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High pH thresholding of beef with VNIR hyperspectral imaging.

Stuart O J Crichton¹, Sascha M Kirchner¹, Victoria Porley²

¹Postharvest Technologies and Processing Group, Department of Agricultural Engineering, University of Kassel, Witzenhausen, Germany.

|July 26, 2017

Summary

This study shows that pH testing with support vector machine (SVM) analysis can accurately classify beef freshness. This non-destructive method improves meat quality assessment, avoiding invasive sampling.

Keywords:

Beef Classification Hyperspectral imaging Non-invasive measurement pH

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Area of Science:

Food Science
Analytical Chemistry
Machine Learning

Background:

Traditional meat quality grading relies on invasive pH testing.
Specific pH thresholds identify meat spoilage issues like DFD and PSE.
Current methods can be destructive and time-consuming.

Purpose of the Study:

To evaluate pH threshold detection for classifying beef freshness.
To assess the accuracy of support vector machine (SVM) analysis for meat quality.
To explore non-invasive methods for determining beef freshness.

Main Methods:

Utilized support vector machine (SVM) analysis.
Measured pH levels in beef samples of varying freshness (fresh, frozen-thawed, matured).
Correlated pH values with meat quality classifications.

Main Results:

Achieved 91% accuracy in classifying beef pH above 5.9.
Achieved 99% accuracy in classifying beef pH below 5.6.
Demonstrated SVM's effectiveness in differentiating meat freshness based on pH.

Conclusions:

Support vector machine (SVM) analysis offers a highly accurate, non-invasive method for meat quality assessment.
pH threshold detection using SVM can reliably classify beef freshness.
This approach enhances objective meat grading and reduces sample destruction.