution. NIRS technology has been extensively utilized in qualitative analysis of organic materials because of its advantage like nondestructive and high-efficiency. There are many researchers who have made use of NIRS to quality inspection and adulteration identification of oil. Katrul Nadia Basri et al. [6] found that NIRS combined with soft independent modeling class analog (SIMCA) could be applied to identify palm oil, the accuracy of classification model was over 95%. For predicting the content of gutter oil, Bing-fang et al. [7] established the quantitative analysis models of partial least squares and back propagation artificial neural network by using of NIRS and fiber-optic sensing technology, as concluded that the determination coefficient of validation set were 0.961 and 0.952, respectively. Moreover, when SIMCA classification model was created based on NIRS, S. Laroussi-Mezghani et al. [8] discovered that the accuracy of identifying 3 kinds of virgin olive oil reached 89.55%, 92.5% and 98.50%, respectively. 1

In recent years, although some studies [9, 10] have discussed about the spectral change of frying oil, but there are not visually described for the frying degree. Therefore, we selected soybean oil and frozen fries as frying oil and frying material, selected frying time as the intuitive indication for the fried degree, applied NIRS technology to qualitative detection the soybean oil.

2 Materials and methods

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2.1 Experimental materials and instruments All of soybean oil and frozen French fries were purchased from the local supermarket, which were produced by Nine Three Oil Industry Group Co., Ltd. (Harbin, Heilongjiang, China). Oil was sealed and preserved at the room temperature, fries were stored on the day of purchase. The spectral acquisition was measured by NIRS DS2500 analyzer (FOSS, Hilleroed, Denmark). The scanned range is 400-2500nm, sampling mode is diffuse reflection. During the frying experiment, the electronic balance with an accuracy of 0.01g measured out frozen fries, the oil-frying was conducted by induction cooker and frying pan (Supor, Zhejiang, China). Meanwhile, the infrared thermometer was utilized to perform a real-time monitoring for the oil temperature to maintain the consistency of experimental condition. At the time of sampling, oil sample was placed in test tube with a stoppered glass to avoid chemical reaction between sample and air. 2.2 Sample preparation and data acquisition The working mode of induction cooker was set to “Frying”, 2L soybean oil was weighed and poured into the pan to fry at 220℃. When the temperature reached 180℃, 500g fries were put into the pan, then recorded the frying time immediately. After lasting for 14min and 20s (it was the best taste, which was verified in study [11]), we turned off the induction cooker, fished out the golden yellow fries and drained off oil. When the temperature was naturally cooled to 30℃, 15ml oil sample was poured into a clean test tube, then sealed and preserved at the room temperature until the spectral acquisition. The process was repeated for 15 times, 15 samples were collected at each experiment, 180 samples were gained after carried out 12 experiments. To avoid the affect of chemical reaction on the accurate of experimental data, spectra were immediate collected. In each experiment, sample was measured 5 times and each duplicate was scanned for 7 times, the result of 35 scans was averaged to obtain an average spectrum, 180 average spectra were obtained at last. Besides, sample cup was thoroughly cleaned to prevent mutual interference. The study performed data processing in accordance with the Fig.1.

Fig.1. Flowchart of classification for frying times.

2.3 Classification algorithm of frying times 2.3.1 Sample set partitioning and classifying of frying times 2

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During the spectral analysis, the first 9 experiments were severally partitioned in a ratio of 2:1 by the sample set partitioning based on joint X-Y distance (SPXY) algorithm, 90 of the 135 samples were calibration set for establishing model, 45 samples were test set for evaluating model. For the calibration set, the way of hierarchical classification was employed for dividing 15 fryings into two stages: primary and secondary, which was highlighted in this study. Roughly, 15 fryings were divided into few major classes, then the sub-segmentations were performed separately for major categories. Fig.2 illustrates the four divisions according to different criteria. In contrast, mode 1 is shown as Fig.2.a, 15 fryings are divided into 15 categories according to the ascending order. As a supervised method, SIMCA[6, 8] was used to calculate the principal components (PCs) of samples, then partitioned the calibration set according to the aggregation relationship of frying times. Table 1 is the variance contribution (VC) rates and cumulative contribution (CC) rates of the first 10 PCs, VC rates of the first and second PCs are 63.96% and 22.79%, respectively, its CC rate is up to 86.75%. There was to say that the original variable could be replaced by few PCs when CC rate was more than 85% [12]. Meanwhile, the PC1-PC2 score of average spectra is shown in Fig.2.b. Obviously, 15 fryings have clustering trend in four quadrants, so the (1st, 5th, 11th, 14th, 15th), (2nd, 9th, 12th, 13th), (7th, 8th), (3rd, 4th, 6th, 10th) fryings were classified as class I, II, III, IV, respectively. Besides, 15 fryings can be partitioned according to the combinations of 3x5 and 5x3, which aims to reduce the difficulty of classification. Specifically, Fig.2.c displays that 15 fryings are equally divided into I, II, III class via mode 3, the 1-5th, 6-10th, 11-15th fryings are class I, II, III, respectively. Fig.2.d shows that the 1-3th, 4-6th, 7-9th, 10-12th, 13-15th fryings belong to class I, II, III, IV, V, respectively. The above 4 modes were applied to gradually subdivide the frying times under the premise of accurately dividing the major classes. Noted that each class was renumbered, the classification models were built in turn. PCs PC2

