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Nondestructive quantifying total volatile basic nitrogen (TVB-N) content in chicken using hyperspectral imaging (HSI)

Urmila Khulal1, Jiewen Zhao1, Weiwei Hu1

  • 1School of Food & Biological Engineering, Jiangsu University, Zhenjiang 212013, PR China.

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|December 18, 2015
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Summary

Hyperspectral imaging (HSI) effectively assesses chicken quality by reducing data dimensions with Ant Colony Optimization (ACO), outperforming PCA. This non-destructive method accurately quantifies TVB-N content, crucial for food safety.

Keywords:
ACO algorithmChicken spoilageHyperspectral imaging (HSI)Texture analysisWavelength selection

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Area of Science:

  • Agricultural Science
  • Food Science
  • Analytical Chemistry

Background:

  • Assessing chicken quality is vital for food safety and consumer trust.
  • Traditional methods for determining chicken freshness, like TVB-N content, are often destructive and time-consuming.
  • Hyperspectral imaging (HSI) offers a promising non-destructive alternative for rapid quality assessment.

Purpose of the Study:

  • To evaluate the efficacy of Hyperspectral Imaging (HSI) for non-destructive quantification of TVB-N content in chicken.
  • To compare the performance of Principle Component Analysis (PCA) and Ant Colony Optimization (ACO) for data dimension reduction in HSI.
  • To develop and optimize a Back Propagation Artificial Neural Network (BPANN) model for predicting chicken freshness.

Main Methods:

  • Selected 5 dominant wavelength images from chicken hyperspectral data using PCA and ACO.
  • Extracted 30 textural variables from dominant wavelength images based on statistical moments.
  • Developed and compared PCA-BPANN and ACO-BPANN models for predicting TVB-N content.

Main Results:

  • ACO demonstrated superiority over PCA in selecting dominant wavelengths for dimension reduction.
  • The optimized ACO-BPANN model achieved high prediction accuracy with RMSEP=6.3834 mg/100g and R=0.7542.
  • The study confirmed the effectiveness of integrating spectral and spatial information from HSI.

Conclusions:

  • HSI is a powerful tool for rapid, non-destructive quantification of chicken freshness (TVB-N content).
  • Ant Colony Optimization (ACO) is a superior method for dimension reduction in hyperspectral data compared to PCA.
  • The developed ACO-BPANN model shows significant potential for practical application in the poultry industry.