PDF(Pubmed)
Abstract：
There is an evident requirement for a rapid, efficient, and simple method to screen the authenticity of milk products in the market. Fourier transform infrared (FTIR) spectroscopy stands out as a promising solution. This work employed FTIR spectroscopy and modern statistical machine learning algorithms for the identification and quantification of pasteurized milk adulteration. Comparative results demonstrate modern statistical machine learning algorithms will improve the ability of FTIR spectroscopy to predict milk adulteration compared to partial least square (PLS). To discern the types of substances utilized in milk adulteration, a top-performing multiclassification model was established using multi-layer perceptron (MLP) algorithm, delivering an impressive prediction accuracy of 97.4 %. For quantification purposes, bayesian regularized neural networks (BRNN) provided the best results for the determination of both melamine, urea and milk powder adulteration, while extreme gradient boosting (XGB) and projection pursuit regression (PPR) gave better results in predicting sucrose and water adulteration levels, respectively. The regression models provided suitable predictive accuracy with the ratio of performance to deviation (RPD) values higher than 3. The proposed methodology proved to be a cost-effective and fast tool for screening the authenticity of pasteurized milk in the market.
摘要：
有一个明显的要求，快速，高效,和简单的方法来筛选牛奶产品在市场上的真实性。傅里叶变换红外（FTIR）光谱是一种有前途的解决方案。这项工作采用FTIR光谱和现代统计机器学习算法来识别和定量巴氏杀菌牛奶掺假。比较结果表明，与偏最小二乘（PLS）相比，现代统计机器学习算法将提高FTIR光谱预测牛奶掺假的能力。为了辨别牛奶掺假中使用的物质类型，使用多层感知器（MLP）算法建立了性能最好的多分类模型，提供了令人印象深刻的97.4%的预测精度。出于量化目的，贝叶斯正则化神经网络(BRNN)为两种三聚氰胺的测定提供了最佳结果，尿素和奶粉掺假，而极端梯度提升（XGB）和投影追踪回归（PPR）在预测蔗糖和水掺假水平方面给出了更好的结果，分别。回归模型提供了合适的预测准确性，性能与偏差（RPD）值的比率高于3。所提出的方法被证明是一种经济有效且快速的工具，用于筛选市场上巴氏杀菌奶的真实性。
