Rapid detection of three quality parameters and classification of wine based on Vis-NIR spectroscopy with wavelength selection by ACO and CARS algorithms

• Published in: Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy Vol. 205; pp. 574 - 581
• Main Authors: Hu, Leqian, Yin, Chunling, Ma, Shuai, Liu, Zhimin
• Format: Journal Article
• Language: English
• Published: England Elsevier B.V 05.12.2018
• Subjects: Algorithms; Least-Squares Analysis; Modified ant colony optimization algorithm; Principal Component Analysis; Quality parameters; Spectroscopy, Near-Infrared - methods; Visible and near infrared spectroscopy; Wavelength selection; Wine; Wine - analysis; Wine - classification; Wine; Modified ant colony optimization algorithm; Wavelength selection; Quality parameters; Visible and near infrared spectroscopy
• ISSN: 1386-1425; 1873-3557
• DOI: 10.1016/j.saa.2018.07.054

The feasibility of rapid detection of three quality parameters and classification of wines based on visible and near infrared spectroscopy (Vis-NIRs) was investigated. A modified ant colony optimization (ACO) algorithm for wavelength selection in Vis-NIR spectral analysis was proposed to improve the prediction performance of partial least squares regression (PLSR) model. The result proved that feature wavelengths/variables can be selected by the proposed method for building a high performance PLSR model. The root mean square error of total acid, total sugar and alcohol obtained by ACO-PLS were 0.00122 mol/l, 0.0893 g/l and 0.206 (v/v), respectively. Their correlation coefficients obtained by ACO-PLS reach to 0.973, 0.994 and 0.928, respectively. Compared with full-spectral PLS and PLS based on competitive adaptive reweighted sampling (CARS-PLS) method, the application of ACO wavelength selection provided a notably improved regression model. The prediction results were significantly better than the full-spectral PLS model and slightly better than the CARS-PLS method. Meanwhile, a classification study was also constructed based on the ACO-Principal component analysis (ACO-PCA) model showed that Vis-NIR spectra could be used to classify wines according to the geographical origins. Therefore, it can be concluded that the Vis-NIR spectroscopy technique based on ACO wavelength selection has high potential to differentiate the wine origins and predict the quality parameters in a nondestructive way. [Display omitted] •Rapid detection of three parameters and classification of wines based on Vis-NIRs was investigated.•Modified ACO algorithm was proposed to select the feature wavelength in Vis-NIR spectral analysis.•Application of ACO wavelength selection provided a notably improved PLSR regression model.•Vis-NIRs based on ACO-PLSR has high potential to predict the quality parameters of wines.