Hyperspectral Imaging for Tomato Bruising Damage Assessment of Simulated Harvesting Process Impact Using Wavelength Interval Selection and Multivariate Analysis

Highlights The hidden internal damage of falling impact on tomatoes will reduce the quality of products. Hyperspectral imaging and VIS/NIR spectrum analysis, including wavelength selection and classification model construction, have the possibility as a non-destructive and fast method to predict the effect of drop impact grades on tomato bruising damage. Abstract . Mechanical damage usually causes hidden internal damage to tomatoes (Solanum lycopersicum L.), which can reduce the product quality and can cause economic losses to farmers. The visible and near-infrared (VIS/NIR) spectra of tomato fruits were analyzed by using the wavelength selection algorithm (the combination of ant colony optimization and variable importance in projection), and the influence of impact grades of simulated transport on tomato fruit bruising was evaluated. A VIS/NIR hyperspectral imaging system was developed to capture hyperspectral images of tomatoes from 392–1034 nm spectral region and the part used in actual data analysis was 442-984 nm. Multivariate analysis classifier models (partial least squares discrimination analysis and ANN) were set up based on the original spectral dataset. On the basis of selected wavelength intervals, multivariate analysis classifier models were re-established. The overall classification accuracies of all models in the validation set are good, ranging from 64.29% to 100%. Especially in the two types of classification (bruising and normal), the range of correct accuracy is 89.29% to 100%, which shows very high predicted performance. The prediction performance of the model based on the selected wavelengths decreases slightly, but the prediction time is shortened by more than 70%. The results demonstrated that hyperspectral imaging and VIS/NIR spectrum analysis, including wavelength selection and classification model construction, have the possibility as a non-destructive and fast method to predict the effect of drop impact grades on tomato bruising damage. Keywords: ANN, Ant colony optimization (ACO), Partial least squares discrimination analysis (PLS-DA), Variable importance in projection (VIP), VIS/NIR hyperspectral imaging system, wavelength interval selection.