Non-Destructive Prediction of Papaya Maturity Level Using Near-Infrared Spectroscopy and Artificial Neural Network

Abstract

This paper presents a non-invasive technique for evaluating the maturity of Carica Papaya L. by predicting its Soluble Solid Content (SSC) using Near-Infrared (NIR) spectroscopy integrated with an Artificial Neural Network (ANN) model. Traditional destructive methods using refractometers hinder quality preservation and consumer usability. In contrast, the proposed approach utilizes NIR spectral reflectance data, pre-processed with Savitzky-Golay (SG) smoothing and derivatives, and is analyzed via a nonlinear ANN regression model. Experimental results based on 49 papaya samples show high predictive accuracy (R² is 0.9063 for training, 0.8768 for testing; RMSE is 0.4406 and 0.7047 respectively) using second derivative data. This study demonstrates the feasibility of portable NIR systems for real-time fruit maturity classification, supporting broader applications in agricultural and supply chain contexts.