Botanical origin discrimination of Greek honeys: physicochemical parameters versus Raman spectroscopy

Analytical insights for ensuring authenticity of Greek agriculture products: Unveiling chemical marker applications

Food authentication, including the differentiation of geographical or botanical origin, the method of production i.e. organic vs. conventional farming as well as the detection of food fraud/adulteration, has been a rapidly growing field over the past two decades due to increasing public awareness regarding food quality and safety, nutrition, and health. Concerned parties include consumers, producers, and legislators. Thus, the development of rapid, accurate, sensitive, and reproducible analytical methods to guarantee the authenticity of foods is of primary interest to scientists and technologists. The aim of the present article is to summarize research work carried out on the authentication of Greek agricultural products using spectroscopic (NIR, FTIR, UV-Vis, Raman and fluorescence spectroscopy, NMR, IRMS, ICP-OES, ICP-MS) and chromatographic (GC, GC/MS, HPLC, HPLC/MS, etc.) methods of analysis in combination with chemometrics highlighting the chemical markers that enable product authentication. The review identified a large number of chemical markers including volatiles, phenolic substances, natural pigments, elements, isotopes, etc. which can be used for (i) the differentiation of botanical/geographical origin; conventional from organic farming; production procedure and vintage year, etc. and (ii) detection of adulteration of high quality plant and animal origin foods with lower value substitutes. Finally, the constant development of reliable analytical techniques in combination with law enforcement authorities will ensure authentic foods in terms of quality and safety for consumers.

Rapid analysis technologies with chemometrics for food authenticity field: A review

In recent years, the problem of food adulteration has become increasingly rampant, seriously hindering the development of food production, consumption, and management. The common analytical methods used to determine food authenticity present challenges, such as complicated analysis processes and time-consuming procedures, necessitating the development of rapid, efficient analysis technology for food authentication. Spectroscopic techniques, ambient ionization mass spectrometry (AIMS), electronic sensors, and DNA-based technology have gradually been applied for food authentication due to advantages such as rapid analysis and simple operation. This paper summarizes the current research on rapid food authenticity analysis technology from three perspectives, including breeds or species determination, quality fraud detection, and geographical origin identification, and introduces chemometrics method adapted to rapid analysis techniques. It aims to promote the development of rapid analysis technology in the food authenticity field.

Determination of sugar content in honey using LC-Raman and programmable pump-Raman methods

We combined (i) liquid chromatography and Raman spectrometry (LC-Raman) and (ii) programmable pump and Raman spectrometry (PP-Raman) to separate and identify compounds in a mixture. These techniques were applied to conduct a quantitative analysis of the sugars in honey. The spectral and temporal axes of the LC-Raman data were analyzed using the MCR-ALS analysis procedure, which enabled the separation and identification of four sugars (glucose, fructose, sucrose, and trehalose). The PP-Raman method was employed to examine the sugar concentration dependence of the intensity pattern of the Raman spectrum, and the linear concentration dependence of the intensity was obtained. The sugar contents were quantitatively determined from the integrated area of the elution peaks. The result was consistent with those derived from mass spectrometry and previous studies. The origin of the errors in the derived sugar contents is discussed. Our study presents a novel quantitative LC-Raman spectrometric method that does not rely on resonance or surface enhancement effects.

Insight into the Recent Application of Chemometrics in Quality Analysis and Characterization of Bee Honey during Processing and Storage

The application of chemometrics, a widely used science in food studies (and not only food studies) has begun to increase in importance with chemometrics being a very powerful tool in analyzing large numbers of results. In the case of honey, chemometrics is usually used for assessing honey authenticity and quality control, combined with well-established analytical methods. Research related to investigation of the quality changes in honey due to modifications after processing and storage is rare, with a visibly increasing tendency in the last decade (and concentrated on investigating novel methods to preserve the honey quality, such as ultrasound or high-pressure treatment). This review presents the evolution in the last few years in using chemometrics in analyzing honey quality during processing and storage. The advantages of using chemometrics in assessing honey quality during storage and processing are presented, together with the main characteristics of some well-known chemometric methods. Chemometrics prove to be a successful tool to differentiate honey samples based on changes of characteristics during storage and processing.

