Visible and Near-Infrared Spectroscopic Discriminant Analysis Applied to Brand Identification of Wine

High-end wine brand is made through the use of high-quality grape variety and yeast strain, and through a unique process. Not only is it rich in nutrients, but also it has a unique taste and a fragrant scent. Brand identification of wine is difficult and complex because of high similarity. In this paper, visible and near-infrared (NIR) spectroscopy combined with partial least squares discriminant analysis (PLS-DA) was used to explore the feasibility of wine brand identification. Chilean Aoyo wine (2016 vintage) was selected as the identification brand (negative, 100 samples), and various other brands of wine were used as interference brands (positive, 373 samples). Samples of each type were randomly divided into the calibration, prediction and validation sets. For comparison, the PLS-DA models were established in three independent and two complex wavebands of visible (400 - 780 nm), short-NIR (780 - 1100 nm), long-NIR (1100 - 2498 nm), whole NIR (780 - 2498 nm) and whole scanning (400 - 2498 nm). In independent validation, the five models all achieved good discriminant effects. Among them, the visible region model achieved the best effect. The recognition-accuracy rates in validation of negative, positive and total samples achieved 100%, 95.6% and 97.5%, respectively. The results indicated the feasibility of wine brand identification with Vis-NIR spectroscopy.

Rapid and simultaneous analysis of multiple wine quality indicators through near-infrared spectroscopy with twice optimization for wavelength model

Alcohol,total sugar,total acid,and total phenol contents are the main indicators of wine quality detection.This study aims to establish simultaneous analysis models for the four indicators through near-infrared(NIR)spectroscopy with wavelength optimization.A Norris derivative filter(NDF)platform with multiparameter optimization was established for spectral pretreatment.The optimal parameters(i.e.,derivative order,number of smoothing points,and number of differential gaps)were(2,9,3)for alcohol,(1,19,5)for total sugar,(1,17,11)for total acid,and(1,1,1)for total phenol.The equidistant combinationpartial least squares(EC-PLS)was used for large-scale wavelength screening.The wavelength step-by-step phaseout PLS(WSP-PLS)and exhaustive methods were used for secondary optimization.The final optimization models for the four indicators included 7,10,15,and 13 wavelengths located in the overtone or combination regions,respectively.In an independent validation,the root mean square errors,correlation coefficient for prediction(i.e.,SEP and RP),and ratio of performance-to-deviation(RPD)were 0.41 v/v,0.947,and 3.2 for alcohol;1.48 g/L,0.992,and 6.8 for total sugar;0.68 g/L,0.981,and5.1 for total acid;and 0.181 g/L,0.948,and 2.9 for total phenol.The results indicate high correlation,low error,and good overall prediction performance.Consequently,the established reagent-free NIR analytical models are important in the rapid and real-time quality detection of the wine fermentation process and finished products.The proposed wavelength models provide a valuable reference for designing small dedicated instruments.