期刊
FOOD CHEMISTRY
卷 436, 期 -, 页码 -出版社
ELSEVIER SCI LTD
DOI: 10.1016/j.foodchem.2023.137682
关键词
Transfer learning; Transfer component analysis; Hyperspectral; Adulterated rice; Food fraud
A transfer learning method (TCA-LSSVR) has been developed to accurately assess industrial paraffin contamination levels in rice. The algorithm integrates transfer component analysis (TCA) with domain adaptive capabilities to produce accurate estimates, which have been validated through experiments with rice from different regions and industrial paraffins.
Accurate assessment of industrial paraffin contamination levels (IPCLs) in rice is critical for food safety. However, time-consuming and labor-intensive experiments to produce labels for targeted adulterated rice have hindered the development of IPCL estimation methods. In this paper, a transfer learning method (TCA-LSSVR) has been developed. The algorithm integrates transfer component analysis (TCA) with domain adaptive capa-bilities to produce accurate estimates. Rice from 7 different regions and 3 industrial paraffins were used to generate 4,680 samples from 9 datasets for benchmarking. The test results showed that the established algorithm achieved good estimation performance in various modelling strategies, and only 20 % of off-site samples were needed to supplement the source dataset, the average determination coefficient R2 reached 0.7045, the average RMSE reached 0.140 %, and the average RPD reached 2.023. This work highlights the prospect of rapidly developing a new generation of adulteration detection algorithms using only previous trial data.
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