Pollen Identification Research Articles

Identification of pollen types of beekeeping interest by non‐targeted mass spectrometry

AbstractBackgroundThe identification of pollen is important in the field of beekeeping for the determination of the botanical origin of bee products and investigations of bee diet. Until now, it has been performed by melissopalynology, the microscopic examination of pollen grains. However, this technique has some limitations, such as the necessity of experienced analysts and identification restricted to the family level for some pollen types. Although many techniques have been proposed as alternatives or complements to melissopalynology and omics techniques have been explored to gather information on the botanical origin of honey, no study has yet been conducted on a large set of pollen types.ResultsThe study dataset consisted of 34 different pollen types of pellets collected by honeybees in Switzerland and analyzed in multiple biological replications, leading to 150 observations. The pollen samples were analyzed after tryptic digestion using a non‐targeted mass spectrometry‐based method. Liquid chromatography coupled with mass spectrometry (LC–MS) was employed to identify pollen, and melissopalynology was used as a reference method for the identification. We built an OPLS‐DA prediction model for the 34 pollen types. The model clearly identified new samples in their membership group (Acer sp., n = 10) and a new pollen type at the species‐specific level for Quercus sp. Less predictable results were achieved for Composita H and pollen collected directly from the plant.ConclusionThe use of a non‐targeted mass spectrometry‐based method and chemometrics resulted in a promising tool for pollen identification as a replacement/supplement method to traditional melissopalynology.

AIpollen: An Analytic Website for Pollen Identification Through Convolutional Neural Networks.

With the rapid development of artificial intelligence, deep learning has been widely applied to complex tasks such as computer vision and natural language processing, demonstrating its outstanding performance. This study aims to exploit the high precision and efficiency of deep learning to develop a system for the identification of pollen. To this end, we constructed a dataset across 36 distinct genera. In terms of model selection, we employed a pre-trained ResNet34 network and fine-tuned its architecture to suit our specific task. For the optimization algorithm, we opted for the Adam optimizer and utilized the cross-entropy loss function. Additionally, we implemented ELU activation function, data augmentation, learning rate decay, and early stopping strategies to enhance the training efficiency and generalization capability of the model. After training for 203 epochs, our model achieved an accuracy of 97.01% on the test set and 99.89% on the training set. Further evaluation metrics, such as an F1 score of 95.9%, indicate that the model exhibits good balance and robustness across all categories. To facilitate the use of the model, we develop a user-friendly web interface. Users can upload images of pollen grains through the URL link provided in this article) and immediately receive predicted results of their genus names. Altogether, this study has successfully trained and validated a high-precision pollen grain identification model, providing a powerful tool for the identification of pollen.

Label-free quantitative phase imaging and analysis of airborne pollen

Abstract Pollen morphology, involving the physical characteristics of pollen grains from seed plants during reproduction, plays an important role in plant biology, ecology, and evolution. High pollen concentrations in the air can degrade air quality and exacerbate respiratory conditions such as asthma. Understanding pollen morphology and its implications for air quality is significant for mitigating respiratory health risks. Conventionally, fluorescence microscopy is used for pollen imaging, but photobleaching, quenching, and phototoxicity affect the surface morphology and do not provide quantitative data on the pollen grains. For this study, we used bright field (BF) imaging and quantitative phase imaging (QPI), a label-free interferometric microscopy method, to look at differences in the shape of pollen. BF imaging provides information about the shape and size of the different pollens but has a limitation of low contrast. To obtain high-contrast images and quantitative data on the pollen grains without any exogenous agents, we employed QPI and BF imaging in the present study. QPI enables the extraction of detailed information regarding the cell wall, aperture, and thickness of pollen while also maintaining their natural state without the need for chemical treatments. In the present work, we sampled the ambient air from May 2023 to January 2024 on the IIT Delhi campus. Subsequently, QPI and BF imaging have been done for pollen identification and phase analysis of arboreal and non-arboreal pollen. Further, by utilizing the information obtained from BF microscopy and QPI, different species of pollen have been identified, and a pollen calendar has been prepared for exhibiting pollen season throughout the year. To the best of the authors’ knowledge, they have conducted the QPI of airborne pollen grains for the first time; this technique holds great potential for characterizing airborne pollens without the need for staining or sample preparation.

