Applied Organic Chemistry Laboratory, Sciences and Techniques Faculty, Sidi Mohamed Ben Abedallah University, BP 2202 route d'Immouzer, Fès, Morocco.
Laboratoire de Biologie, Département d'Écologie et Génie de l'Environnement, Faculté des Sciences de la Nature et de la Vie & Sciences de la Terre et l'Univers, Université 8 Mai 1945 Guelma, BP 401, Guelma 24000, Algeria.
Spectrochim Acta A Mol Biomol Spectrosc. 2023 Nov 5;300:122932. doi: 10.1016/j.saa.2023.122932. Epub 2023 May 29.
In the present study, the focus was to evaluate the potential of three spectroscopic techniques (Mid Infrared -MIR-, fluorescence, and multispectral imaging -MSI-) to check the level of adulteration in camel milk with goat, cow, and ewe milks. Camel milk was adulterated with goat, ewe, and cow milks, respectively, at 6 different levels viz. 0.5, 1, 2, 5, 10, and 15%. After preprocessing the data with standard normal variate (SNV), multiplicative scattering correction (MSC), and normalization (area under spectrum = 1), partial least squares regression (PLSR) and partial least squares discriminant analysis (PLSDA) were used to predict the adulteration level and their belonging group, respectively. The PLSR and PLSDA models, validated using external data, highlighted that fluorescence spectroscopy was the most accurate technique giving a Rp ranging between 0.63 and 0.96 and an accuracy ranging between 67 and 83%. However, no technique has allowed the construction of robust PLSR and PLSDA models for the simultaneous prediction of contamination of camel milk by the three milks.
在本研究中，重点评估了三种光谱技术（中红外-MIR-、荧光和多光谱成像-MS I-）的潜力，以检查骆驼奶中与山羊、牛和绵羊奶混合的程度。骆驼奶分别与山羊、绵羊和牛奶以 6 个不同的水平混合，即 0.5、1、2、5、10 和 15%。对数据进行标准正态变量（SNV）、乘法散射校正（MSC）和归一化（光谱下面积=1）预处理后，使用偏最小二乘回归（PLSR）和偏最小二乘判别分析（PLSDA）分别预测掺假水平及其所属组。使用外部数据验证的 PLSR 和 PLSDA 模型表明，荧光光谱是最准确的技术，其 Rp 值在 0.63 到 0.96 之间，准确性在 67%到 83%之间。然而，没有一种技术能够构建稳健的 PLSR 和 PLSDA 模型，以同时预测三种奶对骆驼奶的污染。
