Novel estimation of tomato soluble solids content using linearly transformed reflectance-based spectral indices.

ABSTRACT: Rapid and non-destructive estimation of soluble solids content (SSC) is essential for tomato quality evaluation, yet the generalization ability of many existing spectral models remains limited when applied across multiple cultivars. In this study, hyperspectral reflectance was combined with a genetic algorithm (GA)-based optimization strategy to develop a robust SSC prediction framework applicable to diverse tomato types. Spectral reflectance and SSC (°Brix) were measured for 152 fruits representing 13 cultivars, including large red, medium red, red cherry, and yellow cherry types. To overcome the structural rigidity of conventional fixed-form spectral indices, reflectance spectra were linearly transformed to construct three novel indices: the linearly transformed difference spectral index

SUBMITTER: Liu N 

PROVIDER: S-EPMC12916681 | biostudies-literature | 2026

REPOSITORIES: biostudies-literature