Statistical Differentiation of Pesto Products Using SPME-GCxGC-TOFMS and ChromaTOF Tile Software

Importance of Topic

Pesto aroma composition strongly influences consumer acceptance, product quality, and brand differentiation. Comprehensive profiling of volatile components enables optimization of formulations, quality control, and targeted product development in the food industry.

Study Objectives and Overview

This study applied non-targeted SPME-GC×GC-TOFMS combined with ChromaTOF Tile software to differentiate eleven commercial pesto samples from three producers. The main goal was to identify statistically significant aroma markers that distinguish product classes and support rapid quality assessment.

Methodology

Samples were prepared by headspace-SPME using a DVB/CAR/PDMS fiber. Each pesto aliquot (1 g) was incubated at 60 °C for 15 min, followed by 15 min extraction. Fibers were thermally desorbed in the GC inlet. Retention indices were determined using n-alkane standards (C7–C30) to improve compound identification. Data processing employed ChromaTOF Tile’s Fisher-ratio algorithm to tile the 2D chromatograms and compute statistical differences across sample classes.

Instrumentation

• GC×GC System: LECO Pegasus BT 4D with QuadJet™ thermal modulator
• Column Set: Primary Rxi-5SilMS (30 m×0.25 mm×0.25 µm) and secondary Rxi-17SilMS (0.6 m×0.18 mm×0.18 µm)
• Carrier Gas: Helium at 1.2 mL/min
• Oven Program: 40 °C (2 min) → 5 °C/min to 210 °C → 20 °C/min to 280 °C; secondary oven +5 °C; modulation period 2.7 s
• Injection: 2 min fiber desorption at 220 °C, split 20:1
• Mass Spectrometer: TOFMS range 40–550 m/z, acquisition 200 spectra/s, ion source 250 °C

Key Results and Discussion

PCA clustering revealed clear separation of pesto classes by producer. Loadings analysis highlighted several distinguishing compounds:

1. 1,2-Propanediol: abundant only in Producer 1 samples.
2. Butanoic acid and other short-chain acids (hexanoic, propanedioic): markers for Producer 3.
3. 7-Hydroxyocta-2,4-dienoic acid: exclusive to Producer 2.
4. Estragole: high in Producer 1, low in Producer 3, absent in Producer 2.

The Fisher-ratio tiling approach efficiently detected low-abundance features that may be overlooked by visual inspection of 2D chromatograms.

Benefits and Practical Applications

The combination of GC×GC-TOFMS with automated tile-based statistics accelerates non-targeted aroma profiling. Rapid identification of class-specific markers supports:

• Product formulation optimization
• Quality control and authentication
• Sensory correlation studies
• Streamlined data analysis workflows

Future Trends and Opportunities

Integration of advanced chemometric tools with multidimensional chromatography is expected to drive further improvements in foodomics. Potential developments include:

• Real-time data processing and cloud-based analytics
• Machine learning models for predictive quality assessment
• Expanded libraries for non-targeted screening in complex matrices
• Cross-platform compatibility for combined spectroscopy and separation data

Conclusion

This application note demonstrates that ChromaTOF Tile enables rapid and robust differentiation of complex sample sets in non-targeted GC×GC-TOFMS studies. By highlighting key aroma compounds responsible for class separation, this approach facilitates focused product development and reduces time-consuming manual data mining.

Reference

1. Marney LC, Siegler WC, Parsons BA, Hoggard JC, Wright BW, Synovec RE. Talanta. 2013;115:887–895.
2. Parsons BA, Marney LC, Siegler WC, Hoggard JC, Wright BW, Synovec RE. Anal Chem. 2015;87:3812–3819.