DNA Barcoding Detects Floral Origin of Indian Honey Samples

Draft

DNA Barcoding Detects Floral Origin of Indian Honey Samples

Journal: Genome

Manuscript ID gen-2018-0058.R4

Manuscript Type: Note

Date Submitted by the Author: 10-Jan-2019

Complete List of Authors: Saravanan, Mohanasundaram; Bharathiar University, Department of BiotechnologyMohanapriya, Gunasekaran; Bharathiar University, Department of BiotechnologyLaha, Ramachandra; Mizoram University, Department of Botany; Mizoram University, Department of BotanyRamalingam, Sathishkumar; Bharathiar University, Department of Biotechnology

Keyword: Honey, Melissopalynology, DNA barcoding, Floral composition

Is the invited manuscript for consideration in a Special

Issue? :7th International Barcode of Life

https://mc06.manuscriptcentral.com/genome-pubs

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DNA Barcoding Detects Floral Origin of Indian Honey Samples

Mohanasundaram Saravanan1†, Gunasekaran Mohanapriya1, Ramachandra Laha2 and

Ramalingam Sathishkumar 1*

1Plant Genetic Engineering Laboratory, Department of Biotechnology, Bharathiar University, Coimbatore- 641 046, Tamil Nadu, India.

2Department of Botany, Mizoram University, Aizawl - 796004, Mizoram, India.

*Corresponding Author, email: [email protected]

†Genome Canada Travel Awardee

Mohanasundaram Saravanan1†

Research ScholarPlant Genetic Engineering LaboratoryDepartment of BiotechnologyBharathiar UniversityCoimbatore - 641046, IndiaE.Mail ID: [email protected]

Gunasekaran Mohanapriya1

Research ScholarPlant Genetic Engineering LaboratoryDepartment of BiotechnologyBharathiar UniversityCoimbatore - 641046, IndiaE. Mail ID: [email protected]

Ramachandra Laha2 (Deceased)

ProfessorDepartment of BiotechnologyMizoram University, TanhrilAizawl, Mizoram -796004E. Mail: [email protected]

Ramalingam Sathishkumar 1*

Professor & Group LeaderPlant Genetic Engineering LaboratoryDepartment of BiotechnologyBharathiar UniversityCoimbatore - 641046, IndiaE. Mail: [email protected]

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Abstract

The unique medicinal and nutritional properties of honey are determined by its chemical

composition. To evaluate the quality of honey, it is essential to study the surrounding vegetation

where honeybees forage. In this study we used conventional melissopalynological and DNA

barcoding techniques to determine the floral source of honey samples collected from different

districts of the state of Mizoram, India. Pollen grains were isolated and genomic DNA was

extracted from the honey samples. PCR amplification was carried out using universal barcode

candidates ITS2 and rbcL to identify the plant species. Furthermore, TA cloning was carried out

to screen the PCR amplicon libraries to identify the presence of multiple plant species. Results

from both the melissopalynological and DNA barcoding analyses identified almost exactly the

same 22 species, suggesting that both methods are suitable for analysis. However, DNA

barcoding is an easier and high-throughput method. Hence, it can be concluded that DNA

barcoding is a useful tool in determining the medicinal and commercial value of honey.

Key words: Honey, Melissopalynology, DNA barcoding, Floral composition

Graphical Abstract attached

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Introduction

Honey is one of the most widely consumed naturally sweet substances and is sought after

for its unique nutritional and medicinal properties (FAO 1981). Honey is produced by

honeybees, Apis mellifera, A. cerana, A. florea, A. and A. dorsata, which forage for plant nectar

or secretions. The medicinal properties of honey are based on the floral origin of the nectar

(Bogdanov 1997). The honeybees process the nectar they collect by combining it with specific

bodily enzymes; thereafter it is deposited in comb cells, dehydrated, and stored to allow it to

ripen (Codex Alimentarius Commission 2001a and b). Honey is a reserve food source for

honeybees. A variety of approaches are used to assess honey attributes and to evaluate its quality

(Kempf et al. 2010). The customary approach for identification of pollens is

melissopalynology—the study of pollen present in honey. The sources of nectar used for honey

production can be identified through direct collection of pollen using pollen traps, or observation

of the pollen found in honey (Synge 1947; Louveaux et al. 1978). Pollen analysis indicates the

floral origin, quality of honey, and also the foraging range of the honeybees (Louveaux et al.

