Rio de Janeiro State University, Chemical Engineering Graduate Program, Rio de Janeiro 20550-013, RJ, Brazil.
Rio de Janeiro State University, Chemical Engineering Graduate Program, Rio de Janeiro 20550-013, RJ, Brazil.
Food Chem. 2022 Mar 1;371:131063. doi: 10.1016/j.foodchem.2021.131063. Epub 2021 Sep 7.
This work aims the study chemometric methods for the classification of the origin of coffee samples. Samples of finely pulverized coffee grains were analyzed by synchronous molecular fluorescence spectroscopy to carry out the classification. The spectral data of the samples were obtained in triplicate in two offsets: 10 nm (with emission wavelengths from 240 nm to 600 nm) and 40 nm (from 240 nm to 560 nm), all with 1 nm resolution. Different strategies were performed using the spectra obtained with the offsets of 10 nm and 40 nm and fused data at mid-level (10 nm + 40 nm). The performances of linear and nonlinear methods were compared, the best results were obtained from the raw data from the fusion at low-level of the 10 nm and 40 nm offset spectra with the Pareto optimization criterion.
本工作旨在研究化学计量学方法,以对咖啡样品的产地进行分类。对精细研磨的咖啡颗粒样品进行同步分子荧光光谱分析,以进行分类。在两个偏移量下(10nm 和 40nm,发射波长范围分别为 240nm 至 600nm 和 240nm 至 560nm,分辨率均为 1nm)对样品的光谱数据进行了三次重复测量。使用 10nm 和 40nm 偏移量获得的光谱以及中间水平(10nm+40nm)融合数据执行了不同的策略。比较了线性和非线性方法的性能,在低水平融合的情况下,使用 Pareto 优化标准从原始数据中获得了最佳结果 10nm 和 40nm 偏移光谱。