用FTIR光谱学对油和黄油进行分类,并配合机器学习
Christabel Y E Tachie1, Daniel Obiri-Ananey2, Marcela Alfaro-Cordoba3
1Delaware State University, College of Agriculture, Science and Technology, Department of Human Ecology (Food Science & Biotechnology Program), 1200 N DuPont Highway, Dover, DE 19901, USA.
Food chemistry
|August 23, 2023
概括
这项研究将ATR-FTIR光谱学和机器学习 (ML) 结合起来,准确地识别和分类纯和伪造的食用油和黄油. 该KNN模型在分类纯油和量化伪造水平方面表现出卓越的性能.
科学领域:
- 分析化学 分析化学
- 化学测量 化学测量 化学测量
- 食品科学 食品科学 食品科学
背景情况:
- 食用油的改是一个重大的问题,影响食品安全和经济完整性.
- 精确识别和量化油成分对于质量控制至关重要.
研究的目的:
- 评估ATR-FTIR光谱学和机器学习 (ML) 的联合有效性,用于识别和分类纯和伪造的食用油和黄油.
- 量化各种油混合物的改程度.
主要方法:
- 使用减弱总反射度-里埃变换红外光谱法 (ATR-FTIR) 来获得油和黄油的光谱指纹.
- 机器学习算法,包括K-Nearest Neighbors (KNN),物流回归 (LR),支持矢量机 (SVM),LightGBM和决策树 (DT) 被用于分类和回归分析.
- 主要组件分析 (PCA) 用于数据可视化和维度缩小.
主要成果:
- 纯玛格林的分类准确率达到了100%. 在纯油分类方面,KNN实现了97%的准确性,优于其他ML模型.
- 回归模型在量化改方面表现良好,不同油类和改剂的R2值在45%至99%之间.
- PCA有效地可视化了不同油样的分离和聚类.
结论:
- 综合ATR-FTIR光谱和ML提供了一种强大而准确的方法来检测食用油和黄油的真实性和伪造.
- 在这项研究中,KNN成为了对分类和量化任务最有效的ML模型.
- 这种方法为食品行业的质量保证提供了一个有前途的工具,确保产品完整性和消费者安全.
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