Simple and Accessible Food Testing with RADIAN™ ASAP

Importance of the Topic

Ensuring the integrity and safety of food products across the supply chain is critical in light of high-profile adulteration incidents. Rapid, minimally invasive analytical approaches enable timely screening and support traceability systems.

Objectives and Overview of the Study

This white paper presents the RADIAN ASAP system—a compact direct-analysis platform combining a single-quadrupole mass spectrometer (ACQUITY QDa) with an ambient Atmospheric Pressure Solids Analysis Probe—and the LiveID 2.0 informatics for rapid (<1 min) profiling of liquids and solids.

Methodology and Embedded Instrumentation

Key features and workflows:

• Instrumentation: RADIAN ASAP with ACQUITY QDa mass analyzer, ASAP ionization source, heated nitrogen gas for volatization and ionization.
• Sample Introduction: Disposable glass capillaries for direct dabbing of solids or dipping/pipetting liquids; optional dilution or solvent extraction to mitigate matrix effects.
• Ionization Mechanism: APCI-like proton transfer facilitated by ambient moisture.
• Instrument Parameters: Typical settings include cone voltage at 15 V, corona current 3 µA (positive) or 2.5 µA (negative), scan speeds of 1–2 Hz, and gas heater control by isothermal or stepped temperature programs.
• Data Workflows:
  
  • Chemometric Analysis: PCA, LDA or PCA-LDA for classification, one-class and multiclass models, and multivariate calibration for quantitation.
  • Library Matching: Building spectral libraries across multiple cone voltages and real-time reverse matching with match scores (0–999).

Main Results and Discussion

Three case studies illustrate performance:

• Oolong Tea Authentication: Direct analysis of ground tea leaves or methanolic extracts achieved clear discrimination of Guangdong Dancong, Taiwan Dongding, and Anxi Tieguanyin varieties.
• Hop Cultivar Classification: Ethanol extracts of Strata, Cent, and Cas hops were differentiated by PCA-LDA based on volatile and non-volatile markers.
• Oregano Adulteration Detection: SIMCA models identified non-aromatic adulterants in dried oregano down to 5–20% substitution levels.

Benefits and Practical Applications of the Method

• High sensitivity and selectivity with minimal sample preparation.
• Rapid throughput (<1 min per sample) and compact footprint suitable for diverse lab environments.
• Intuitive LiveID 2.0 workflows with live recognition for on-the-fly classification and compound identification.

Future Trends and Opportunities

• Expansion of spectral libraries and chemometric models to cover additional food matrices and adulterants.
• Integration with complementary techniques (e.g., LC-MS) for comprehensive profiling and quantitation.
• Development of portable or on-site testing solutions for in-field food surveillance.
• Adoption in regulatory, QA/QC, and supply-chain risk-management laboratories.

Conclusion

The RADIAN ASAP platform, combined with LiveID 2.0 informatics, streamlines food testing by uniting direct ambient-ionization mass spectrometry with user-friendly chemometric and library-matching workflows, enhancing throughput, reducing complexity, and supporting rapid decision making for food authentication and safety assessment.

Reference

• Walker MJ, Burns M, Burns DT. Horse Meat in Beef Products – Species Substitution 2013. J Assoc Public Anal. 2013;41:67–106.
• Gossner CM-E, Schlundt J, Ben Embarek P, et al. The Melamine Incident: Implications for International Food and Feed Safety. Environ Health Perspect. 2009;117(12):1803–1808.
• Jafari S, Guercetti J, Geballa-Koukoula A, et al. ASSURED Point-of-Need Food Safety Screening: A Critical Assessment of Portable Food Analyzers. Foods. 2021;10(6):1399.
• Horning EC, Horning MG, Carroll DI, Dzidic I, Stillwell RN. New Picogram Detection System Based on a Mass Spectrometer with an External Ionization Source at Atmospheric Pressure. Anal Chem. 1973;45(6):936–943.
• ASAP Application Notebook. Waters Application Notebook 720003907en. 2011.
• Atmospheric Pressure Ionization Sources: Their Use and Applicability. Waters White Paper 720005935en. 2017.
• Tan HR, Chan LY, Lee HH, Xu Y-Q, Zhou W. Rapid Authentication of Chinese Oolong Teas Using Atmospheric Solids Analysis Probe-Mass Spectrometry (ASAP-MS). Food Control. 2022;134:108736.
• Yu J, Wang Y, Lin J, Li J, Qiu W. Application of RADIAN ASAP-LiveID Platform in Flavor Type Discrimination of Chinese Baijiu and Authentication of Maotai. Waters Application Note 720007188en. 2021.
• Chan LY, Chang Y, Peng H, Zhang G. Authentication of Cocoa Butter by Direct Analysis Using RADIAN ASAP with LiveID. Waters Application Note 720007100en. 2021.
• Damiani T, Dreolin N, Stead S, Dall’Asta C. Critical Evaluation of Ambient Mass Spectrometry Coupled with Chemometrics for the Early Detection of Adulteration Scenarios in Origanum vulgare L. Talanta. 2021;227:122116.
• Stead S, Dreolin N, Damiani T, Sammarco G, Suman M, Dall’Asta C. RADIAN ASAP LiveID as a Routine Screening Solution for Substitution Fraud in Dried Herbs. Waters Application Note 720007045en. 2020.
• Li J, Xing Z, Yu J, Qiu W, Wang F. Rapid Identification of Adulteration in Edible Oils Using Direct Analysis Mass Detection Platform (RADIAN ASAP-LiveID). Waters Application Note 720007189en. 2021.
• Ng D. Evaluation of Multivariate Calibration with RADIAN ASAP Data for the Quantitation of Edible Oil Blends. Waters Application Note 720007437en. 2021.
• Lee HH, Ng D. Rapid Discrimination of Authentic Honey and Adulterants Using RADIAN ASAP. Waters Application Note 720007135en. 2021.
• Loh LX, Lee HH, Stead S, Ng DHJ. Manuka Honey Authentication by a Compact Atmospheric Solids Analysis Probe Mass Spectrometer. J Food Compos Anal. 2022;105:104254.
• An N, Cai W-J, Zhu Q-F, Wang W, Hussain D, Feng Y-Q. Metabolic Profiling of Organic Acids in Honey by Stable Isotope Labeling Assisted Liquid Chromatography-Mass Spectrometry. J Food Compos Anal. 2020;87:103423.
• Jones M, Riches E. RADIAN ASAP for Simple Mass Spectral Screening of Polymer Formulations. Waters Application Note 720007270en. 2021.
• Callao MP, Ruisánchez I. An Overview of Multivariate Qualitative Methods for Food Fraud Detection. Food Control. 2018;86:283–293.
• Oliveri P. Class-modelling in Food Analytical Chemistry: Development, Sampling, Optimization, and Validation Issues – a Tutorial. Anal Chim Acta. 2017;982:9–19.
• Berrueta LA, Alonso-Salces RM, Héberger K. Supervised Pattern Recognition in Food Analysis. J Chromatogr A. 2007;1158(1):196–214.