Ecosystem discrimination and fingerprinting of Romanian propolis by hierarchical fuzzy clustering and image analysis of TLC patterns

Propolis: chemical diversity and challenges in quality control

Propolis is a resinous natural product produced by honeybees using beeswax and plant exudates. The chemical composition of propolis is highly complex, and varies with region and season. This inherent chemical variability presents several challenges to its standardisation and quality control. The present review was aimed at highlighting marker compounds for different types of propolis, produced by the species Apis mellifera, from different geographical origins and that display different biological activities, and to discuss strategies for quality control. Over 800 compounds have been reported in the different propolises such as temperate, tropical, birch, Mediterranean, and Pacific propolis; these mainly include alcohols, acids and their esters, benzofuranes, benzopyranes, chalcones, flavonoids and their esters, glycosides (flavonoid and diterpene), glycerol and its esters, lignans, phenylpropanoids, steroids, terpenes and terpenoids. Among these, flavonoids (> 140), terpenes and terpenoids (> 160) were major components. A broad range of biological activities, such as anti-oxidant, antimicrobial, anti-inflammatory, immunomodulatory, and anticancer activities, have been ascribed to propolis constituents, as well as the potential of these compounds to be biomarkers. Several analytical techniques, including non-separation and separation methods have been described in the literature for the quality control assessment of propolis. Mass spectrometry coupled with separation methods, followed by chemometric analysis of the data, was found to be a valuable tool for the profiling and classification of propolis samples, including (bio)marker identification. Due to the rampant chemotypic variability, a multiple-marker assessment strategy considering geographical and biological activity marker(s) with chemometric analysis may be a promising approach for propolis quality assessment.

SUPPLEMENTARY INFORMATION

The online version contains supplementary material available at 10.1007/s11101-022-09816-1.

Application of HPTLC Multiwavelength Imaging and Color Scale Fingerprinting Approach Combined with Multivariate Chemometric Methods for Medicinal Plant Clustering According to Their Species

In the current study, multiwavelength detection combined with color scales HPTLC fingerprinting procedure and chemometric approach were applied for direct clustering of a set of medicinal plants with different geographical growing areas. The fingerprints profiles of the hydroalcoholic extracts obtained after single and double development and detection under 254 nm and 365 nm, before and after selective spraying with specific derivatization reagents were evaluated by chemometric approaches. Principal component analysis (PCA) with factor analysis (FA) methods were used to reveal the contribution of red (R), green (G), blue (B) and, respectively, gray (K) color scale fingerprints to HPTLC classification of the analyzed samples. Hierarchical cluster analysis (HCA) was used to classify the medicinal plants based on measure of similarity of color scale fingerprint patterns. The 1-Pearson distance measurement with Ward’s amalgamation procedure proved to be the most convenient approach for the correct clustering of samples. Data from color scale fingerprints obtained for double development procedure and multiple visualization modes combined with appropriate chemometric methods proved to detect the similar medicinal plant extracts even though they are from different geographical regions, have different storage conditions and no specific markers are individually extracted. This approach could be proposed as a promising tool for authentication and identification studies of plant materials based on HPTLC fingerprinting analysis.

Thin-layer chromatography in the authenticity testing of bee-products

Quality control, nutritional value and the monitoring of hazardous residues in honey bee- products have become major topics for both producers and consumers. Due to its potential role in human health, bee-products rich in bioactive compounds are becoming increasingly popular. This review aims to provide an overview of thin-layer chromatography methods used in quality control,authenticity testing and chemical profiling of bee-products in order to help scientists engaged in the field of bee-products chemistry to utilize the advantages of this technique in the detection and elimination of fraudulent practices in bee-product manufacturing. Recently, hyphenation of thin-layer chromatography, image analysis and chemometrics support bee-products analysisbysimultaneousdeterminationofanalytes with different detection principles, identification of individual bioactive compounds as well as structure elucidation of compounds. Highlighted opportunities of thin-layer chromatography could encourage further investigations that would lead to improvements in the detection and elimination of marketing fraudulent practices.

