Abstract Infrared spectroscopy (IRs) coupling with chemometric methods were used to predict principal quality parameters in wine. A new strategy of variable (wavelength) selection named as Fisher Discriminant-Variable Selection (FD-VS) model was constructed. Characteristic variables were selected from Infrared spectra based on the absolute values of eigenvector obtained by Fisher Discriminant Function. The FD-VS method was combined with quantitative models including Principal Component Regression (PCR), Partial Least Squares (PLS) and Least Squares Support Vector Regression (LSSVR), which were utilized for prediction of multiple principal quality parameters of red wine. It is shown that FD-VS method obviously improves the performances of PCR, PLS and LSSVR models. Then four variable selection methods based on PLS regression including Competitive Adaptive Reweighted Sampling (CARS)-PLS, Uninformative Variable Elimination (UVE)-PLS, Interval Partial Least Squares (IPLS) and Moving Windows Partial Least Squares (MWPLS) were also compared. The results also show good performance of FD-VS-LSSVR in terms of prediction accuracy or robustness. Therefore, the FD-VS method provides an effective and credible variable selection way for IR spectrum to predict quality parameters of wine.

中文翻译：

---

使用红外光谱结合化学计量学方法快速预测多个葡萄酒质量参数

摘要 结合化学计量学方法的红外光谱 (IR) 被用于预测葡萄酒的主要质量参数。构建了一种新的变量（波长）选择策略，称为Fisher判别变量选择（FD-VS）模型。基于通过Fisher判别函数获得的特征向量的绝对值，从红外光谱中选择特征变量。FD-VS 方法结合包括主成分回归 (PCR)、偏最小二乘法 (PLS) 和最小二乘支持向量回归 (LSSVR) 在内的定量模型，用于预测红酒的多个主要质量参数。结果表明，FD-VS 方法明显提高了 PCR、PLS 和 LSSVR 模型的性能。然后还比较了基于 PLS 回归的四种变量选择方法，包括竞争性自适应重加权采样 (CARS)-PLS、无信息变量消除 (UVE)-PLS、区间偏最小二乘法 (IPLS) 和移动窗口偏最小二乘法 (MWPLS)。结果还表明 FD-VS-LSSVR 在预测精度或鲁棒性方面的良好性能。因此，FD-VS方法为红外光谱预测葡萄酒的质量参数提供了一种有效且可信的变量选择方法。