Pollen atlas from bat guano deposits in southeastern Amazonia

The botanical literature lacks pollen resources such as atlases and identification keys for bat guano deposits. To address this gap, our dataset describes modern pollen and spore assemblages from five caves in the Carajás region, southeastern Amazonia. A comprehensive atlas featuring illustrations and detailed descriptions of pollen and spores was created using both original and published data. It aims to simplify the identification of various pollen and spore taxa within bat guano deposits, and features light micrographs and descriptions of 105 pollen taxa, including 81 arboreal and shrub taxa, 11 lianas, seven herbaceous taxa, three palm taxa, and three ferns. Among the total pollen assemblage, entomophilous plant taxa represented 42.8%, followed by wind-pollinated taxa at 9.5% and bat-pollinated taxa at 7.6%. Our data indicate that bat guano can provide valuable information on plant taxa that are underrepresented in more traditional pollen sampling sites such as peat bogs and lake sediments.

Atlas of the Oman pollen flora – Pollen morphology of the arid Arabian Peninsula vegetation

A precise and consistent fossil pollen identification is crucial for the reconstruction of (long-term) vegetation and climate dynamics. The inspection of local reference material serves as the best basis for meaningful results. Digital pollen reference collections, especially for the hyper arid regions of North Africa and the Arabian Peninsula are still sparse. Therefore, we want to contribute a descriptive and illustrated atlas of pollen from plants of the Sultanate of Oman. The atlas comprises around 400 pollen taxa on 43 plates from various habitats (e.g., mountains, wadis, deserts, coastal areas, and places shaped by human activities) characteristic for the arid environments of the Arabian Peninsula. It covers approximately 45% of all plant genera found in Oman. We provide high-resolution images of 402 taxa of 70 families as well as useful information on habitus, pollination, and habitat for each plant species as a useful guide for environmental reconstructions and interpretations from palynological investigations as well as the identification of pollen in honey.

Amaranthaceae Pollen Trends in the Air

Understanding the trends of airborne pollen allergens is of great importance given the high prevalence and socioeconomic impact of pollen-related allergic diseases globally. To study the morphological features of Amaranthaceae pollen, assess its contribution to the aerobiological situation in the city of Karakol and identify periods of increased concentration in order to improve the diagnosis and prevention of pollinosis symptoms in patients. Aerobiological sampling using a Lanzoni pollen trap was carried out from April to October over a three-year period (2015–2017) in Karakol city. Pollen identification and differential counting were performed according to current standard methodology and specially developed guides and atlases. Pollen characteristics of 4 Amaranthaceae species were visualized using scanning electron microscopy (Hitachi) to provide a detailed understanding of the surface structure and ornamentation of pollen grains. The decadal curve of Amaranthaceae pollen concentration in Karakol is unimodal, with often overlapping quantitative characteristics: 6615 (2nd decade of August 2015), 1326 (1st decade of 2016), 444 pollen grains / m3 (1st decade of 2017). At the same time, strong interannual variability of atmospheric pollen levels was observed, depending both on the pollen production of the plants themselves and on meteorological parameters, especially temperature and precipitation, changes in the traditional farming system. The response of summer-flowering Amaranthaceae weeds to climate change was demonstrated, which consists in a decrease in the annual pollen index, which corresponds to the global trend. Peak concentrations of Amaranthaceae pollen many times exceeded clinical threshold values in the air. On the surface of the Bassia scopariapollen exine, the ultrastructure study revealed the smallest (1.5-2.0 μm) granules of sporopollenin or orbicules (Ubisch bodies), which play a role in the spread of allergens. The obtained data are important in terms of increasing public awareness of the dominant pollen of the Amaranthaceae family, optimizing diagnostics, treatment and prevention of pollinosis.

