Use of near-infrared spectroscopy and least-squares support vector machine to determine quality change of tomato juice

Near-infrared (NIR) transmittance spectroscopy combined with least-squares support vector machine (LS-SVM) was investigated to study the quality change of tomato juice during the storage. A total of 100 tomato juice samples were used. The spectrum of each tomato juice was collected twice: the first measurement was taken when the tomato juice was fresh and had not undergone any changes, and the second measurement was taken after a month. Principal component analysis (PCA) was used to examine a potential capability of separating juice before and after the storage. The soluble solid content (SSC) and pH of the juice samples were determined. The results show that changes in certain compounds between tomato juice before and after the storage period were obvious. An excellent precision was achieved by LS-SVM model compared with discriminant partial least-squares (DPLS), soft independent modeling of class analogy (SIMCA), and discriminant analysis (DA) models, with 100% of a total accuracy. It can be found that NIR spectroscopy coupled with LS-SVM, DPLS, SIMCA, and DA can be used to control the quality change of tomato juice during the storage.

[1]  Johan A. K. Suykens,et al.  Benchmarking Least Squares Support Vector Machine Classifiers , 2004, Machine Learning.

[2]  F. J. Acevedo,et al.  Classification of wines produced in specific regions by UV-visible spectroscopy combined with support vector machines. , 2007, Journal of agricultural and food chemistry.

[3]  H. Ramon,et al.  Classification of Soils into Different Moisture Content Levels based on VIS-NIR Spectra , 2006 .

[4]  G. W. Small,et al.  Comparison of combination and first overtone spectral regions for near-infrared calibration models for glucose and other biomolecules in aqueous solutions. , 2004, Analytical chemistry.

[5]  M. P. Gómez-Carracedo,et al.  Classification of apple beverages using artificial neural networks with previous variable selection , 2004 .

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

[7]  R. Poppi,et al.  Least-squares support vector machines and near infrared spectroscopy for quantification of common adulterants in powdered milk. , 2006, Analytica chimica acta.

[8]  Johan A. K. Suykens,et al.  Systematic benchmarking of microarray data classification: assessing the role of non-linearity and dimensionality reduction , 2004, Bioinform..

[9]  David J. Hewson,et al.  Classifying NIR spectra of textile products with kernel methods , 2007, Eng. Appl. Artif. Intell..

[10]  Vladimir Vapnik,et al.  Three remarks on the support vector method of function estimation , 1999 .

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

[12]  James B. Callis,et al.  Quantification of hydrofluoric acid species by chemical-modeling regression of near-infrared spectra , 1997 .

[13]  Vladimir N. Vapnik,et al.  The Nature of Statistical Learning Theory , 2000, Statistics for Engineering and Information Science.

[14]  L. Buydens,et al.  Multivariate calibration with least-squares support vector machines. , 2004, Analytical chemistry.

[15]  D. Cozzolino,et al.  Geographic classification of spanish and Australian tempranillo red wines by visible and near-infrared spectroscopy combined with multivariate analysis. , 2006, Journal of agricultural and food chemistry.

[16]  D. Cozzolino,et al.  Prediction of phenolic compounds in red wine fermentations by visible and near infrared spectroscopy , 2004 .

[17]  Xingyi Huang,et al.  Qualitative identification of tea categories by near infrared spectroscopy and support vector machine. , 2006, Journal of pharmaceutical and biomedical analysis.

[18]  Xueguang Shao,et al.  A consensus least squares support vector regression (LS-SVR) for analysis of near-infrared spectra of plant samples. , 2007, Talanta.

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