Rapid Detecting Total Acid Content and Classifying Different Types of Vinegar based on Near Infrared Spectroscopy and Ant Colony Optimization Partial Least-Squares Analysis

Authors

  • Zhu Yao-Di Jiangsu university
  • Zou Xiao-Bo Key Laboratory of Modern Agricultural Equipment and Technology
  • Huang Xiao-Wei Jiangsu university
  • Shi Ji-Yong Jiangsu university
  • Zhao Jie-Wen Jiangsu university
  • Li Yanxiao Jiangsu university
  • Hao Limin The Research Center of China Hemp Materials
  • Zhang Jianchun The Research Center of China Hemp Materials

DOI:

https://doi.org/10.6000/1929-5030.2013.02.01.4

Keywords:

Near infrared spectroscopy, Ant colony optimization, Vinegar, Total acid content, Principle component analysis, Partial least-square

Abstract

Abstract: More than 3.2 million litres of vinegar is consumed every day in China. Traditional Chinese vinegars are prepared through solid-state fermentation (SFF) and made from different sorts of cereals. Chinese vinegars have specific local features. Every region has its own manufacturers, who produce vinegar in specific processes, using particular raw materials. How to control the quality of vinegar is problem. Near infrared spectroscopy (NIR) transmission technique was applied to achieve this purpose. 46 traditional vinegar samples were collected. They were classified into Sanxi vinegar, Zhenjiang vinegar, Micu vinegar, and Baonin vinegar according to their origin. Micu vinegar and Baonin vinegar were separated from the other categories in the two-dimension principal component space of NIR after principle component analysis (PCA). Ant colony optimization partial least-squares analysis (ACO-PLS) was firstly applied to identify the four categories vinegar. The accuracies of identification were more than 85%. As total acid content (TAC) is highly connecting with the quality of vinegar, NIR was used to predicate the TAC of samples. ACO-PLS was applied to building the TAC prediction model based on spectral transmission rate. Compared with full spectral partial least-square (PLS) model, ACO-PLS model gave better precision and accuracy in predicting TAC. The determination coefficient for prediction (Rp) of the ACO-PLS model was 0.921 and root mean square error for prediction (RMSEP) was 0.3031. This work demonstrated that near infrared spectroscopy technique coupled with ACO-PLS could be used as a quality control method for vinegar.

