Assis Camila, Gama Ednilton Moreira, Nascentes Clésia Cristina, de Oliveira Leandro Soares, Anzanello Michel José, Sena Marcelo Martins
Departamento de Química, Instituto de Ciências Exatas (ICEx), Universidade Federal de Minas Gerais (UFMG), 31270-901 Belo Horizonte, MG, Brazil.
Departamento de Engenharia Mecânica, Escola de Engenharia, Universidade Federal de Minas Gerais (UFMG), 31270-901 Belo Horizonte, MG, Brazil.
Food Chem. 2020 Apr 30;325:126953. doi: 10.1016/j.foodchem.2020.126953.
This article aims to develop and validate a multivariate model for quantifying Robusta-Arabica coffee blends by combining near infrared spectroscopy (NIRS) and total reflection X-ray fluorescence (TXRF). For this aim, 80 coffee blends (0.0-33.0%) were formulated. NIR spectra were obtained in the wavenumber range 11100-4950 cm and 14 elements were determined by TXRF. Partial least squares models were built using data fusion at low and medium levels. In addition, selection of predictive variables based on their importance indices (SVPII) improved results. The best model reduced the number of variables from 1114 to 75 and root mean square error of prediction from 4.1% to 1.7%. SVPII selected NIR regions correlated with coffee components, and the following elements were chosen: Ti, Mn, Fe, Cu, Zn, Br, Rb, Sr. The model interpretation took advantage of the data fusion between atomic and molecular spectra in order to characterize the differences between these coffee varieties.
本文旨在通过结合近红外光谱（NIRS）和全反射X射线荧光光谱（TXRF）来开发并验证一种用于定量罗布斯塔 - 阿拉比卡咖啡混合物的多元模型。为此，制备了80种咖啡混合物（0.0 - 33.0%）。在11100 - 4950 cm波数范围内获取近红外光谱，并通过TXRF测定14种元素。使用低水平和中等水平的数据融合建立偏最小二乘模型。此外，基于重要性指数选择预测变量（SVPII）改善了结果。最佳模型将变量数量从1114个减少到75个，预测均方根误差从4.1%降至1.7%。SVPII选择了与咖啡成分相关的近红外区域，并选择了以下元素：Ti、Mn、Fe、Cu、Zn、Br、Rb、Sr。模型解释利用了原子光谱和分子光谱之间的数据融合，以表征这些咖啡品种之间的差异。
