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. 2019 May 30;281:71-77. doi: 10.1016/j.foodchem.2018.12.044. Epub 2018 Dec 19.
This paper describes a robust multivariate model for quantifying and characterizing blends of Robusta and Arabica coffees. At different degrees of roasting, 120 ground coffee blends (0.0-33.0%) were formulated. Spectra were obtained by two different techniques, attenuated total reflectance Fourier transform infrared (ATR-FTIR) spectroscopy and paper spray mass spectrometry (PS-MS). Partial least squares (PLS) models were built individually with the two types of spectra. Nevertheless, better predictions were obtained by low and medium-level data fusion, taking advantage from the synergy between these two data sets. Data fusion models were improved by variable selection, using genetic algorithms (GA) and ordered predictors selection (OPS). The smallest prediction errors were provided by OPS low-level data fusion model. The number of variables used for regression was reduced from 2145 (full spectra) to 230. Model interpretation was performed by assigning some of the selected variables to specific coffee components, such as trigonelline and chlorogenic acids.
本文描述了一种稳健的多元模型，用于量化和表征罗布斯塔和阿拉比卡咖啡的混合。在不同的烘焙程度下，配制了 120 种研磨咖啡混合物（0.0-33.0%）。通过两种不同的技术获得光谱，衰减全反射傅里叶变换红外（ATR-FTIR）光谱和纸喷雾质谱（PS-MS）。分别用这两种光谱建立了偏最小二乘（PLS）模型。然而，通过低水平和中等水平的数据融合，可以更好地进行预测，利用这两个数据集之间的协同作用。通过遗传算法（GA）和有序预测器选择（OPS）进行变量选择，改进了数据融合模型。OPS 低水平数据融合模型提供了最小的预测误差。用于回归的变量数量从 2145 个（全光谱）减少到 230 个。通过将某些选定的变量分配给特定的咖啡成分，如葫芦巴碱和绿原酸，对模型进行了解释。