Morphological Changes in Allergenic Pollen as a Bioindicator

Since the anther opens and the pollen grains travel in the atmosphere, they can come into contact with solid and gaseous particles, and the exine can become contaminated with various adherent particles. Pollen and solid particles are among the most common triggers of bronchial asthma. Increased allergenicity of pollen grains will be determined by chemical reactions between pollutants and pollen, as well as non-specific modulation of the mechanisms of synthesis of many proteins, lipids and enzymes in response to environmental stress due to urbanization and climate change. This situation seriously complicates the identification of pollen to a taxon during traditional monitoring and further complicates the calculation and interpretation of the results obtained during research and automatic determination. To classify tetramorphic pollen from aerobiological samples and explain the mechanism of the identified changes. Determination of mutagenic contamination and a method for integral assessment of pollen viability and damage under a light microscope. Pollen from aerobiological samples was examined under a scanning electron microscope (Hitachi): a high-power imaging technique that uses electron beams to create detailed images of the ultrastructure of pollen grains. Unidentified, tetramorphic pollen from aerobiological samples from sites with varying degrees of contamination can be conditionally classified into 4 types: 1) shapeless lumps of sporopollenin with various outgrowths, cracks and cavities that arose during pollen ontogenesis; 2) atypical ornamentation of sculpture, such as large tubercles - swellings with a smooth surface; 3) atypical form, but with partially preserved sculpture, where in places species-specific features can be identified and pollen can be identified to a taxon; 4) conglomerates. The study of the vitality (viability) and damage of the cocksfoot pollen under a light microscope from observation points with different anthropogenic loads showed the dependence of these indicators on the level of pollutants: with their increase, viability decreases, although it remains quite high. The data obtained are important for assessing allergenic pollen as a bioindicator of environmental changes. An integrated assessment of the enzymatic activity of pollen and the degree of its damage can be used for the purposes of bioecological monitoring.

Stanley and Acetolysis: Enhanced Method for Pollen Identification and Geolocation of Cocaine Samples

The concept of palynological assemblage, a set of all identifiable pollen grains and spores, is very useful in geolocation determinations in Forensic Studies. To identify plant genera, palynologists must recognize detailed aspects of apertures, ornamentation and layers on the cell wall. Pollen and spores recovered from higher purity cocaine samples represent an alternative to indicate drug source regions. After following the method established by Stanley in 1992, we added the acetolysis procedure in samples seized by the Civil Police of the State of Sao Paulo, Brazil, in 2017, in order to enhance morphological traits observation and taxonomical identification of different pollen taxa, allowing reliable environmental and geolocation reconstructions.

Pollen atlas and morphological analysis of the selected types from southeastern Tibet, China

The accuracy of fossil pollen identification is crucial for understanding past plant diversity, vegetation landscape and associated climate change, while dispersed/fossil pollen assignments heavily rely on how robust modern pollen reference is. There is high species richness in southeastern Tibet, China, however, pollen morphological studies in this area are still poorly documented. Here, we present pollen atlas of 57 species (40 genera, 22 families) collected from the Ranwu Lake Basin. Pollen grains were imaged with light microscope (LM, 100X oil-immersion objective lens) and scanning electron microscope (SEM). All pollen types are ordered and presented according to different aperture types and sculpture patterns, facilitating comparison with fossil pollen. Moreover, we selected some representative species mainly based on potential palaeoecological importance, including Sibiraea (Rosaceae), Koenigia cathayana (Polygonaceae), Stellera chamaejasme (Thymelaeaceae), Hordeum vulgare subsp. vulgare and Poa annua (Poaceae), to discuss their pollen identification characteristics for the first time and assess potential applications in Quaternary palaeoecological interpretation. This study enriches modern pollen atlas for Quaternary pollen analysis in the study area and the adjacent regions. Our results provide robust modern reference in pollen-based reconstruction of vegetation history, palaeoclimate and palaeoenvironment, and tracing pollen signal of local human activities as well.

