Pollen Identification Research Articles

Grass pollen morphology investigation as a basis for monitoring of allergenic biological particles in an automatic mode

A clear distinction between the morphology of allergenic pollen grains of various genera of the Poaceae family is an important task in determining the causal allergenic factors in the population. It allows significant improvement of the efficiency of seasonal allergy diagnostics caused by grass pollen. Moreover, it let to perform better predictions of allergenic risks for people, suffering from pollinosis caused by Poaceae pollen. Therefore, the aim of our study was to establish the morphological difference between the pollen grains of plants of various species of Poaceae family in order to further determination of the possibility to use the established distinctions for the identification of pollen in aerobiological studies. For this, both herbarium samples and pollen of the studied plants were collected in the field during May-June 2019 in Vinnytsia. The pollen was shaken off the anthers directly onto a glass slide, immediately stained with basic fuchsin, and covered with a cover slip. The sizes of pollen grains – their width and length – were determined and analyzed using the PhotoM 1.21 program, and the obtained data on the sizes of pollen were divided into categories by the quartile method in Excel. Three categories of pollen sizes were identified: large, medium and small. Large grains had width and length parameters of 40 μm or more, average grains ranged from 26 to 39 μm, and small grains had a size of 26 μm or lesser in width and length. The large category includes the pollen of Hordeum morinum (39.5-53.1 μm), Elytrigia repens (41-48 μm), Secale cereale (48.4-62.5 μm) and Bromus arvensis (42.2-52.7 μm). The medium grain category included pollen from Dactylis glomerata (29.2-38.1 μm), Poa spp. (26.1-37.3 μm), Panicum capillare (33.3-39.5 μm), Lolium perenne (30.4-35.3 μm), Bromus sterilis (28.3-30.8 μm). The pollen size of B. ramosus ranged from 26.1 to 39.5 µm, and B. tectorum was from 35 to 38.4 µm. The pollen grains of Poa pratense (22.1-25.9 μm) and Piptatherum spp were assigned to the category of the smallest pollen (20.3-24.1 microns). Agrostis gigantea was the only grass pollen type whose size fitted for each category. We found out large, medium-sized and grains of 25.0-27.7 microns, which lie between categories 2 and 3, for different populations of this plant. Consequently, some genera and species of Poaceae can be distinguished by the size of their pollen, while in others the size of pollen grains varies considerably. It is necessary to carry out further research that will help to establish the morphology of pollen of a larger number of Grass family plants. This will significantly improve the diagnosis and prevention of seasonal allergy caused by grass pollen in Ukraine.

Pollen analysis using multispectral imaging flow cytometry and deep learning.

Pollen identification and quantification are crucial but challenging tasks in addressing a variety of evolutionary and ecological questions (pollination, paleobotany), but also for other fields of research (e.g. allergology, honey analysis or forensics). Researchers are exploring alternative methods to automate these tasks but, for several reasons, manual microscopy is still the gold standard. In this study, we present a new method for pollen analysis using multispectral imaging flow cytometry in combination with deep learning. We demonstrate that our method allows fast measurement while delivering high accuracy pollen identification. A dataset of 426876 images depicting pollen from 35 plant species was used to train a convolutional neural network classifier. We found the best-performing classifier to yield a species-averaged accuracy of 96%. Even species that are difficult to differentiate using microscopy could be clearly separated. Our approach also allows a detailed determination of morphological pollen traits, such as size, symmetry or structure. Our phylogenetic analyses suggest phylogenetic conservatism in some of these traits. Given a comprehensive pollen reference database, we provide a powerful tool to be used in any pollen study with a need for rapid and accurate species identification, pollen grain quantification and trait extraction of recent pollen.

