Predicting piperine content in javanese long pepper using fluorescence imaging and machine learning model

The conventional method for determining piperine content involves a series of labor-intensive steps, including drying the pepper samples, grinding them, and then extracting them using high-grade ethanol through a reflux method. While effective, this process is time-consuming and resource-intensive, posing limitations in terms of efficiency and the ability to address potential variations. Therefore, there is an urgent need to explore more efficient and rapid approaches for accurately measuring and predicting piperine content, with machine learning approach. This research aims to explore the potential of using fluorescence imaging methods and ANN models to increase the efficiency of measuring piperine content on Javanese long pepper. We propose a machine learning approach using UV-induced fluorescence imaging of Javanese long pepper. UV LEDs (365 nm) induced fluorescence, with color variation indicating piperine content. An artificial neural network (ANN) model, trained on color texture features from fluorescence images, predicted piperine content, achieving an R² value of 0.88025 with ten selected features using the One-R attribute. The final ANN, configured with 'trainoss' learning, 'tansig' activation, 0.1 learning rate, and 10-40-10 nodes, demonstrated a testing R² of 0.8943 and MSE of 0.0875. LED-induced fluorescence enhances machine learning's piperine content prediction. This research contributes to more efficient piperine content measurement methods.