Nondestructive determination of soluble solids content of persimmons by using dielectric spectroscopy

• Published in: International journal of food properties Vol. 20; no. sup3; pp. S2596 - S2611
• Main Authors: Liu, Dayang, Guo, Wenchuan
• Format: Journal Article
• Language: English
• Published: Abingdon Taylor & Francis 10.01.2018; Taylor & Francis Ltd
• Subjects: Characteristic variables selection; correlation; Dielectric constant; dielectric properties; dielectric spectroscopy; Dielectric spectroscopy technique; Diospyros; least squares; Persimmons; prediction; selection methods; Soluble solids content; Spectroscopy; Spectrum analysis; storage time; support vector machines; total soluble solids
• ISSN: 1094-2912; 1532-2386; 1532-2386
• DOI: 10.1080/10942912.2017.1381114

To explore the feasibility of dielectric spectroscopy in predicting soluble solids content (SSC) of persimmons during postharvest storage period, the dielectric constant spectra and dielectric loss factor spectra of 105 'Shui' persimmons were measured from 20 MHz to 4500 MHz. Based on the joint x-y distances algorithm, the persimmon samples were divided into two sets: 70 samples in calibration set and 35 samples in prediction set. One hundred and seventy-four, 14, and 24 variables were extracted as characteristic variables from full dielectric spectra (FS) by uninformative variables elimination (UVE), successive projection algorithm (SPA), and competitive adaptive reweighted sampling (CARS), respectively. Partial least squares (PLS) and least squares support vector machine (LSSVM) were applied to build SSC prediction models using FS and characteristic variables extracted by UVE, SPA, and CARS. The results indicated that LSSVM models offered better performance than PLS models at same input variables. CARS-LSSVM had the best SSC determination performance with the correlation coefficient and root-mean-square error of prediction set of 0.970 and 0.494°Brix. This study indicates that dielectric spectroscopy technique combined with characteristic variables selection methods is promising for determining SSC of persimmons.