College of Mechanical and Electrical Engineering, Sichuan Agriculture University, Ya'an 625014, China.
College of Mechanical and Electrical Engineering, Sichuan Agriculture University, Ya'an 625014, China; Sichuan Intelligent Agriculture Engineering Technology Research Center, Ya'an 625014, China.
Spectrochim Acta A Mol Biomol Spectrosc. 2025 Feb 5;326:125183. doi: 10.1016/j.saa.2024.125183. Epub 2024 Sep 21.
Terahertz spectroscopy is an emerging rapid detection method that can be used to detect and analyze food quality issues. However, models developed based on various spectral characteristics of terahertz have shown different performances in food identification. Therefore, we preliminarily analyzed the effect of terahertz spectral characteristics on the identification and quantification of collagen powder adulterated with food powders (plant protein powder, corn starch, wheat flour) with the use of random forest (RF), linear discriminant analysis (LDA), and partial least squares regression (PLSR), and determined the spectral characteristics suitable for identification and quantitative analysis. Then, the selected spectral characteristics data were preprocessed using baseline correction (BC), gaussian filter (GF), moving average (MA), and savitzky-golay (SG). Feature variables were extracted from preprocessed spectral characteristics data using genetic algorithm (GA), random forest (RF), and least angle regression (LAR). The study indicated that the BC-GA-LDA classification model based on the absorption coefficient spectra achieved an accuracy of 96.96% in identifying adulterated collagen powder. Additionally, the GA-PLSR model developed based on the power spectra demonstrated excellent performance in predicting adulteration levels, with the coefficient of determination (R) values ranging from 0.93 to 0.99. The results showed that the rational selection of terahertz spectral characteristics is highly feasible for the accurate detection of collagen powder adulteration.
太赫兹光谱学是一种新兴的快速检测方法，可用于检测和分析食品质量问题。然而，基于太赫兹各种光谱特征开发的模型在食品识别中表现出不同的性能。因此，我们初步分析了太赫兹光谱特征对掺有食品粉末（植物蛋白粉、玉米淀粉、面粉）的胶原蛋白粉的识别和定量的影响，使用随机森林（RF）、线性判别分析（LDA）和偏最小二乘回归（PLSR），确定了适合识别和定量分析的光谱特征。然后，使用基线校正（BC）、高斯滤波（GF）、移动平均（MA）和 Savitzky-Golay（SG）对选定的光谱特征数据进行预处理。使用遗传算法（GA）、随机森林（RF）和最小角回归（LAR）从预处理后的光谱特征数据中提取特征变量。研究表明，基于吸收系数光谱的 BC-GA-LDA 分类模型在识别掺假胶原蛋白粉方面的准确率达到 96.96%。此外，基于功率谱开发的 GA-PLSR 模型在预测掺假水平方面表现出优异的性能，决定系数（R）值范围为 0.93 至 0.99。结果表明，合理选择太赫兹光谱特征对于准确检测胶原蛋白粉掺假是高度可行的。
