Group of Analytical Chemistry of Wine and Food, Department of Analytical Chemistry and Organic Chemistry, Universitat Rovira i Virgili, Campus Sescelades, Facultat d'Enologia, 43007 Tarragona, Spain.
Anal Bioanal Chem. 2010 Aug;397(7):3043-9. doi: 10.1007/s00216-010-3852-z. Epub 2010 Jun 2.
In this work, the ability of an electronic tongue based on Fourier-Transform Mid Infrared (FT-MIR) spectroscopy as a gustative sensor is assessed by emulating the responses of a tasting panel for the gustative mouthfeel "tannin amount". The FT-MIR spectra were modeled against the sensory responses evaluated in 37 red wines by means of partial least squares (PLS) regression models. In order to find the wavenumbers more correlated with the sensorial attribute and thus providing the best predictive models, six different variable selection techniques were tested. The iterative predictor weighting IPW-PLS technique showed the best results with the smallest RMSEC and RMSECV values (0.07 and 0.13, respectively) using 20 selected wavenumbers. The coincident wavenumbers selected by the six variable selection techniques were interpreted based on the absorption bands of tannin and then a calibration model using these wavenumbers was built to validate the interpretation made.
在这项工作中，通过模拟味觉口感“单宁含量”品尝小组的响应，评估基于傅里叶变换中红外（FT-MIR）光谱的电子舌作为味觉传感器的能力。使用偏最小二乘（PLS）回归模型，将 FT-MIR 光谱与通过感官评估的 37 种红葡萄酒的响应进行建模。为了找到与感官属性更相关的波数，从而提供最佳的预测模型，测试了六种不同的变量选择技术。迭代预测加权 IPW-PLS 技术使用 20 个选定的波数，表现出最好的结果，具有最小的 RMSEC 和 RMSECV 值（分别为 0.07 和 0.13）。根据单宁的吸收带解释六种变量选择技术选择的一致波数，然后使用这些波数建立校准模型以验证所做的解释。
