Pollen analysis is a method used for verification of the botanical and geographical honey origin. Currently, much effort is being made to introduce automated systems with the use of image analysis programs. The automatic analysis is impeded by the insufficient depth of field of objects when using a light microscope, however, this can be avoided by using image reconstruction from images obtained from different focal planes. In this method, testing was performed on the normal focus (NF) and extended-depth-of-focus (EDF) images. These two methods were compared and statistically evaluated. The number of pollen grains and selected morphometric characteristics were compared. For EDF images, a higher number of pollen grains was obtained for the analysis, and except for the length/width ratio, a statistically significant difference was observed in the characteristics of pollen grains between the compared NF and EDF methods.
One of the important pieces of information for consumers of honey is its geographical and botanical origin (
Pollen analysis is a very demanding method. The examiner performing it must be able to recognize the species or at least the genus of pollen grains contained in a microscopic sample of honey (
Pollen grains differ in appearance, color, and shape. Morphological characteristics of pollen grains can be divided into several groups, according to their shape, size, pollen unit, polarity, symmetry, number and size of apertures, pollen surface, stratification of sporoderms, or exine ornamentation (
The scientific hypothesis was to verify whether using an EDF image allows for detecting and identifying more pollen grains than scanning a single focal plane image.
In this work, a sample of honey from the market network, which came from the Czech Republic, was tested.
The amount of 10.0 g of the sample measured to the nearest 0.1 g was used for the examination. This amount was dissolved in conical centrifuge tubes in 20 mL of distilled water tempered at 40 °C. Subsequently, the obtained solution was centrifuged in a centrifuge (Centric 322A, Technica, SLO) for 10 minutes at a speed corresponding to 1000 g. After the centrifugation, the supernatant was removed. Again, 20 mL of tempered distilled water was added and centrifugation was performed. After removing the supernatant again, the remaining sediment was transferred to a microscope slide using a Pasteur pipette and allowed to dry on a heated plate (Vezas spol. ltd, CZE) at 40 °C. Before placing the sample on the slide, a square of 22 × 22 mm had been drawn using a barrier marker (Elite Mini PAP Pen, USA) to prevent the sediment from spilling over a larger area. After the sediment had dried, the sample was mounted with Kaiser’s glycerol gelatine.
Subsequent sample scanning was performed with a DFK 23U274 camera (Imaging Source, GER) using an Eclipse Ci-L microscope (Nikon, JPN) with a Prosca III motorized stage (Prior, USA). NIS-Elements AR 5.20 software (Laboratory Imaging, CZE) was used to scan the samples. The program performed a random selection of 100 fields of view, on which the automatic counting of pollen grains was performed. Scanning was performed in the acquisition mode in five focal planes. The distance between the individual focal planes was 8 μm. Thresholding, counting, and measurement of morphometric properties of pollen grains were performed in the ideal focal plane (NF) according to the evaluator’s choice and also after merging images from 5 different focal planes after creating an extended depth of focus (EDF) image. The actual scanning and automatic counting of pollen grains were performed in ten replicates per 100 images for each repetition. The two individual methods were then compared and statistically evaluated.
In the next step, the following parameters were measured by the image analysis software of NIS-Elements AR 5.20 (Laboratory Imaging, CZE) for both methods: pollen grain area (basic quantity indicating the size of the object in μm2), its length (calculation of the lengths of the central axes of thin objects is used and is given in μm), width (indicating the ratio between the area and length of the object in μm), and MaxFeret90 (indicating the projection length perpendicular to the maximum Feret’s projection, Figure
Measured morphometrical criteria.
The data were processed statistically using the MATLAB 2019b (MathWorks, USA). The Anderson-Darling test, Student’s t-test, sign test, and Wilcoxon test, two-sample test, and nonparametric Mann-Whitney test were used to evaluate the obtained results.
The analyzed sample was scanned automatically in different focal planes. The sharpest image was selected for NF according to the experience of the human evaluator. For EDF, all the scanned Z levels were combined into one super-sharp image. Z-images were combined into one focused image by picking the focused regions from each frame and the pieces combined. In our study, balanced algorithms of Laboratory Imaging software were used. Algorithms for extended depth of focus were developed in the past 20 years. The primary application of EDF is for transmitted light microscopy systems (
When merging multiple focal planes, a higher number of pollen grains was recorded in 80% of the measurements. 5397 pollen grains were counted in NF scanning and 5689 pollen grains in EDF scanning for all 10 repetitions on 100 randomly scanned images. This is consistent with the results of a study by
Number of pollen particles in NF and EDF images for each repetition.
