Non-Destructive Prediction of Piperine in Javanese Chilli (Piper Retrofractum Vahl) Based on Color and Texture Analysis Using Artificial Neural Network

Abstract

Measurement of active substances in herbal plants is currently becoming the major focus of the health industry in Indonesia. Previously, measurement of the active substance had been carried out destructively by extraction methods such as maceration and reflux, which was done by damaged the substrate and required a complicated step. The Artificial Neural Network (ANN), which combined with color and textural analysis methods provides a fast, easy process, does not damage the sample, and minimize errors due to human factors. This study aims to determine and build a relation model between image parameters and the piperine content of Javanese chilli using an optimized ANN with feature selection. The best topology in this study was obtained with an 8-30-40-1 structure (8 inputs, 30 hidden layers 1 node, 40 hidden layers 2 nodes, and 1 output) with a learning rate of 0.1 and a momentum of 0.9; traincgf as a learning function and an activation function of tansig-tansig-purelin. The ANN structure produces a training correlation coefficient (R) of 0.0975, an R validation of 0.9457, a training Mean Square Error (MSE) value of 0.01, and a validation MSE value of 0.0215.