Microwave-assisted sample preparation of Hungarian raw propolis in quartz vessels and element analysis by ICP-OES and ICP-MS for geographical identification

One of the aims of this study was to improve the sample throughput of a microwave-assisted closed vessel digestion system by using small quartz vials in polytetrafluoroethylene (PTFE) vessels for the sample preparation of raw propolis samples in small amounts. The digested samples were measured by inductively coupled plasma optical emission spectrometry (ICP-OES) and inductively coupled plasma mass spectrometry (ICP-MS) analyzing 36 elements. Limit of detection was low enough to measure all the elements, with the exception of La, in all raw propolis samples. There were no cross-contamination between the quartz vials, therefore independent samples can be prepared in the same PTFE vessel. Accuracy of the method was checked by spike recoveries and by analyzing BCR 189 wholemeal flour and two plant samples from a collaborative study. The means of RSDs were 5.3%-14.4% in the case of measured elements. The sample throughput was increased by three times using quartz vials in PFTE vessels besides matching with the requirements of green chemistry. Another goal was the characterization of the element content and thereby geographical identification of Hungarian raw propolis. In total, 252 samples were analyzed and their statistical characteristics were described. We cannot find globally such results of propolis element content, which is representing one country and with such a number of elements and samples. All the elements have positive skew and positive kurtosis. Concentration range is above two orders of magnitude in the case of Ba, Zn, V, Cr, Ni, Cd and Eu elements. The decimal logarithm of element concentrations was used for geographical identification of raw propolis samples originating from seven regions of Hungary by linear discriminant analysis (LDA). Grouping of the samples of the Northern Great Plain was the most effective with 96.3% and 77.8% based on the original method and the cross validation, respectively. The same indicators for all the groups are 76.6% and 61.5%.

Fuzzy Divisive Hierarchical Associative-Clustering Applied to Different Varieties of White Wines According to Their Multi-Elemental Profiles

Wine data are usually characterized by high variability, in terms of compounds and concentration ranges. Chemometric methods can be efficiently used to extract and exploit the meaningful information contained in such data. Therefore, the fuzzy divisive hierarchical associative-clustering (FDHAC) method was efficiently applied in this study, for the classification of several varieties of Romanian white wines, using the elemental profile (concentrations of 30 elements analyzed by ICP-MS). The investigated wines were produced in four different geographical areas of Romania (Transylvania, Moldova, Muntenia and Oltenia). The FDHAC algorithm provided not only a fuzzy partition of the investigated white wines, but also a fuzzy partition of considered characteristics. Furthermore, this method is unique because it allows a 3D bi-plot representation of membership degrees corresponding to wine samples and elements. In this way, it was possible to identify the most specific elements (in terms of highest, smallest or intermediate concentration values) to each fuzzy partition (group) of wine samples. The chemical elements that appeared to be more powerful for the differentiation of the wines produced in different Romanian areas were: K, Rb, P, Ca, B, Na.

Fuzzy Divisive Hierarchical Clustering of Solvents According to Their Experimentally and Theoretically Predicted Descriptors

The present study describes a simple procedure to separate into patterns of similarity a large group of solvents, 259 in total, presented by 15 specific descriptors (experimentally found and theoretically predicted physicochemical parameters). Solvent data is usually characterized by its high variability, different molecular symmetry, and spatial orientation. Methods of chemometrics can usefully be used to extract and explore accurately the information contained in such data. In this order, advanced fuzzy divisive hierarchical-clustering methods were efficiently applied in the present study of a large group of solvents using specific descriptors. The fuzzy divisive hierarchical associative-clustering algorithm provides not only a fuzzy partition of the solvents investigated, but also a fuzzy partition of descriptors considered. In this way, it is possible to identify the most specific descriptors (in terms of higher, smallest, or intermediate values) to each fuzzy partition (group) of solvents. Additionally, the partitioning performed could be interpreted with respect to the molecular symmetry. The chemometric approach used for this goal is fuzzy c-means method being a semi-supervised clustering procedure. The advantage of such a clustering process is the opportunity to achieve separation of the solvents into similarity patterns with a certain degree of membership of each solvent to a certain pattern, as well as to consider possible membership of the same object (solvent) in another cluster. Partitioning based on a hybrid approach of the theoretical molecular descriptors and experimentally obtained ones permits a more straightforward separation into groups of similarity and acceptable interpretation. It was shown that an important link between objects’ groups of similarity and similarity groups of variables is achieved. Ten classes of solvents are interpreted depending on their specific descriptors, as one of the classes includes a single object and could be interpreted as an outlier. Setting the results of this research into broader perspective, it has been shown that the fuzzy clustering approach provides a useful tool for partitioning by the variables related to the main physicochemical properties of the solvents. It gets possible to offer a simple guide for solvents recognition based on theoretically calculated or experimentally found descriptors related to the physicochemical properties of the solvents.

