Bison muscle discrimination and color stability prediction using near-infrared hyperspectral imaging

Muhammad Mudassir Arif Chaudhry, Md Mahmudul Hasan, Chyngyz Erkinbaev, Jitendra Paliwal, Surendranath Suman, Argenis Rodas-Gonzalez

Research output: Contribution to journalArticlepeer-review

12 Scopus citations

Abstract

A novel photonics-based multivariate pattern recognition technique is presented to segregate bison meat samples based on muscle type, ageing, and retail display period. The technique uses color attributes obtained from visible to near-infrared hyperspectral images (400–1000 nm) to predict the stability of bison muscle samples. Unsupervised and supervised classification methods were implemented with an aim to discriminate muscle samples based on muscle type, ageing period, and retail display period. The wavelength region from 500 to 690 nm which is associated with the a∗ value in the CIE Lab color space was found to be significantly important for the classification of muscle samples over the storage period. Partial least squares discriminant analysis (PLS-DA) demonstrated classification accuracies from 0.88 to 0.94 for the classification of muscle type, ageing period and retail display followed by development of classification maps. For the estimation of color changes in the muscle samples over the storage and retail display period, a∗ value was predicted with an R2 of calibration of 0.89, and R2 of cross-validation of 0.88. Conclusively, the wavelength range from 550 to 690 nm can significantly contribute to sorting and predicting freshness of bison muscle samples based on muscle type, color stability and storage period.

Original languageEnglish
Pages (from-to)1-13
Number of pages13
JournalBiosystems Engineering
Volume209
DOIs
StatePublished - Sep 2021

Bibliographical note

Publisher Copyright:
© 2021 IAgrE

Keywords

  • Classification
  • Meat color
  • Meat quality
  • Predictive modelling
  • a∗-value

ASJC Scopus subject areas

  • Food Science
  • Agronomy and Crop Science
  • Control and Systems Engineering
  • Soil Science

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