Study on the Mutton Freshness Using Multivariate Analysis Based on Texture Characteristics - Archive ouverte HAL Access content directly
Conference Papers Year : 2016

Study on the Mutton Freshness Using Multivariate Analysis Based on Texture Characteristics

(1) , (2) , (1) , (1) , (1)
1
2
Xiaojing Tian
  • Function : Author
  • PersonId : 1020373
Jun Wang
  • Function : Author
  • PersonId : 988302
Jutian Yang
  • Function : Author
  • PersonId : 1020374
Shien Chen
  • Function : Author
  • PersonId : 1020375
Zhongren Ma
  • Function : Author
  • PersonId : 1020376

Abstract

Aiming at discrimination and prediction of mutton freshness by texture profile, the texture parameters of mutton stored at 1°C, 4°C and Room temperature were analyzed. The analysis methods of Canonical Discriminant Analysis (CDA) and Principal component analysis (PCA) were used to analyze texture parameters of mutton. The results of PCA showed that mutton sample stored at three temperatures clustered into groups according to their freshness, changing along the direction of PC1. Better classification results were found by CDA. The changing trends of mutton freshness were described by Multiple Linear Regression (MLR) and Partial Least Square analysis (PLS), and effective predictive models were found for indices of days stored, TVB-N and pH using texture parameters. With optimum analysis methods, texture parameters could classify and predict freshness of mutton stored at three temperatures. Texture profiles were proved to be a fast and objective tool for the prediction of mutton freshness.
Fichier principal
Vignette du fichier
434298_1_En_15_Chapter.pdf (623.12 Ko) Télécharger le fichier
Origin : Files produced by the author(s)
Loading...

Dates and versions

hal-01614168 , version 1 (10-10-2017)

Licence

Attribution - CC BY 4.0

Identifiers

Cite

Xiaojing Tian, Jun Wang, Jutian Yang, Shien Chen, Zhongren Ma. Study on the Mutton Freshness Using Multivariate Analysis Based on Texture Characteristics. 9th International Conference on Computer and Computing Technologies in Agriculture (CCTA), Sep 2015, Beijing, China. pp.143-154, ⟨10.1007/978-3-319-48354-2_15⟩. ⟨hal-01614168⟩
69 View
106 Download

Altmetric

Share

Gmail Facebook Twitter LinkedIn More