Quantifying the joint effects of background material and light polarization on hyperspectral reflectance for enhanced internal biochemical prediction in postharvest lettuce

Abstract

Hyperspectral imaging (HSI) is a powerful tool for nondestructive quality assessment of fresh produce, providing pixel-level spectral and spatial information. However, its performance strongly depends on imaging configuration illumination geometry, background material, and light polarization specifically in optically complex, highly scattering samples such as leafy vegetables. Nevertheless, no clear criteria exist for tuning HSI setups according to sample type or analytical goal. This study investigates six short-wave infrared (SWIR, 1000-1700 nm) HSI configurations combining three polarization states (parallel, cross, and non-polarized), each paired with either a black or white background, to examine their effects on reflectance spectral quality and ascorbic-acid content interpretability in lettuce (Lactuca sativa L.) stored under 5°C, 20°C and 35°C for 6 days. Partial least-squares regression models were built from spectra acquired under six configurations; informative wavelengths were selected with variable importance-in-projection (VIP) scores > 1. Spectra recorded against a black background consistently outperformed those from a white background. Across polarization states, predictive power ranked cross-polarized > non-polarized > parallel-polarized, with the cross polarized/black-background setup achieving the best performance (R² = 0.61, RMSE = 0.69 mg 100 g⁻¹). This was attributed to the suppression of background reflection interference and surface glare, enhancing isolation of internal spectral information. Notably, the non polarized/black-background configuration also yielded favorable performance, suggesting the potential benefits of integrating both surface and subsurface spectral information. Conversely, under white background conditions, re-entering reflected light and surface interference were found to reduce model accuracy. These results demonstrate that optimizing HSI configurations in accordance with the optical properties of the target sample improves the accuracy of non destructive biochemical analysis. Configurations that minimize recursive reflection and surface glare, thereby isolating internal spectral signals derived from internal components, were validated as most effective. These findings make a significant contribution to the advancement of sensing technologies in postharvest engineering from general HSI toward application-specific biochemical assessment.