| Summary: | For decades, the headspace Gas Chromatography Mass Spectrometry (GCMS) technique has been employed to analyse Volatile Organic Compounds (VOCs), extracting chromatographic signals and identifying chemical components. In practical scenarios, identifying major chemical compounds has been a useful approach for herb experts to recognize and differentiate species. However, this process has been manual and lacked an automated herb recognition system that incorporates GCMS technology. To address this gap, a GCMS herb recognition system has been proposed, integrating the GCMS system with a pattern recognition approach. Innovatively, a new feature extraction method using the Weighted Histogram Analysis Method (WHAM) has been introduced. This method employs a reweighting technique that utilizes the peak area and peak height of VOCs to generate a unique pattern for each herb species. A comparison of classification performance between systems with WHAM shows that the Support Vector Machine (SVM) method achieves a higher percentage of accuracy, ranging from 92.32 to 95.67, compared to without WHAM, which achieves an accuracy ranging from 57.43 to 62.11. This method has demonstrated promising results in identifying herb species, and the classification method based on machine learning algorithms has proven successful in recognizing and distinguishing herb species
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