LAUSR

Abstract Prawns are highly popular with consumers but present many technical difficulties for the evaluation of their internal quality when intact (in-shell prawns). This study proposed a nondestructive method to assess the internal quality of intact prawns (Fenneropenaeus chinensis) using spatially offset Raman spectroscopy (SORS) technique combined with data modeling analysis. This technique holds promise due to the capability of SORS to obtain chemical information nondestructively from below the surface of a sample material. Raman scattering image data for 100 fresh prawns (approximately 15 g each) were collected using a line-scan Raman imaging system over the course of seven days with 24 h measurement intervals. Measurement anomalies due to physical prawn irregularities were eliminated using a peak identification method. Twenty feature bands selected by Random Forest (RF) method were input to Partial Least Squares Regression (PLSR), Support Vector Regression (SVR), and Extremely Randomized Tree (ET) models to predict the freshness of prawns during the storage time. The prediction model based on SORS enhanced data and combining RF feature band selection with SVR demonstrated the best performance, with RMSEP, R2, and RPD values of 0.71, 0.88, and 2.63, respectively. This rapid and nondestructive method for quality evaluation may be feasible as a practical means of assessing internal quality of materials that demonstrate surface interference, such as in-shell prawns.