1978). However, melissopalynology is tedious, requires skilled labor, and its results are difficult

to interpret to clearly identify the plant species (Khansari et al. 2012). Species belonging to

Campanulaceae, Lamiaceae, and Poaceae have high levels of morphological variation in their

pollen grains, which makes classification difficult (Khansari et al. 2012; Galimberti et al. 2014;

Kraaijeveld et al. 2015).

DNA-based identification is a high-throughput technology that reduces processing time,

and can discriminate at species level without taxonomic expertise (Laube et al. 2010). The

selection of universal markers is very important to identify the floral composition of honey, and

to differentiate between closely related taxa (Sandionigi et al. 2012). The Plant Working Group

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of the Consortium for DNA Barcode of Life have suggested the use of rbcL and matK as core

barcodes for land plant identification (Figure 1); additional regions like ITS2 and trnH-psbA can

be used for analyzing closely related taxa using a tiered approach. These universal barcode

candidates are used for the identification of plant species from pollen DNA. This approach is

rapid and considered a standard method for pollen-based identification of species, and its

successful and varied application has been widely reported (Galimberti et al. 2014; Hawkins et

al. 2015; Kraaijeveld et al. 2015). The limitation of using pollen is that the samples are normally

available as mixtures of multiple species, so, identification of all the species by conventional

Sanger DNA sequencing is difficult. Still, this technique is advantageous when detailed

information is required from unknown samples.

Recently, natural antioxidant potential for protection against oxidative damage has been

explored (Chua et al. 2013). Honey is an important commodity owing to its use in the food and

medicinal industries. There are many cottage industries that process honey, and its consumption

is widespread. However, there is inadequate scientific information that could increase the

profitability of this business. Hence, the aim of the current study was to apply DNA barcoding to

determine the floral origin of honey samples collected from Mizoram, which determines the

quality and commercial value of honey.

Materials and Methods

Honey sample collection

Twenty-nine honey samples were collected from five different districts of the state of

Mizoram in northeast India (Figure 2), which is known for its apiculture industry and rich

biodiversity. All the collected samples were labelled (HB001 to HB029) with the location details

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(Table 1). Samples were stored at 21˚C temperature, and samples were not stored for more than

six months.

Melissopalynological studies

Two grams of honey were mixed with 40 mL of 0.5% sulfuric acid solution. Samples

were incubated in a water bath at 80°C for 5 min. The samples were filtered using a 5 µm filter,

washed using 8 mL of glacial acetic acid to dehydrate the samples, and then centrifuged at 800 g

for 2 min. The pellet was then re-suspended in 1 mL of acetolysis mixture and placed in a water

bath at 80°C for 10 min. The centrifugation process was repeated, and the pellet was re-

suspended in 1 mL of acetolysis mixture (Moore et al. 1991; Reille 1995; 1999). A few drops of

the acetolysis mixture were mounted on a microscope slide and examined with a light

microscope (Magnus, India) at 40X magnification. An average of 300 pollen grains were

observed for each sample for species identification.

DNA Barcoding

Genomic DNA extraction and PCR

Three DNA extractions were carried out for each honey sample to maximize the chances

of identification of all species. Total genomic DNA was isolated from 10 g of honey using a

Nucleospin Food Mini DNA Extraction Kit (Macherey-Nagel, Germany). The isolated DNA was

subjected to PCR amplification in 20 µL reaction mixture containing 10 ng of genomic DNA, 2.5

µL of 10X OneTaq buffer with MgSO4 (NEB, USA), 2.5 µL of 2 mM dNTPs (Fermentas,

USA), 0.5 µL of each forward and reverse primers (10 pM), and 0.2 µL of 2.5 U of OneTaq

DNA Polymerase (NEB, USA) using a thermal cycler (Bio-Rad, USA). The primers and the

reaction conditions were as per published literature (for rbcL- Hollingsworth et al. 2009; Selvaraj

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et al. 2012; for ITS2- Chen et al. 2010). The PCR products were further purified and then sent for

amplicon sequencing. Bidirectional sequencing was performed for the amplicons using 3730XL

automated DNA Sequencer (BioServe Biotechnologies, India). The chromatographic traces were

aligned and contigs were generated using CodonCode Aligner ver. 3.0 (CodonCode, USA). DNA

sequences were deposited in GenBank (MH109162- MH109178).

TA cloning of amplicons

The ITS2 amplicons from the samples HB012 and HB013 were cloned using the pGEM-

T Easy vector system (Genejet PCR Cloning kit, Thermo Fisher Scientific, USA) (Bruni et al.