Enhancement of the Antioxidant Capacity of Thyme and Chestnut Honey by Addition of Bee Products

Honey consumption and imports have increased in recent years, and it is considered by consumers to be a healthy alternative to more commonly used sweeteners. Honey contains a mixture of polyphenols and antioxidant compounds, and the botanical origin and geographical area of collection play an important role on its chemical composition. The present study investigated the physicochemical properties, total phenolic content and antioxidant capacity of Spanish thyme honey and chestnut honey, and their mixtures with royal jelly (2% and 10%) and propolis (2% and 10%). The analysis of the physicochemical parameters of both honey samples showed values within the established limits. Propolis showed the highest value of total phenolic content (17.21–266.83 mg GAE/100 g) and antioxidant capacity (DPPH, ORAC and ABTS assays; 0.63–24.10 µg eq. Tx/g, 1.61–40.82 µg eq. Tx/g and 1.89–68.54 µg eq. Tx/g, respectively), and significantly reduced ROS production in human hepatoma cells. In addition, mixtures of honey with 10% of propolis improved the results obtained with natural honey, increasing the value of total phenolic content and antioxidant capacity. A significant positive correlation was observed between total phenolic compounds and antioxidant capacity. Therefore, the antioxidant capacity could be attributed to the phenolic compounds present in the samples, at least partially. In conclusion, our results indicated that thyme and chestnut honey supplemented with propolis can be an excellent natural source of antioxidants and could be incorporated as a potential food ingredient with biological properties of technological interest, added as a preservative. Moreover, these mixtures could be used as natural sweeteners enriched in antioxidants and other bioactive compounds.

Physicochemical Characterization and Biological Properties of Pine Honey Produced across Greece

Pine honey is a honeydew honey produced in the East Mediterranean region (Greece and Turkey) from the secretions of the plant sucking insect Marchalina hellenica (Gennadius) (Coccoidea: Marchalini-dae) feeding on living parts of Pinus species. Nowadays, honeydew honey has attracted great attention due to its biological activities. The aim of this study was to study unifloral pine honey samples produced in Greece regarding their physicochemical parameters and antioxidant and antibacterial activity against five nosocomial and foodborne pathogens. These honeys showed physicochemical and microscopic characteristics within the legal limits, except for diastase activity, a parameter known to be highly variable, depending on various factors. Substantially higher levels of H2O2 were estimated compared to other types of honeydew honey, whereas protein content was similar. The total phenolic content was 451.38 ± 120.38 mg GAE/kg and antiradical activity ranged from 42.43 to 79.33%, while FRAP values (1.87 to 9.43 mmol Fe+2/kg) were in general higher than those reported in the literature. Various correlations could be identified among these parameters. This is the first attempt to investigate in depth the antibacterial activity of pine honey from Greece and correlate it with honey quality parameters. All tested honeys exerted variable but significant antibacterial activity, expressed as MIC and MBC values, comparable or even superior to manuka honey for some tested samples. Although honey antibacterial activity is mainly attributed to hydrogen peroxide and proteins in some cases (demonstrated by elevated MICs after catalase and Proteinase K treatment, respectively), no strong correlation between the antibacterial activity and hydrogen peroxide concentration or total protein content was demonstrated in this study. However, there was a statistically significant correlation of moisture, antioxidant and antibacterial activity against Klebsiella pneuomoniae, as well as antioxidant and antibacterial activity against Salmonella ser. Typhimurium. Interestingly, a statistically significant negative correlation has been observed between diastase activity and Staphylococcus aureus antibacterial activity. Overall, our data indicate multiple mechanisms of antibacterial activity exerted by pine honey.

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

BACKGROUND

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.

RESULTS

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.

CONCLUSION

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. © 2021 Society of Chemical Industry.