Computerized Principal Component–Canonical Discriminant Analysis Classification Model for Pollen Grain Identification Using Surface-Enhanced Raman Spectroscopy

Pollen identification and quantification are employed in various applications, and studies have been conducted to achieve precise automated pollen recognition to minimize the manual labor and subjectivity associated with human identification. The objective of the study was to evaluate the potential of surface-enhanced Raman spectroscopy (SERS) properties of pollen grain spectra, specifically within the spectral range of 582–1563 cm–1, using the convective assembly combined principal component–canonical discriminant analysis (PC-CDA) classification model. This research aimed to explore the feasibility of using this approach for the automated identification of pollen in the future. This approach facilitates the optimal arrangement of pollen grains and colloidal silver nanoprobes, thereby enhancing SERS spectra reproducibility using the convective assembly sampling method. Pollen grains can be characterized at a microscopic level without the need for purification-based analysis procedures. The findings demonstrate the possibility of rapid and accurate classification of pollen grains with the PC-CDA detection model with a 99.3% sensitivity, 97.6% specificity, and 98.5% accuracy. Research study indicates that the SERS properties of pollen spectra could serve as a promising method for automated pollen identification using the PC–CDA classification model.

Poaceae Pollen as the Leading Aeroallergen

The relevance of aerobiological topics is evidenced by the fact that from July 1 to July 5, 2024, an unusual event was held in Vilnius (Lithuania), including the 8th European Symposium on Aerobiology (8th ESA), the 12th International Congress on Aerobiology (12th ICA) and the 5th International Ragweed Conference (5th IRC). World Aerobiology 2024 is an event of unprecedented scale. It discussed the use of artificial intelligence (AI) for calculating and interpreting aerobiological monitoring data. But so far, AI cannot identify all taxa, so careful counting and identification of plant pollen and fungal spores is only possible for a human researcher. Aerobiological monitoring using volumetric traps remains the gold standard of research. In the world community, there are networks of biological monitoring of air quality with the study of the concentration of plant pollen and fungal spores in the air. Through an application on a mobile phone, you can find out the forecast of periods of maximum risk, during which patients with hay fever are exposed to the main aeroallergens. Popular queries from Internet users relate to the complex problem of climate change, environmental pollution and aeroallergens. There is no National Aerobiological Monitoring system in Kyrgyzstan, but the Meteonova service has launched a pollen forecast application for mobile phones, which is still in the testing stage and does not reflect the real features of the aerobiological spectrum. It is known that in the world there are more than 12,000 species in the Poaceae family, classified into 771 genera belonging to 12 subfamilies. In Kyrgyzstan, 300 anemophilous species of annual perennial plants grow, releasing a huge amount of pollen into the atmosphere in the spring and summer. Poaceae pollen is still one of the top ten global aeroallergens, being the leading cause of hay fever, by predominant size, classified as biological pollutants (PM10). The concentration of cereal pollen was consistently observed in the air of Karakol during all seasons of the study and ranged from 167 to 174 days. Its daily maximum concentrations were detected in June-July; the distribution of pollen in the air is unimodal. The influence of the anthropogenic triad on fluctuations in the concentration of cereal pollen was established: climate change (temperature) and land use systems, as well as environmental pollution, which manifested itself in the degradation of pollen grains and the appearance of damaged and unidentified biological particles. The purpose of this article is to study the concentration of cereal pollen in the air, their integrative contribution to the aerobiological spectrum in the city of Karakol as dominant pollen allergens.

Blooming calendar and polliniferous plants in Djuanda Great Forest Park, West Java, Indonesia: Implication for beekeeping management

Abstract. Husodo T, Wulandari I, Saputri Y, Kasmara H, Hermawan W, Kusmoro J, Shanida SS, Khairina F, Haydar MRA, Asyraf MN, Febrian RR. 2024. Blooming calendar and polliniferous plants in Djuanda Great Forest Park, West Java, Indonesia: Implication for beekeeping management. Biodiversitas 25: 3216-3229. Information was unavailable regarding the types of pollinator plants and their blooming periods in Djuanda Great Forest Park, West Java, Indonesia. Information about flower blooming seasons was useful for various purposes. These findings will provide a basis for understanding the importance of Apis cerana Fabricius, 1793 in plant pollination and plant species resources for honey bees so that beekeeping practices can be carried out sustainably. The blossoming period of a plant is determined by its blooming period at the time of observation. The blooming period is observed every month in the second and fourth weeks for six months (December 2021-May 2022). Pollen collection was carried out in August 2021 (dry season). Pollen identification was carried out using the acetolysis method, which involves dissolving the non-pollen components of a sample to reveal the pollen grains, thereby enabling accurate identification. An overview of the observation records revealed the presence of 249 plant species from 82 families. The Asteraceae family has the most abundant species richness. Most plants were identified as pollinator plants (30.52%), nectariferous plants (10.04%), and plants that produce both (18.47%). Flowering plants of melliferous plants for honeybees are available throughout the rest of the year, especially from December to May. Therefore, to optimize honey harvesting, it is important to establish multiple colonies during periods of large honey flows. Honeybees may use alternative sugar sources beekeepers provide, such as fruit juice, during nectar scarcity. We would like to know more about the year-round bloom calendar. Sharing knowledge with farmers about the availability of melliferous plants in certain months will empower them to make decisions for sustainable beekeeping.

