Evaluation of biomarkers that influence the freshness of beef during storage using VIS/NIR hyperspectral imaging
Biomarkers influencing the freshness of beef during storage were detected using VIS/NIR hyperspectral imaging (HSI). A total of 18 cuts of eye round from three cattle were vacuum-packaged and wet-aged at 4 ± 2 °C for 27 days. Throughout this period, freshness was maintained as evidenced by a signifi...
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| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | en |
| Published: |
Academic Press
2025
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| Subjects: | |
| Online Access: | http://psasir.upm.edu.my/id/eprint/123253/1/123253.pdf http://psasir.upm.edu.my/id/eprint/123253/ https://www.sciencedirect.com/science/article/pii/S0023643824015858 |
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| Summary: | Biomarkers influencing the freshness of beef during storage were detected using VIS/NIR hyperspectral imaging (HSI). A total of 18 cuts of eye round from three cattle were vacuum-packaged and wet-aged at 4 ± 2 °C for 27 days. Throughout this period, freshness was maintained as evidenced by a significant decrease in pH, stable color, and total bacterial count (TBC) and volatile basic nitrogen (VBN) remaining below spoilage thresholds at 5.78 Log CFU/g and 14.47 mg/100 g, respectively. Metabolite profiling revealed correlations between freshness indicators-ethanol, 5′-inosine monophosphate, acetate, histamine-and TBC and VBN values, highlighting their importance in freshness. Integrating HSI with partial least squares regression (PLSR) proved more reliable than artificial neural networks for predicting metabolite profiles and correlating them with quality traits, confirming its effectiveness in meat quality monitoring. With PLSR, the model performance for TBC was similar (R2 = 0.77 from HSI and 0.74 from metabolite predictions), while VBN performance improved significantly from R2 = 0.63 to 0.81 with predicted metabolite data. This integration was essential for monitoring beef quality during wet aging and for developing effective assessment strategies. |
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