Comparison and Determination of Acetic Acid of Plum Vinegar Using Visible/Near Infrared Spectroscopy and Multivariate Calibration

Visible and near infrared (Vis/NIR) spectroscopy was investigated to determine the acetic acid of plum vinegar based on three different calibration methods, including partial least squares analysis (PLS), multiple linear regression (MLR) and least squares-support vector machine (LS-SVM). Five concentration levels (100%, 80%, 60%, 40% and 20%) of plum vinegar were studied with 60 samples for each level. PLS was the calibration method as well as extraction method for latent variables (LVs). Simultaneously, five effective wavelengths (EW) were selected by regression coefficients. The LVs and EWs were employed as the inputs of MLR and LS-SVM models. The optimal prediction results were achieved by LV-LS-SVM model, and the correlation coefficient (r), root mean square error of prediction (RMSEP) and bias for validation set were 0.9994, 0.2361 and 0.0064, respectively. The results indicated that Vis/NIR spectroscopy combined with chemometrics could be utilized as a parsimonious and efficient way for the determination of acetic acid of plum vinegar.

[1]  José L. F. C. Lima,et al.  Simultaneous automatic potentiometric determination of acidity, chloride and fluoride in vinegar , 1995 .

[2]  Johan A. K. Suykens,et al.  Least Squares Support Vector Machine Classifiers , 1999, Neural Processing Letters.

[3]  Giorgia Foca,et al.  Application of a wavelet-based algorithm on HS-SPME/GC signals for the classification of balsamic vinegars , 2004 .

[4]  A. Caligiani,et al.  Identification and quantification of the main organic components of vinegars by high resolution 1H NMR spectroscopy. , 2007, Analytica chimica acta.

[5]  Consuelo Pizarro,et al.  Prediction of organic acids and other quality parameters of wine vinegar by near-infrared spectroscopy. A feasibility study , 2006 .

[6]  Fei Liu,et al.  Comparison of calibrations for the determination of soluble solids content and pH of rice vinegars using visible and short-wave near infrared spectroscopy. , 2008, Analytica chimica acta.

[7]  Wang Ling Method for Selecting Parameters of Least Squares Support Vector Machines and Application , 2006 .

[8]  A. M. Troncoso,et al.  Spectrophotometric determination of total procyanidins in wine vinegars. , 1997, Talanta.

[9]  Fei Liu,et al.  Determination of effective wavelengths for discrimination of fruit vinegars using near infrared spectroscopy and multivariate analysis. , 2008, Analytica chimica acta.

[10]  B. Kowalski,et al.  Partial least-squares regression: a tutorial , 1986 .

[11]  Bin Chen,et al.  Application of wavelet transforms to improve prediction precision of near infrared spectra , 2005 .

[12]  Johan A. K. Suykens,et al.  Least Squares Support Vector Machines , 2002 .

[13]  M. Forina,et al.  Study of the aging and oxidation processes of vinegar samples from different origins during storage by near-infrared spectroscopy , 2006 .

[14]  José Anchieta Gomes Neto,et al.  Tungsten permanent chemical modifier with co-injection of Pd(NO3)2 + Mg(NO3)2 for direct determination of Pb in vinegar by graphite furnace atomic absorption spectrometry , 2007 .

[15]  Markus Lipp,et al.  Characterisation of Italian vinegar by pyrolysis–mass spectrometry and a sensor device (‘electronic nose’) , 1998 .