LAUSR

BACKGROUND Various spectral profiles, including reflectance (RS), absorbance (Abs)) and Kubelka-Munk spectra (K-Ms), have been derived from hyperspectral images and used to develop multivariate models to evaluate changes in the quality of meat and meat products as a function of processing. However, neither of these have the capacity to produce images of structural changes often associated with processing. The present study explored the feasibility of combining hyperspectral imaging (HSI) with confocal laser scanning microscopy (CLSM) to examine the impact of the processing on microstructural changes and the evolution of moisture. Reflectance spectra features were obtained and transformed into Abs and K-Ms and their ability to predict moisture content using models established on partial least squares regression (PLSR) were evaluated. RESULTS The PLSR model (full-band wavelength) dubbed Rs-MSC yielded the best result with Rc 2 = 0.967, RMSEC = 0.127, Rcv 2 = 0.949, RMSECV = 0.418, Rp 2 = 0.937, RMSEP = 0.824). Next, a total of 16 optimum wavelengths were selected using the competitive adaptive reweighted sampling algorithm. These wavelengths also yielded good results for Rs-MSC with Rc 2 = 0.958, RMSEC = 0.840, Rcv 2 = 0.931, RMSECV = 0.118, Rp 2 = 0.926, RMSEP = 0.121). Regarding moisture distribution and microstructure analysis, HSI and CLSM were able to reveal MC distribution and conformational differences in microstructure in the test samples. CONCLUSION Using HSI in synergy with CLSM may offer a reliable means for assessing both the chemical and structural changes that occur in other congener food products during processing. This article is protected by copyright. All rights reserved.