RADIAN ASAP LiveID as a Routine Screening Solution for Substitution Fraud in Dried Herbs

Importance of the Topic

Substitution fraud in dried culinary herbs such as oregano poses a significant economic and safety risk in the food industry. Routine screening methods based on spectroscopy or DNA analysis can be time-consuming or require extensive sample preparation. Direct mass spectrometry (MS) techniques offer faster, more sensitive, and more selective detection of adulterants, providing critical diagnostic chemical information to ensure product authenticity and protect consumer trust.

Objectives and Study Overview

This study evaluated the combination of the RADIAN ASAP direct MS platform with the LiveID chemometrics software to develop a rapid, robust screening solution for detecting substitution of dried Mediterranean oregano with bulking agents such as olive and cistus leaves. The aim was to build a binary classification model capable of distinguishing authentic oregano from adulterants and to assess its predictive performance in real-time quality control testing.

Methodology and Used Instrumentation

Sample Preparation and Extraction

• Dried, ground plant material (0.2 g) was extracted with methanol (10 mL) by rotary shaking (10 min) and centrifugation. A 400 µL aliquot of supernatant was used for analysis.

Sample Introduction

• Disposable glass capillaries were dipped into wet extract for 5 s and inserted into the RADIAN ASAP probe.

Instrumentation

• RADIAN ASAP direct mass detector fitted with an ACQUITY QDa mass analyzer and Atmospheric Solids Analysis Probe (ASAP) ion source using atmospheric pressure chemical ionization (APCI).
• LiveID chemometrics software (version 2.0) for model building, validation, and real-time recognition.

Chemometric Modeling

• Mass spectra were normalized to total ion current and mean-centered.
• PCA was conducted over m/z 300–750 with bin width 1 Da; three principal components (96% variance) fed into LDA for supervised classification.
• An outlier threshold of three standard deviations (97% confidence) defined class boundaries.

Main Results and Discussion

Model Training and Cross-Validation

• Authentic oregano (n=35) and olive leaves (n=18) spectra produced repeatable, feature-rich profiles.
• PCA/LDA model achieved 100% predictive accuracy in both “leave one file out” and “leave one group out” cross-validation, demonstrating robust discrimination and negligible inter-day variation.

Independent Validation

• Seven unseen oregano and two olive samples analyzed across four instruments by three analysts returned 100% correct classifications in real time.
• Four non-model herb species (marjoram, thyme, mint, cistus) were correctly flagged as outliers.

Screening Detection Limit

• Adulteration blends (10% and 30%) of olive and cistus in oregano yielded a reliable screening threshold of ≤30% substitution (w/w).

Benefits and Practical Applications

The RADIAN ASAP LiveID workflow delivers:

• Minimal sample preparation and instrument optimization for non-experts.
• Rapid analysis-to-decision turnaround in seconds.
• Compact footprint enabling deployment in routine QC laboratories.
• High sensitivity and selectivity for a broad range of thermally stable, polar compounds.

Future Trends and Potential Applications

Extensions may include:

• Expanding chemometric models to cover additional herb species and complex adulterant mixtures.
• Integration with laboratory information management systems for automated quality control.
• Application to other food commodities (spices, teas, processed ingredients) and forensic authenticity testing.
• Adoption of advanced machine learning algorithms for multi-class classification and quantitation at lower adulteration levels.

Conclusion

The combination of RADIAN ASAP and LiveID chemometrics provides an accurate, robust, and routine screening solution for detecting substitution fraud in dried herbs. With minimal method development, the platform delivers real-time recognition of adulteration scenarios and supports industry deployable quality control across food and beverage matrices.

References

1. Black C., Haughey S. A., Chevallier O. P., et al. A Comprehensive Strategy to Detect the Fraudulent Adulteration of Herbs: The Oregano Approach. Food Chemistry. 2016;210:551–557.
2. Drabova L., Alvarez-Rivera G., Suchanova M., et al. Food Fraud in Oregano: Pesticide Residues as Adulteration Markers. Food Chemistry. 2019;276:726–734.
3. Marieschi M., Torelli A., Bianchi A., Bruni R. Detecting Satureja montana L. and Origanum majorana L. by Means of SCAR-PCR in Commercial Samples of Mediterranean Oregano. Food Control. 2011;22(3–4):542–548.
4. Wielogorska E., Chevallier O., Black C., et al. Development of a Comprehensive Analytical Platform for the Detection and Quantitation of Food Fraud using a Biomarker Approach: The Oregano Adulteration Case Study. Food Chemistry. 2018;239:32–39.
5. Black C., Chevallier O. P., Elliott C. T. The Current and Potential Applications of Ambient Mass Spectrometry in Detecting Food Fraud. Trends in Analytical Chemistry. 2016;82:268–278.
6. Black C., Stead S., Chevallier O., Martin N., Elliott C. T. A Real Time Lipidomics Approach for Detecting Fish Fraud using Rapid Evaporative Ionisation Mass Spectrometry and LiveID software. Waters Corporation Application Note 720006205EN. 2018.