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Incorporating brand variability into classification of edible oils by Raman spectroscopy

食用油 拉曼光谱 化学计量学 化学 分析化学(期刊) 数学 模式识别(心理学) 材料科学 色谱法 食品科学 人工智能 计算机科学 物理 光学
作者
Francis Kwofie,Barry K. Lavine,J. M. Ottaway,Karl S. Booksh
出处
期刊:Journal of Chemometrics [Wiley]
卷期号:34 (7) 被引量:10
标识
DOI:10.1002/cem.3173
摘要

Abstract 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.
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