Finding specific peaks (markers) using fuzzy divisive hierarchical associative-clustering based on the chromatographic profiles of medicinal plant extracts obtained at various detection wavelengths

Advanced chemometric methods, such as fuzzy c-means (FCM), a fuzzy divisive hierarchical clustering algorithm (FDHC), and fuzzy divisive hierarchical associative-clustering (FDHAC), which offer the excellent possibility to associate each fuzzy partition of samples with a fuzzy set of characteristics (features), have been successfully applied in this study. FDHAC, a method that utilizes specific regions of chromatographic fingerprints or specific peaks as a fuzzy set of characteristics, was effectively applied to the characterization and classification of medicinal plant extracts according to their antioxidant capacities, using their chromatographic profiles monitored at 242, 260, 280, 320, 340, and 380 nm via HPLC with a multistep isocratic and gradient elution system and diode array detection (HPLC-DAD). What is quite new is the partitioning of the chromatographic retention time ranges and peaks (markers) and their association with different plant extract samples with high, moderate or low antioxidant capacity. Furthermore, the degrees of membership of fingerprints (fuzzy markers) are highly relevant with respect to the (dis)similarity of samples because they indicate both the positions and degrees of association of chromatographic peaks from different classes or individual samples. The obtained results clearly demonstrate the efficiency and information power of these advanced fuzzy methods for medicinal plant characterization and authentication, and this study generates the premise for a new chemometrics approach with high-impact for use in analytical chemistry and other fields.

The phytochemistry of the honeybee

Honeybees rely on plants for everything they need to keep the colony running; plant nectar and pollen are their only carbohydrate and protein food sources. By foraging to satisfy their basic nutritional demand, honeybees inevitably gather specialized plant metabolites as part of the nectar and pollen. In general, these compounds possess biological activity which may become relevant in fighting pests and pathogens in the hive. The third plant derived bee product, besides honey and bee pollen, is propolis (bee glue), which comes from plant resins. It is not a food; it is used as a building material and a defensive substance. Thus, the beehive is rich in specialized plant metabolites, produced by many different plant species and the expression "Phytochemistry of honeybees" is not inappropriate. However, it is virtually impossible to perform a detailed overview of the phytochemical features of honey and pollen in a review article of this nature, for reasons of space. The present review deals with propolis, because it is the bee product with highest concentration of specialized plant metabolites and has valuable pharmacological activities. The most recent developments concerning plant sources of propolis, bees' preferences to particular plants, the application of metabolomic approaches and chemometrics to propolis research and the problems concerning standardization of propolis are summarized. The overview covers the literature published in the last decade, after 2007.

A Critical Review on Ultrasensitive, Spectroscopic-based Methods for High-throughput Monitoring of Bacteria during Infection Treatment

The world is in the midst of a pre-emptive public health emergency, one that is just as dramatic as the global aggressive viruses-related crises (Ebola, Zika, or SARS), but not as visible. The "superbugs" and their antimicrobial resistance do not cause much public alarm or awareness, but provoke financial losses of $100 trillion annually (WHO, http://www.who.int/mediacentre/commentaries/superbugs-action-now/en/ ). This status quo review offers an overview of ultrasensitive methods for high-throughput monitoring of bacteria during infection treatment, the effects of antibiotics on bacteria at single-cell level and the challenges we will face in their detection due to the extraordinary capability of these "superbugs" to gain and constantly improve multiresistance to antibiotics. A special emphasis is put on the ultrasensitive spectroscopic-based analysis techniques, using nanotechnology or not necessarily, that are more and more promising alternatives to conventional culture-based ones. The particular case of Mycobacteria detection is discussed based on recent reported work.

