Measurement of Soluble Solid Contents and pH of White Vinegars Using VIS/NIR Spectroscopy and Least Squares Support Vector Machine

Food and Bioprocess Technology

Volumn 7, Issue 1, 2014, Pages 54-61

  Bao, Yidan ^a   Liu, Fei ^a   Kong, Wenwen ^a   Sun, Da Wen ^b   He, Yong ^a   Qiu, Zhengjun ^a  

^a ZHEJIANG UNIVERSITY   (China)

^b UNIVERSITY COLLEGE DUBLIN   (Ireland)

Author keywords

Least squares support vector machine; Partial least squares analysis; pH; Soluble solid content; VIS NIR spectroscopy; White vinegars

Indexed keywords

LEAST SQUARES SUPPORT VECTOR MACHINES; PARTIAL LEAST SQUARES ANALYSIS; SOLUBLE SOLID CONTENT; VIS/NIR SPECTROSCOPY; WHITE VINEGARS;

ACETIC ACID; FERMENTATION; FORECASTING; LEAST SQUARES APPROXIMATIONS; MEAN SQUARE ERROR; PH; RADIAL BASIS FUNCTION NETWORKS; REGRESSION ANALYSIS; SPECTRUM ANALYSIS;

SUPPORT VECTOR MACHINES;

EID: 84891627915     PISSN: 19355130     EISSN: 19355149     Source Type: Journal    
DOI: 10.1007/s11947-013-1065-0     Document Type: Article

References (49)

