siPLS-LASSO的近红外特征波长选择及其应用

近红外技术广泛应用于食品、药品等生产过程和产品质量检测，具有样品无需预处理、成本低、无破坏性、测定速度快等优点。但是，全光谱数据维数高、冗余信息多，直接应用于建模会导致模型复杂性高、稳定性差等问题。siPLS是最常见的光谱数据降维方法，但是难以处理光谱数据的共线性问题。LASSO是一种相对新的数据降维方法，但在小样本应用中具有不稳定性。针对siPLS和LASSO在近红外光谱数据应用中存在的问题，提出了基于siPLS-LASSO的近红外特征波长选择方法，并将其应用于秸秆饲料蛋白固态发酵过程pH值监测。该方法首先采用siPLS算法，实现对光谱波长最佳联合子区间的优选；然后，对优选联合子区间使用LASSO算法进行特征波长选择，在此基础上建立PLS校正模型。同时，将siPLS-LASSO方法与其他传统特征波长选择方法进行了对比。结果表明：建立在siPLS-LASSO方法优选33个特征波长基础上的PLS模型预测结果更好，其预测方差(RMSEP)和相关系数(R_p)分别为0.071 1和0.980 8；所提siPLS-LASSO方法有效选取了特征波长，提高了模型预测性能。

Wavelength Selection by siPLS-LASSO for NIR Spectroscopy and Its Application

Near-infrared spectroscopy (NIR) is widely used in entire production processes and product quality test, especially in food and drug industries. It has many advantages, e. g. no requirement of sample pretreatment, low cost, non-destructive detection, and fast determination. However, the application of the whole spectrum data in modeling can lead to complexity and poor stability. The synergy interval PLS(siPLS) is the most common dimensionality reduction method for spectral data. However, it cannot deal with the collinearity problem of spectral data. Least absolute shrinkage and selection operator (LASSO) is a relatively new method for data dimensionality reduction. However, when it comes to small samples, its instability cannot be ignored. For disadvantages of siPLS and LASSO in NIR calibration, a novel wavelength selection method named siPLS-LASSO was proposed. It was validated in a wheat-straw solid-state fermentation process by monitoring pH values. In the method, siPLS was firstly used to selected intervals of NIR spectroscopy. Secondly, LASSO was used to select wavelengths on the selected intervals. Finally, the selected wavelengths were used to construct PLS model for prediction. For comparisons, several conventional wavelength selection methods were also studied. In the case study, 33 wavelengths were eventually selected by the siPLS-LASSO method and used for PLS modelling. The RMSEP and Rp of the model were 0.071 1 and 0.980 8 respectively. Results showed that the proposed siPLS-LASSO was an effective method of wavelength selection and can improve prediction performance of models.