[1] FRANKEL E N, GERMAN J B, KINSELLA J E, et al.. Inhibition of oxidation of human low-density lipoprotein by phenolic substances in red wine ［J］. The Lancet, 1993, 341(8843):454-457.

[2] BIANCA F, ALEXANDRA L, MICHAEL A. Consumption of red wine with meals reduces the susceptibility of human plasma and low-density lipoprotein to lipid peroxidation ［J］. Am. J. Clinic.Nutrit., 1995, 61(3):549-554.

[3] FERRARI E, FOCA G, VIGNALI M, et al.. Adulteration of the anthocyanin content of red wines: perspectives for authentication by Fourier transform-near infrared and 1H NMR spectroscopies ［J］. Anal. Chim. Acta, 2011, 701(2):139-151.

[4] URBANO C M, LUQUE D C M, PEREZ J P, et al.. Comparison and joint use of near infrared spectroscopy and Fourier transform mid infrared spectroscopy for the determination of wine parameters ［J］. Talanta, 2005, 66(1):218-224.

[5] GEANA E, POPESCU R, COSTINEL D, et al.. Verifying the red wines adulteration through isotopic and chromatographic investigations coupled with multivariate statistic interpretation of the data ［J］. Food Control, 2016, 62:1-9.

[6] 陈士恒, 章晴, 史晓梅, 等. DSPE-GC/MS快速检测葡萄酒中52种农药残留 ［J］. 食品科学技术学报, 2014, 32(3):14-22.

    CHEN S H, ZHANG Q, SHI X M, et al.. Fast determination of 52 pesticide residues in grape wine by DSPEGC/MS ［J］. J. Food Sci. Technol., 2014, 32(3):14-22. (in Chinese)

[7] 吴正宗, 王芳, 徐恩波, 等. 傅立叶红外光谱技术快速检测黄酒中的氨基酸质量浓度 ［J］. 食品与生物技术学报, 2017, 36(1):34-40.

    WU Z Z, WANG F, XU E B, et al.. Rapid determination of amino acids in chinese rice wine by fourier transform infrared spectroscopy ［J］. J. Food Sci. Biotechnol., 2017, 36(1):34-40. (in Chinese)

[8] BASALEKOU M, PAPPAS C, KOTSERIDIS Y, et al.. Red wine age estimation by the alteration of its color parameters: Fourier transform infrared spectroscopy as a tool to monitor wine maturation time ［J］. J. Anal. Methods Chem., 2017, 2017:1-9.

[9] URBANO-CUARDRADO M, LUQUE D C M, PEREZ J P, et al.. Near infrared reflectance spectroscopy and multivariate analysis in enology ［J］. Anal. Chim. Acta, 2004, 527(1):81-88.

[10] COZZOLINO D, KWIATOWSKI M, PARKER M, et al.. Prediction of phenolic compounds in red wine fermentations by visible and near infrared spectroscopy ［J］. Anal. Chim. Acta, 2004, 513(1):73-80.

[11] SWIERENGA H, WULFERT F, NOORD O, et al.. Development of robust calibration models in near infrared spectrometric applications ［J］. Anal. Chim. Acta, 2000, 411(1):121-135.

[12] LEARDI R, LUPIANEZ G A. Genetic algorithms applied to feature selection in PLS regression: how and when to use them ［J］. Chemometrics Intellig. Lab. Syst., 1998, 41(2):195-207.

[13] BLANCO M, COELL J, ITURRIAGA H, et al.. NIR calibration in non-linear systems: different PLS approaches and artificial neural networks ［J］. Chemometrics Intellig. Lab. Syst., 2000, 50(1):75-82.

[14] CENTNER V, MASSART D L, NOORD O, et al.. Elimination of uninformative variables for multivariate calibration ［J］. Anal. Chem., 1996, 68(21):3851-3858.

[15] ARAUJO M, SALDANHA T, GALVAO R, et al.. The successive projections algorithm for variable selection in spectroscopic multicomponent analysis ［J］. Chemometrics Intellig. Lab. Syst., 2001, 57(2):65-73.

[16] ZHENG J, SONG Z, GE Z. Probabilistic learning of partial least squares regression model: theory and industrial applications ［J］. Chemometrics Intellig. Lab. Syst., 2016, 158:80-90.

[17] 陈家益, 赵忠盖, 刘飞. 鲁棒PPLS模型及其在过程监控中的应用 ［J］. 化工学报, 2016, 67(7):2907-2915.

    CHEN J Y, ZHAO Z G, LIU F. Robust PPLS model and its applications in process monitoring ［J］. J. Chem. Ind. Eng., 2016, 67(7):2907-2915. (in Chinese)

[18] GE Z. Supervised latent factor analysis for process data regression modeling and soft sensor application ［J］. IEEE Trans. Control Syst. Technol., 2016, 24(3):203-209.

[19] LI C, ZHAO T, LI C, et al.. Determination of gossypol content in cottonseeds by near infrared spectroscopy based on Monte Carlo uninformative variable elimination and nonlinear calibration methods ［J］. Food Chem., 2017, 221:990-996.

[20] JIE D, XIE L, FU X, et al.. Variable selection for partial least squares analysis of soluble solids content in watermelon using near-infrared diffuse transmission technique ［J］. J. Food Eng., 2013, 118(4):387-392.

[21] 马红辉, 王中江, 袁天军, 等. 烟草中淀粉近红外光谱变量的筛选及校正模型的建立 ［J］. 烟草科技, 2015, 48(8):37-43.

    MA H H, WANG Z J, YUAN T J, et al.. Screening of NIR spectral variables of starch in tobacco and establishment of calibration model ［J］. Tobacco Sci. Technol., 2015, 48(8):37-43. (in Chinese)

[22] COZZOLINO D, CYNKAR W, SHAH N, et al.. Multivariate data analysis applied to spectroscopy: potential application to juice and fruit quality ［J］. Food Res. Int., 2011, 44(7):1888-1896.

[23] 向伶俐, 李梦华, 李景明, 等. 近、中红外光谱法融合判定葡萄酒产地 ［J］. 光谱学与光谱分析, 2014, 34(10):2662-2666.

    XIANG L L, LI M H, LI J M, et al.. Determination of wine original regions using information fusion of NIR and MIR spectroscopy ［J］. Spectrosc. Spect. Anal., 2014, 34(10):2662-2666. (in Chinese)