The application of hyperspectral imaging technique in the wavelength range of 400-1000 nm to estimate some of the maturity parameters of mangoes was investigated. Mangoes with different quality levels were grouped using principle component analysis (PCA). Feature wavelengths were identified to predict total soluble solids content, water content and firmness using simple correlation, first derivative, partial least square (PLS) regression analysis and measured values. Calibration models were developed using the selected wavelengths from correlation coefficients, first derivative, partial least square (PLS) regression analysis and corresponding maturity parameters employing artificial neural network model to predict total soluble solids content, water content and firmness of the fruit. Performance of the models was compared using the correlation coefficient (r) values. Fruit firmness was predicted with high correlation coefficient (r=0.88) followed by water content (r=0.81) and total soluble solids (r=0.78) using wavelengths selected from simple correlation of first derivative data with the parameters and ANN model. The results of the study demonstrated the scope for further research on maturity and quality evaluation of fruits using hyperspectral imaging technique.

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