Desert dust has a notable impact on aerobiological measurements in Europe

• Notably higher concentrations measured from Aralkum desert dust. • Particle concentration comparable to mass concentration. • Desert dust adds to the uncertainty of airborne pollen identification. • Bioaerosol monitoring could support for validation dust dispersion models. Dust emitted from the surface of arid and semi-arid regions is a natural source of coarse particulate matter (PM) in the atmosphere. Long-distance transport episodes of desert dust to Europe can have a large impact on air quality, and high atmospheric concentrations of particulate matter can also affect the routine sampling and analysis of airborne pollen grains and fungal spores. Here, I examine two desert dust episodes recorded in Novi Sad, Serbia, and discuss the possible implications such events could have on the continuous outdoor aerobiological monitoring. Continuous sampling was conducted using the traditional Hirst-type volumetric trap, state-of-the-art Plair SA Rapid-E automatic sampler, and the GRIMM EDM 180 optical particle sizer and counter. Large quantities of airborne particles collected during the dust events densely covered the impaction surface in samples collected using the Hirst trap, and made identification of biological particles challenging. Such airborne particles can also have an impact of the life of the 337 nm laser used in the Rapid-E. Nevertheless, significant correlations between the hourly particle concentrations (Rapid-E) and the mass concentrations (GRIMM EDM 180) suggest that the automatic aerobiological networks could also be mobilized for providing additional data points for the validation and assimilation of dust dispersion models.

Identification of pollen and pistil polygalacturonases in Nicotiana tabacum and their function in interspecific stigma compatibility

The pollen and pistil polygalacturonases in Nicotiana tabacum were identified and found to regulate pollen tube growth and interspecific compatibility. Polygalacturonase (PG) is one of the enzymes catalyzing the hydrolysis of pectin. This process plays important roles in the pollen and pistil. In this research, the pollen and pistil PGs in Nicotiana tabacum (NtPGs) were identified, and their expression, localization and the potential function in the pollen and interspecific stigma incompatibility were explored. The results showed that 118 NtPGs were retrieved from the genome of N. tabacum. The phylogenetic tree and RT-qPCR analysis led to the identification of 10 pollen PGs; among them, two, seven and one showed specifically higher expression levels in the early development of anthers, during pollen maturation and in mature anthers, respectively, indicating their function difference. Immunofluorescence analysis showed that PGs were located in the cytoplasm of (1) mature pollen and (2) in vitro grown pollen tubes, as well as in the wall of in vivo grown pollen tubes. Four NtPGs in clade A were identified as the pistil PGs, and the pistil PGs were not found in clade E. Significantly higher PGs expression was recorded after incompatible pollination in comparison with the compatible stigma, indicating a potential function of PGs in regulating stigma incompatibility. The influence of PGs on pollen tube growth was explored in vitro and partly in vivo, showing that high PGs activity inhibited pollen tube growth. The application of PGs on the otherwise compatible stigma resulted in pollen tube growth inhibition or failure of germination. These results further supported that increased PGs expression in incompatible stigma might be partially responsible for the interspecific stigma incompatibility in Nicotiana.

Identifying Counterfeit Cigarettes Using Environmental Pollen Analysis: An Improved Procedure.

Traditional pollen preparation techniques provide clear residues for pollen identification; however, such methods are time-consuming, requiring repeated centrifugation, heating, and digestion with high-concentration hazardous chemicals. Tobacco leaves can effectively trap environmental pollen due to hairy surface and terpene-rich exudates. A new tobacco sample processing method was developed by using different extraction chemistry with surfactant. Marlboro Gold cigarettes were employed as model samples for method development. Parameters critical for pollen extraction, which include number of cigarette sticks used, extraction solution, and extraction temperature, were optimized. By using 1% dishwashing detergent to treat three cigarettes at room temperature, the improved method was able to recover sufficient pollen for microscopic analysis in three repeated centrifuge-washing steps and omit hazardous chemicals involved in traditional methods. We focused on the pollen of common ragweed (Ambrosia artemisiifolia), a plant native to North America, as an indicator to differentiate genuine and counterfeit U.S. brand cigarettes. Results from analyzing randomly purchased genuine (authenticated by forensic examination) and known counterfeit Marlboro Gold provided by law enforcement revealed that a significant amount (39%) of Ambrosia were consistently present in all genuine samples, while counterfeit contained none or only trace count. Similar results were found in other counterfeit U.S. brand cigarettes (all seized in the U.S.) involved in this study as well. Lack of Ambrosia in cigarette strongly indicates the product was not originated in the United States.

