Assessing soluble solid content and texture of pear during shelf-life period by single snapshot spatial frequency domain imaging. (December 2021)

• Record Type: Journal Article
• Authors: He, Xueming; Yang, Xiaoyun; Fu, Xiaping; Jiang, Xu; Rao, Xiuqin
• Is Part Of: Biosystems engineering. Volume 212(2021)
• Page Start: 252
• Page End: 263
• Publication Date: 2021-12
• Subjects: Spatial frequency domain imaging -- Single snapshot -- Absorption -- Scattering -- Pear; Bioengineering -- Periodicals; Agricultural engineering -- Periodicals; Biological systems -- Periodicals; Génie rural -- Périodiques; Systèmes biologiques -- Périodiques; 631
• DOI: 10.1016/j.biosystemseng.2021.10.016
• Languages: English
• ISSNs: 1537-5110

Abstract

Abstract : 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 ( μ a ) and reduced scattering coefficients ( μ′ 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 μ a and μ′ 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 μ a or μ′ s . Finally, the variation trends of μ a, μ′ 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 μ a and μ′ s respectively. The results showed that for texture estimation, the prediction effect was relative well by using μ′ s, especially for brittleness and adhesiveness, while the accuracy for SSC was limited by only six μ a features. Future research should focus on the acquisition of more spectral information to improve model accuracy. Highlights: Calibrated SFDI system and validated linearity for modulated light. Validated single snapshot method and LSSVR models for μ a, μ′ s at six wavelengths. Analysed the variations of μ a, μ′ s SSC and texture parameters of pear in shelf-life. Built ANN models for SSC and texture of pear based on μ a, μ′ s respectively.