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| Review on the Application of Spectroscopy Technology in Food Detection |
| LI Xin-xing1, 2, ZHANG Ying-gang1, MA Dian-kun1, TIAN Jian-jun3, ZHANG Bao-jun3, CHEN Jing4* |
1. Beijing Laboratory of Food Quality and Safety, College of Information and Electrical Engineering, China Agricultural University, Beijing 100083, China
2. Energy and Environment Engineering Institute, Nanchang Institute of Technology, Nanchang 330044, China
3. College of Food Science and Engineering, Inner Mongolia Agricultural University, Huhhot 010018, China
4. School of Logistics, Beijing Wuzi University, Beijing 101149, China
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Abstract With the progress of society, people's dietary requirements are constantly improving, which is gradually changed from the previous “eat full” to today's “eat well”. People are paying more attention to food safety. Therefore, fast and non-destructive food detection technology is needed to meet the imminent demand for food safety. Spectral technology can calculate the material characteristics and composition of food samples according to their physical structure and chemical composition. It has a broad application prospect in adulteration detection, freshness detection, and residue detection of harmful substances. Compared with the traditional detection technology in food detection, spectral technology has the advantages of rapid, high precision, no sample loss, and good repeatability, and it has become an important development direction in food detection. In this paper, related domestic and international literature on spectral techniques applied to food detection in the last five years is discussed, focusing on data pretreatment method, characteristic band selection algorithm and data modeling method to systematically review the application and progress of spectral technology in food detection. In this paper, the application of spectral technology in food detection is discussed, including the preprocessing of spectral data by multiplicative scatter correction (MSC), standard normal variate transform (SNV), and Savitzky-Golay smoothing (SG) algorithm; successive projections algorithm (SPA), principal component analysis (PCA), and competitive adaptive reweighted sampling (CARS) were used to select characteristic bands; partial least squares (PLS), support vector machine (SVM), and artificial neural network (ANN) were used to analyze collected data. Simultaneously, this paper summarizes the prospects for the application of spectral technology in food detection: the integration of spectral detection technology and a variety of food detection technology will become a new development direction in the future; combining spectral detection technology with on-line detection technology to realize on-line and real-time detection of food samples will obtain more valuable detection results; the development of portable spectral detection equipment will be more convenient for on-site food detection, and this equipment will significantly improve the efficiency of food detection and has excellent market potential.
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Received: 2021-11-24
Accepted: 2022-11-09
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Corresponding Authors:
CHEN Jing
E-mail: chenjing@bwu.edu.cn
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