VC（%）

63.96

22.79

CC（%）

63.96

86.75

PC3

PC4

PC5

PC6

PC7

PC8

PC9

PC10

7.81

3.23

0.47

0.23

0.12

0.09

0.03

0.02

94.56

97.79

98.26

98.49

98.61

98.70

98.73

98.75

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Table 1 Accumulative contribution rates and variance contribution rates of the first 10 PCs.

Fig.2. Four classification modes of 15 frying times. 3

2.3.2 Spectral data preprocessing To create stable and accurate models, standard normal variate (SNV), multiplicative scatter correction (MSC), first derivatives (1st), second derivative (2nd), Savitzky-Golay (S-G) smoothing and detrend are selected for preprocessing spectra. Specifically, SNV [13] and MSC [13] aim to turn down the scattering effects. Derivative [14] attempts to eliminate the spectral offset (1st) and remove baselines effect (2nd). S-G [15] can effectively eliminate random noise of high frequency. Detrend [16] can remove the offset. In this study, the optimal pretreatment way is chosen to improve the prediction ability and robustness of model. 2.3.3 Spectral variables selected by SPA

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Not all wavelengths are related to the measured object according to the principle and detection process of NIRS. The change of internal chemical functional groups within the heated soybean oil is only related to few sensitive wavelengths. Thus, SPA was employed for extracting feature wavelengths to improve the stability and interpretability of model. SPA is a forward variable selection algorithm that minimizes vector space collinearity [17], which not only reduces the data dimension, but also overcomes the linear correlation between raw data, and improves the reliability and accuracy. It is supposed that XM  N is the spectral absorbance matrix of frying oil, Y M 1 is the vector of frying times, Xcal is the spectral matrix of calibration set, xk is the initial

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iteration variable, T is the range of the feature wavelength. In addition, M is the number of sample and N is the number of wavelength. The specific calculation steps are as follows: Step1：When it is initial iteration ( m  1 ), the column k ( k  1......N ) in Xcal is chosen for xk , and xk was normalized. Step2：The remaining spectra among Xcal form the matrix S , S  k ,1  k  N , k  var(0), var(1,..., var(m  1) , then calculate the orthogonal projection Px T T 1 of xk to column vector xvar( m 1) , Px  xk  ( x k xvar( m1) ) xvar( m1) ( x var( m1) xvar( m1) ) . Where P is a projection operator, k  S . Step3：The value of var(m) is arg(max( Px ), k  S) , acquire the position of wavelength corresponding to the maximum projection value. Meanwhile, xk is set to Px , k  S , the maximum k

k

k

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projection value is used as the initial value of the next iteration. Step4： m is equal to m  1 , if m  T , then return to step 2 for the loop calculation. Step5: The gained wavelengths are var(m)  0,1, 2,..., T  1 at the position of XM  N .

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Finally, the dimensionalized spectra were used to build models, the feature wavelengths were corresponding to the minimum value of mean square error (MSE) in all models. 2.3.4 Establishment and evaluation of classification models To avoid the contingency of single model, the machine learning algorithms, including logistic regression (LR), back-propagation neural network (BPNN), are applied to establish classification models. Specifically, LR gets the actual prediction function H ( x) through the function Sigmoid , and obtains the optimal solution of loss function by using the gradient descent method. BPNN [18] is a multi-layer feedforward network, which can achieve a high degree of nonlinear mapping between output and input. As an iterative algorithm, Adaboost [19] is built into a strong classifier by training weak classifiers. SVM is a nonlinear modeling method of minimize structural risk, which has been widely applied in pattern recognition [20], the kernel function and relative parameters have an important influence on the performance of model. Especially, GS [21] is employed for acquiring the optimal penalty parameter C and kernel function parameter g . Usually, both of C and g have a range of 25  25 and an interval of 0.1. 4

3 Results and discussion

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3.1 Feasibility analysis The spectra from the first 9 experiments are illustrated in Fig.3, each subgraph contains 15 curves with corresponding to the average spectrum after 15 consecutive fryings. As seen that they are very similar of the position of absorption bands. However, there are subtle differences, the absorbance in the range of 900-1160nm, 1700-1800nm, 2250-2500nm are not exactly the same. These slight differences may be related to the environment or instruments.

