Assessing soluble solid content and texture of pear during shelf-life period by single snapshot spatial frequency domain imaging

Spatial frequency domain imaging (SFDI) is a promising technique for its merits of noncontact and wide-field detection. However, considering the cost and detection speed, it has not been put into widespread application in agro-products. In this study, the low-cost, fast SFDI system and matching software were adopted to realize determination of absorption (mu(a)) and reduced scattering coefficients (mu'(s)) of pears. The spatial frequencies (f(x)) were calibrated and system linearity was verified, the results showed that excellent linearity was obtained. Single snapshot demodulation was adopted, the validation results conducted on 390 liquid phantoms indicated that the optimal f(x) for snapshot method is 1/3 mm(-1). Fast calculation models for mu(a) and mu'(s) developed by least squares support vector regression (LSSVR) based on Monte Carlo (MC) simulations were then applied and validated at six wavelengths (460, 503, 527, 630, 658 and 675 nm). The results demonstrated that the LSSVR models could realize precise calculation for mu(a) or mu'(s). Finally, the variation trends of mu(a), mu'(s), soluble solids content (SSC) and texture (MT firmness, flesh firmness, stiffness, brittleness and adhesiveness) of 9 batches of pears were analysed, and prediction models were developed by artificial neural network (ANN) based on mu(a) and mu'(s) respectively. The results showed that for texture estimation, the prediction effect was relative well by using mu'(s), especially for brittleness and adhesiveness, while the accuracy for SSC was limited by only six mu(a) features. Future research should focus on the acquisition of more spectral information to improve model accuracy. (C) 2021 IAgrE. Published by Elsevier Ltd. All rights reserved.