Department of Chemistry, Federal University of Paraíba, C.P. 5093, 58051-970 João Pessoa, PB, Brazil.
Department of Chemistry, Federal University of Paraíba, C.P. 5093, 58051-970 João Pessoa, PB, Brazil.
Food Chem. 2019 Feb 1;273:31-38. doi: 10.1016/j.foodchem.2018.04.136. Epub 2018 Apr 30.
This work presents a simple and low-cost analytical approach to detect adulterations in ground roasted coffee by using voltammetry and chemometrics. The voltammogram of a coffee extract (prepared as simulating a home-made coffee cup) obtained with a single working electrode is submitted to pattern recognition analysis preceded by variable selection to detect the addition of coffee husks and sticks (adulterated/unadulterated), or evaluate the shelf-life condition (expired/unexpired). Two pattern recognition methods were tested: linear discriminant analysis (LDA) with variable selection by successive projections algorithm (SPA), or genetic algorithm (GA); and partial least squares discriminant analysis (PLS-DA). Both LDA models presented satisfactory results. The voltammograms were also evaluated for the quantitative determination of the percentage of impurities in ground roasted coffees. PLS and multivariate linear regression (MLR) preceded by variable selection with SPA or GA were evaluated. An excellent predictive power (RMSEP = 0.05%) was obtained with MLR aided by GA.
本工作提出了一种简单且低成本的分析方法，通过伏安法和化学计量学来检测研磨咖啡中的掺杂物。使用单工作电极获得的咖啡提取物（模拟自制咖啡杯）的伏安图经过变量选择进行模式识别分析，以检测咖啡壳和棒（掺假/未掺假）的添加，或评估保质期条件（过期/未过期）。测试了两种模式识别方法：逐步投影算法（SPA）或遗传算法（GA）变量选择的线性判别分析（LDA）；和偏最小二乘判别分析（PLS-DA）。两种 LDA 模型均取得了令人满意的结果。还评估了伏安图用于定量测定研磨咖啡中杂质的百分比。对 SPA 或 GA 变量选择预处理的偏最小二乘和多元线性回归（MLR）进行了评估。通过 GA 辅助的 MLR 获得了优异的预测能力（RMSEP=0.05%）。
