Soluble solids content prediction of pineapple based on visible-near infrared hyperspectral image

Pineapple is widely favored by consumers for its rich proteins, vitamin C and other nutrients. Soluble solids content (SSC) has long been the core indicator for pineapple quality assessment, directly affecting its market acceptability and sales. To accurately detect pineapple SSC, this study used a hyperspectral imaging system to collect hyperspectral images in the 400-1700 nm range, with SSC measured by an Atago PAL-1 digital sugar meter as the reference. Five pretreatments (including multiple scattering correction (MSC), polynomial smoothing (SG) and mathematical transformations) were applied to raw spectral data, and three prediction models (partial least squares regression (PLSR), Lasso regression, ridge regression (RR)) were established. All models performed well: PLSR showed R²=0.9459 and RMSE = 0.5746, Lasso R²=0.8965 and RMSE = 1.0221, RR R²=0.8560 and RMSE = 1.2632. After screening characteristic bands via Successive Projections Algorithm (SPA) and re-modeling, the ddA-PLSR model was optimal (R²=0.9869, RMSE = 0.1250), with four key wavelengths (673-676nm, 711-715nm, 971-990nm, 1357-1367nm) extracted. This confirms hyperspectral imaging (HSI) enables efficient and accurate SSC detection in pineapples, with great application potential in pineapple quality identification.