For identifying discriminatory biomarkers between green tea (GT) and decaffeinated green tea (dGT), in-tube extraction (ITEX)-gas chromatograph-mass spectrometer (GC-MS) was optimized to determine volatile organic compounds (VOCs) from tea products. Biomarker selection between GT and dGT was then conducted by random forest (RF). Optimized ITEX parameters by central composite design were an incubation temperature of 92 °C, incubation time of 12 mins, and 36 for syringe strokes. A training group of 24 samples and testing group of 21 samples were used to RF classification of biomarkers identification. Results revealed that 2-hexenal, 2-ethyl furan, indole, and β-ocimene were selected as discriminatory biomarkers between GT and dGT in the training group. Using these biomarkers with RF classification algorithms, prediction accuracy for dGT and GT were 88.9% and 100%, respectively, which was higher than for other classification algorithms. This implies that ITEX-GC-MS can be a promising tool for quality control of commercial tea products.