Evaluation and prediction of salt effects on pig muscle by deep UV and machine learning

Abstract

The salting process for meat transformation is a crucial step in conventional industry. Recent developments in label-free spectrometry techniques combined with machine learning hold great promise for high-precision salt processing. In this study, we applied UV fluorescence to characterize salting treatments in pig's Teres major muscle and predict NaCl concentrations. t-SNE analyses based on spectral measurements revealed clear differences between NaCl-free and salted treatments. However, salt treatments were not clearly identified. We then highlighted and exploited a variability seen in the emission spectra at the wavelengths 300, 318, and 360 nm, which reflected structural or compositional changes. Using this information, predictive models could accurately identify the five salted treatments with a high specificity and sensitivity or predict salt concentrations. This study paves the way toward the possibility for industrials to precisely adjust NaCl concentrations with precision during processing.

Keywords: Deep-UV; Machine learning; Meat; PLS; Pig meat; Salting; Spectroscopy.