Metabolomics study of Populus type propolis

Herein, we propose rapid and simple spectroscopic methods to determine the chemical composition of propolis derived from various Populus species using a metabolomics approach. In order to correlate variability in Populus type propolis composition with the altitude of its collection, NMR, IR, and UV spectroscopy followed by OPLS was conducted. The botanical origin of propolis was established by comparing propolis spectral data to those of buds of various Populus species. An O2PLS method was utilized to integrate two blocks of data. According to OPLS and O2PLS, the major compounds in propolis samples, collected from temperate continental climate above 500m, were phenolic glycerides originating from P. tremula buds. Flavonoids were predominant in propolis samples collected below 400m, originating from P. nigra and P. x euramericana buds. Samples collected at 400-500m were of mixed origin, with variable amounts of all detected metabolites.

Proof-of-Principle of rTLC, an Open-Source Software Developed for Image Evaluation and Multivariate Analysis of Planar Chromatograms

High-performance thin-layer chromatography (HPTLC) is an advantageous analytical technique for analysis of complex samples. Combined with multivariate data analysis, it turns out to be a powerful tool for profiling of many samples in parallel. So far, chromatogram analysis has been time-consuming and required the application of at least two software packages to convert HPTLC chromatograms into a numerical data matrix. Hence, this study aimed to develop a powerful, all in one open-source software for user-friendly image processing and multivariate analysis of HPTLC chromatograms. Using the caret package for machine learning, the software was set up in the R programming language with an HTML-user interface created by the shiny package. The newly developed software, called rTLC, is deployed online, and instructions for direct use as a web application and for local installation, if required, are available on GitHub. rTLC was created especially for routine use in planar chromatography. It provides the necessary tools to guide the user in a fast protocol to the statistical data output (e.g., data extraction, preprocessing techniques, variable selection, and data analysis). rTLC offers a standardized procedure and informative visualization tools that allow the user to explore the data in a reproducible and comprehensive way. As proof-of-principle of rTLC, German propolis samples were analyzed using pattern recognition techniques, principal component analysis, hierarchic cluster analysis, and predictive techniques, such as random forest and support vector machines.

Antimicrobial Activity of Serbian Propolis Evaluated by Means of MIC, HPTLC, Bioautography and Chemometrics

New information has come to light about the biological activity of propolis and the quality of natural products which requires a rapid and reliable assessment method such as High Performance Thin-Layer Chromatography (HPTLC) fingerprinting. This study investigates chromatographic and chemometric approaches for determining the antimicrobial activity of propolis of Serbian origin against various bacterial species. A linear multivariate calibration technique, using Partial Least Squares, was used to extract the relevant information from the chromatographic fingerprints, i.e. to indicate peaks which represent phenolic compounds that are potentially responsible for the antimicrobial capacity of the samples. In addition, direct bioautography was performed to localize the antibacterial activity on chromatograms. The biological activity of the propolis samples against various bacterial species was determined by a minimum inhibitory concentration assay, confirming their affiliation with the European poplar type of propolis and revealing the existence of two types (blue and orange) according to botanical origin. The strongest antibacterial activity was exhibited by sample 26 against Staphylococcus aureus, with a MIC value of 0.5 mg/mL, and Listeria monocytogenes, with a MIC as low as 0.1 mg/mL, which was also the lowest effective concentration observed in our study. Generally, the orange type of propolis shows higher antimicrobial activity compared to the blue type. PLS modelling was performed on the HPTLC data set and the resulting models might qualitatively indicate compounds that play an important role in the activity exhibited by the propolis samples. The most relevant peaks influencing the antimicrobial activity of propolis against all bacterial strains were phenolic compounds at R_F values of 0.37, 0.40, 0.45, 0.51, 0.60 and 0.70. The knowledge gained through this study could be important for attributing the antimicrobial activity of propolis to specific chemical compounds, as well as the verification of HPTLC fingerprinting as a reliable method for the identification of compounds that are potentially responsible for antimicrobial activity. This is the first report on the activity of Serbian propolis as determined by several combined methods, including the modelling of antimicrobial activity by HPTLC fingerprinting.