Pollen identification of three notorious illicit drug plants and its potential applications in forensic practice.

Opium poppy, coca and cannabis are raw materials for three notorious illicit drugs. For a long time, drug lords have been growing and smuggling these drugs in a variety of ways and channels and are continually finding new ways of trafficking their wares, which has led to the increasing difficulty of global drug enforcement. In the present paper, we propose an innovative pollen identification system for these important drug plants, which provides a tool for screening and detection of the drugs to aid in drug enforcement. By utilizing the characteristics of these fine particles, their abundant production, and high resistance to decay, we believe this tool could be applied in the following scenarios: detecting and dynamically monitoring drug cultivation activities; determining whether a suspect has been to fields of drug plants and determining whether the site has ever been planted with a drug plant and/or was involved in drug production. In the future, combined with microscope automatic image acquisition technology and intelligent image recognition technology, this pollen identification system is expected to be used to screen three notorious illicit drug plants, thus enhancing the efficiency of drug related crime investigations.

Effects of floral resources on honey bee populations in Mexico: Using dietary metabarcoding to examine landscape quality in agroecosystems.

The decline of honey bee populations significantly impacts the human food supply due to poor pollination and yield decreases of essential crop species. Given the reduction of pollinators, research into critical landscape components, such as floral resource availability and land use change, might provide valuable information about the nutritional status and health of honey bee colonies. To address this issue, we examine the effects of landscape factors like agricultural area, urban area, and climatic factors, including maximum temperature, minimum temperature, relative humidity, and precipitation, on honey bee hive populations and nutritional health of 326 honey bee colonies across varying landscapes in Mexico. DNA metabarcoding facilitated the precise identification of pollen from 267 plant species, encompassing 243 genera and 80 families, revealing a primary herb-based diet. Areas characterized by high landscape diversity exhibited greater pollen diversity within the colony. Conversely, colonies situated in regions with higher proportions of agricultural and urban landscapes demonstrated lower bee density. The maximum ambient temperature outside hives positively correlated with pollen diversity, aligning with a simultaneous decrease in bee density. Conversely, higher relative humidity positively influenced both the bee density of the colony and the diversity of foraged pollen. Our national-level study investigated pollen dietary availability and colony size in different habitat types, latitudes, climatic conditions, and varied levels and types of disturbances. This effort was taken to gain a better insight into the mechanisms driving declines in honey bee populations. This study illustrates the need for more biodiverse agricultural landscapes, the preservation of diverse habitats, and the conservation of natural and semi-natural spaces. These measures can help to improve the habitat quality of other bee species, as well as restore essential ecosystem processes, such as pollination and pest control.

Pollen identification through convolutional neural networks: First application on a full fossil pollen sequence.

