Rapid Discrimination of Authentic Honey and Adulterants Using RADIAN ASAP

Significance of the topic

The authentication of honey is critical for ensuring product quality and consumer trust. Adulteration with cheaper plant-based syrups undermines market integrity and poses economic and health concerns. Developing fast, reliable screening tools supports quality control in food production and raw ingredient verification.

Purpose and Overview of the study

This study evaluates the use of RADIAN ASAP ambient mass spectrometry combined with LiveID Software to rapidly distinguish authentic honey, common syrup adulterants, and intentionally adulterated honey. The goal is to establish a minimal-preparation workflow for point-of-control testing in food manufacturing.

Methodology and Instrumentation

A total of 11 genuine honey samples, five plant-based syrup adulterants, and one honey sample spiked with 35% syrup were analyzed. Samples underwent a simple solvent extraction to reduce complexity, then introduced directly into the RADIAN ASAP ion source via glass capillary dipping. Mass spectra were acquired over m/z 200–800. Chemometric modeling was performed in LiveID v2.0, employing principal component analysis (PCA) and linear discriminant analysis (PCA-LDA).

Main Results and Discussion

Unsupervised PCA accounted for 90% of total variance in the first three components, effectively grouping samples. Supervised PCA-LDA separated authentic honey, adulterants, and adulterated honey into distinct clusters: LD1 discriminated genuine honey from syrups, while LD2 isolated the adulterated sample. Loading plots highlighted two key mass regions—m/z 200–350 and 500–700—that drive sample differentiation. The rapid fingerprinting capability of RADIAN ASAP facilitates high-throughput screening without extensive preparation.

Benefits and Practical Applications

• Rapid, user-friendly fingerprinting for quality control in food manufacturing.
• Minimal sample preparation suitable for non-mass spectrometry experts.
• Integrated chemometric modeling via LiveID Software for quick discrimination of honey authenticity.

Future Trends and Opportunities

Advancements may include expanding ambient MS screening to a wider range of food matrices and integrating results with machine learning and IoT platforms for real-time monitoring. Further coupling with detailed LC-MS workflows can deepen compositional insights following initial screening.

Conclusion

This proof-of-principle work demonstrates that RADIAN ASAP combined with LiveID Software offers a rapid, reliable platform to distinguish authentic honey, adulterant syrups, and adulterated products. The approach supports point-of-control testing in raw material screening and finished product verification, enhancing food quality assurance processes.

Reference

1. Chin NL, Sowndhararajan K. In: Arnaut de Toledo VA, Chambo ED, eds. IntechOpen; 2020; DOI:10.5772/intechopen.90232.
2. Soares S, Amaral JS, Oliveira MBPP, Mafra I. Comprehensive Reviews in Food Science and Food Safety. 2017;16(5):1072–1100.
3. Wu L, Du B, Heyden Y, Chen L, Zhao L, Wang M, Xue X. Trends in Analytical Chemistry. 2017;86:25–38.
4. Black C, Chevallier O, Elliott C. Trends in Analytical Chemistry. 2016;82:268–278.
5. Lu H, Zhang H, Chingin K, Xiong J, Fang X, Chen H. Trends in Analytical Chemistry. 2018;107:99–115.
6. Kandova R, Stead S, Chevallier O. Waters Application Note 720006409EN. 2018.
7. Stead S, Jandova R, Eccles R, Goodall I. Waters Application Note 720006530EN. 2019.