An operational robotic pollen monitoring network based on automatic image recognition

There is high demand for online, real-time and high-quality pollen data. To the moment pollen monitoring has been done manually by highly specialized experts.Here we evaluate the electronic Pollen Information Network (ePIN) comprising 8 automatic BAA500 pollen monitors in Bavaria, Germany. Automatic BAA500 and manual Hirst-type pollen traps were run simultaneously at the same locations for one pollen season. Classifications by BAA500 were checked by experts in pollen identification, which is traditionally considered to be the “gold standard” for pollen monitoring.BAA500 had a multiclass accuracy of over 90%. Correct identification of any individual pollen taxa was always >85%, except for Populus (73%) and Alnus (64%). The BAA500 was more precise than the manual method, with less discrepancies between determinations by pairs of automatic pollen monitors than between pairs of humans. The BAA500 was online for 97% of the time. There was a significant correlation of 0.84 between airborne pollen concentrations from the BAA500 and Hirst-type pollen traps. Due to the lack of calibration samples it is unknown which instrument gives the true concentration.The automatic BAA500 network delivered pollen data rapidly (3 h delay with real-time), reliably and online. We consider the ability to retrospectively check the accuracy of the reported classification essential for any automatic system.

Mixture analyses of air-sampled pollen extracts can accurately differentiate pollen taxa

The daily pollen forecast provides crucial information for allergic patients to avoid exposure to specific pollen. Pollen counts are typically measured with air samplers and analyzed with microscopy by trained experts. In contrast, this study evaluated the effectiveness of identifying the component pollens using the metabolites extracted from an air-sampled pollen mixture. Ambient air-sampled pollen from Munich in 2016 and 2017 was visually identified from reference pollens and extracts were prepared. The extracts were lyophilized, rehydrated in optimal NMR buffers, and filtered to remove large proteins. NMR spectra were analyzed for pollen associated metabolites. Regression and decision-tree based algorithms using the concentration of metabolites calculated from the NMR spectra outperformed algorithms using the NMR spectra themselves as input data for pollen identification. Categorical prediction algorithms trained for low, medium, high, and very high pollen count groups had accuracies of 74% for the tree, 82% for the grass, and 93% for the weed pollen count. Deep learning models using convolutional neural networks performed better than regression models using NMR spectral input, and were the overall best method in terms of relative error and classification accuracy (86% for tree, 89% for grass, and 93% for weed pollen count). This study demonstrates that NMR spectra of air-sampled pollen extracts can be used in an automated fashion to provide taxa and type-specific measures of the daily pollen count.

A Hybrid Grey Wolves Optimizer and Convolutional Neural Network for Pollen Grain Recognition

Melissopalynology, or pollen analysis of honey, is one of the areas that benefited greatly from image processing and analysis techniques, where melissopalynology is the science that studies the pollen contained in honey, using a microscopic examination. Nowadays, developing an automatic classification system for pollen identification presents a challenge. This article presents a metaheuristic for image segmentation to detect pollen grains in images. It is a swarm intelligence technique inspired from grey wolf hunting behavior in nature, centered around respecting the hierarchy of a pack. It was tested on a set of microscopic images of pollen grains. To evaluate pollen detection, we represented the detected pollen grains using two methods, grey-level based representations where we kept grey value of each pixel, and a binary mask-based technique, where a pixel could have only two values (1 or 0). Then, we used a convolutional neural network (CNN) technique for image classification to predict the specie of each pollen. The proposed system was tested on a set of microscopic images of pollen grains. The obtained performance measures of the system proved that the system is very successful.

Discrimination of grass pollen of different species by FTIR spectroscopy of individual pollen grains

Fourier-transform infrared (FTIR) spectroscopy enables the chemical characterization and identification of pollen samples, leading to a wide range of applications, such as paleoecology and allergology. This is of particular interest in the identification of grass (Poaceae) species since they have pollen grains of very similar morphology. Unfortunately, the correct identification of FTIR microspectroscopy spectra of single pollen grains is hindered by strong spectral contributions from Mie scattering. Embedding of pollen samples in paraffin helps to retrieve infrared spectra without scattering artifacts. In this study, pollen samples from 10 different populations of five grass species (Anthoxanthum odoratum, Bromus inermis, Hordeum bulbosum, Lolium perenne, and Poa alpina) were embedded in paraffin, and their single grain spectra were obtained by FTIR microspectroscopy. Spectra were subjected to different preprocessing in order to suppress paraffin influence on spectral classification. It is shown that decomposition by non-negative matrix factorization (NMF) and extended multiplicative signal correction (EMSC) that utilizes a paraffin constituent spectrum, respectively, leads to good success rates for the classification of spectra with respect to species by a partial least square discriminant analysis (PLS-DA) model in full cross-validation for several species. PLS-DA, artificial neural network, and random forest classifiers were applied on the EMSC-corrected spectra using an independent validation to assign spectra from unknown populations to the species. Variation within and between species, together with the differences in classification results, is in agreement with the systematics within the Poaceae family. The results illustrate the great potential of FTIR microspectroscopy for automated classification and identification of grass pollen, possibly together with other, complementary methods for single pollen chemical characterization.

