Cross-Correlation for Compound Detection Consistency in Aquilaria Oil Analysis

This study investigates the application of crosscorrelation analysis to evaluate detection consistency and temporal alignment between Gas Chromatography-Mass Spectrometry (GC-MS) and GC-MS coupled with Gas Chromatography-Flame Ionization Detector (GC-FID) in profiling Aquilaria oil compounds. Two datasets comprising 7 compounds from high-quality GC-MS data and 6 compounds from four Aquilaria oil species via GC-MS/GC-FID were analyzed, producing 42 compound pair combinations. Crosscorrelation coefficients (R) and lag values were used to assess signal similarity and retention time shifts. The results revealed several highly correlated pairs ($\mathbf{R} \boldsymbol{\gt} \mathbf{0 . 7}$), such as $\boldsymbol{\gamma}$-eudesmol from the GC-MS dataset paired with dihydro- $\boldsymbol{\beta}$-agarofuran from the GC-MS/GC-FID dataset, $\gamma$-eudesmol with $\gamma$-eudesmol, and 10 -epi- $\gamma$-eudesmol with $\gamma$-eudesmol, with strong or perfect alignment, indicating their reliability as synchronized marker compounds. Moderate correlations ($0.6 \leq R \leq 0.7$) were also identified, particularly among $\boldsymbol{\beta}$-agarofuran and $\boldsymbol{\alpha}$-agarofuran pairs, showing acceptable consistency. Although compound pairs involving longifolol, hexadecanol and eudesmol exhibited weaker correlations ($R\lt 0.5$), select pairs like hexadecanol with dihyro- $\boldsymbol{\beta}$-agarofuran and longifolol with dihyro- $\boldsymbol{\beta}$-agarofuran demonstrated good alignment despite low $\mathbf{R}$ values. This analysis highlights the value of cross-correlation as a statistical tool to enhance compound matching across analytical techniques, contributing to improved quality classification and chemical marker identification in agarwood oil analysis.