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Characterising variances of milk powder and instrumentation for the development of a non-targeted Raman spectroscopy and chemometrics detection method for the evaluation of authenticity

机译：表征奶粉的差异和用于开发非目标拉曼光谱和化学计量学检测方法的仪器以评估真伪

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摘要

There is a need to develop rapid tools to screen milk products for economically motivated adulteration. An understanding of the physiochemical variability within skim milk powder (SMP) and non-fat dry milk (NFDM) is the key to establishing the natural differences of these commodities prior to the development of non-targeted detection methods. This study explored the sources of variance in 71 commercial SMP and NFDM samples using Raman spectroscopy and principal component analysis (PCA) and characterised the largest number of commercial milk powders acquired from a broad number of international manufacturers. Spectral pre-processing using a gap-segment derivative transformation (gap size = 5, segment width = 9, fourth derivative) in combination with sample normalisation was necessary to reduce the fluorescence background of the milk powder samples. PC scores plots revealed no clear trends for various parameters, including day of analysis, powder type, supplier and processing temperatures, while the largest variance was due to irreproducibility in sample positioning. Significant chemical sources of variances were explained by using the spectral features in the PC loadings plots where four samples from the same manufacturer were determined to likely contain an additional component or lactose anomers, and one additional sample was identified as an outlier and likely containing an adulterant or differing quality components. The variance study discussed herein with this large, diverse set of milk powders holds promise for future use as a non-targeted screening method that could be applied to commercial milk powders.
机译：需要开发快速的工具来筛选牛奶产品以进行出于经济动机的掺假。了解脱脂奶粉（SMP）和脱脂奶粉（NFDM）内的理化变异性是在开发非目标检测方法之前确定这些商品的自然差异的关键。这项研究使用拉曼光谱和主成分分析（PCA）探索了71种商业SMP和NFDM样品中的方差来源，并表征了从众多国际制造商处购得的最大数量的商业奶粉。使用间隙段导数转换（间隙大小= 5，片段宽度= 9，四阶导数）和样品归一化进行光谱预处理对于减少奶粉样品的荧光背景是必要的。 PC分数图显示各种参数（包括分析日，粉末类型，供应商和加工温度）没有明确的趋势，而最大的差异是由于样品定位的不可再现性。通过使用PC装载图中的光谱特征解释了重要的化学差异来源，其中确定了来自同一制造商的四个样品可能含有其他成分或乳糖异构体，另外一个样品被鉴定为离群值且可能包含掺假物或不同质量的组件。本文讨论的使用这种大而多样的奶粉组合进行的方差研究为将来用作可用于商业奶粉的非目标筛选方法提供了希望。

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