How biased is our perception of plant-pollinator networks? A comparison of visit- and pollen-based representations of the same networks

Most plant-pollinator networks are based on observations of contact between an insect and a flower in the field. Despite significant sampling efforts, some links are easier to report, while others remain unobserved. Therefore, visit-based networks represent a subsample of possible interactions in which the ignored part is variable. Pollen is a natural marker of insect visits to flowers. The identification of pollen found on insect bodies can be used as an alternative method to study plant-pollinator interactions, with a potentially lower risk of bias than the observation of visits, since it increases the number of interactions in the network. Here we compare plant-pollinator networks constructed (i) from direct observation of pollinator visits and (ii) from identification of pollen found on the same insects. We focused on three calcareous grasslands in France, with different plant and pollinator species diversities. Since pollen identification always yields richer, more connected networks, we focused our comparisons on sampling bias at equal network connectance. To do so, we first compared network structures with an analysis of latent blocks and motifs. We then compared species roles between both types of networks with an analysis of specialization and species positions within motifs. Our results suggest that the sampling from observations of insect visits does not lead to the construction of a network intrinsically different from the one obtained using pollen found on insect bodies, at least when field sampling strives to be exhaustive. Most of the significant differences are found at the species level, not at the network structure level, with singleton species accounting for a respectable fraction of these differences. Overall, this suggests that recording plant-pollinator interactions from pollinator visit observation does not provide a biased picture of the network structure, regardless of species richness; however, it provided less information on species roles than the pollen-based network.

Automated recognition by multiple convolutional neural networks of modern, fossil, intact and damaged pollen grains

Pollen grains are valuable paleoclimate and paleovegetation proxies which require extensive knowledge of morphotypes and long acquisition time under the microscope. The abundance of damaged, folded, and broken pollen grains in the fossil register and sometimes also in modern soil and sediment samples, has so far prevented automation of pollen identification. Recent improvements in machine learning, however, have allowed reconsidering this approach. Here we present an automated approach which is capable of assisting palynologists with poorly preserved pollen samples. Called multi-CNNs, this approach is based on multiple convolutional neural networks (CNNs) integrated in a decision tree system. To test it, we built a system designed for three botanical families very common in the modern and fossil pollen assemblages of Eastern Africa, namely Amaranthaceae, Poaceae, and Cyperaceae. Our system was tested on stacked optical images of 8 pollen types (6 Amaranthaceae, 1 Poaceae, 1 Cyperaceae) using a training dataset of 1102 intact pollen grains and three validation datasets of intact (276 grains), damaged (223 grains), and fossil pollen (97 grains). We show that our system successfully recognizes intact, damaged, and fossil pollen grains with very low misclassification rates of 0%, 2.8%, and 3.7%, respectively. The use of augmentation on stacked optical images during the training increases classification accuracy. Following a palynologist's approach, our system allows grains without obvious characters to be classified into a class of high taxonomic level or as indeterminable pollen. This is the first software able to process grains with a wide range of taphonomical stages, which makes it the first truly applicable to automated pollen identification of fossil material.

Chemical Analysis of Pollen by FT-Raman and FTIR Spectroscopies.

