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| Research Progress of Near-Infrared Spectroscopy in the Detection of
Edible Oil Adulteration |
| WU Cheng-zhao1, WANG Yi-tao1, HU Dong1, SUN Tong1, 2* |
1. College of Optical, Mechanical and Electrical Engineering, Zhejiang A&F University, Hangzhou 311300, China
2. College of Engineering, Jiangxi Agricultural University, Nanchang 330045, China
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Abstract Edible oil is a necessity in daily diet, providing heat energy and fatty acid for the human body. It is an important organic matter that promotes the absorption of fat-soluble vitamins. With the improvement of people’s living standards, high-grade edible oil has entered the table of the public and is deeply welcomed. Due to the high selling price of high-grade edible oil in the market, some illegal manufacturers mix cheap edible oil into high-grade edible oil for sale to make huge profits. And this leads to the adulteration of edible oil from time to time, which has aroused widespread concern of the government and the public. In order to protect the legitimate interests of consumers and maintain the normal order of the edible oil market, it is urgent to detect the adulteration of edible oil quickly and effectively. Near-infrared spectroscopy(NIR)has the advantages of simple, rapid, nondestructive and no sample pretreatment, and it is widely used in the analysis of adulteration of edible oil. This paper summarizes the basic principle of NIR technology and reviews the research progress of NIR technology in adulteration detection of edible oils such as olive oil, camellia oil, sesame oil and walnut oil in recent ten years. Different test devices, test methods and data processing methods(pretreatment, wavelength selection and modeling methods) are mainly used to detect the binary, ternary and multivariant adulteration of edible oil to improve the accuracy and application range of edible oil adulteration detection and establish a more effective quantitative detection and qualitative identification model for edible oil adulteration. Then, it summarizes the existing problems in the detection of adulteration of edible oil by near-infrared spectroscopy, such as the detection mechanism of adulteration of edible oil is unclear. The prepared adulterated edible oil samples are difficult to meet the actual complex adulteration forms, the adulteration detection by sampling can only realize part of the spot sampling inspection, and the unified standard specification of adulteration detection of edible oil is not established. At last, it points out the development trend in future to integrate NIR with other rapid detection technologies to obtain more accurate and reliable detection models, and the construction of edible oil NIR database using the internet of things and big data technology to realize spectral data sharing and online upgrade and remote update of adulteration detection models. This paper aims to provide references and solutions for detecting adulteration of edible oil in China.
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Received: 2022-01-07
Accepted: 2022-06-27
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Corresponding Authors:
SUN Tong
E-mail: suntong980@163.com
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[1] LU Wan-zhen(陆婉珍). Modern Near Infrared Spectroscopic Analysis Technology(现代近红外光谱分析技术). Beijing: China Petrochemical Press(北京: 中国石化出版社), 2007. 395.
[2] ZHU Yu-tian, LI Jin-cai, GAO Su-jun, et al(朱雨田, 李锦才, 高素君，等). China Oils and Fats(中国油脂), 2017, 42(7): 140.
[3] CHU Xiao-li(褚小立). Molecular Spectroscopy Analytical Technology Combined With Chemometrics and Its Applications(化学计量学方法与分子光谱分析技术). Beijing: Chemical Industry Press(北京: 化学工业出版社), 2011. 394.
[4] Meenu M, Cai Q, Xu B. Trends in Food Science & Technology, 2019, 91: 391.
[5] Mignani A G, Ciaccheri L, Ottevaere H，et al. Analyticaland Bioanalytical Chemistry, 2011, 399(3): 1315.
[6] Mendes T O, Da Rocha R A, Porto B L S，et al. Food Analytical Methods, 2015, 8(9): 2339.
[7] Azizian H, Mossoba M M, Fardin-Kia A R，et al. Lipids, 2015, 50(7): 705.
[8] Vanstone N, Moore A, Martos P，et al. Food Quality and Safety, 2018, 2(4): 189.
[9] Jiménez-Carvelo A M, Lozano V A, Olivieri A C. Food Control, 2019, 96: 22.