The numbers of pollen grains recorded by both methods were compared using a paired test, using the difference in the results of individual methods for the same sites instead of the original data. However, the commonly used paired Student’s t-test assumes a normal distribution of these differences. This assumption was tested by the Anderson- Darling test described in the study by
By imperfect focusing of the evaluated image, especially smaller pollen grains can completely disappear from the image. A similar problem was addressed in the study by
In the case of melissopalynological analyses, it is common for both, small as well as large pollen grains, to be present in honey. Small pollen grains typically come from members of the
NF and EDF image. Note: A – C: NF scanning, D: EDF scanning.
None of the images at different NF levels (Figure
As part of the statistical evaluation, it was first tested whether the images obtained by both methods (NF and EDF) detect the same number of pollen grains. For this comparison, all 100 x 10 measured images were considered as a single statistical file. Due to the availability of pairwise comparisons for the number of pollen grains detected by both methods (NF and EDF), it was possible to “filter out” inter-image variability using the paired statistical test. As shown by the histogram in Figure
Histogram of differences in the number of pollen grains obtained by NF-EDF.
A similar deficiency is indicated by the Normal Probability plot (Figure
Normal probability plot of differences in the number of pollen grains NF-EDF.
The rejection of normality meant that it was not possible to use the standard (parametric) paired variant of the Student’s t-test. Thus, to demonstrate a statistically significant difference between the number of grains detected by both methods, it was necessary to use paired nonparametric tests (sign and Wilcoxon test, both with continuity correction). The null hypothesis of these tests in this case was the zero medians of differences (NF-EDF). The sign test rejected the null hypothesis (p-value which contained the first valid digit in the eighth decimal place, 8.5045*10-7). The result of the Wilcoxon test was then yet by two orders of magnitude stronger rejection of the null hypothesis (
The amount of pollen grains in honey also varies naturally. Beekeeping practices, most importantly the method of honey extraction, are mainly responsible for the variability of the pollen grains amount in honey. As to concerns about honey adulteration, it is not common to filter honey through sieves with meshes smaller than 0.2 mm. This filtration is only allowed to remove foreign matter (
Thus, the number of pollen grains usable for melissopalynological analysis is not the only criterion for evaluating the suitability of the compared methods. The pollen grains of each botanical taxon have their typical morphometric properties, which may vary according to the botanical species of the plants. The literature describes both, differences between plant species (
All the observed geometric parameters (except the length/width ratio) can be argued that the smaller median indicates that EDF is a better method. In the case of imperfect focusing on NF, the observed pollen grain may contain image information “mixed” with the background, which increases all the monitored morphometric parameters (Figure
Pollen sample of Brassica napus. Note: A – E: NF scanning, F: EDF scanning.
Pollen sample of Pinus sylvestris. Note: A – E: NF scanning, F: EDF scanning.
Further reduction of the median is caused by more grains detected. The “lost” pollen grains (not detected by the NF method) can be expected to have a smaller length, width, and area.
To compare the suitability of morphometric parameters in NF and EDF, the null hypothesis of the median equality of two statistical sets was tested by the nonparametric Mann- Whitney test. The medians of all observed morphometric characteristics are summarized in Table
Medians of the observed geometric parameters of pollen grains.
Method | Area [µm2] | Length [µm] | Width [µm] | MaxFeret90 [µm] | Length/Width Ratio |
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NF | 531.24 | 28.39 | 18.78 | 25.96 | 1.52 |
EDF | 490.68 | 27.31 | 18.00995 | 25.11 | 1.53 |
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0.7851 |
Hence, it is clear that except for the length/width ratio, there is a statistically significant difference between NF and EDF in all observed geometric characteristics of pollen grains. Since the medians of all statistically significantly different geometric characteristics of EDF are smaller than the medians of NF, it can be argued that the identification of pollen grains in EDF images will be more efficient than in NF images. This rule was not confirmed for the recalculated length/width factor. This result confirms that this factor is a suitable criterion especially for individual evaluation of pollen grains (
In this work, the extended depth of focus obtained from images from different focal planes and normal focused images was compared. The calculation and measurements of morphometric characteristics of pollen grains were used for comparison. The EDF method can detect a larger number of pollen grains in the evaluated images. A statistically significant difference between NF and EDF was demonstrated in all the observed morphometric characteristics of pollen grains except the length/width ratio. Identification of pollen grains in images based on morphometric criteria obtained by the EDF method is more efficient than the identification of pollen grains in images obtained by the NF method. Based on our results, the EDF scanning method is a method more suitable for image-based melissopalynological analysis, providing better results than the technique of scanning a single depth of field in NF.
This work was supported by Applied Research Programme of the Ministry of Agriculture for the 2017–2025 period – ZEMĚ (THE LAND), number QK1920344.
The authors declare no conflict of interest.