Development of a novel quantitative function between spectral value and metmyoglobin content in Tan mutton

Area of Science:

• Food Science
• Spectroscopy
• Quantitative Analysis

Background:

• Metmyoglobin (MetMb) content is a key indicator of meat color and quality.
• Accurate and rapid assessment of MetMb is crucial for the meat industry.
• Traditional methods for MetMb determination can be time-consuming and destructive.

Purpose of the Study:

• To establish a quantitative relationship between spectral reflectance and MetMb content in Tan mutton during refrigeration.
• To identify characteristic spectral bands for predicting MetMb content using hyperspectral imaging.
• To evaluate the effectiveness of generalized two-dimensional correlation spectroscopy (G2D-COS) in analyzing spectral changes.

Main Methods:

• Near-infrared (NIR) hyperspectral imaging was employed to collect spectral data from Tan mutton.
• Generalized two-dimensional correlation spectroscopy (G2D-COS) was used to analyze spectral data and identify characteristic bands.
• Partial Least Squares Regression (PLSR) was applied to build predictive models for MetMb content.

Main Results:

• G2D-COS analysis successfully identified characteristic wavebands that significantly improved MetMb prediction.
• The PLSR model using characteristic bands achieved high prediction accuracy (R²p = 0.849, RPD = 2.786).
• G2D-COS proved to be a powerful tool for understanding spectral intensity changes related to MetMb.

Conclusions:

• A convenient, rapid, and non-destructive quantitative method for assessing MetMb content in Tan mutton was established.
• This approach enables accurate prediction of meat color changes during refrigeration.
• The integration of NIR hyperspectral imaging and G2D-COS offers a promising tool for meat quality monitoring.