Fourier transform infrared (FT-IR) spectroscopy combined with chemometric methods was used to detect multiple adulterants in milk samples simultaneously. PLS-DA (partial least squares discriminant analysis) and SVM (support vector machine) were used for the 100% accurate classification of samples to differentiate the adulterants. RCGA (real coded genetic algorithm) was used to obtain 20, 30, and 40 different fingerprint wavenumbers from milk FT-IR spectra when spiked with starch, urea, and sucrose. Amongst the four algorithms tested, the performance of LS-SVM was observed to be superior having higher values for correlation coefficient (${\text{R}}_{\text{p}}^2$) for prediction of 0.9843, 0.9763, and 0.9964 and lower root-mean-square error of prediction (RMSEP) of 0.4197, 0.2617, and 0.3771 for starch, urea, and sucrose, respectively. RCGA was established as an efficient feature selection algorithm for obtaining user-defined fingerprints. Also, LS-SVM was demonstrated as a robust non-linear regression algorithm for simultaneous detection of milk adulterants.

傅里叶变换红外 (FT-IR) 光谱结合化学计量学方法用于同时检测牛奶样品中的多种掺假物。PLS-DA（偏最小二乘判别分析）和 SVM（支持向量机）用于对样品进行 100% 准确分类以区分掺假品。当添加淀粉、尿素和蔗糖时，RCGA（实数编码遗传算法）用于从牛奶 FT-IR 光谱中获得 20、30 和 40 个不同的指纹波数。在测试的四种算法中，LS-SVM 的性能被观察到具有更高的相关系数值（${\text{电阻}}_{\text{磷}}^2$) 的预测值为 0.9843、0.9763 和 0.9964，而淀粉、尿素和蔗糖的预测均方根误差 (RMSEP) 分别为 0.4197、0.2617 和 0.3771。RCGA 被确立为一种有效的特征选择算法，用于获取用户定义的指纹。此外，LS-SVM 被证明是一种鲁棒的非线性回归算法，用于同时检测牛奶掺假。