Near-infrared spectroscopy assisted by random forest for predicting the physicochemical indicators of yak milk powder

IF 9.8 1区农林科学 Q1 CHEMISTRY, APPLIED

Food Chemistry Pub Date : 2025-03-01 DOI:10.1016/j.foodchem.2025.143555

Haiyang Peng , Lunzhao Yi , Xuejing Fan , Jiawen Zhang , Ying Gu , Shuo Wang

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Abstract

High-efficiency and cost-effective detection of physicochemical indicators is essential for the quality control of yak milk powder. Herein, a rapid and simultaneous detection method based on miniaturized near-infrared (NIR) spectroscopy and chemometrics for four physicochemical indicators (protein, fat, and moisture contents as well as acidity) of yak milk powder was developed. By comparing partial least squares combined with support vector regression (PLS-SVR), ridge regression (RR), and random forest (RF), the optimal prediction models were identified. The results indicated that the combination of RF and NIR spectroscopy achieved excellent performance in predicting the four indicators, with correlation coefficients of 0.9846, 0.9642, and 0.9915 for the protein, fat, and moisture contents, respectively, and 0.9819 for acidity. This method enables rapid and accurate prediction of yak milk powder quality, providing a reliable tool for production quality control. Future work should explore its scalability and integration into real-time monitoring systems.

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随机森林辅助近红外光谱法预测牦牛奶粉的理化指标

高效、经济的理化指标检测是牦牛奶粉质量控制的关键。本文建立了一种基于小型化近红外（NIR）光谱和化学计量学的牦牛奶粉蛋白质、脂肪、水分和酸度四项理化指标的快速同时检测方法。通过比较偏最小二乘联合支持向量回归（PLS-SVR）、脊回归（RR）和随机森林（RF），确定了最优预测模型。结果表明，射频光谱与近红外光谱结合预测4项指标效果良好，蛋白质、脂肪和水分含量的相关系数分别为0.9846、0.9642和0.9915，酸度的相关系数为0.9819。该方法能够快速、准确地预测牦牛奶粉的质量，为生产质量控制提供了可靠的工具。未来的工作应探索其可扩展性和集成到实时监控系统。

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来源期刊

Food Chemistry 工程技术-食品科技

CiteScore

16.30

自引率

10.20%

发文量

3130

审稿时长

122 days

期刊介绍： Food Chemistry publishes original research papers dealing with the advancement of the chemistry and biochemistry of foods or the analytical methods/ approach used. All papers should focus on the novelty of the research carried out.