Author Biographies

Zhu Yao-Di, Jiangsu university

School of Food and Biological Engineering

Huang Xiao-Wei, Jiangsu university

School of Food and Biological Engineering

Shi Ji-Yong, Jiangsu university

School of Food and Biological Engineering

Zhao Jie-Wen, Jiangsu university

School of Food and Biological Engineering

Li Yanxiao, Jiangsu university

School of Food and Biological Engineering

References


[1] Ji-Cheng Chen QCQG. Characterization of Chinese vinegars by electronic nose. Food Chem 2010; 122: 1247-52.
[2] Goodarzi M, Freitas MP, Heyden YV. Linear and nonlinear quantitative structure–activity relationship modeling of the HIV-1 reverse transcriptase inhibiting activities of thiocarbamates. Analyt Chim Acta 2011; 705(1-2): 166-73. http://dx.doi.org/10.1016/j.aca.2011.04.046
[3] Moros J, Inon FA, Garrigues S, de la Guardia A. Determination of vinegar acidity by attenuated total reflectance infrared measurements through the use of second-order absorbance-pH matrices and parallel factor analysis. Talanta 2008; 74: 632-41. http://dx.doi.org/10.1016/j.talanta.2007.06.046
[4] Casale M, Abajo MJS, Sáiz JMG, Pizarro C, Forina M. Study of the aging and oxidation processes of vinegar samples from different origins during storage by near-infrared spectroscopy. Analyt Chim Acta 2006; 557: 360-66. http://dx.doi.org/10.1016/j.aca.2005.10.063
[5] Xu QP, Tao WY, Ao ZH. Antioxidant activity of vinegar melanoidins. Food Chem 2007; 102(3): 841-49. http://dx.doi.org/10.1016/j.foodchem.2006.06.013
[6] García-Parrillaa MC, Camachob ML, Herediaa FJ, Troncoso AM. Separation and identification of phenolic acids in wine vinegars by HPLC. Food Chem 1994; 50(3): 313-15. http://dx.doi.org/10.1016/0308-8146(94)90140-6
[7] Romero EG, Muñoz GS, Alvarez PJM, Ibáñez MDC. Determination of organic acids in grape musts, wines and vinegars by high-performance liquid chromatography. J Chromatogr A 1993; 655(1): 111-17. http://dx.doi.org/10.1016/0021-9673(93)87018-H
[8] De Vero L, Gala E, Gullo M, Solieri L, Landi S, Giudici P. Application of denaturing gradient gel electrophoresis (DGGE) analysis to evaluate acetic acid bacteria in traditional balsamic vinegar. Food Microbiol 2006; 23(8): 809-13. http://dx.doi.org/10.1016/j.fm.2006.01.006
[9] Plessi M, Bertelli D, Miglietta F. Extraction and identification by GC-MS of phenolic acids in traditional balsamic vinegar from Modena. J Food Comp Anal 2006; 19(1): 49-54. http://dx.doi.org/10.1016/j.jfca.2004.10.008
[10] Cocchi M, Durante C, Grandi M, Lambertini P, Manzinib D, Marchetti A. Simultaneous determination of sugars and organic acids in aged vinegars and chemometric data analysis. Talanta 2006; 69(5): 1166-75. http://dx.doi.org/10.1016/j.talanta.2005.12.032
[11] Sáiz-Abajo MJ, González-Sáiz JM, Pizarro C. Prediction of organic acids and other quality parameters of wine vinegar by near-infrared spectroscopy. A feasibility study. Food Chem 2006; 99; 615-21. http://dx.doi.org/10.1016/j.foodchem.2005.08.006
[12] Fu XG, Yan GZ, Chen B, Li HB. Application of wavelet transforms to improve prediction precision of near infrared spectra. J Food Eng 2005; 69: 461-66. http://dx.doi.org/10.1016/j.jfoodeng.2004.08.039
[13] García-Parrilla MC, Heredia FJ, Troncoso AM, González AG. Spectrophotometric determination of total procyanidins in wine vinegars. Talanta 1997; 44: 119-23. http://dx.doi.org/10.1016/S0039-9140(96)02012-7
[14] Liu F, He Y, Wang L. Comparison of calibrations for the determination of soluble solids content and pH of rice vinegars using visible and short-wave near infrared spectroscopy. Analyt Chim Acta 2008a; 610: 196-204. http://dx.doi.org/10.1016/j.aca.2008.01.039
[15] Liu F, He Y, Wang L. Determination of effective wavelengths for discrimination of fruit vinegars using near infrared spectroscopy and multivariate analysis. Analyt Chim Acta 2008b; 615: 10-17. http://dx.doi.org/10.1016/j.aca.2008.03.030
[16] Xiaobo Z, Jiewen Z, Povey MJW, Holmes M, Hanpin M. Variables selection methods in near-infrared spectroscopy. Analyt Chim Acta 2010; 667(1-2): 14-32. http://dx.doi.org/10.1016/j.aca.2010.03.048
[17] Xiaobo Z, Jiewen Z, Xingyi H, Yanxiao L. Use of FT-NIR spectrometry in non-invasive measurements of soluble solid contents (SSC) of 'Fuji' apple based on different PLS models. Chemometr Intellig Lab Syst 2007; 87(1): 43-51. http://dx.doi.org/10.1016/j.chemolab.2006.09.003
[18] Shamsipur M, Zare-Shahabadi V, Hemmateenejad B, Akhond M. Ant colony optimisation: a powerful tool for wavelength selection. J Chemometr 2006; 20(3-4): 146-57. http://dx.doi.org/10.1002/cem.1002
[19] Allegrini F, Olivieri AC. A new and efficient variable selection algorithm based on ant colony optimization. Applications to near infrared spectroscopy/partial least-squares analysis. Analyt Chim Acta 2011; 699(1): 18-25. http://dx.doi.org/10.1016/j.aca.2011.04.061
[20] Pan Y, Jiang JC, Wang R, Jiang JJ. Predicting the net heat of combustion of organic compounds from molecular structures based on ant colony optimization. J Loss Preven Proc Ind 2011; 24(1): 85-89. http://dx.doi.org/10.1016/j.jlp.2010.11.001
[21] Goodarzi M, Freitas MP, Jensen R. Ant colony optimization as a feature selection method in the QSAR modeling of antiHIV-1 activities of 3-(3,5-dimethylbenzyl)uracil derivatives using MLR, PLS and SVM regressions. Chemometr Intellig Lab Syst 2009; 98(2): 123-29. http://dx.doi.org/10.1016/j.chemolab.2009.05.005
[22] Tang Y, Wu M. The simultaneous separation and determination of five organic acids in food by capillary electrophoresis. Food Chem 2005; 103(1): 243-48. http://dx.doi.org/10.1016/j.foodchem.2005.09.022
[23] Workman J, Weyer L. Practical guide to interpretive nearinfrared spectroscopy. Boca Raton: CRC Press 2007.

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Published

2013-03-01

How to Cite

Yao-Di, Z., Xiao-Bo, Z., Xiao-Wei, H., Ji-Yong, S., Jie-Wen, Z., Yanxiao, L., Limin, H., & Jianchun, Z. (2013). Rapid Detecting Total Acid Content and Classifying Different Types of Vinegar based on Near Infrared Spectroscopy and Ant Colony Optimization Partial Least-Squares Analysis. Journal of Applied Solution Chemistry and Modeling, 2(1), 25–32. https://doi.org/10.6000/1929-5030.2013.02.01.4

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General Articles