Two‐hundred and fifteen Raman spectra of 15 edible oils or blends of edible oils from 53 samples spanning multiple brands purchased over 3 years were investigated using a genetic algorithm for spectral pattern recognition. Using a hierarchical approach to classification, the 15 edible oils could be divided into two groups based on their degree of unsaturation. While edible oils from any particular batch within a class are well clustered and can be differentiated from other varieties of edible oils that are also from a single source, incorporating uncontrolled variability from sources (by purchasing edible oils under different brand names) and seasons (by purchasing edible oils over a 3‐year period) presented a far more challenging classification problem for edible oils within the same group. The between‐source and yearly variability within one class of edible oils is often comparable to differences between the average spectra of the different varieties of edible oils, thereby preventing either a reliable classification of the edible oils or the detection of adulterants in an edible oil if a single model, spanning all sources and years of oils, is to be constructed. The novelty of this study arises from the incorporation of edible oils gathered systematically over the span of 3 years, introducing a heretofore unseen variance to the chemical compositions of the edible oils that are being classified. This is the first time that many different edible oils and commercially available brands thereof have been classified simultaneously.

中文翻译：

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通过拉曼光谱将品牌变异性纳入食用油分类

使用遗传算法进行光谱模式识别，研究了来自 3 年内购买的多个品牌的 53 个样品的 15 种食用油或食用油混合物的 215 拉曼光谱。使用分层方法进行分类，15 种食用油可根据其不饱和度分为两组。虽然同一类别中任何特定批次的食用油都很好地聚集在一起，并且可以与同样来自单一来源的其他食用油品种区分开来，但结合了来源（通过购买不同品牌名称的食用油）和季节（通过3 年期间购买食用油）对同一组内的食用油提出了更具挑战性的分类问题。一类食用油的来源间和年度变化通常与不同食用油品种的平均光谱之间的差异相当，因此，如果出现以下情况，则无法对食用油进行可靠分类或检测食用油中的掺假物。将构建一个单一模型，涵盖所有来源和年份的石油。这项研究的新颖之处在于纳入了 3 年间系统收集的食用油，为正在分类的食用油的化学成分引入了迄今为止未见的差异。这是第一次将许多不同的食用油及其市售品牌同时分类。