Determinación de la textura en caqui "Rojo brillante" mediante imagen hyiperspectral Vis-NIR - Castillo-Gironés, Salvador; González-Muelas, Ángel; Cubero, Sergio; Rodríguez, Alejandro; Munera, Sandra; Salvador, Alejandra; Guirao, Alberto; Gómez-Sanchis, Juan; Blasco, José (2024)
Quality of persimmon cv 'Rojo Brillante' fruits can be affected by changes in texture during postharvest storage if storage conditions are not appropriate, which can impact consumer acceptance. Therefore, developing accurate tools to predict texture before marketing is of great interest. Hyperspectral imaging is a non-destructive technology that has been proven effective for predicting internal attributes. This study aimed to evaluate the usefulness of hyperspectral images for predicting flesh firmness. A total of 3,340 persimmons were stored for three months under different temperature conditions (0°C, 1°C, and 5°C) to induce varying changes in flesh texture. Hyperspectral images were acquired at harvest and every month for each storage condition. Flesh firmness was measured using a texturometer immediately after image acquisition. Samples were clustered into three groups using k-Means based on their firmness data and mean spectra was extracted from each persimmon. Samples were randomly divided into training (70%) and test (30%) sets. Besides, wavelength reduction was performed using Partial Least Squares Discriminant Analysis (PLS) coefficients. The training data was used to train PLS, Support Vector Machine, and Random Forest models using all spectra and selected wavelengths. All models achieved high accuracies, indicating that even using a few wavelengths, it is possible to accurately predict the flesh firmness of persimmon fruits.