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Visible and Near-Infrared Spectroscopy Combined With Bayes Classifier Based on Wavelength Model Optimization Applied

Tao Pan1, Jiaqi Li1, Chunli Fu2

  • 1Department of Optoelectronic Engineering, Jinan University, Guangzhou, China.

Frontiers in Nutrition
|August 5, 2022
PubMed
Summary

This study introduces an optimized Bayes classifier using wavelength selection to accurately identify wine brands using spectroscopy. The new method achieves high recognition accuracy, offering a quick and efficient solution for wine authentication.

Keywords:
Bayes classifierequidistant combination wavelength screeningmultibrand identificationvisible and near-infrared spectroscopywavelength step-by-step phase-outwine

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Area of Science:

  • Chemometrics
  • Spectroscopy
  • Machine Learning

Background:

  • Wine brand identification is crucial to prevent fraud and protect stakeholders.
  • Conventional methods for wine authentication are complex and time-consuming.
  • Naive Bayes (NB) classifiers show promise but are limited by wavelength independence assumptions in spectral analysis.

Purpose of the Study:

  • To develop an improved Bayes classifier for accurate wine brand identification.
  • To address the limitations of traditional methods and the standard NB classifier in spectral pattern recognition.
  • To enhance the applicability of Bayesian methods in analyzing complex spectral data for authentication.

Main Methods:

  • Proposed a novel Bayes classifier algorithm incorporating wavelength optimization techniques: equidistant combination (EC) and wavelength step-by-step phase-out (WSP).
  • Applied the EC-WSP-Bayes method to discriminant analysis of five wine categories using visible and near-infrared (Vis-NIR) spectroscopy.
  • Selected an optimal model with six key wavelengths (404, 600, 992, 2070, 2266, 2462 nm).

Main Results:

  • The optimized EC-WSP-Bayes model achieved high recognition accuracy rates (RAR) of 98.1% in modeling and 97.6% in independent validation.
  • The method effectively reduced wavelength correlation, enhancing discrimination accuracy.
  • Identified a parsimonious set of six wavelengths for efficient wine brand identification.

Conclusions:

  • The developed EC-WSP-Bayes method offers a fast, simple, and highly accurate approach for wine brand identification using Vis-NIR spectroscopy.
  • The optimized, low-wavelength model (N=6) is suitable for developing compact, specialized instruments.
  • This chemometric approach significantly improves the accuracy and applicability of Bayesian methods for spectral authentication, aiding market regulation.