A feature-selection algorithm based on Support Vector Machine-Multiclass for hyperspectral visible spectral analysis

Abstract Quality and safety of foods is one of the world’s top topics. Using high-precision spectral devices is a main technology trends by its high accuracy and nondestructive of food inspection, but the common obstacle is how to extract informative variables from raw data without losing significant information. This article proposes a novel feature selection algorithm named Support Vector Machine-Multiclass Forward Feature Selection (SVM-MFFS). SVM-MFFS adopts the wrapper and forward feature selection strategy, explores the stability of spectral variables, and uses classical SVM as classification and regression model to select the most relevant wavelengths from hundreds of spectral data. We compare SVM-MFFS with Successive Projection Analysis and Uninformative Variable Elimination in the experiment of identifying different brands of sesame oil. The results show that SVM-MFFS outperforms in accuracy, Receiver Operating Characteristic curve, Prediction and Cumulative Stability, and it will provide a reliable and rapid method in food quality inspection.