Fast Analysis of Food and Beverage Products using a Mass Spectrometry Based Chemical Sensor

Significance of the topic

Rapid detection of contamination, adulteration, and quality inconsistencies is critical for food and beverage safety and consumer acceptance. Mass spectrometry based chemical sensors offer high throughput screening without chromatographic separation, enabling timely on-site decision-making in quality control workflows.

Objectives and Study Overview

This study evaluates a mass spectrometry based chemical sensor equipped with three sample introduction techniques: headspace injection, solid phase microextraction (SPME), and headspace sorptive extraction (HSSE). Three food and beverage matrices (strawberry yogurt, olive oil, and coffee) were analyzed to demonstrate the sensor’s ability to classify product quality, detect spoilage or adulteration, and differentiate origin or processing status.

Methodology and Instrumentation

• Sample Introduction Techniques:
  • Headspace (HS) injection for yogurt aroma profiling.
  • Solid Phase MicroExtraction (SPME) for olive oil volatile analysis using a 75 μm Carboxen/PDMS fiber.
  • Headspace Sorptive Extraction (HSSE) with Twister stir bar for coffee headspace trapping.
• Instrumentation:
  • GERSTEL ChemSensor interface with Agilent 5973 mass spectrometer.
  • MPS 2 multipurpose autosampler and GERSTEL CIS 4 PTV inlet.
  • GERSTEL TDS A/TDS 2 system for thermal desorption in HSSE experiments.
  • HP5-MS columns for rapid temperature ramp GC separations.
  • Chemometric software (Pirouette, Instep) for PCA, HCA, SIMCA, and KNN modeling.

Main Results and Discussion

• Yogurt Analysis: Headspace–MS fingerprints and SIMCA models distinguished fresh and aged samples and correctly identified different strawberry flavors. The m/z 44 signal was key for quality discrimination, while higher masses reflected flavor additives.
• Olive Oil Analysis: SPME–MS data enabled clear separation of pure versus degassed oils using SIMCA. Five pure oil varieties were individually classified, while degassed samples exhibited lower volatile signals.
• Coffee Analysis: HSSE–TDS–MS combined with SIMCA differentiated Arabica and Robusta species and further discriminated among Arabica origins (Brazil, Java, Kenya) and Robusta types (Grain Noir, Soft African, Vietnam).

Benefits and Practical Applications

• High throughput screening with sub-4-min analysis cycles and minimal sample preparation.
• Elimination of full chromatographic separation reduces analysis time and cost.
• Chemometric classification models offer robust pass/fail decisions for QC and authentication.
• Versatile sensor configuration accommodates diverse matrices and workflows.

Future Trends and Opportunities

Advances in sensor miniaturization, integration of advanced machine learning algorithms, and expansion to additional food and beverage categories can further enhance the applicability of MS-based chemical sensors. Real-time monitoring in production lines and wireless data processing will support Industry 4.0 quality control paradigms.

Conclusion

The mass spectrometry based chemical sensor demonstrates rapid, reliable classification of yogurt, olive oil, and coffee samples without chromatographic separation. Combined with multivariate data analysis, this approach enables effective detection of spoilage, adulteration, and product origin, supporting robust quality control operations.

References

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