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Fig.3. Spectra of frying oil in the first 9 experiments.

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Besides, in Fig.3, the curves are reciprocal chiasma and disorderly distributed, Fig.4 just illustrated with the partial magnification of average spectrum of the 9 experiments (the labels correspond to the frying times). In the range of 920-930nm and 2195-2205nm, the arrangement of curves are irregular, some of which are overlapped and difficult to distinguish. So that raw spectra are not suitable for modeling, it is necessary to optimize data.

Fig.4. The partial enlargement for average spectra of the first 9 experiments.

In the study, six methods were separately applied to preprocess the raw spectra, and Fig.5 recorded the processed results. As seen that the 15 curves in 6 subgraphs are evenly arranged and

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increased in turn, the optimization effect of MSC, detrend, S-G, SNV, 1st, 2nd are successively decreasing, the pre-treated spectra can be employed for qualitatively analyzing the frying times.

Fig.5. Influence of pretreatment methods on the spectra of frying oil.

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3.2 Selection of primary classification model of frying times After conducted the preliminary division for 15 frying times, the models of LR, BPNN, Adaboost, SVM were built, Fig.5 shows the classification scores of models. As known that the classification effect depend on the various combinations of modeling methods and division modes. Specifically, all of combinations can improve the classification score, but the model established by the combination of mode 3 and Adaboost composed of 50 binary classifiers is best, its score is as high as 98%. Meanwhile, all the combinations of BPNN and 4 modes have a poor effect. Therefore, Adaboost and mode 3 was chosen to build the primary classification model.

Fig.6. Comparison the effect between different hierarchical methods and modeling methods on the primary classification models.

3.3 Selection of secondary classification model of frying times Based on mode 3, the sample label of I, II, III class were reset to 1, 2, 3, 4, 5, the secondary classification models were severally established after combined with the pre-processing methods. 6

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The classification scores are shown in Fig.7. For the raw spectra, the model performance is different and its scores are generally low. Meanwhile, the difference of models depend on the pretreatment methods and classification methods. Firstly, pretreatment methods except 2nd can improve the model performance, SNV and MSC are obviously superior to others, 2nd has a negative effect. Secondly, after the optimization of SNV and MSC, the scores of SVM (RBF) models were 67% and 76%, respectively, which are significant improvement. For improving the model performance, MSC was utilized for preprocessing spectra, and SVM (RBF) was chosen as the secondary classification method, the accuracy was increased from 42% to 76%.

Fig.7. Comparison the effect between different pretreatment methods and modeling methods on the secondary classification models .

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The combinations of pretreatment methods were investigated to further reduce interference. The comparison of accuracy is presented in Fig.7. It can be observed that after preprocessed by the combinations of pretreatment methods, the accuracy of SVM (RBF) models are generally improved. When MSC combined with any of the five methods, the performance can be further improved. Above all, it is marked in the red circle that the performance of model optimized by MSC+SNV is best, its accuracy increased to 88.89%. In terms of the perspective of physical properties, the scattering effect in spectra is heightened because of the liquid properties of frying oil, MSC+SNV just eliminates it. The last, the sub-diagonal lines in table also indicate that pretreatment methods are inadvisable for reusing; otherwise the modeling effect may be worse.

Fig.8. Influence of different pretreatment methods and their combinations on model performance.

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Fig.9. The change of MSE with feature wavelengths.

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3.4 Selection of feature wavelengths Although the accuracy of SVM (RBF) model was meaningfully increased from 42% to 88.89% when spectra were preprocessed by MSC+SNV, wavelengths were too many to speed up the analysis. Next, SPA was applied to reduce the spectral dimension, Fig.9 shows the comparison of MSE. It is indicated that MSE decreases sharply with the increase of the effective feature wavelengths, the selected wavelengths contain more useful information which have a positive impact on model. Moreover, when MSE is the minimum value, SPA extracts the main spectral information. For taking into account the high accuracy and low data dimension, the study picks 14, 6, 8 wavelengths from I, II, III class, respectively. And GS optimizes the parameters of SVM (RBF) model, the best values of C , g are 17.15, 0.07, respectively, the MSE of three models reach the minimum of 0.1230, 0.3687, 0.4524, the average accuracy increases to 93.33%.

