Classification of adulterated honeys by multivariate analysis.

Area of Science:

• Food Science
• Analytical Chemistry
• Agricultural Science

Background:

• Honey adulteration with cheaper syrups like date syrup (DS) and invert sugar syrup (IS) is a global concern.
• Authenticity testing is vital for consumer protection and maintaining market integrity.
• Accurate detection methods are needed to differentiate genuine honey from adulterated products.

Purpose of the Study:

• To develop and validate a method for detecting adulteration of honey with date syrup and invert sugar syrup.
• To evaluate the effectiveness of various analytical parameters (color, rheological, physical, chemical) in identifying adulterants.
• To apply multivariate statistical techniques for classifying adulterated honey samples.

Main Methods:

• Preparation of 102 adulterated honey samples with DS and IS at 7%, 15%, and 30% concentrations.
• Determination of 32 parameters including color indices, rheological, physical, and chemical properties.
• Application of multivariate analysis: Principal Component Analysis (PCA) followed by Linear Discriminant Analysis (LDA).

Main Results:

• Color indices and rheological properties achieved moderate identification rates (62.75% and 67.65%, respectively).
• Physical properties demonstrated high discriminatory power (97.06% accuracy).
• Chemical properties provided the best separation, with one set achieving 100% accuracy (lactone, diastase activity, sucrose) and another 95% (free acidity, HMF, ash).

Conclusions:

• Multivariate analysis, particularly LDA, is effective for classifying honey adulterated with DS and IS.
• Chemical properties are the most reliable indicators for detecting honey adulteration.
• The study provides a robust analytical framework for ensuring honey authenticity.