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As stingless bee honey (SBH) is gaining in popularity in the Malaysian market, it is now prone to adulteration. The higher price of SBH compared to floral honey has led to the use of unusual adulterants such as vinegar and even floral honey to mimic the unique taste and appearance of SBH. Since the current AOAC 998.12 method fails to detect these adulterants as their δ

As stingless bee honey (SBH) is gaining in popularity in the Malaysian market, it is now prone to adulteration. The higher price of SBH compared to floral honey has led to the use of unusual adulterants such as vinegar and even floral honey to mimic the unique taste and appearance of SBH. Since the current AOAC 998.12 method fails to detect these adulterants as their δ13C values are in the range for C3 plants, untargeted 1H NMR metabolomics was proposed. Principal component analysis of SBH 1H NMR fingerprints was able to distinguish authentic SBHs from adulterated ones down to 1% adulteration level for selected adulterants. Discriminant analysis showed promising results in distinguishing the preliminary datasets of authentic SBHs from the adulterated ones, including discriminating SBHs adulterated with different adulterants derived from C3 and C4 plants. Hence, to assure any emerging adulterant can be detected, all 1H NMR regions should be considered.

Honey is a high value food commodity with recognized nutraceutical properties. A primary driver of the value of honey is its floral origin. The feasibility of applying multivariate data analysis to various chemical parameters for the discrimination of honeys was explored. This approach was applied to four authentic honeys with different floral origins (rata, kamahi, clover and manuka) obtained from producers in New Zealand. Results from elemental profiling, stable isotope analysis, metabolomics (UPLC-QToF MS), and NIR, FT-IR, and Raman spectroscopic fingerprinting were analyzed. Orthogonal partial least square discriminant analysis (OPLS-DA) was used to determine which technique or combination of techniques provided the best classification and prediction abilities. Good prediction values were achieved using metabolite data (for all four honeys, Q(2)=0.52; for manuka and clover, Q(2)=0.76) and the trace element/isotopic data (for manuka and clover, Q(2)=0.65), while the other chemical parameters showed promise when combined (for manuka and clover, Q(2)=0.43).

A novel procedure for the rapid isotope analysis of the carbon-bound non-exchangeable (CBNE) hydrogen in mono and disaccharides has been developed to demonstrate the feasibility of detecting undeclared addition of exogenous sugar products in foods and beverages susceptible to economically motivated adulteration. The procedure utilizes a simple one-step reaction, with the derivatising agent N-methyl-bis-trifluoroacetamide, to substitute the exchangeable hydroxyl-hydrogens with trifluoroacetate derivatives that are sufficiently volatile to be separated and measured by a gas chromatograph coupled to an isotope ratio mass spectrometer. The conversion of the derivatised sugars into the measuring gas is achieved using a high temperature chromium-silver reactor that retains carbon, oxygen and fluorine whilst releasing hydrogen gas for stable isotope measurement. The new procedure has advantages over existing methods in terms of ease of use, analysis time and compound-specific information. Sugars from fruit juice and honey have been measured to demonstrate the feasibility of using this technique.

In recent years, with the growing complexity of global food supply chains and trade, food fraud, including adulteration of high value foods with cheaper substitutes, has become an increasingly important issue. A metabolomics approach can be applied to discover biomarkers that can be used to trace food adulteration. A study was undertaken to discover novel, potential biomarkers for the rapid detection of the adulteration of fruit juices with cheaper alternatives. Pineapple, orange, grapefruit, apple, clementine, and pomelo were investigated. Untargeted metabolite fingerprinting was performed by UPLC-QToF MS with multivariate data analysis. Twenty-one differential metabolites were selected, contributing to the separation between pineapple, orange and grapefruit juices, and their admixtures down to 1% adulteration level. A targeted metabolomics method was then optimised and adulteration could be detected at 1%. The results demonstrate that metabolomics has potential as a screening tool for the rapid detection of food adulteration.