1. Anklam, E., & Battaglia, R. (2001). Food analysis and consumer protection. Trends in Food Science and Technology, 12(5-6), 197-202.
2. Anklam, E., Lipp, M., Radovic, B., Chiavaro, E., & Palla, G. (1998). Characterization of Italian vinegar by pyrolysis-mass spectrometry and a sensor device ('electronic nose'). Food Chemistry, 61, 243-248.
3. Antonucci, F., Pallottino, F., Paglia, G., Palma, A., D'Aquino, S., & Menesatti, P. (2011a). Non-destructive estimation of mandarin maturity status through portable VIS-NIR spectrophotometer. Food and Bioprocess Technology, 4(5), 809-813.
4. Antonucci, F., Pallottino, F., Costa, C., Rimatori, V., Giorgi, S., Papetti, P., et al. (2011b). Development of a rapid soil water content detection technique using active infrared thermal methods for in-field applications. Sensors, 11, 10114-10128.
5. Belousov, A. I., Verzakov, S. A., & von Frese, J. (2002). Applicational aspects of support vector machines. Journal of Chemometrics, 16, 482-489.
6. 1H NMR spectroscopy. Analytica Chimica Acta, 585, 110-119.
7. Casale, M., Abajo, M. J. S., Sáiz, J. M. G., Pizarro, C., & Forina, M. (2006). Study of the aging and oxidation processes of vinegar samples from different origins during storage by near-infrared spectroscopy. Analytica Chimica Acta, 557, 360-366.
8. Cen, H. Y., He, Y., & Huang, M. (2006). Measurement of soluble solids contents and pH in orange juice using chemometrics and VIS/NIRS. Journal of Agricultural and Food Chemistry, 54, 7437-7443.
9. Chauchard, F., Cogdill, R., Roussel, S., Roger, J. M., & Bellon-Maurel, V. (2004). Application of LS-SVM to non-linear phenomena in NIR spectroscopy: development of a robust and portable sensor for acidity prediction in grapes. Chemometrics and Intelligent Laboratory Systems, 71, 141-150.
10. Chen, Q. S., Zhao, J. W., Fang, C. H., & Wang, D. M. (2007). Feasibility study on identification of green, black and Oolong teas using near-infrared reflectance spectroscopy based on support vector machine (SVM). Spectrochimica Acta Part A, 66(3), 568-574.
11. Chen, Q. S., Ding, J., Cai, J. R., Sun, Z. B., & Zhao, J. W. (2012). Simultaneous measurement of total acid content and soluble salt-free solids content in Chinese vinegar using near-infrared spectroscopy. Journal of Food Science, 77(2), 222-227.
12. Cocchi, M., Durante, C., Foca, G., Manzini, D., Marchetti, A., & Ulrici, A. (2004). Application of a wavelet-based algorithm on HS-SPME/GC signals for the classification of balsamic vinegars. Chemometrics and Intelligent Laboratory Systems, 71, 129-140.
13. Cocchi, M., Durante, C., Grandi, M., Lambertini, P., Manzini, D., & Marchetti, A. (2006). Simultaneous determination of sugars and organic acids in aged vinegars and chemometric data analysis. Talanta, 69, 1166-1175.
14. Durante, C., Cocchi, M., Grandi, M., Marchetti, A., & Bro, R. (2006). Application of N-PLS to gas chromatographic and sensory data of traditional balsamic vinegars of Modena. Chemometrics and Intelligent Laboratory Systems, 83, 54-65.
15. Fan, W., Li, H. D., Shan, Y., Lv, H. Y., Zhang, H. X., & Liang, Y. Z. (2011). Classification of vinegar samples based on near infrared spectroscopy combined with wavelength selection. Analytical Methods, 3, 1872-1876.
16. 2. NIR News, 13, 12-14.
17. Fu, X. G., Yan, G. Z., Chen, B., & Li, H. B. (2005). Application of wavelet transforms to improve prediction precision of near infrared spectra. Journal of Food Engineering, 69, 461-466.
18. García-Parrilla, M. C., Heredia, F. J., Troncoso, A. M., & González, A. G. (1997). Spectrophotometric determination of total procyanidins in wine vinegars. Talanta, 44, 119-123.
19. Guo, H., Liu, H. P., & Wang, L. (2006). Method for selecting parameters of least squares support vector machines and application. Journal of System Simulation, 18(7), 2033-2036.
20. Lapa, R. A. S., Lima, J. L. F. C., Pérez-Olmos, R., & Pilar Ruiz, M. (1995). Simultaneous automatic potentiometric determination of acidity, chloride and fluoride in vinegar. Food Control, 6, 155-159.
21. Lipp, M., Radovic, B. S., & Anklam, F. (1998). Characterisation of vinegar by pyrolysis-mass spectrometry. Food Control, 9, 349-355.
22. Liu, F., & He, Y. (2008). Classification of brands of instant noodles using VIS/NIR spectroscopy and chemometrics. Food Research International, 41(5), 562-567.
23. Liu, F., & He, Y. (2009). Application of successive projections algorithm for variable selection to determine organic acids of plum vinegar. Food Chemistry, 115(4), 1430-1436.
24. Liu, F., He, Y., & Wang, L. (2008a). Determination of effective wavelengths for discrimination of fruit vinegars using near infrared spectroscopy and multivariate analysis. Analytica Chimica Acta, 615(1), 10-17.
25. Liu, F., He, Y., & Wang, L. (2008b). Comparison of calibrations for the determination of soluble solids content and pH of rice vinegars using visible and short-wave near infrared spectroscopy. Analytica Chimica Acta, 610, 196-204.
