Use of FTIR-ATR Spectroscopy Combined with Multivariate Analysis as a Screening Tool to Identify Adulterants in Raw Milk

The objective of this study was to use Fourier transform infrared (FTIR) spectroscopy combined with multivariate analysis to identify adulterations in raw milk and in samples from producers. Five levels of concentration of sodium bicarbonate, sodium hydroxide, hydrogen peroxide, starch, sucrose and urea were used. A total of 620 samples previously adulterated, frozen and lyophilized were analyzed in FTIR-attenuated total reflection (ATR) equipment and 15 peaks of the spectra were obtained. With the multiple linear regression method for samples adulterated with sodium bicarbonate, sucrose and urea, a coefficient greater than 75% was obtained, and with artificial neural networks all adulterated samples obtained a percentage of correctness greater than 76.6%, making it possible to identify adulterants from 0.1%. Of the 249 samples of producers analyzed, 2.4% were adulterated. With the use of FTIR allied to the multivariate analysis as a screening method, it was possible to obtain a satisfactory classification for the adulterated samples in this study.

本研究旨在采用傅里叶变换红外（Fourier Transform Infrared, FTIR）光谱法结合多元分析技术，识别生乳及生产者样品中的掺假问题。本次实验设置了碳酸氢钠、氢氧化钠、过氧化氢、淀粉、蔗糖及尿素共6种掺假物的5级浓度梯度。总计620份预先完成掺假处理的冷冻冻干样品，通过傅里叶变换红外衰减全反射（Attenuated Total Reflection, ATR）设备进行光谱检测，最终获得15个光谱特征峰。针对掺入碳酸氢钠、蔗糖及尿素的样品，采用多元线性回归法分析时，模型拟合系数均高于75%；而采用人工神经网络对所有掺假样品进行判别时，正确率均超过76.6%，可实现0.1%及以上含量掺假物的有效识别。在249份生产者送检样品中，有2.4%的样品存在掺假情况。本研究表明，将FTIR光谱法与多元分析结合作为筛查手段，可对掺假生乳样品实现令人满意的分类识别效果。