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The distribution of feature wavelengths is shown in Fig.10. According to the importance of wavelengths, 14 feature wavelengths of class I are 2312, 2148, 424, 1900, 2468, 1708, 2428, 1170, 2462, 2306, 564, 1173, 2398, 2256nm; 6 feature wavelengths of class II are 1948, 1168, 2150, 426, 1710, 1732 nm; 8 feature wavelengths of class III are 754, 426, 1170, 1900, 2468, 1732, 2254, 1710nm. The higher the arrangement, the greater the influence on frying times. Meanwhile, the feature wavelengths are mainly distributed in the region where the absorbance of spectral curve is significantly changed, which confirms the effectiveness with SPA to extract sensitive wavelengths. And the distribution of feature wavelengths is similar. For example, in the red box, the wavelengths of 426, 1170, 1710, 1712nm are common of I, II, III class. In the black box, the common wavelength of I, II class is 2150nm, the common wavelengths of I, III class are 2254, 2468nm. Yet, they are arranged differently in sequence and take different effect on frying times. For the soybean oil with different frying times, the mainly distinction is reflected on the degree of chemical reaction between oil and oxygen. In the view of the physical properties, 424, 426, 564, 754nm are part of the visible light. The non-volatile decomposition ingredients in oil are gradually accumulated with the increase of frying times, resulting in an increase in the absorption of visible light [22], showing in the deepening of oil color [23]. Besides, the absorption peaks at 1168, 1170nm are just related to the secondary frequency doubling of stretching frequency of C=C bond and C-H bond of olefin in frying oil. The absorption peaks at 1708, 1710, 1732nm reflect the stretching vibration of the methyl C-H bond in alcohol. At the high temperature, the acid substance in oil is further oxidized to produce alcohol, the wavelength at 1900nm just reflect the 2nd-order frequency doubling of stretching vibration of C=O bond. The wavelengths at 2148, 8

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2150, 2398, 2428, 2462, 2468nm reflect the change of aryl C-H bond in the absorption band of benzene. The wavelengths at 2254, 2256nm reflect the stretching vibration of N-H bond. The wavelengths at 2312, 2312nm are connected with the 1st-order frequency doubling of the anti-symmetric stretching vibration fundamental frequency, which show that substances like grease are decomposed into aldehydes at high temperatures.

Fig.10. Distribution of the feature wavelengths of I, II, III class.

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3.5 Verification and comparison of qualitative analysis models After the extraction of feature wavelengths, the built Adaboost-SVM (RBF) classification model had the best effect. As a supplement, the residual 45 samples were selected for detecting the prediction effect, Adaboost-SVM (RBF) model was evaluated comprehensively by comparing with other calibration models. These models shared the same data and were optimized by the same strategy to rationalize the comparison. Table 2 shows the prediction results. Table 2 Evaluation results of the verification model classification performance.

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Accuracy (%) Validation set

Level-2

Model

I

88.89

BPNN Adaboost SVM(Linear)

SVM(RBF)

II

III

Mean

53.33

80

68.89

73.33

80

66.67

86.67

77.78

95.56

66.67

46.67

60

57.78

93.33

73.33

33.33

53.33

53.33

84.44

86.67

73.33

86.67

82.22

86.67

93.33

86.67

93.33

91.11

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SVM(Polynomial)

73.33

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LR

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In Table 2, compared with other primary models, Adaboost generated the highest accuracy of 95.56%. For the secondary classification, the accuracy of SVM (RBF) models for I, II, III class were respectively 93.33%, 86.67%, 93.33%, the average accuracy was 91.11%, which realized the classified purpose of frying times.

4 Conclusions A novel technique based on NIRS and the optimal Adaboost-SVM (RBF) classification model had been evaluated in detail for nondestructive detecting the frying times of soybean oil. The created model was based on the analysis, optimization and dimensionality reduction of spectra via suitable chemometrics. The specific steps as follows: (1) After the feature analysis and spectral pretreatment in turn, the distribution of curves tended to be ordered. When 15 fryings were classified into the primary and secondary stages. 9

Mode 3 combined with Adaboost by composed of 50 weak classifiers constructed the best primary classification model, its accuracy was 98%. By using of MSC + SNV, the secondary model of SVM (RBF) reached the better accuracy of 88.89%. (2) Apply SPA to reduce the spectral dimension, the result was that 14 feature wavelengths of 2312, 2148, 424, 1900, 2468, 1708, 2428, 1170, 2462, 2306, 564, 1173, 2398, 2256nm, 6 feature wavelengths of 1948, 1168, 2150, 426, 1710, 1732nm, 8 feature wavelengths of 754, 426, 1170, 1900, 2468, 1732, 2254, 1710nm were extracted and corresponded to I, II, III class. When GS determined C and g as 17.15, 0.07, respectively, the average accuracy of SVM (RBF) model achieved to 93.33%. (3) Finally, the verified results proved that the proposed method can realize the detection of frying times of soybean oil.

Conflict of Interest The authors declare that there are no conflicts of interest.

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Acknowledgement

This work was supported by the funding of Heilongjiang University with the grant number YJSCX2019-168HLJU. The gratitude would like to express to the student Li Ruonan for her great assistance.

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