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Vis-NIR spectroscopy Combined with Wavelengths Selection by PSO Optimization Algorithm for Simultaneous Determination of Four Quality Parameters and Classification of Soy Sauce

  • Leqian Hu
  • Chunling Yin
  • Shuai Ma
  • Zhimin Liu
Article
  • 20 Downloads

Abstract

The performance of Vis-NIR techniques combined with variable select by a simple modified particle swarm optimization (PSO) algorithm for the determination of four quality parameters in soy sauce was evaluated. Compared with full-spectral support vector machine regression (Full-SVMR) and SVMR based on competitive adaptive reweighted sampling (CARS-SVM) method, the application of PSO wavelength selection provided a notably improved SVM regression model. The root-mean-square error of amino acid nitrogen, salt, total acid content, and color ratio obtained by PSO-SVMR are 0.0075 g/100 ml, 0.2176 g/100 ml, 0.0077 g/100 ml, and 0.0506 in predicted sets, respectively. The correlation coefficients of predicted sets obtained by PSO-SVMR reached 0.9997, 0.9462, 0.9996, and 0.9998, respectively. Meanwhile, a classification study constructed with principal component analysis and SVM classification model based on the feature wavelengths selected by PSO shows that Vis-NIR spectra can be used to classify soy sauce according to their brands and quality. The result showed that the Vis-NIR spectroscopy technique based on PSO wavelength selection has high potential to predict the quality parameters in a nondestructive way. This analytical tool may also contribute to the detection of fraud and mislabeling in the soy sauce market and certainly contribute to improvement in quality and reliability of the soy sauce market.

Keyword

Visible and near-infrared spectroscopy Soy sauce Quality parameters Wavelength selection Modified particle swarm optimization algorithm 

Notes

Funding Information

The work was financially supported by the National Natural Science Foundation of China (Grant no. 21275039), Natural Science Foundation of Henan Province of China (Grant no. 182102310681), Grain & Corn Engineering Technology Research Center, State Administration of Grain (Grant no. GA2017008), and Foundation of Henan University of Technology (Grant no. 2014JCYJ08).

Compliance with Ethical Standards

Conflict of Interest

Leqian Hu declares that he has no conflict of interest. Chunling Yin declares that she has no conflict of interest. Shuai Ma declares that he has no conflict of interest. Zhimin Liu declares that she has no conflict of interest.

Ethical Approval

This article does not contain any studies with human and animal subjects.

Informed Consent

Not applicable.

Supplementary material

12161_2018_1407_MOESM1_ESM.docx (25 kb)
ESM 1 (DOCX 24 kb)

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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Engineering Technology Research Center for Grain & Oil Food, State Administration of GrainHenan University of TechnologyZhengzhouPeople’s Republic of China
  2. 2.College of Chemistry, Chemical and Environmental EngineeringHenan University of TechnologyZhengzhouPeople’s Republic of China

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