Nondestructive Evaluation of Soluble Solid Content of Cucumbers Based on VIS–NIR and SWIR Hyperspectral Images

Abstract

Soluble solid content (SSC) is a key indicator for evaluating cucumber quality, directly influencing its commercial value. In China's cucumber sorting factories, SSC is typically assessed using random sampling and destructive methods, which are unsuitable for large-scale and continuous detection. Therefore, this study employs hyperspectral imaging technology to evaluate the capability of visible–near infrared (VIS–NIR) and shortwave infrared (SWIR) spectroscopy for nondestructive SSC detection. In the experiment, hyperspectral data of cucumbers at different growth stages were collected in the VIS–NIR and SWIR. Using a partial least squares regression (PLSR) model, SSC prediction performance was compared across three spectral preprocessing methods and three sensitive wavelength selection methods. The optimal prediction models for SSC in the VIS–NIR and SWIR spectral ranges were established. The results showed that the optimal model in the VIS–NIR was the savitzky–golay smoothing (SG)-fullwave-PLSR model, with an R2p of 0.827, an RMSEP of 0.176, and an RPD of 2.403. In the SWIR, the optimal model was the multiplicative scatter correction (MSC)-competitive adaptive reweighted sampling (CARS)-PLSR, with an R2p of 0.818, an RMSEP of 0.177, and an RPD of 2.344. The study demonstrates that hyperspectral imaging in both VIS–NIR and SWIR can be applied for nondestructive SSC detection in cucumber sorting factories. Considering both prediction accuracy and cost, VIS–NIR is more suitable for online monitoring of cucumber SSC.