Comparative study using classical chemical analytical techniques, combined with innovative spectroscopic methods based on photonics and machine learning for the authenticity and origin detection of Greek honey
Start Date: 12/05/2020,     End Date: 11/05/2023

Honey is a natural product of great nutritional value, highly demanded from consumers. However, the common practice of adulterating high-quality honey by adding substances (sugars, water) or mixing it with
lower quality honey (based on its botanical origin) is a common practice in the market. The control of adulteration is important, especially in our country where the sale of low quality honeys leads to consumer
deception. Until now the methods used to analyze honey origin and quality are time- and cost-consuming and they are performed by experienced and highly-specialized personnel. The aim of the present research
proposal is to develop and validate a new innovative, facile and cost-effective method to detect honey adulteration towards of a final high-quality, verified product (Greek honey). In the framework of the project,
a combination of classical analytical methods and modern optical spectroscopic techniques combined with machine learning methods will be deployed in the field of honey analysis. Combining the aforementioned
techniques, we expect to achieve a highly efficient screening and more accurate and cost-effective results concerning the different types of honey and detection of adulteration. The results of the proposal will be
commercially exploited by the participating company, leading to a framework that will result in a high quality verified honey for customers’ benefits.

The objectives of the project are:

a. Development of methods, mechanisms and tools for verifying the authenticity of honey and protecting consumers from fraud by adulteration in Greek traditional products and foods of high added value (honey).

b. Investigation of alternative screening techniques, such as spectroscopy methods based on innovative photonic approaches in conjunction with the application of computational methods of artificial intelligence pattern recognition, which provide speed, ease of measurement and low cost, for the verification of the authenticity of honey, with direct application to food businesses.

c. Commercial exploitation of the above in the CHEMICOTECHNIKI laboratory, which is based in Crete, in order to attract customers from Greece and abroad.

Principal Investigator

Dr. Velegrakis Michalis
Research Director

Technical Staff

Ms. Stamataki Katerina

Research Associates

Dr. Zoumi Aikaterini
PostDoctoral Fellow


Mr. Orfanakis Emmanouil
Ph.D. student

Optical spectroscopy methods combined with multivariate statistical analysis for the classification of Cretan thyme, multi-floral and honeydew honey

The botanical origin of honey attracts both commercial and research interest. Consumers’ preferences and medicinal uses of particular honey types drive the demand for the determination of their authenticity with regard to their botanical origin. This study presents the discrimination of thyme, multi-floral. and honeydew honeys by Fourier-transform infrared (FTIR) and ultraviolet (UV) absorption spectroscopy combined with multivariate statistical analysis. UV absorption spectroscopy was applied without any dilution of the sample using a custom-made cuvette. FTIR and UV absorption spectroscopic
data were processed by means of the orthogonal partial least squares discriminant analysis.
The optimal classification of floral and honeydew honeys was accomplished with UV spectroscopy with a successful estimation of 92.65% for floral honey and 91.30% for honeydew honey. The discrimination of thyme versus the multi-floral honey was best achieved with FTIR, with a correct classification of 95.56% and 100% for multi-floral and thyme honey respectively. Furthermore, our findings revealed the region of 2400–4000 cm−1 of the FTIR spectra as the most significant for this discrimination.
This work demonstrates that optical spectroscopic techniques in combination with multivariate statistical analysis can be a rapid, low-cost, easy-to-use approach for the determination of the botanical origin of honey without sample pretreatment.



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