Pollen studies are important for the assessment of present and past environment, including biodiversity, sexual reproduction of plants and plant-pollinator interactions, monitoring of aeroallergens, and impact of climate and pollution on wild communities and cultivated crops. Although information on chemical composition of pollen is of importance in all of those research areas, pollen chemistry has been rarely measured due to complex and time-consuming analyses. Vibrational spectroscopies, coupled with multivariate data analysis, have shown great potential for rapid chemical characterization, identification and classification of pollen. This study, comprising 219 species from all principal taxa of seed plants, has demonstrated that high-quality Raman spectra of pollen can be obtained by Fourier transform (FT) Raman spectroscopy. In combination with Fourier transform infrared spectroscopy (FTIR), FT-Raman spectroscopy is obtaining comprehensive information on pollen chemistry. Presence of all the main biochemical constituents of pollen, such as proteins, lipids, carbohydrates, carotenoids and sporopollenins, have been identified and detected in the spectra, and the study shows approaches to measure relative and absolute content of these constituents. The results show that FT-Raman spectroscopy has clear advantage over standard dispersive Raman measurements, in particular for measurement of pollen samples with high pigment content. FT-Raman spectra are strongly biased toward chemical composition of pollen wall constituents, namely sporopollenins and pigments. This makes Raman spectra complementary to FTIR spectra, which over-represent chemical constituents of the grain interior, such as lipids and carbohydrates. The results show a large variability in pollen chemistry for families, genera and even congeneric species, revealing wide range of reproductive strategies, from storage of nutrients to variation in carotenoids and phenylpropanoids. The information on pollen’s chemical patterns for major plant taxa should be of outstanding value for various studies in plant biology and ecology, including aerobiology, palaeoecology, forensics, community ecology, plant-pollinator interactions, and climate effects on plants.

Results of a Beta Test Evaluating Automated Pollen Identification During Ragweed Pollen Season

The fatigue behavior of polystyrene (PS) in strain controlled torsion rectangular oscillatory tests was analyzed via Fourier transform (FT) to better understand the time evolution of linear and nonlinear mechanical parameters, and to establish fingerprints related to failure onset. The tests were performed under large amplitude oscillatory shear (LAOS), so the stress response was no longer perfectly sinusoidal and higher harmonics could be detected and quantified in the FT spectra as a function of time or number of cycles N. A linear parameter, the storage modulus (G′(N)), was analyzed, as well as nonlinear parameters of the ratios of the second (I2(N)) and the third (I3(N)) harmonics over the fundamental one (I1(N)). The nonlinear parameter I2/1(N) is very low for undamaged samples, but its intensity was found to increase when defects were created in the structure to a point where cracks became visible in the sample. On the other hand, the I3/1(N) parameter increased steadily during a test up to a local maximum where a macroscopic crack occurs in the sample. Both parameters I2/1(N) and I3/1(N) are proposed as new criteria to detect the onset of part failure under the conditions tested and can be used as safety limits for partial damage.

Assessment of Pollen Viability for Wheat.

Wheat sheds tricellular short-lived pollen at maturity. The identification of viable pollen required for high seed set is important for breeders and conservators. The present study aims to evaluate and improve pollen viability tests and to identify factors influencing viability of pollen. In fresh wheat pollen, sucrose was the most abundant soluble sugar (90%). Raffinose was present in minor amounts. However, the analyses of pollen tube growth on 112 liquid and 45 solid media revealed that solid medium with 594 mM raffinose, 0.81 mM H3BO3, 2.04 mM CaCl2 at pH5.8 showed highest pollen germination. Partly or complete substitution of raffinose by sucrose, maltose, or sorbitol reduced in vitro germination of the pollen assuming a higher metabolic efficiency or antioxidant activity of raffinose. In vitro pollen germination varied between 26 lines (P < 0.001); between winter (15.3 ± 8.5%) and spring types (30.2 ± 13.3%) and was highest for the spring wheat TRI 2443 (50.1 ± 20.0%). Alexander staining failed to discriminate between viable, fresh pollen, and non-viable pollen inactivated by ambient storage for >60 min. Viability of fresh wheat pollen assessed by fluorescein diacetate (FDA) staining and impedance flow (IF) cytometry was 79.2 ± 4.2% and 88.1 ± 2.7%, respectively; and, when non-viable, stored pollen was additionally tested, it correlated at r = 0.54 (P < 0.05) and r = 0.67 (P < 0.001) with in vitro germination, respectively. When fresh pollen was used to assess the pollen viability of 19 wheat, 25 rye, 11 barley, and 4 maize lines, correlations were absent and in vitro germination was lower for rye (11.7 ± 8.5%), barley (6.8 ± 4.3%), and maize (2.1 ± 1.8%) pollen compared to wheat. Concluding, FDA staining and IF cytometry are used for a range of pollen species, whereas media for in vitro pollen germination require specific adaptations; in wheat, a solid medium with raffinose was chosen. On adapted media, the pollen tube growth can be exactly analyzed whereas results achieved by FDA staining and IF cytometry are higher and may overestimate pollen tube growth. Hence, as the exact viability and fertilization potential of a larger pollen batch remains elusive, a combination of pollen viability tests may provide reasonable indications of the ability of pollen to germinate and grow.

