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Honey is the most popular natural sweetener worldwide, with a wide range of consumers relying on its sweet taste to add sweetness to baked goods or to improve tea’s palatability. While honey is a widely available product, many honey products’ authenticity has been called into question over recent years.
Commonly used methods for detecting sugar-based adulterated honey are not generally effective. This is because of similarities in the chemical compositions of authentic honey and sugar-based adulterants.
Honey is made up of around 70% to 80% carbohydrates. These include glucose and fructose alongside minor components such as enzymes, organic acids, amino acids, polysaccharides, lipids, minerals, vitamins, proteins, volatile chemicals, and phenolic acids.
Adulterated honey: A widespread problem requiring a solution
Honey is one such food that is reported to be incredibly vulnerable to food fraud, accounting for approximately 90% of all sweetener-related adulterations across Europe.
People facilitating this fraud are often based in China, a country that is a major producer of honey as well as one of the world’s leading global honey providers. It is estimated that 200,000 or more tons of honey are produced in China every year, with around half of the Chinese honey output being exported to the global market.
The need for accurate, sensitive tools to detect adulterated honey has increased, because many existing methods are unable to produce optimal findings. Adulteration of honey lowers the product quality, as well as potentially downgrading the region’s market credit rate.
Adulterants also pose a prospective health risk, partially resulting from its increased levels of low-density lipoprotein and cholesterol.
The nature of the contemporary food industry poses additional challenges in the tracing of products’ origins, as well as in the identification of these products’ components. Analytical techniques, like thin-layer chromatography, tend to be limited due to their high detection rate of false positives.
The role of nuclear magnetic resonance for identifying adulterated honey
One of the most promising tools in the detection of food adulteration is nuclear magnetic resonance (NMR) imaging. This technique can measure differing compounds and allow thorough examination of the structural information of compounds contained in a mixture.
Bruker’s NMR Food-Screener includes a Honey-Profiling Module, and this is just one example of the range of NMR commercial tools specifically designed for the identification of adulterated honey. Through the use of spectral libraries of a known mixture, NMR spectra for a test sample can identify whether a food’s components are either added or missing.
Bruker’s Food-Screener offers an NMR fingerprint that is specific to a sample – for example a honey sample – and this can be compared to a sizeable database of honey samples to ascertain if there is a discrepancy or a match. The platform provides an innovative tool, capable of improving quality control monitoring across the honey production industry.
A single NMR experiment can produce reproducible, repeatable findings. The NMR experiment is generally only five minutes long, as well as being high-throughput, and generally considered to be non-destructive to the sample. Spectroscopic methods are also highly cost-effective, an attractive quality in many research labs.
Study accurately differentiates between natural and adulterated honey
In a recent study, researchers from China utilized an advanced NMR technique to analyze major and minor components in Chinese honey, in order to ascertain whether or not the technique was able to identify adulteration.
The researchers employed a 600 MHz Bruker Ascend™ NMR spectrometer to acquire the 1H NMR spectra of 75 adulterated and 90 authentic Chinese honey samples. The NMR spectrometer was set at 600.38 MHz and 298 K with no sample rotation in place.
A principal component analysis (PCA) score scatter plot revealed a metabolically similar cluster of total honey samples. The researchers believe that this was a result of the tailoring of adulterated honey samples to mimic natural honey’s carbohydrate profile.
A PCA/linear discriminate analysis model that utilized 1H NMR spectral data was able to accurately classify adulterated honey and true honey by 94.0% and 98.3% in the training set, respectively. The test set displayed similar results.
The researchers were also able to use NMR data to ‘screen out’ contributing components that discriminated adulterated honey from true honey samples. A large number of these contributing components included xylobiose, proline, turanose, uridine, melezitose, β-glucose, and lysine.
The researchers were able to conclude that these markers, when examined in combination with NMR analysis, may aid scientists in establishing a rapid tool suitable for the identification of authentic honey. This tool can then be employed prior to the honey entering the Chinese and world markets.
NMR: The future of food monitoring
As research around the applications of NMR technologies in nutritional sciences continues to expand, consumers will likely be more confident in the quality of testing procedures applied to their honey products.
Not only does testing using an accurate system guarantee a better quality end product, it has the potential to mitigate the possible negative health effects that have been linked to adulterated honey.
Additional research may be required to fully validate this study’s findings; but, these promising results do indicate that NMR can be effectively and safely employed for honey analysis in Chinese honey exports.
Reference
Hea C, Liub Y, Liu H, et al. Compositional identification and authentication of Chinese honeys by 1H T NMR combined with multivariate analysis. Food Research International. doi: 10.1016/j.foodres.2019.108936.
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