数字农科院2.0

Predicting the chemical composition of pet food with hyperspectral imaging

文献类型： 外文期刊

作者： Liu Xiaolu;Li Shouxue;Yao Ting;Feng Yuchao;Yao Shujiao;Fan Xia

作者机构：

关键词： Cat food;Dog food;Hyperspectral imaging;Partial least squares regression

期刊名称： Microchemical Journal

ISSN： 0026-265X

年卷期： 2024 年 203 卷

页码：

收录情况： SCIE（2024版）

摘要： Performing fast analyses to control the nutritional quality of pet food products is of great interest in the pet food industry. The purpose of this study was to test the efficacy of near-infrared hyperspectral imaging (HSI) in determining chemical composition content of cat and dog food, and evaluate the influence of mixed model and characteristic wavelength on the performance of the quantitative model. Seventy cat food and thirty-six dog food samples were characterised using reference methods for moisture, crude protein (CP), crude fat (CF), crude fibre (CFe), crude ash (CA), calcium (Ca) and total phosphorus (TP) content. The Partial Least Squares regression (PLSR) was used to establish the quantitative models that involved the cat food model and mixed model. The characteristic wavelength was selected using a competitive adaptive reweighted-sampling (CARS) algorithm. Results showed that, except for CFe, the performance of the cat food model was similar to the mixed model. When using feature band, the prediction results of the mixed models for CP, CF, moisture, and CFe were optimized with R2p between 0.73 and 0.96 and RPD in the range 2.22–5.20, but the prediction results of TP, CA, and Ca did not meet the actual demand. Optimal quantitative models were used to visualise the sample chemical composition distributions. The results of this study provide theoretical and technical support for the rapid online quality control of pet food.

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