Prediction of K value for fish flesh based on ultraviolet-visible spectroscopy of fish eye fluid using partial least squares regression

A method to predict K value of fish flesh using ultraviolet-visible (UV-VIS) spectral properties (250-600 nm) of its eye fluid and a partial least squares (PLS) regression method is investigated. UV-VIS absorbance of eye fluid was monitored for 240 fresh fish (Japanese dace) while simultaneously measuring the K value of the fish flesh by a paper electrophoresis technique. Several spectral pre-processing techniques, such as moving average smoothing, normalization, multiplicative scatter correction (MSC), Savitzky-Golay first-order derivative and Savitzky-Golay second-order derivatives were compared. The results showed that the regression model developed by PLS based on MSC preprocessed spectra resulted in better performance compared to models developed by other preprocessing methods, with a determination coefficient of prediction (Rpred2) of 0.87 and a root mean square error of prediction (RMSEP) of 7.87%. Therefore, the use of UV-VIS spectroscopy combined with appropriate multivariate analysis has the potential to accurately predict K value of fish flesh.