Determination of cow's milk fat content based on segmented dielectric relaxation parameters combined with characteristic variables

IF 4 2区农林科学 Q2 CHEMISTRY, APPLIED

Journal of Food Composition and Analysis Pub Date : 2025-04-17 DOI:10.1016/j.jfca.2025.107623

Yue Li , Ke Yang , Wei Liu , Donggen Fang , Jiahui Zhang , Wenchuan Guo , Xinhua Zhu

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引用次数: 0

Abstract

Rapid and accurate online detection of cow's milk composition content is significant. This study aimed to clarify the correlation between dielectric relaxation parameters (DRPs) and fat content and explore methods to improve milk fat content prediction accuracy with segmented DRPs. Dielectric spectra were collected from 270 milk samples and divided into two relaxation segments using Cole-Cole plots. The segmented DRPs were obtained by fitting the segmented dielectric spectra using a modified Debye model. The prediction models for milk fat content based on segmented DRPs were built, and their performance was evaluated using root mean square errors of prediction set (RMSEP) and residual prediction deviation (RPD). The results showed an enhanced correlation between segmented DRPs and milk fat content. The prediction accuracy of the models with the segmented DRPs was higher than that of the unsegmented DRPs. The milk fat content prediction model based on the combination of the selected DRPs and successive projections algorithm (SPA) had the best prediction accuracy, with an RPD of 4.4 and an RMSEP of 2.05 g·kg⁻¹. This study improved the prediction accuracy of milk fat based on the segmented DRPs combing with characteristic variables. This study provides a new method for accurately detecting complex systems such as cow's milk based on dielectric spectroscopy.

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基于分段介电松弛参数与特征变量相结合的牛奶脂肪含量测定

快速准确的在线检测牛奶成分含量具有重要意义。本研究旨在阐明介质弛豫参数（DRPs）与脂肪含量的相关性，并探索利用分段DRPs提高乳脂含量预测精度的方法。采集270份牛奶样品的介电光谱，用Cole-Cole图将其划分为两个松弛段。采用改进的Debye模型拟合分段电介质谱，得到了分段电介质谱。建立了基于分段DRPs的乳脂含量预测模型，并利用预测集均方根误差（RMSEP）和残差预测偏差（RPD）对其性能进行了评价。结果表明，分段DRPs与乳脂含量之间的相关性增强。采用分段DRPs模型的预测精度高于未分段DRPs模型。选取的DRPs与逐次预测算法（SPA）相结合的乳脂含量预测模型预测精度最高，RPD为4.4，RMSEP为2.05 g·kg−1。本研究基于分段drp与特征变量相结合，提高了乳脂的预测精度。本研究提供了一种基于介电光谱技术精确检测牛奶等复杂体系的新方法。

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来源期刊

Journal of Food Composition and Analysis 工程技术-食品科技

CiteScore

6.20

自引率

11.60%

发文量

601

审稿时长

53 days

期刊介绍： The Journal of Food Composition and Analysis publishes manuscripts on scientific aspects of data on the chemical composition of human foods, with particular emphasis on actual data on composition of foods; analytical methods; studies on the manipulation, storage, distribution and use of food composition data; and studies on the statistics, use and distribution of such data and data systems. The Journal''s basis is nutrient composition, with increasing emphasis on bioactive non-nutrient and anti-nutrient components. Papers must provide sufficient description of the food samples, analytical methods, quality control procedures and statistical treatments of the data to permit the end users of the food composition data to evaluate the appropriateness of such data in their projects. The Journal does not publish papers on: microbiological compounds; sensory quality; aromatics/volatiles in food and wine; essential oils; organoleptic characteristics of food; physical properties; or clinical papers and pharmacology-related papers.