[10] Karunathilaka S R, Kia A F, Srigley C，et al. Journal of Food Science, 2016, 81(10): C2390.
[11] Jiang H, Chen Q. Molecules, 2019, 24(11): 2134.
[12] Sohng W, Park Y, Jang D，et al. Talanta, 2020, 212: 120748.
[13] Li Y, Xiong Y, Min S. Vibrational Spectroscopy, 2019, 101: 20.
[14] Cheng Y T, Lu C C, Yen G C. Journal of Nutritional Science & Vitaminology, 2015, 61 Suppl(Supplement): S100.
[15] YUAN Jiao-jiao, WANG Cheng-zhang, CHEN Hong-xia(原姣姣, 王成章, 陈虹霞). Journal of the Chinese Cereals and Oils Association(中国粮油学报), 2012, 27(3): 110.
[16] SUN Tong, HU Tian, XU Wen-li, et al(孙 通, 胡 田, 许文丽, 等). China Oils and Fats(中国油脂), 2013, 38(10): 75.
[17] Yuan J J, Wang C Z, Chen H X，et al. International Journal of Food Properties, 2016, 19(2): 300.
[18] Chu X, Wang W, Li C，et al. Journal of Innovative Optical Health Sciences, 2018, 11(02): 1850006.
[19] Li S, Zhu X, Zhang J，et al. Journal of Food Science, 2012, 77(4): C374.
[20] SUN Tong, WU Yi-qing, LI Xiao-zhen, et al(孙 通, 吴宜青, 李晓珍, 等). Acta Optica Sinica(光学学报), 2015, 35(6): 350.
[21] SUN Tong, WU Yi-qing, XU Peng, et al(孙 通, 吴宜青, 许 朋, 等). Journal of Nuclear Agricultural Sciences(核农学报), 2015, 29(5): 925.
[22] Du Q, Zhu M, Shi T，et al. Food Control, 2021, 121: 107577.
[23] SUN Tong, WU Yi-qing, XU Peng, et al(孙 通, 吴宜青, 许 朋, 等). Spectroscopy and Spectral Analysis(光谱学与光谱分析), 2015, 35(7): 1894.
[24] Hu O, Chen J, Gao P，et al. Journal of the Science of Food and Agriculture, 2019, 99(5): 2285.
[25] QIN Yu-chuan, LIU Ben-tong, XUE Jin-song, et al(秦玉川, 刘本同, 薛锦松, 等). Journal of the Chinese Cereals and Oils Association(中国粮油学报), 2020, 35(5): 97.
[26] Dossa K, Diouf D, Wang L H, et al. Frontiers in Plant Science, 2017, 8: 1154.
[27] LIU Yan-de, WAN Chang-lan(刘燕德, 万常斓). Transactions of the Chinese Society for Agricultural Machinery(农业机械学报), 2012, 43(7): 136.
[28] Chen H, Lin Z, Tan C. Vibrational Spectroscopy, 2018, 99: 178.
[29] CHEN Hong-liang, ZENG Shan, WANG Bin(陈洪亮, 曾 山, 王 斌). China Oils and Fats(中国油脂), 2020, 45(2): 86.
[30] ZHANG Jing, SHAN Hui-yong, YANG Ren-jie, et al(张 婧, 单慧勇, 杨仁杰, 等). Acta Photonica Sinica(光子学报), 2019, 48(6): 62.
[31] Zeng L L, Song Z Q, Zheng X，et al. Applied Mechanics and Materials, 2014, 687-691: 795.
[32] Castro R C, Ribeiro D S M, Santos J L M，et al. Talanta, 2021, 230: 122373.
[33] PENG Xing-xing, CHEN Wen-min, QIAO Xi-hua, et al(彭星星, 陈文敏, 乔茜华, 等). Journal of the Chinese Cereals and Oils Association(中国粮油学报), 2015, 30(12): 106.
[34] Basri K N, Laili A R, Tuhaime N A，et al. Analytical Methods, 2018, 10(34): 4143.
[35] Yuan Z, Zhang L, Wang D，et al. LWT-Food Science and Technology, 2020, 125: 10927.
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