Multi-Window Feature Extraction for SVM-Based Electronic Nose Classification of Four Herbal Essential Oils

Rapid authentication of herbal essential oils requires sensor models that are accurate, fast, and chemically interpretable. This paper presents an electronic-nose workflow for four oils, namely red ginger, white turmeric, turmeric, and lemongrass, using multi-window feature extraction and support vector machine classification. Signals were collected from a ten-sensor metal-oxide-semiconductor array during an exposure period from 10 to 70 seconds after baseline correction. Features were extracted from short, medium, and long windows corresponding to onset, peak development, and tail dynamics, then classified using radial-basis-function support vector machines under nested cross-validation. Gas chromatography-mass spectrometry profiles were used as class-level chemical anchors rather than direct regression targets. Early windows already contained strong class information, especially for citral-rich lemongrass, whereas mid and late windows improved interpretation for slower terpene-rich oils. The compact window triad produced competitive macro-averaged F1-score performance while preserving an auditable link between sensor features and volatile kinetics. The workflow supports rapid screening of herbal essential oils with a parsimonious and chemically interpretable feature set.