The automation of pollen identification has seen vast improvements in the past years, with Convolutional Neural Networks coming out as the preferred tool to train models. Still, only a small portion of works published on the matter address the identification of fossil pollen. Fossil pollen is commonly extracted from organic sediment cores and are used by paleoecologists to reconstruct past environments, flora, vegetation, and their evolution through time. The automation of fossil pollen identification would allow paleoecologists to save both time and money while reducing bias and uncertainty. However, Convolutional Neural Networks require a large amount of data for training and databases of fossilized pollen are rare and often incomplete. Since machine learning models are usually trained using labelled fresh pollen associated with many different species, there exists a gap between the training data and target data. We propose a method for a large-scale fossil pollen identification workflow. Our proposed method employs an accelerated fossil pollen extraction protocol and Convolutional Neural Networks trained on the labelled fresh pollen of the species most commonly found in Northeastern American organic sediments. We first test our model on fresh pollen and then on a full fossil pollen sequence totalling 196,526 images. Our model achieved an average per class accuracy of 91.2% when tested against fresh pollen. However, we find that our model does not perform as well when tested on fossil data. While our model is overconfident in its predictions, the general abundance patterns remain consistent with the traditional palynologist IDs. Although not yet capable of accurately classifying a whole fossil pollen sequence, our model serves as a proof of concept towards creating a full large-scale identification workflow.

Initial study of trap and identification of environmental pollens and fungi spores in Samborondon, Ecuador

Initial study of trap and identification of environmental pollens and fungi spores in Samborondon, Ecuador Introduction: The identification of local aeroallergens in any geographical area is essential for the specific diagnosis and treatment of allergic diseases. Our objective was to identify and registry the most important aeroallergens trapped at the aerobiology station in the city of Samborondon, Ecuador.Material and methods:Pollen grains and fungal spore counts were performed according to the standardized technique with a Hirst-type collection equipment, Burkard spore trap for seven days, following the recommendations of the National Allergy Bureau (NAB) of the American Academy of Allergy Asthma and Immunology (AAAAI). The equipment was installed on the roof of the Universidad de Especialidades Espiritu Santo (UEES), 25 m above ground level, coordinates: 2°07 ́57 ́ ́S 79°52 ́06 ́ ́W, in the city of Samborondon. The sampling period was performed from November 2022 to April 2023.Results:We identified the following pollen families: Poaceae (258 grains/m3), Apocynaceae (Plumeria rubra pc) 214 grains/m3, Lamiaceae (180 grains/m3), Asteraceae - Ambrosía spp.- (60 grains/m3), Chenopodiacea (27 grains/m3), Myrtaceae (17 grains/m3), Pinaceae (11 grains/m3), Betulaceae (7 grains/m3).Also identified fungical spores: Fuzariella spp./Leptosphaeria spp. (1899/m3), Cladosporium spp. (1407/m3), Nigrospora spp. (1183/m3), Dreschlera/Helmintosporum spp. (329/m3), Alternaria spp. (98/m3), Pithomyces spp. (79/m3), Curvularia spp. (48/m3), Stemphylium spp. (46/m3).Conclusions:We reported the first study of aerobiology (capture and identification of environmental pollens and fungi) in the city of Samborondon. The inhabitants of this area are exposed to different aeroallergens with a predominance of Poaceaes pollen and Fuzzariella spp./Leptosphaeria spp. spores. The identified allergens should be part of the usual allergy studies. The results of this first preliminary study should be compared with information from the forthcoming years, which will help to identify variations in the concentration of seasonal aeroallergens, annual fluctuations, and extend the traps to other parts of the city.Key words: aerobiology, allergic rhinitis, allergic conjunctivitis, pollens.

Initial study of trap and identification of environmental pollens and fungi spores in Samborondon, Ecuador

