Rapid non-destructive evaluation of texture properties changes in crispy tilapia during crispiness using hyperspectral imaging and data fusion

Abstract

In this study, two distinct hyperspectral imaging systems, the Visible and Near-Infrared Spectroscopy (VNIR) and the Short-Wave Infrared Spectroscopy (SWIR), were employed in combine with chemometric techniques to examine the alterations in the texture properties (hardness, resilience, chewiness, and cohesiveness) of tilapia during the crispy process. Partial least squares regression (PLSR) was used to establish a linear relationship between the spectral reflectance values and the different texture parameters of tilapia, and various preprocessing, variable selection, and data fusion (Low-Level Fusion (LLF) and Mid-Level Fusion (MLF)) methods were considered to optimize the calibration model. For hardness, with RP, RMSEP, %RMSEP and RPD values of 0.63, 5.84, 21.65% and 1.26 respectively. For resilience, the LLF presented superior performance, with RP, RMSEP, %RMSEP and RPD values of 0.92, 0.03, 6.8% and 2.52 respectively. For chewiness, the MLF presented superior performance, with RP, RMSEP, %RMSEP and RPD values of 0.68, 4.03, 23.05% and 1.36. For cohesiveness, the MLF exhibited the best performance, with RP, RMSEP, %RMSEP and RPD values of 0.82, 0.03, 4.85% and 1.65 respectively. It was demonstrated that the fusion of both hyperspectral datasets provides a promising method for non-destructive texture measurement of tilapia at different stages of crispiness.