Linear Discriminant Analysis vs. Genetic Algorithm Neural Network with Principal Component Analysis for Hyperdimensional Data Analysis: A study on Ripeness Grading of Oil Palm (Elaeis guineensis Jacq.) Fresh Fruit








Divo Dharma Silalahi, Consorcia E. Reaño, Felino P. Lansigan, Rolando G. Panopio and Nathaniel C. Bantayan


Using Near Infrared Spectroscopy (NIRS) spectral data, the Linear Discriminant Analysis (LDA) performance was compared with the Genetic Algorithm Neural Network (GANN) to solve the classification or assigning problem for ripeness grading of oil palm fresh fruit. The LDA is known as one of the famous classical statistical techniques used in classification problem and dimensionality reduction. The GANN is a modern computational statistical method in terms of soft computing with some adaptive nature in the system. The first four new components variables as result of Principal Component Analysis (PCA) also were used as input variables to increase the efficiency and made the data analysis process faster. Based on the results, both in training and validation phase GANN technique had lower Mean Absolute Error (MAE) and Root Mean Square Error (RMSE), higher percentage of correct classification and suitable to handle large amount of data compared to the LDA technique. Therefore, the GANN technique is superior in terms of precision and less error rate to handle a hyperdimensional problem for data analysis in ripeness classification of oil palm fresh fruit compared to the LDA.


Near Infrared Spectroscopy, Neural Network, Genetic Algorithm, Linear Discriminant Analysis, Principal Component Analysis, Oil Palm, Ripeness


Teacher's Corner: