Work

Quantitative analysis and visualization of live spotted seabass (Lateolabrax maculatus) flesh texture using hyperspectral imaging and machine learning

Year: 2026

Type: article

Abstract: Flesh texture is one of the most important quality traits determining consumer satisfaction and perception of fish. Traditional methods for measuring flesh texture characteristics present several limitations. This study developed a rapid, efficient and non-invasive approach based on hyperspectral imaging (HSI) technology for evaluating texture profile analysis (TPA) parameters in live spotted seabass. Three types of surface hyperspectral data (skin with scales, SWS; skin without scales, SOS; and reversed skin without scales, RSOS) were collected from the dorsal region of 300 live fish across 400–1000 nm wavelength range. After applying five spectral pre-processing methods, five machine learning algorithms, including partial least squares regression (PLSR), least square support vector machine regression (LS-SVR), random forest (RF), convolutional neural network (CNN) and back propagation artificial neural network (BP-ANN), were performed to construct optimal prediction models for seven TPA parameters. Gumminess was predicted most effectively, with the RF model achieving a prediction set coefficient of determination (R 2 P ) of 0.923, a ratio of performance deviation (RPD) of a mean absolute percentage error (MAPE) of 6.378%. The optimal models enabled quantitative prediction of gumminess, chewiness, and hardness (RPD > 3.0 and MAPE<10%). For cohesiveness and springiness, the models performed satisfactory with R 2 P values of 0.859 and 0.854, RPD values of 2.890 and 2.613, and MAPE were 6.858% and 3.002%, respectively. However, prediction accuracy for adhesiveness and resilience was lower, with R 2 P values of 0.552 and 0.341, and RPD values of 1.494 and 1.134, respectively. Visualization maps generated from the optimal models facilitated the direct assessment of TPA parameters distribution in the muscle. This study demonstrated that HSI combined with machine learning offers an effective and non-invasive method for assessing flesh quality in live spotted seabass, thereby accelerating the selection of germplasm with superior textural attributes.

Source: Food Chemistry X

DOI: 10.1016/j.fochx.2026.103773