Enhanced prediction of total purine content in hyperspectral images of diverse livestock meat samples using...

This research looks at rapid detection and algorithm optimization of total purine in livestock meat with the goal of supporting the creation of low-purine diets. First, the prediction models for total purine content of single species were constructed based on chemical and hyperspectral data. Next, the correlation of spectral curves between different species by Hausdorff distance and Pearson correlation coefficient were analyzed to explore differential purine variation in mixed livestock meat. Finally, the optimal prediction models of total purines in mixed livestock meat were obtained, which were the interval-variable-iterative-spatial-contraction-method-sparrow-search-algorithm-bidirectional-long-short-term-memory-mmulti-head-attention (SSA-Bi-LSTM-MHA) in visible near-infrared hyperspectral imaging technology (Vis-NIR HSI) and iteratively-retain-informative-variables-SSA-Bi-LSTM-MHA in NIR-HSI, respectively, with Rp² of 0.7820 and 0.7766. Totally, the conclusion that model performance of mixed samples due to increased computational complexity was lower than the ideal model of single samples was obtained and validated. This study demonstrated the potential of HSI for rapid detection of purine content, which further promoted the industrialization of online monitoring of livestock meat quality.