Poplar-type Propolis: Chemical Composition, Botanical Origin and Biological Activity

Propolis is one of the most used natural products known for centuries for its beneficial effects. Due to significant differences in chemical composition of samples originating from different geographic and climatic zones it is crucial to characterize reliably each type of propolis. This article comprises the latest findings concerning the poplar type propolis, i.e. it gives a cross section of chemical composition, botanical origin and biological activity of poplar type propolis in order to encourage further investigations that would indicate its beneficial effects.

Ultrahigh-performance liquid chromatography and mass spectrometry (UHPLC-LTQ/Orbitrap/MS/MS) study of phenolic profile of Serbian poplar type propolis

INTRODUCTION

Propolis is a resinous natural substance collected by honeybees from different plant sources. Due to the presence of various phytochemicals, this bee-product exhibits numerous biological activities, including anti-bacterial, anti-viral, anti-inflammatory, anti-oxidant, immunostimulating and anti-tumour effects. As the chemical composition and biological activity of propolis depend on its botanical and geographical origin, searching for new bioactive substances in various types of propolis from unexplored regions is of great importance.

OBJECTIVE

The aim of this study is the evaluation of the phenolic profile of poplar propolis samples in order to characterise Serbian propolis, to identify possible new constituents and to specify the phenolic components relevant for differentiation of poplar propolis samples into two subgroups through simultaneous analysis of poplar bud extracts.

METHODS

Ethanolic extracts of propolis and poplar buds were comprehensively analysed using ultrahigh-performance liquid chromatography coupled with hybrid mass spectrometry, which combines the linear trap quadrupole and Orbitrap MS/MS mass analyser together with chemometric methods.

RESULTS

Extensive fingerprint analysis of Serbian propolis was achieved for the first time. Seventy-five phenolic compounds were detected. Eight of them were identified in propolis for the first time. Pattern-recognition methods applied to the content of ten quantified phenolics verified the existence of two subgroups of propolis, with galangin, chrysin and pinocembrin as the most influential distinguishing factors.

CONCLUSION

The phenolic composition of the analysed propolis samples confirm their affiliation to the European poplar type propolis and the existence of two subgroups according to botanical origin.

Thin-layer chromatographic identification of Chinese propolis using chemometric fingerprinting

INTRODUCTION

Poplar tree gum has a similar chemical composition and appearance to Chinese propolis (bee glue) and has been widely used as a counterfeit propolis because Chinese propolis is typically the poplar-type propolis, the chemical composition of which is determined mainly by the resin of poplar trees. The discrimination of Chinese propolis from poplar tree gum is a challenging task.

OBJECTIVE

To develop a rapid thin-layer chromatographic (TLC) identification method using chemometric fingerprinting to discriminate Chinese propolis from poplar tree gum.

METHODS

A new TLC method using a combination of ammonia and hydrogen peroxide vapours as the visualisation reagent was developed to characterise the chemical profile of Chinese propolis. Three separate people performed TLC on eight Chinese propolis samples and three poplar tree gum samples of varying origins. Five chemometric methods, including similarity analysis, hierarchical clustering, k-means clustering, neural network and support vector machine, were compared for use in classifying the samples based on their densitograms obtained from the TLC chromatograms via image analysis.

RESULTS

Hierarchical clustering, neural network and support vector machine analyses achieved a correct classification rate of 100% in classifying the samples. A strategy for TLC identification of Chinese propolis using chemometric fingerprinting was proposed and it provided accurate sample classification.

CONCLUSION

The study has shown that the TLC identification method using chemometric fingerprinting is a rapid, low-cost method for the discrimination of Chinese propolis from poplar tree gum and may be used for the quality control of Chinese propolis.

Important developments in romanian propolis research

The most important developments in propolis analysis and pharmacological properties are discussed. In order to help in the Romanian propolis standardization, different methodologies for chemical composition analysis (UV-VIS, HP-TLC, and HPLC-DAD) are reviewed using new approaches and software (fuzzy divisive hierarchical clustering approach and ChromQuest software) and compared with international studies made until now in propolis research. Practical applications of Romanian propolis in medicinal therapy and cosmetics are reviewed, and quality criteria for further standardization are proposed.