Non-destructive assessment of soluble solids content and firmness in tomatoes using dual-mode hyperspectral imaging...

Non-destructive assessment of tomato internal quality, including soluble solids content (SSC) and firmness, is important for grading and postharvest management. However, the varying capabilities of reflectance and transmittance hyperspectral imaging for predicting biochemical and mechanical quality attributes have not been sufficiently compared.

In this study, a dual-mode hyperspectral imaging system covering 500-950 nm was developed to evaluate SSC and firmness in 160 'Yuan Wei No. 1' tomatoes. Four preprocessing methods, including Savitzky-Golay smoothing (SG), standard normal variate (SNV), multiplicative scatter correction (MSC), and orthogonal signal correction (OSC), and three feature-wavelength selection strategies, including uninformative variable elimination (UVE), competitive adaptive reweighted sampling (CARS), and UVE-CARS, were compared using partial least squares regression. Transmittance spectra outperformed reflectance spectra for SSC prediction. The CARS filtered transmittance model achieved the best performance, with R _p = 0.9256 and residual predictive deviation (RPD) = 2.4208. Firmness prediction was less accurate; the best model was obtained using reflectance spectra combined with SG-SNV preprocessing and UVE-CARS feature selection, yielding R _p = 0.8008 and RPD = 1.6696.

Dual-mode hyperspectral imaging is effective for non-destructive SSC prediction in tomatoes, whereas firmness prediction remains limited because mechanical quality attributes are less directly represented by visible-near-infrared spectral information. The results provide a basis for tomato quality assessment and suggest that future firmness prediction may benefit from multi-modal data fusion. © 2026 Society of Chemical Industry.