2015). The cloned plasmids were transformed to E. coli DH5α using the heat shock method

(Froger and Hall 2007). Then the recombinant plasmids were isolated using MN Plasmid DNA

Extraction Kit (Macherey-Nagel, Germany). From the HB012 and HB013 samples, 100 and 64

clones were screened, respectively. The amplicons were sequenced bidirectionally using 3730XL

automated DNA Sequencer (BioServe Biotechnologies, India). The chromatographic traces were

aligned and contigs were generated using CodonCode Aligner ver. 3.0 (CodonCode, USA). To

identify the plant species, the edited sequences were subjected to Basic Local Alignment Search

Tool (BLAST) of National Center for Biotechnology Information (NCBI) database with a

minimum cut off of 97% identity for a top match (Dhivya et al. 2016). In case of multiple species

with similar scores, the sequence was mapped against the Barcode of Life Database (BOLD)

identification system for the species identification.

Results

A high percentage of species identification was achieved (98-100%) from the

melissopalynological studies (Table 2). The honey samples were found to contain a wide variety

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of pollen grains from multiple plant species. The number of species detected with

melissopalynology ranged from 3-4 per sample (Table 2). A total of 22 plant species were

recorded from the samples using both conventional melissopalynology and DNA barcoding

technique.

In BLAST analysis, rbcLa was not able to identify a species with 99% similarity.

Hence, in the tiered approach, ITS2 and rbcLa were used separately to improve their resolution,

by which we could successfully identify 22 different plant species (Table 2). The analyzed

honey samples exhibited a prevalence of pollen types from a variety of floral sources that are

widely distributed in Mizoram, including Combretum indicum (L.) DeFilipps, Nicotiana

tabacum L., Mikania micrantha Kunth, Cucumis sativus L., Strychnos erichsonii Rich., and

Amaranthus tricolor L. Plant species identified through DNA barcoding also showed the

presence of horticultural species, which may be due to the presence of bee hives in human

settlements.

For the samples HB012 and HB013, the PCR amplicons were cloned in TA vectors, for

detection of multiple species. The clones obtained were sequenced and 94 clones were obtained

for sample HB012, and 64 clones for HB013. (Table 3). Sequencing results detected the same

number of species as detected by melissopalynology, while TA cloning analysis performed with

the ITS2 candidate identified only three plant species each in the samples, namely Amaranthus

tricolor L., Cucumis sativus L., and Nicotiana tabacum L. in HB012, and Amaranthus tricolor

L., Cucumis sativus L., and Datura stramonium L. in HB013. Whilst there were minor

differences in the species identified by melissopalynology and DNA barcoding, the dominant

floral composition of honey identified by both methods was identical.

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Discussion

The aim of this study was to compare the efficiency of DNA barcoding and

melissopalynology to determine the floral origin of honey, and the range within which the nectar

was collected. Heterogeneity in the floral composition of honey requires multiple DNA

extraction from honey samples to identify the low-abundance pollens. The variation in the

characteristics of pollens from different plants, including size, shape, and cell wall composition

affects DNA extraction from honey samples, which might affect PCR amplification of the

candidate genes from pollens in honey. DNA extraction efficiency and amplification in PCR for

different species is affected by the shape and size of pollen grains (Borg and Twell 2011).

Universal barcodes like ITS2 and rbcL were used for amplification in this study; however, rbcL

has been reported to be limited in its ability to identify species below the genus level (Newmaster

et al. 2013). These findings support the use of a tiered approach using conventional DNA

barcode markers to differentiate between taxa. In the TA cloning approach, there is a chance of

failing to identify some plant species, since it is based on the probability of the PCR clones.

Melissopalynology has certain limitations; for example, coverage of pollen grains is low, as only

a limited number of pollen grains are used for analysis. Although each method has its own

limitations, they are both able to establish the floral composition and origin of unknown honey

samples.

The current study of honey collected from Mizoram indicates that the honeybees had

foraged mostly on horticultural plants and native wild plants, which are present in the

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surrounding areas. The results suggest that the honeybees mostly forage on only a few plant

species, while a small number of plant species supplement this forage array. Current studies on

foraging of honeybees suggests that the quality and quantity of nectar is also important since the

medicinal property of honey based on its nectar source. The major species represented in the

honey samples were Combretum indicum L., Nicotiana tabacum L., Cucumis sativus L.,

Amaranthus tricolor L., and Mikania spp., as honeybee colonies make an independent choice

among the species available. The abundance of plant species may be the reason the frequency of

pollens of certain species is higher than that of the others in the honey sample. Our results proved

that honeybees foraged mostly on horticultural plants more than on wild plants; this may have

been due to the deforestation. The plant species representation in honey could be used to trace

the geographical origin of honey. The techniques used in this study could be used in the

detection of fraudulently- or mistakenly-labeled commercial honey.

