Using Rapid Aroma Profiling to Classify Honey

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Nowadays, consumers are highly discerning when it comes to quality. This is especially true for honey, which is obtained from natural resources like the honeycombs of beehives, buckwheat, etc. The probability of bacteria and fungi growth occurring in honey is surprisingly high. Honey may also contain dormant endospores of bacteria that can transform into a toxin-producing form of bacteria, causing severe illness and, in some cases, even death.

For this reason, the safety, nutritional value, functionality, and aesthetics (such as color, texture and flavor) of honey must conform to very high standards prior to consumption. Quality Assurance teams are, therefore, duty bound to monitor the quality and standards of honey before it reaches customers.

Fortunately, there are several available choices that cater to the stringent testing requirements of the market. EST Inc.’s zNose® is a superior technology that utilizes the chromatogram approach as well as the spectral approach to conduct swift aroma profiling of honey.

Two experiments were conducted for at-line flavor quality control and off-flavor assessment. Sugar solutions were included in the tests due to their similarity to honey and because they are frequently used as alternatives for honey within adulteration practices.

In the first experiment, samples of three different honey varieties (buckwheat, clover, and orange blossom) were examined. Ten independent samples were analyzed for each honey variety and sugar solution. In the second experiment, samples of six different honey varieties from different geographical origins (buckwheat, clover, orange blossom, black locust, mint, and carrot), were compared with the varieties used in the first experiment. In addition, ten independent samples per honey variety and sugar solution were recorded.

As the zNose® is a mixture of sensor-based detection and regular GC analysis, the data obtained from measurements taken from the zNose® were analyzed in two different ways:

1. Results of Experiment 1

  • Analyzing Samples Using the Chromatogram Approach:

With the chromatogram approach, a PCA analysis on the total dataset of all relative peak areas of three honey types (buckwheat, clover, and orange blossom) and two sugar solutions (beet invert and cane invert) showed a division between all products with PC1 and PC2, which explained 90% of the total variance.

  • Analyzing Samples Using the Spectral Approach:

Using the spectral approach, both the positive and negative values in original derivative plots of all honey samples and sugar solutions were incorporated into the PCA analysis, which initially did not result in a good separation. Only the aroma fingerprint of buckwheat appeared distinct enough to be separated from the rest in a PCA plot, with PC1 and PC2 explaining 80% of the total variance.

2. Results of Experiment 2

  • Analyzing Samples Using the Chromatogram Approach:

Using the chromatogram approach, the twelve most abundant honey volatiles were selected and used directly as explanatory variables in the discriminant analysis. This model performed well in terms of classification. Only one of the sixteen external validation samples were classified from the carrot honey in the group of the clover honey.

  • Analyzing Samples Using the Spectral Approach:

Concerning corrected spectral data, it was not possible to apply the full spectra directly to undertake the discriminant analysis as the number of variables exceeded the number of observations and resulted in an overfit. Data reduction techniques, such as principal components or canonical variable analysis, were used and visually discriminated all honey samples and sugar solutions.

Results acquired implied that the validation measurements match with the corresponding calibration measurements, bar one carrot honey validation observation (which was categorized with the clover honey observations). Overall, it correctly classified 94% of the external validation samples.

This demonstrates how EST Inc.’s zNose® can accurately discriminate adulterant sugars from pure honey samples as well as the aromas of different varieties of honey. It is a fast aroma fingerprinting technique that conducts aroma profiling of honey within a minute, with speed, accuracy and precision.

zNose® provides a one-stop solution that allows Quality Assurance teams to efficiently assess the quality and standard of honey, before it is presented to consumers.


This information has been sourced, reviewed and adapted from materials provided by Electronic Sensor Technology.

For more information on this source, please visit Electronic Sensor Technology.

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