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33. Evaluation of methods for the detection of hazardous substances in food based on machine learning.

Author

Zhu, Long-yu, Yan, Lijuan, Zhao, Fang, Guo, Xuewen, Xu, Dunming, Lv, Jingzhang, Ding, Lin, Niu, Na, Qiao, Jun-qin, Ma, Shumian, Huang, Xindi, Liu, Huiling, and Lian, Hong-zhen

Subjects
HAZARDOUS substances, MACHINE learning, SUPERVISED learning, EVALUATION methodology, BACK propagation, INTRUSION detection systems (Computer security)
Abstract

With new detection methods for hazards in food being created, it is essential to build an evaluation model of these methods in order to improve the level and efficiency of food safety related detection. Back propagation neural network (BPNN), classification and regression tree (CART), support vector regression (SVR) and random forest regression (RFR) are the supervised machine learning methods used to build the model. In this study, we used these four approaches to evaluate hazard detection methods. First, the factors that affect the test (considering the accuracy, efficiency and cost of the methods) were considered, and 8 indices were selected to establish an evaluation index system for chemical hazards and biological hazards respectively. Then, 727 methods related to the detection of chemical hazards and 91 methods related to the detection of biological hazards in food were analyzed, and experts were invited to score each method as machine learning training data. Then, four machine learning models were used for training and verification. It turns out that for the detection methods of chemical hazards, the BPNN, CART and SVR models require a shorter period of time to adjust parameters, perform better on the test set with great fitting ability and lower mean square error, and the goodness of fit is 0.969, 0.962 and 0.988, respectively. While for the detection methods of biological hazards, BPNN and SVR models fit better with goodness of fit values of 0.988 and 0.999, respectively, and give more accurate prediction. It can be concluded that BPNN and SVR models perform well for both chemical and biological hazards detection methods, which can be used as a reference for artificial selection before testing. [ABSTRACT FROM AUTHOR]

Published
2024
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50. Determination of Industrial Acotiamide Hydrochloride Trihydrate by High Performance Liquid Chromatography

Author

ZHOU Wei-qun, QIAO Jun-qin, and LIAN Hong-zhen

Subjects
high performance liquid chromatography (hplc), acotiamide hydrochloride trihydrate, sodium octanesulfonate, methanol-ammonium acetate, quality control, Geology, QE1-996.5, Ecology, QH540-549.5
Abstract

Acotiamide hydrochloride trihydrate, which is the first gastrointestinal prokinetic agent, has been proven effective in treating functional malnutrition. There is little method in analyzing this new medicine. The only reported method is using High Performance Liquid Chromatography (HPLC), with methanol-monopotassium phosphate-sodium octanesulfonate as the mobile phase. However, this method is time wasting and unstable because of the using of an ion paring agent in the mobile phase. In this paper, a new HPLC method is established to quantitatively analyze industrial acotiamide hydrochloride trihydrate based on its amphoteric structure. The separation is accomplished in a C18 (4.6 mm×150 mm, 5 μm) column with methanol and 20 mmol/L ammonium acetate aqueous solution (pH 6.8) (45:55, V/V) as the mobile phase. The detection wavelength is set at UV 280 nm. The retention time of acotiamide hydrochloride trihydrate is 8 min with good peak shape, and the total analysis time is 23 min. Under the optimized chromatographic condition, the calibration curve of acotiamide hydrochloride trihydrate is linear in the range of 0.0006 and 1.0 mg/mL, detection limit is 0.0002 mg/mL, and both the intra-and inter-day relative standard deviations (RSDs) are lower than 1%. This method contains no ion paring agent, benefiting the rapid balance of chromatographic condition and improving the analysis repeatability and maneuverability. The method can be performed for rapid detection of acotiamide hydrochloride trihydrate and used in the quality control of raw medicine.

Published
2014

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