A Screening Method Based on Headspace-Ion Mobility Spectrometry to Identify Adulterated Honey

Area of Science:

• Analytical Chemistry
• Food Science
• Spectrometry

Background:

• Honey adulteration with cheaper sweeteners like high fructose corn syrup (HFCS) is a prevalent economic fraud.
• Food adulteration poses significant risks to consumer health and erodes market trust.
• There is a critical need for rapid, cost-effective, and user-friendly analytical methods for food quality control.

Purpose of the Study:

• To develop and optimize a novel headspace-ion mobility spectrometry (HS-IMS) method for detecting honey adulterated with HFCS.
• To evaluate the precision and repeatability of the developed HS-IMS method.
• To assess the classification performance of different analytical techniques for identifying adulterated honey samples.

Main Methods:

• Optimization of the HS-IMS procedure using a Box-Behnken design and response surface methodology.
• Assessment of method performance through intermediate precision and repeatability studies.
• Application of Hierarchical Cluster Analysis (HCA), Principal Component Analysis (PCA), and Linear Discriminant Analysis (LDA) for sample classification.

Main Results:

• The HS-IMS method demonstrated good precision (CV=4.90%) and repeatability (CV=4.27%).
• HCA and PCA indicated a trend in sample classification based on adulteration levels but did not achieve perfect separation.
• Linear Discriminant Analysis (LDA) successfully achieved 100% classification of honey samples according to HFCS adulteration levels.

Conclusions:

• The developed HS-IMS method is a novel and effective technique for the automatic determination of honey adulterated with HFCS.
• LDA provides a robust classification tool when combined with HS-IMS data for accurate adulteration detection.
• This study establishes HS-IMS as a promising analytical tool for ensuring honey authenticity and combating food fraud.