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

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.

[1]  Alejandro C. Olivieri,et al.  Visible/near infrared-partial least-squares analysis of Brix in sugar cane juice: A test field for variable selection methods , 2010 .

[2]  S. Wold,et al.  PLS-regression: a basic tool of chemometrics , 2001 .

[3]  M. C. U. Araújo,et al.  The successive projections algorithm for variable selection in spectroscopic multicomponent analysis , 2001 .

[4]  Jian-Hui Jiang,et al.  Modified Ant Colony Optimization Algorithm for Variable Selection in QSAR Modeling: QSAR Studies of Cyclooxygenase Inhibitors , 2005, J. Chem. Inf. Model..

[5]  C. Du,et al.  A new HPLC method for simultaneously measuring chloride, sugars, organic acids and alcohols in food samples , 2017 .

[6]  M. Pérez-Coello,et al.  Wine science in the metabolomics era , 2015 .

[7]  Turgut Cabaroğlu,et al.  HPLC determination of organic acids, sugars, phenolic compositions and antioxidant capacity of orange juice and orange wine made from a Turkish cv. Kozan , 2009 .

[8]  J. Huidobro,et al.  A review of the analytical methods to determine organic acids in grape juices and wines , 2005 .

[9]  Franco Allegrini,et al.  A new and efficient variable selection algorithm based on ant colony optimization. Applications to near infrared spectroscopy/partial least-squares analysis. , 2011, Analytica chimica acta.

[10]  Hongdong Li,et al.  Key wavelengths screening using competitive adaptive reweighted sampling method for multivariate calibration. , 2009, Analytica chimica acta.

[11]  Nathalie Locquet,et al.  Using pH variations to improve the discrimination of wines by 3D front face fluorescence spectroscopy associated to Independent Components Analysis. , 2016, Talanta.

[12]  Ernestina Casiraghi,et al.  NIR and MIR spectroscopy as rapid methods to monitor red wine fermentation , 2010 .

[13]  Wenxiu Pan,et al.  Real-time monitoring of process parameters in rice wine fermentation by a portable spectral analytical system combined with multivariate analysis. , 2016, Food chemistry.

[14]  R. Leardi,et al.  Sequential application of backward interval partial least squares and genetic algorithms for the selection of relevant spectral regions , 2004 .

[15]  Marco S. Reis,et al.  Madeira wine ageing prediction based on different analytical techniques: UV–vis, GC-MS, HPLC-DAD , 2011 .

[16]  Di Wu,et al.  Determination of alpha-linolenic acid and linoleic acid in edible oils using near-infrared spectroscopy improved by wavelet transform and uninformative variable elimination. , 2009, Analytica chimica acta.

[17]  Paul Geladi,et al.  Principal Component Analysis , 1987, Comprehensive Chemometrics.

[18]  Wei Li,et al.  Improvement of near infrared spectroscopic (NIRS) analysis of caffeine in roasted Arabica coffee by variable selection method of stability competitive adaptive reweighted sampling (SCARS). , 2013, Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy.

[19]  G. Downey,et al.  Hyperspectral imaging combined with principal component analysis for bruise damage detection on white mushrooms (Agaricus bisporus) , 2008 .

[20]  Delia B. Rodriguez-Amaya,et al.  Rapid method for the determination of organic acids in wine by capillary electrophoresis with indirect UV detection , 2009 .

[21]  Pedro Melo-Pinto,et al.  Comparison of different approaches for the prediction of sugar content in new vintages of whole Port wine grape berries using hyperspectral imaging , 2017, Comput. Electron. Agric..

[22]  Xueguang Shao,et al.  A wavelength selection method based on randomization test for near-infrared spectral analysis , 2009 .

[23]  R. Lamuela-Raventós,et al.  Effects of wine, alcohol and polyphenols on cardiovascular disease risk factors: evidences from human studies. , 2013, Alcohol and alcoholism.

[24]  G. Foca,et al.  Prediction of parameters related to grape ripening by multivariate calibration of voltammetric signals acquired by an electronic tongue. , 2018, Talanta.

[25]  Yibin Ying,et al.  A feasibility study on on-line determination of rice wine composition by Vis–NIR spectroscopy and least-squares support vector machines , 2009 .

[26]  B. Cho,et al.  Rapid monitoring of the fermentation process for Korean traditional rice wine ‘Makgeolli’ using FT-NIR spectroscopy , 2015 .

[27]  William J. Astle,et al.  Risk thresholds for alcohol consumption: combined analysis of individual-participant data for 599 912 current drinkers in 83 prospective studies , 2018, The Lancet.

[28]  A. Lourenço,et al.  Voltammetric determination of tartaric acid in wines by electrocatalytic oxidation on a cobalt(II)-phthalocyanine-modified electrode associated with multiway calibration. , 2018, Analytica chimica acta.

[29]  Riccardo Leardi,et al.  Application of genetic algorithm–PLS for feature selection in spectral data sets , 2000 .