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due complex nature chemical compositions ignitable liquids il interferences fire debris matrices interpreting chromatographic data poses challenges analysts work artificial intelligence ai developed transfer learning convolutional neural network cnn googlenet image classification ai finetuned create intelligent classification systems discriminate samples containing gasoline residues burned substrates ground truth samples analyzed headspace solidphase microextraction hsspme coupled gas chromatograph mass spectrometer gcms hsspmegcms data transformed three types image presentations heatmaps extracted ion heatmaps total ion chromatograms abundance masstocharge ratios scan converted image patterns characteristic chemical profiles gasoline transfer learning data labeled gasoline present gasoline absent classes assessment results demonstrated ai models achieved 100 0 accuracy identifying neat gasoline models assessed using spiked samples ai model developed using extracted ion heatmap obtained highest accuracy rate 959 04 greater obtained machine learning models ranging 173 07 787 07 proposed work demonstrated heatmaps created gcms data represent chemical features samples additionally pretrained cnn models readily available transfer learning workflow develop ai gcms data interpretation fire debris analysis