Department of Chemistry, University of La Rioja, C/Madre de Dios 51, 26006 Logroño, La Rioja, Spain.
Anal Bioanal Chem. 2011 Feb;399(6):2061-72. doi: 10.1007/s00216-010-4356-6. Epub 2010 Nov 3.
Fourier-transform mid-infrared (FT-MIR) spectroscopy, combined with partial least-squares (PLS) regression and IPW as feature selection method, was used to develop reduced-spectrum calibration models based on a few IR bands to provide near-real-time predictions of two key parameters for the characterization of finished red wines, which are essential from a quality assurance standpoint: total and volatile acidity. Separate PLS calibration models, correlating IR data (only considering those regions showing a high signal to noise ratio) with each response studied, were developed. Wavenumber selection was also performed applying IPW-PLS to take into account only significant predictors, in an attempt to improve the quality of the final models constructed. Using both PLS and IPW-PLS regression, prediction of the two responses modelled was performed with very high reliability, with RMSECV and RMSEP values on the order of 1% (comparable in terms of accuracy to the results provided by the respective reference analysis methods). An important advantage derived from the application of the IPW-PLS method had to do with the low number of original variables needed for modelling both total acidity (22 significant wavenumbers) and volatile acidity (only 11 selected predictor variables), in such a way that variable selection contributed to enhance the stability and parsimony properties of the final calibration models. The high quality of the calibration models proposed encourages the feasibility of implementing them as a fast and reliable tool in routine analysis for the determination of critical parameters for wine quality.
傅里叶变换中红外（FT-MIR）光谱结合偏最小二乘（PLS）回归和 IPW 作为特征选择方法，用于开发基于少数 IR 波段的简化光谱校准模型，以提供对完成红葡萄酒两个关键参数的近实时预测，这从质量保证的角度来看是至关重要的：总酸度和挥发性酸度。针对每个研究的响应，开发了单独的 PLS 校准模型，将 IR 数据（仅考虑那些显示出高信噪比的区域）与每个响应相关联。还应用了 IPW-PLS 进行波数选择，仅考虑重要的预测因子，以尝试提高构建的最终模型的质量。使用 PLS 和 IPW-PLS 回归，对两个模型响应的预测具有非常高的可靠性，RMSECV 和 RMSEP 值在 1%左右（在准确性方面与各自的参考分析方法提供的结果相当）。应用 IPW-PLS 方法的一个重要优势是，对于建模总酸度（22 个显著波数）和挥发性酸度（仅选择 11 个预测变量），所需的原始变量数量较少，以这种方式，变量选择有助于增强最终校准模型的稳定性和简约性。所提出的校准模型的高质量鼓励将其实施为用于确定葡萄酒质量关键参数的常规分析中的快速可靠工具的可行性。
