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Food Analytical Methods

, Volume 10, Issue 10, pp 3306–3311 | Cite as

Identification of Wine According to Grape Variety Using Near-Infrared Spectroscopy Based on Radial Basis Function Neural Networks and Least-Squares Support Vector Machines

  • Jing Yu
  • Jicheng Zhan
  • Weidong HuangEmail author
Article

Abstract

This paper describes how near-infrared (NIR) spectroscopy combined with radial basis function neural networks (RBFNN) and least-squares support vector machines (LS-SVMs) based on principal component analysis (PCA) can be used to classify wines from grape varieties. The effects of different preprocessing methods (standard normal variate (SNV) and multiplicative scattering correction (MSC)) on classification results were also compared. The results show that the use of NIR preprocessing spectral data with optimum RBFNN parameters produced a very high level of correct classification rate, 90.16–98.36%. For RBF LS-SVM, identification rates were from 91.80 to 98.36%. The results demonstrate that, combined with chemometrics with appropriate spectral data pretreatment, NIR spectroscopy has potential to rapidly and nondestructively differentiate wine according to grape variety. The results of this study are helpful to develop a more rapid and nondestructive detection method of wine.

Keywords

Near-infrared spectroscopy Wine Discrimination Radial basis function neural networks Least-squares support vector machines 

Notes

Compliance with Ethical Standards

Funding

This study was funded by the National “Twelfth Five-Year” Plan for Science and Technology Support (2016YFD0400504).

Conflict of Interest

Jing Yu declares that she has no conflict of interest. Weidong Huang declares that he has no conflict of interest. Jicheng Zhan declares that he has no conflict of interest.

Ethical Approval

This article does not contain any studies with human participants or animals performed by any of the authors.

Informed Consent

Informed consent was obtained from all individual participants included in the study.

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Copyright information

© Springer Science+Business Media New York 2017

Authors and Affiliations

  1. 1.College of Food Science and Nutritional Engineering, Beijing Key Laboratory of Viticulture and EnologyChina Agricultural UniversityBeijingPeople’s Republic of China

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