Identifying Social Transformations and Crisis during the Pre-Monastic to Post-Viking era on Iona: New Insights from a Palynological and Palaeoentomological Perspective

ABSTRACT Iona is renowned for its early monastery, founded following the arrival of Columba in AD 563. This paper uses palaeoecological data to provide insight into the social and environmental transformations that influenced the landscape of Iona during the later prehistoric and historic periods. The identification of cereal pollen suggests that arable farming occurred during the Bronze Age and possibly continued through the Iron Age. Pastoral farming was also practiced. It remains unclear as to whether there were people living on the island at the time of the monastic community’s arrival. Between AD 630 and 1100, woodland clearance and farming occurred. There were also two phases of woodland regeneration and agricultural decline. The first phase coincides with the period of Viking raids and may have witnessed a decline in population. The second phase occurred in conjunction with increased Scandinavian influence and political restructuring across the wider region; however, small-scale farming continued. After c. AD 1000 there was renewed intensification of landscape management prior to the arrival of Benedictine monks and Augustinian nuns (c. AD 1200). This may be linked to climatic amelioration during the Medieval Warm Period and economic growth in the Hebrides.

Cytogenetic and pollen identification of genus Gagnepainia (Zingiberaceae) in Thailand.

Gagnepainia godefroyi K. Schumann, 1904 and G. harmandii K. Schumann, 1904 belong to the genus Gagnepainia K. Schumann, 1904 of the Ginger family. They have the potential to be developed as medicinal and attractive ornamental plants. To date, the knowledge on the cytological and reproductive aspects of Gagnepainia have not been publicly available. Therefore, the aims of this research are to investigate the cytogenetic and pollen characters of Gagnepainia species using light, fluorescence, and scanning electron microscopes. The regular meiotic figures of 15 bivalents are found in both species and presented for the first time. These evidences indicate that Gagnepainia is diploid and contains 2n = 2x = 30 with basic number of x = 15. The mean nuclear DNA contents range from 1.986 pg in Gagnepainia sp., 2.090 pg in G. godefroyi to 2.195 pg in G. harmandii. Pollens of all species are monad, inaperturate, prolate with bilateral symmetry, and thick wall with fossulate exine sculpturing. The pollen size of G. harmandii (74.506 ± 5.075 μm, 56.082 ± 6.459 μm) is significantly larger than that of G. godefroyi (59.968 ± 3.484 μm, 45.439 ± 2.870 μm). Both 2C DNA content and pollen size are the effective characteristics for species discrimination. The reproductive evidence of high meiotic stability and normal pollen production indicate that both Gagnepainia species have high fertility and seed productivity, which are in accordance with the broad distribution. The present study provides good cytogenetic and pollen characters not only for plant identification, but also plant fertility assessment through plant genetic resource management and improvement of Gagnepainia.

Using museum pelt collections to generate pollen prints from high-risk regions: A new palynological forensic strategy for geolocation