26. Liu, F., Zhang, F., Jin, Z. L., He, Y., Fang, H., Ye, Q. F., et al. (2008c). Determination of acetolactate synthase activity and protein content of oilseed rape (Brassica napus L.) leaves using visible/near-infrared spectroscopy. Analytica Chimica Acta, 629(1-2), 56-65.
27. Liu, F., He, Y., Wang, L., & Sun, G. M. (2011). Detection of organic acids and pH of fruit vinegars using near-infrared spectroscopy and multivariate calibration. Food and Bioprocess Technology, 4(8), 1331-1340.
28. Menesatti, P., Antonucci, F., Pallottino, F., Roccuzzo, G., Allegra, M., Stagno, F., et al. (2010). Estimation of plant nutritional status by VIS-NIR spectrophotometric analysis on orange leaves [Citrus sinensis (L) Osbeck cv Tarocco]. Biosystem Engineering, 105, 448-454.
29. 2 for direct determination of Pb in vinegar by graphite furnace atomic absorption spectrometry. Food Chemistry, 105, 236-241.
30. Sabatier, R., Vivein, M., & Amenta, P. (2003). Two approaches for discriminant partial least square. In M. Schader, W. Gaul, & M. Vichi (Eds.), Between data science and applied data analysis (pp. 100-108). Berlin: Springer.
31. Sáiz-Abajo, M. J., Gonzákez-Sáiz, J. M., & Pizarro, C. (2006). Prediction of organic acids and other quality parameters of wine vinegar by near-infrared spectroscopy. A feasibility study. Food Chemistry, 99, 615-621.
32. Šašić, S., & Ozaki, Y. (2001). Short-wave near-infrared spectroscopy of biological fluids. 1. Quantitative analysis of fat, protein, and lactose in raw milk by partial least-squares regression and band assignment. Analytical Chemistry, 73(1), 64-71.
33. Shao, Y. N., & He, Y. (2006). Method for predicting acidity of bayberry juice by using VIS/near infrared spectra. Journal of Infrared and Millimeter Waves, 25(6), 478-480.
34. Singh, C. B., Choudhary, R., Jayas, D. S., & Paliwal, J. (2010). Wavelet analysis of signals in agriculture and food quality inspection. Food and Bioprocess Technology, 3(1), 2-12.
35. Sjöström, M., Wold, S., & Söderström, B. (1986). PLS discrimination plots. In: Gelsema, E. S., Kanals, L. N., (Eds). Pattern recognition in practice II (p. 486). Amsterdam: Elsevier.
36. Suykens, J. A. K., & Vanderwalle, J. (1999). Least squares support vector machine classifiers. Neural Processing Letters, 9, 293-300.
37. Tesfaye, W., Morales, M. L., Benítez, B., García-Parrilla, M. C., & Troncoso, A. M. (2004). Evolution of wine vinegar composition during accelerated aging with oak chips. Analytica Chimica Acta, 513, 239-245.
38. Ulissi, V., Antonucci, F., Benincasa, P., Farneselli, M., Tosti, G., Guiducci, M., et al. (2011). Nitrogen content estimation on tomato leaves by VIS-NIR non-destructive spectral reflectance system. Sensors, 11(6), 6411-6424.
39. Urbano-Cuadrado, M., De Castro, M. D. L., Pérez-Juan, P. M., García-Olmo, J., & Gómez-Nieto, M. A. (2004). Near infrared reflectance spectroscopy and multivariate analysis in enology determination or screening of fifteen parameters in different types of wines. Analytica Chimica Acta, 527, 81-88.
40. Vapnik, V. N. (1995). The nature of statistical learning theory. New York: Springer.
41. Viscarra Rossel, R. A., Taylor, H. J., & McBratney, A. B. (2007). Multivariate calibration of hyperspectral γ-ray energy spectra for proximal soil sensing. European Journal of Soil Science, 58, 343-353.
42. Wang, W. J., Xu, Z. B., Lu, W. Z., & Zhang, X. Y. (2003). Determination of the spread parameter in the Gaussian kernel for classification and regression. Neurocomputing, 55, 643-663.
43. Wang, L., Li, Z. F., He, Y., & Liu, F. (2008). Fast detection of sugar content in fruit vinegar using NIR spectroscopy. Spectroscopy and Spectral Analysis, 28(8), 1810-1813.
44. Williams, P. C. (2001). Near-infrared technology in the agricultural and food industries. St. Paul, MN: AACC, Inc.
45. Williams, P. C., & Sobering, D. C. (1996). How do we do it: a brief summary of the methods we use in developing near infrared calibrations. In A. M. C. Davies & P. C. Williams (Eds.), Near infrared spectroscopy: the future waves (pp. 185-188). Chichester, UK: NIR Publications.
46. Yano, T., Aimi, T., Nakano, Y., & Tamai, M. (1997). Prediction of the concentrations of ethanol and acetic acid in the culture broth of a rice vinegar fermentation using near-infrared spectroscopy. Journal of Fermentation and Bioengineering, 84, 461-465.
47. Yu, H. Y., Lin, H. J., Xu, H. R., Ying, Y. B., Li, B. B., & Pan, X. X. (2008). Prediction of enological parameters and discrimination of rice wine age using least-squares support vector machine and near infrared spectroscopy. Journal of Agricultural and Food Chemistry, 56, 307-313.
48. Zhang, Q. Y., Zhang, S. P., Xie, C. S., Zeng, D. W., Fan, C. Q., Li, D. F., et al. (2006). Characterization of Chinese vinegars by electronic nose. Sensors and Actuators B, 119, 538-546.
49. Zou, X. B., Chen, Z. W., Shi, J. Y., Wang, K. L., Jiang, P., & Huang, X. W. (2011). Near infrared modeling of total acid content in vinegars based on LS-SVM. China Brewing, 3, 63-65.