To identify and registry the most important aeroallergens trapped at the aerobiology station in the city of Samborondon, Ecuador. Pollen grains and fungal spore counts were performed according to the standardized technique with a Hirst-type collection equipment, Burkard spore trap for seven days, following the recommendations of the National Allergy Bureau (NAB) of the American Academy of Allergy Asthma and Immunology (AAAAI). The equipment was installed on the roof of the Universidad de Especialidades Espiritu Santo (UEES), 25 m above ground level, coordinates: 2°07 ́57 ́ ́S 79°52 ́06 ́ ́W, in the city of Samborondon. The sampling period was performed from November 2022 to April 2023. We identified the following pollen families: Poaceae (258 grains/m3), Apocynaceae (Plumeria rubra pc) (214 grains/m3), Lamiaceae (180 grains/m3), Asteraceae Ambrosía spp.- (60 grains/m3), Chenopodiacea (27 grains/m3), Myrtaceae (17 grains/m3), Pinaceae (11 grains/m3), Betulaceae (7 grains/m3). Also identified fungical spores: Fuzariella spp./Leptosphaeria spp. (1899/m3), Cladosporium spp. (1407/m3), Nigrospora spp. (1183/m3), Dreschlera/Helmintosporum spp. (329/m3), Alternaria spp. (98/m3), Pithomyces spp. (79/m3), Curvularia spp. (48/m3), Stemphylium spp. (46/m3). We reported the first study of aerobiology (capture and identification of environmental pollens and fungi) in the city of Samborondon. The inhabitants of this area are exposed to different aeroallergens with a predominance of Poaceaes pollen and Fuzzariella spp./Leptosphaeria spp. spores. The identified allergens should be part of the usual allergy studies. The results of this first preliminary study should be compared with information from the forthcoming years, which will help to identify variations in the concentration of seasonal aeroallergens, annual fluctuations, and extend the traps to other parts of the city.

Assessment of the botanical origin of Saudi Arabian honey samples to identify pollen with chromatographic tools and packing and storage.

The increasing demand for honey purification and authentication necessitates the global utilization of advanced processing tools. Common honey processing techniques, such as chromatography, are commonly used to assess the quality and quantity of valuable honey. In this study, 15 honey samples were authenticated using HPLC and GC-MS chromatographic methods to analyze their pollen spectrum. Various monofloral honey samples were collected, including Acacia, Hypoestes, Lavandula, Tamarix, Trifolium, and Ziziphus species, based on accurate identification by apiarists in 2023 from the Kingdom of Saudi Arabia. Honey analysis revealed the extraction of pollen from 20 different honeybee floral species. Pollen identified from honey samples using advanced chromatographic tools revealed dominant vegetation resources: Ziziphus species (23%), Acacia species (25%), Tamarix species (34%), Lavandula species (26%), Hypoestes species (34%), and Trifolium species (31%). This study uses HPLC to extract phenolic compounds, revealing dominant protocatechuic acid (4.71 mg g-1), and GC-MS to analyze organic compounds in honey pollen. Specifically, 2-dodecanone was detected with a retention time of 7.34 min. The utilization of chromatographic tools in assessing honey samples for pollen identification provides a reliable and efficient method for determining their botanical origins, thereby contributing to the quality control and authentication of honey products.

Pollen morphology of Asteraceae from Garhwal Himalaya (Uttarakhand, India): part I

Palynotaxonomy is crucial for understanding plant evolution, biodiversity, and ecological history by systematically classifying and identifying species based on pollen morphology. In addition, a regional pollen flora is crucial for precise pollen identification in various applied fields of palynology, including melissopalynology, aeropalynology, forensic palynology, paleopalynology, and copropalynology. In this communication, we explore the pollen morphology of 28 species of Asteraceae, employing both light (LM) and scanning electron microscopy (SEM). The results revealed that the grains were monad, isopolar, radially symmetric, tricolporate, and exhibited oblate-spheroidal to prolate-spheroidal shapes. The infratectum was found to be columellate, caveate, or noncaveate. The supratectum exhibited echinate or microechinate characteristics, with the exine surface showing perforate, microreticulate, or perforate regulate features. Among some species, a lophate pattern was observed, revealing two distinct variations: one characterized by a ‘15 lacunae type,’ while the other exhibited a ‘21 lacunae type’. Some unusual variations were observed in the aperture character among certain species, like the occurrence of tricolpate grains and pollen dimorphism due to variation in aperture number. Light microscopic examination was preliminary for resolving columellar structure but it clearly showed a double level of columella in some species. A cluster analysis, incorporating qualitative and quantitative features of pollen grains and an artificial pollen key have been set forth to distinguish studied species, supplemented by LM and SEM micrographs. The current findings have enriched the existing pollen database of Asteraceae and could play an important role not only in palynotaxonomy but also in allied fields.

Canudo-de-pito (Escallonia sp.) honey: a comprehensive analysis of quality, composition, and pollen identification

Canudo-de-pito (Escallonia sp.) honey: a comprehensive analysis of quality, composition, and pollen identification