The use of pollen as a forensic tool for geolocation is a well-established practice worldwide in cases ranging from the provenance of drugs and other illicit materials to tracking the travel of individuals in criminal investigations. Here we propose a novel approach to generation of pollen databases that uses pollen vacuumed from mammal pelts collected historically from international areas that are now deemed too high risk to visit. We present the results of a study we conducted using mammal pelts collected from Mexico. This new investigative technique is important because, although it would seem that the ubiquitous and geo-specific nature of pollen would make pollen analysis among the most promising forensic tools for law enforcement and intelligence agencies, it is not the case. The process is notoriously slow because pollen identification is a tedious task requiring trained specialists (palynologists) who are few in number worldwide, and the reference materials necessary for geolocation usually are rare or absent, especially from regions of the world that are no longer safe to visit because of war or threat of terrorism. Current forensic palynological work is carried out by a few highly trained palynologists who require accurate databases of pollen distribution, especially from sensitive areas, to do their jobs accurately and efficiently. Our project shows the suitability of using the untapped museum pelt resources to support homeland security programs. This first palynological study using museum pelts yielded 133 different pollen and spore types, including 8 moss or fern families, 12 gymnosperm genera and 112 angiosperm species. We show that the palynological print from each region is statistically different with some important clustering, demonstrating the potential to use this technique for geolocation.

Application of High-Throughput Screening Raman Spectroscopy (HTS-RS) for Label-Free Identification and Molecular Characterization of Pollen.

Pollen studies play a critical role in various fields of science. In the last couple of decades, replacement of manual identification of pollen by image-based methods using pollen morphological features was a great leap forward, but challenges for pollen with similar morphology remain, and additional approaches are required. Spectroscopy approaches for identification of pollen, such as Raman spectroscopy has potential benefits over traditional methods, due to the investigation of the intrinsic molecular composition of a sample. However, current Raman-based characterization of pollen is complex and time-consuming, resulting in low throughput and limiting the statistical significance of the acquired data. Previously demonstrated high-throughput screening Raman spectroscopy (HTS-RS) eliminates the complexity as well as human interaction by incorporation full automation of the data acquisition process. Here, we present a customization of HTS-RS for pollen identification, enabling sampling of a large number of pollen in comparison to other state-of-the-art Raman pollen investigations. We show that using Raman spectra we are able to provide a preliminary estimation of pollen types based on growth habits using hierarchical cluster analysis (HCA) as well as good taxonomy of 37 different Pollen using principal component analysis-support vector machine (PCA-SVM) with good accuracy even for the pollen specimens sharing similar morphological features. Our results suggest that HTS-RS platform meets the demands for automated pollen detection making it an alternative method for research concerning pollen.

A Thresholding Approach for Pollen Detection in Images Based on Simulated Annealing Algorithm

Melissopalynology is a field that studies pollen grain origins to identify their species. It consists of studying either the chemical composition of each grain, or their shapes using microscopic images. This paper presents a system of pollen identification based on the microscopic images, it is divided into two parts, first part is the pollen detection using a thresholding method with simulated annealing algorithm. The second step is the pollen classification, in which we used deep convolutional neural network to extract features from the detected pollen grains and represent them in numerical vectors, therefore, we can use these vectors to classify them based on fully connected neural network, SVM or similarity calculation. The obtained results showed a high efficiency of the neural network in which it could recognize 98.07% of the pollen species compared not just to SVM and similarity methods, but also to works from literature.

Quality evaluation of bee pollens by chromatographic fingerprint and simultaneous determination of its major bioactive components

Bee pollens constitute a large number of flavonoids and thus possess great medicinal value. However different varieties of bee pollen flavonoids vary with different species and their content also differ greatly in different region. Herein, the aim of present research is to establish a method based on high performance liquid chromatography (HPLC) for quantitative analysis of flavonoids compounds and chemical fingerprint analysis of bee pollen. Five batches of rape bee pollen collected from different region of China and particularly six bee pollen species obtained in Anhui were used to establish the fingerprint. The feasibility and advantages of the used HPLC fingerprint were verified for its similarity evaluation by systematically comparing chromatograms with professional analytical software. The similarities of liquid chromatography fingerprints for five batches of rape bee pollen were more than 0.994 while six batches of different species of bee pollen were lower than 0.810. In quantitative analysis, the six compounds showed good regression (R ≥ 0.9964) within the test ranges, and all the values for the RSD were lower than 2%. The developed HPLC fingerprint method was found simple, reliable, and it was validated for the quality control and identification of bee pollen. Additionally, simultaneous quantification of six flavonoids ingredients in the bee pollen samples was conducted to reveal the variation in their content. The results indicated that the HPLC fingerprint, as a characteristic distinguishing method combining similarity evaluation and quantification analysis, can be successfully used to assess the quality and also to identify the authenticity of bee pollen.