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Non-Destructive Assessment of Beef Freshness Using Visible and Near-Infrared Spectroscopy with Interpretable Machine

Ruoxin Chen1, Wei Ning1, Xufen Xie2

  • 1College of Food Science and Technology, Dalian Polytechnic University, Dalian 116034, China.

Foods (Basel, Switzerland)
|February 27, 2026
PubMed
Summary
This summary is machine-generated.

This study introduces a new method using visible and near-infrared spectroscopy with machine learning to accurately assess beef freshness. The approach identifies key wavelengths for predicting quality indicators, ensuring safer, higher-quality beef for consumers.

Keywords:
PSOGASHAPVis-NIR spectroscopybeef freshnessfeature selection

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Area of Science:

  • Food Science and Technology
  • Analytical Chemistry
  • Artificial Intelligence in Agriculture

Background:

  • Beef freshness is crucial for consumer health and the meat industry.
  • Rapid, non-destructive methods for assessing beef quality are needed.
  • Visible and near-infrared (Vis-NIR) spectroscopy offers potential for non-destructive analysis.

Purpose of the Study:

  • To develop a novel, non-destructive method for assessing beef freshness.
  • To utilize machine learning (ML) and explainable artificial intelligence (xAI) for improved accuracy and interpretability.
  • To identify key spectral features for predicting beef quality indicators.

Main Methods:

  • Visible and near-infrared (Vis-NIR) spectroscopy was employed for data acquisition.
  • An improved hybrid heuristic method, particle swarm optimization-genetic algorithm (PSOGA), was used for optimal feature (wavelength) selection.
  • eXtreme Gradient Boosting (XGBoost) was utilized for regression modeling.
  • The SHapley Additive exPlanations (SHAP) framework was applied for model interpretability and identification of key wavelengths.

Main Results:

  • The PSOGA-XGBoost model demonstrated high prediction accuracy for beef quality indicators: R²p values of 0.9504 for total volatile basic nitrogen (TVB-N), 0.9540 for L*, 0.8939 for a*, and 0.9416 for b*.
  • PSOGA significantly outperformed traditional feature selection methods.
  • SHAP analysis identified critical wavelengths (e.g., 1236 nm and 1316 nm for TVB-N) contributing to the predictions.
  • The model achieved satisfactory prediction accuracy, outperforming traditional methods.

Conclusions:

  • The developed Vis-NIR spectroscopy combined with PSOGA-XGBoost and SHAP provides an effective and interpretable method for non-destructive beef freshness assessment.
  • This approach offers a promising tool for quality control in the meat industry.
  • The study highlights the synergy of advanced ML and xAI techniques for robust food quality analysis.