Comparative Study of Several Machine Learning Algorithms for Classification of Unifloral Honeys

Unifloral honeys are highly demanded by honey consumers, especially in Europe. To ensure that a honey belongs to a very appreciated botanical class, the classical methodology is palynological analysis to identify and count pollen grains. Highly trained personnel are needed to perform this task, which complicates the characterization of honey botanical origins. Organoleptic assessment of honey by expert personnel helps to confirm such classification. In this study, the ability of different machine learning (ML) algorithms to correctly classify seven types of Spanish honeys of single botanical origins (rosemary, citrus, lavender, sunflower, eucalyptus, heather and forest honeydew) was investigated comparatively. The botanical origin of the samples was ascertained by pollen analysis complemented with organoleptic assessment. Physicochemical parameters such as electrical conductivity, pH, water content, carbohydrates and color of unifloral honeys were used to build the dataset. The following ML algorithms were tested: penalized discriminant analysis (PDA), shrinkage discriminant analysis (SDA), high-dimensional discriminant analysis (HDDA), nearest shrunken centroids (PAM), partial least squares (PLS), C5.0 tree, extremely randomized trees (ET), weighted k-nearest neighbors (KKNN), artificial neural networks (ANN), random forest (RF), support vector machine (SVM) with linear and radial kernels and extreme gradient boosting trees (XGBoost). The ML models were optimized by repeated 10-fold cross-validation primarily on the basis of log loss or accuracy metrics, and their performance was compared on a test set in order to select the best predicting model. Built models using PDA produced the best results in terms of overall accuracy on the test set. ANN, ET, RF and XGBoost models also provided good results, while SVM proved to be the worst.

Response Surface Methodology to Optimize the Isolation of Dominant Volatile Compounds from Monofloral Greek Thyme Honey Using SPME-GC-MS

This study aimed at an experimental design of response surface methodology (RSM) in the optimization of the dominant volatile fraction of Greek thyme honey using solid-phase microextraction (SPME) and analyzed by gas chromatography-mass spectrometry (GC-MS). For this purpose, a multiple response optimization was employed using desirability functions, which demand a search for optimal conditions for a set of responses simultaneously. A test set of eighty thyme honey samples were analyzed under the optimum conditions for validation of the proposed model. The optimized combination of isolation conditions was the temperature (60 °C), equilibration time (15 min), extraction time (30 min), magnetic stirrer speed (700 rpm), sample volume (6 mL), water: honey ratio (1:3 v/w) with total desirability over 0.50. It was found that the magnetic stirrer speed, which has not been evaluated before, had a positive effect, especially in combination with other factors. The above-developed methodology proved to be effective in the optimization of isolation of specific volatile compounds from a difficult matrix, like honey. This study could be a good basis for the development of novel RSM for other monofloral honey samples.

The Use of Right Angle Fluorescence Spectroscopy to Distinguish the Botanical Origin of Greek Common Honey Varieties

The standardization of the botanical origin of honey reflects the commercial value and quality of honey. Nowadays, most consumers are looking for a unifloral honey. The aim of the present study was to develop a novel method for honey classification using chemometric models based on phenolic compounds analyzed with right angle fluorescence spectroscopy, coupled with stepwise linear discriminant analysis (LDA). The deconstructed spectrum from three-dimensional-emission excitation matrix (3D-EEM) spectra provided a correct classification score of 94.9% calibration and cross-validation at an excitation wavelength (λex) of 330 nm. Subsequently, a score of 81.4% and 79.7%, respectively, at an excitation wavelength (λex) of 360 nm was achieved. Each chemometric model confirmed its power through the external validation with a score of 82.1% for both. Differentiation could be correlated with hydroxycinnamic and hydroxybenzoic acids, which absorb in this region of the spectrum. Fluorescence spectroscopy constitutes a rapid and sensitive technique, which, when combined with the stepwise algorithm and LDA method, can be used as a reliable and predictive authentication tool for honey. This study indicates that the developed methodology is a promising technique for determination of the botanical origin of common Greek honey varieties. Our long-term ambition is to support producers and suppliers to remain in a competitive national and international market.