Monitoring postharvest water loss in eggplants (Solanum melongena L.) using UV-induced fluorescence imaging and multivariate analysis

Eggplant (Solanum melongena L.) is susceptible to significant postharvest losses primarily due to water loss during storage, which affects market quality by causing texture and glossiness degradation. We investigated whether UV-induced fluorescence imaging and EEM (Excitation-Emission Matrix) fluorescence spectroscopy can non-destructively monitor WL under four storage regimes (10 °C/95 % RH, 20 °C/95 % RH, 20 °C/75 % RH, 10 °C/75 % RH). EEMs exhibited three regions; a 365/420 nm blue emission increased most under warm, low-humidity storage and is consistent with phenolic/lignin-related fluorescence. Side-view fluorescence (FL) images showed progressive blue-white emission and surface textural changes that tracked gravimetric water loss (WL). A PLSR model using combined color and texture features from FL and reflectance (CL) images achieved R2CV = 0.88 (RMSECV = 3.47 %) with only six features. To test a minimal predictor, we fit an Analysis of Covariance (ANCOVA) using Day-1 FL MeanBlue as a covariate and storage category as a factor with Leave One Out Cross-validation (LOOCV); this forecasted cumulative WL with R2LOOCV = 0.92 and MAE = 1.88 %. Importantly, this ANCOVA model using Day-1 blue-band fluorescence as a covariate was predictive only under 20 °C/75 % RH; under the other conditions, its contribution was weak. Linear Discriminant Analysis (LDA) and Support Vector Machine (SVM) models achieved accuracies of 94.4 % and 85.2 %, respectively, in differentiating storage conditions. These results support low-cost FL imaging as a practical tool to monitor WL and storage stress.