Fourier Transform Infrared (FTIR) and Gas Chromatography Mass Spectrometry (GCMS) are two common instruments used for analysis of edible oils. The output signal is often analysed on the software attached to the workstations. The processing software is usually individualised for a specific source. The output of GCMS cannot be analysed on the FTIR hence analysts often need to juggle between instruments when multiple techniques are employed. This could become exhaustive when a large dataset is involved. This paper reports a synchronised approach for analysis of signal from FTIR and GCMS. The algorithm is demonstrated on a dataset of edible oils to investigate the thermal degradation of seven types of edible oils treated at 100°C and 150°C. The synchronised routines identify peaks present in FTIR and GCMS spectra/chromatograms where the information is subsequently extracted onto peak tables for further analysis. In this study, it is found that palm based products and corn oils were relatively more stable with higher content of antioxidants tocopherols and squalene. As a conclusion, this approach allows simultaneous analysis of signal from multiple sources and samples enhancing the efficiency of the signal processing process. 1. Introduction Fourier Transform Infrared (FTIR) and Gas Chromatography-Mass Spectrometry (GCMS) are two essential techniques applied for analysis of edible oils [1, 2]. Fundamentally, FTIR spectra illustrate absorption bands with characteristic frequency attributed to different functional groups whilst GCMS reveals the compounds eluted at different retention times with mass spectra corresponding to compounds present, indicative of the fatty acid compositions. Conventionally the resultant signals from both instruments are analysed with the software equipped at the workstations for peak integration. The software is typically instrumental and model dependent. The signal processing tool exclusively designed for FTIR is not applicable to GCMS chromatograms due to differences in data nature and characteristics. Therefore to analyse the output from both FTIR and GCMS, an analyst has to juggle between both instruments. When a large volume of sample is involved, the signal processing process can be exhaustive and time consuming. With the advances in computer technology, various alternatives have been made available reducing the dependence on the default signal processing tool; for instance, the digital data in csv format is readable on Microsoft Excel. Numerous algorithms have been developed for analysis of signal from various instrumentation
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