Feature Selection Model Development on Near-Infrared Spectroscopy Data

Abstract

This study aims to develop a feature selection model on Near-Infrared Spectroscopy (NIRS) data. The object used is beef with six quality parameters: color, drip loss, pH, storage time, Total Plate Colony (TPC), and water moisture. The prediction model is a Random Forest Regressor (RFR) with default parameters. The feature selection model is carried out by mapping spectroscopic data into line form. The collection of lines is made into one line by finding the mean value. Next, apply the line simplification method based on angle elimination, starting from the smallest angle to the largest. Each iteration will eliminate one corner, reducing one column of data in the corresponding dataset. Then, the predicted value in the form of R2 will be collected, and the highest value will be considered the best feature selection formation. RFR prediction results with R2 values are as follows: color R2= 0.597, drip loss R2=0.891, pH R2=0.797, storage time R2=0.889, TPC R2=0.721, and water moisture R2=0.540. Meanwhile, after applying the feature selection model, the R2 values for all parameters increased to color R2=0.877, drip loss R2=0.943, pH R2=0.904, storage time R2=0.917, TPC R2=0.951, and water moisture R2=0.893. Based on the results of increasing the R2 value of the six parameters, an average value of increasing prediction accuracy of 17.49% can be taken. So, the feature selection method based on line simplification with an angle elimination system can provide very good results.