Department of Industrial Engineering, Faculty of Engineering, King Abdulaziz University, P.O. Box 80204, Jeddah 21589, Saudi Arabia.
Yıldız Technical University, Davutpaşa Campus, Chemical and Metallurgical Engineering Faculty, Food Engineering Department, 34210 Istanbul, Turkey.
Food Chem. 2020 Dec 1;332:127344. doi: 10.1016/j.foodchem.2020.127344. Epub 2020 Jun 15.
There is a contentious need for robust and rapid methodologies for maintaining the authenticity of foods and food additives. The current paper presented a new Raman spectroscopy-based methodology for detection and quantification of lard in butter. Hierarchical cluster analysis (HCA) and principal component analysis (PCA) were successfully performed for the classification and discrimination of butter and lard-adulterated samples. Strong discrimination pattern was observed in the HCA analysis. Also, partial least squares regression and principal component regression (R = 0.99) were applied for quantification of lard in butter samples. Quite favorable prediction capabilities were observed in the cross-validation of PLS and PCR analysis for the adulteration levels between 0% and 100% lard fat (w/w). Raman spectroscopy coupled chemometrics was employed effectively for quantification of lard fat in butter fat samples with easy, robust, effective, low-cost and reliable application in the quality control of butter.
对于保持食品和食品添加剂真实性的强有力和快速的方法存在争议性需求。本文提出了一种基于拉曼光谱的新方法，用于检测和定量黄油中的猪油。层次聚类分析（HCA）和主成分分析（PCA）成功地用于分类和区分黄油和掺假猪油样品。在 HCA 分析中观察到强烈的区分模式。此外，还应用偏最小二乘回归和主成分回归（R=0.99）来定量黄油中的猪油。在对 0%至 100%猪油脂肪（w/w）掺假水平的 PLS 和 PCR 分析的交叉验证中，观察到相当有利的预测能力。拉曼光谱结合化学计量学有效地用于定量黄油中的猪油脂肪，具有简单、稳健、有效、低成本和可靠的应用，可用于黄油的质量控制。
