Visible/Near-Infrared Spectroscopy Combined With Machine Vision for Dynamic Detection of Aflatoxin B1 Contamination in Peanut
YAN Chen1, JIANG Xue-song1*, SHEN Fei2*, HE Xue-ming2, FANG Yong2, LIU Qin2, ZHOU Hong-ping1, LIU Xing-quan3
1. College of Mechanical and Electronic Engineering, Nanjing Forestry University, Nanjing 210037, China
2. College of Food Science and Engineering, Nanjing University of Finance and Economics, Nanjing 210023, China
3. School of Agriculture and Food Science, Zhejiang Forestry University, Hangzhou 311300, China
Abstract：Peanut is an important oil crop that is susceptible to Aspergillus infection to produce aflatoxins, of which Aflatoxin B1 (AFB1) poses a higher threat to humans and animals. The traditional AFB1 detection method is more complicated, such as cumbersome operation, the material is destroyed and long time-consuming. Therefore, it is of great significance to develop a rapid, non-destructive and suitable online detection method for peanut production and processing. Firstly, peanuts were purchased and stored at 28 ℃ and 85% relative humidity to mold. In the 0, 4, 6, 7 and 8 d time periods, the spectral and image information of peanut samples were collected dynamically at a rate of 0.15 m·s-1. After collecting the information, the AFB1 content in peanuts was determined by enzyme-linked immunosorbent assay (ELISA). Data processing steps are: pre-processing of spectra by multiple scatter correction, baseline correction, standard normal variable correction, and Savitzky-Golay smoothing, principal component analysis of spectral data in the range of 600~1 600 nm, 8 characteristic wavelengths (630, 1 067, 1 150, 1 227, 1 390 and 1 415 nm) are determined according to the principal component weight coefficient; the image is subjected to grayscale and threshold segmentation processing, and 12 image color feature parameters are extracted. Finally, using linear discriminant analysis (LDA) and support vector machine (SVM) to establish a qualitative discriminant analysis model for peanut samples (with a national standard of 20 μg·kg-1). ELISA results showed that AFB1 peanut exceeding 58%. Visible/near-infrared spectrum analysis showed that the absorbance gradually decreased with the increase of toxin intensification at the peak of 1 180 nm. Machine vision analysis showed that with the increase of storage time, the RGB value of peanuts decreased overall, the surface gradually dimmed and covered with hyphae, and the level of toxin infection gradually increased. The principal component analysis showed that the spectrum showed a clear clustering trend, but the clustering trend of image and data fusion was not obvious. LDA and SVM models constructed based on full-spectrum and characteristic wavelengths can quickly identify over- and under-standard samples, the best recognition rate based on the full-spectrum model is 92%, and the best recognition rate based on the characteristic wavelength is 88%; compared with spectral information modeling, the nonlinear SVM model performs better on image color feature parameter modeling , and the best recognition rate is 90%; combining the internal and external information of peanut samples, the SVM model based on spectral and image information fusion has the best discrimination accuracy of 92%. It is feasible to use the visible/near-infrared spectroscopy and machine vision technology combined with stoichiometry to realize the dynamic discrimination of peanut AFB1 content exceeding the standard, which provides a theoretical basis for the quality and safety detection of peanut.
[1] Gell R M, Carbone I. Journal of Microbiological Methods, 2019, 158: 14.
[2] IARC Working Group on the Evaluation of Carcinogenic Risks to Humans. Food Items and Constituents, Chemical Agents and Related Occupations. Lyon (FR), IARC Monographs on the Evaluation of Carcinogenic Risks to Humans, 2012, 100F: 229.
[3] GB 2761—2017. Limits of Mycotoxins in Food（食品中真菌毒素限量). National Standards of the People’s Republic of China（中华人民共和国国家标准).
[4] Xie L, Chen M, Ying Y. Critical Reviews in Food Science and Nutrition, 2016, 56(16): 2642.
[5] Tao F, Yao H, Zhu F, et al. 2018 ASABE American Society of Agricultural and Biological Engineers, Annual International Meeting. Feasibility of Using Visible/Near-Infrared (Vis/NIR) Spectroscopy to Detect Aflatoxigenic Fungus and Aflatoxin Contamination on Corn Kernels. Proc. 2018, 1801006 (https://doi.org/10.13031/aim.201801006).
[6] Shen F, Wu Q, Shao X, et al. Journal of Food Science and Technology, 2018, 55(3): 1175.
[7] Wu Q, Xie L, Xu H. Food Chemistry, 2018, 252: 228.
[8] Tao F, Yao H, Hruska Z, et al. Applied Spectroscopy, 2019, 73(4): 415.
[9] Badaró A T, Morimitsu F L, Ferreira A R, et al. Food Chemistry, 2019, 289: 195.
[10] Shen F, Zhao T, Jiang X, et al. LWT, 2019, 109: 216.
[11] Wang L, Wang Q, Liu H, et al. Journal of the Science of Food and Agriculture, 2013, 93(1): 118.
[12] Siripatrawan U, Makino Y. International Journal of Food Microbiology, 2015, 199: 93.
[13] Wu L, He J, Liu G, et al. Postharvest Biology and Technology, 2016, 112: 134.
[14] Tao F, Yao H, Hruska Z, et al. Proc SPIE, 2018, 10665: 106650K.