Honey is a potential cause of allergic reaction in humans because of its multi-plant origin

and pollen content. Knowing the exact composition of honey can be very important in cases of

hypersensitivity or detection of a potential toxin. Pollen from poisonous plants like Atropa

belladonna (Bruni et al. 2015) and Boraginaceae family members, which produce pyrrolizidine

alkaloids, have been detected in honey (Edgar et al, 2002). To ensure high quality and safety,

understanding the botanical profile of honey is essential (Olivieri et al. 2012). DNA barcoding is

a potentially useful tool for detecting allergens as well as tracing the origin of food (Galimberti et

al. 2013). This technique is well suited for large-scale and standardized analyses, enabling the

analysis of hundreds of samples in a single sequencing experiment (Hawkins et al. 2015; Keller

et al. 2015; Sickel et al. 2015). Valentini et al. (2010) characterized honey samples using trnL

(UAA) successfully. However, DNA metabarcoding approach offers superior levels of

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repeatability relative to melissopalynology. Pollen DNA metabarcoding has advantages over

melissopalynology, owing to its higher taxonomic resolution, with more plant taxa being

identified at the species level (CBOL Plant Working Group, 2009). Hawkins et al. (2015)

identified that, although honeybees prefer a wide range of plant species for foraging, a small

number of plant species were represented as the main source. This foraging preference of

honeybees will help researchers to understand the network between floral availability and health.

Honeybee foraging preference has been used in establishing pollination networks, although this

is not widely employed because of the associated cost factors (Pornon et al. 2016). Taxonomic

identification depends on the presence of the species in the databases for both DNA

metabarcoding and melissopalynology. Certain geographic regions and plant groups are

understudied owing to a lack of representation in both the DNA barcode and pollen morphology

databases. Barcode reference libraries are not comprehensive in most geographic locations, and

some plant groups are possibly unrepresented. Metabarcoding has been used in many diversity

studies (Větrovský et al. 2013; Gibson et al. 2014; Nelson et al. 2014; Hawkins et al. 2015).

Recent advancements in high-throughput sequencing, like Illumina sequencing platforms, are

used for identifying the composition of microbes (Nelson et al. 2014) and fungal communities

(Větrovský et al., 2013) and in biodiversity assessment studies (Gibson et al. 2014). In future,

advancing the multiplexing of barcodes is needed to characterize multiple pollen samples and to

effectively establish regulations for honey quality, with the aim of improving consumer

confidence.

Conclusion

The quality of honey depends on the plants in the foraging range of the honeybees. DNA

barcoding proved to be a valid alternative to melissopalynological analysis, as it is quicker, easy

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to execute, and more robust. There is a need for the development of comprehensive barcode

reference libraries for the flora of geographic locations where apiculture is practiced, to validate

the quality and source of honey from these regions.

Author Contributions

MS carried out the bench work under the supervision of RS. The manuscript was written and

drafted by MS, GM, and RL. Final editing and submission was performed by RS.

Acknowledgements

The authors are thankful to DBT, India for the financial support through NER-Twining scheme.

The first author is thankful to the Genome Canada Travel Award for enabling his attendance at

the 7th IBOL conference held in South Africa. We would also like to thank UGC-SAP and DST-

FIST for the financial support to the Department of Biotechnology, Bharathiar University, India.

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Figure Captions

Figure 1. Schematic representation of the various methods used in this study to detect the floral

origin of honey.

Figure 2. Honey sample collection sites in Mizoram, Northeast India.

Supplementary Table

Species identified through Melissopalynology and Species identified through DNA barcoding

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Table 1. Number of honey samples collected, and species identified in the honey samples from

the study area.

S.

No.

Districts Location No. of

Samples

collected

Species Identified Using

DNA Barcoding

References

1 Aizawl latitude 23.8789° N

and longitude

92.8976° E

5

(HB001,

HB002,

HB003,

HB009 and

HB012)

Combretum indicum (Syn.

Quisqualis indicum), Cucumis

sativus, Mikania micrantha,

Schima crenata,

Oryza sativa, Olmecare flexa

Amaranthus tricolor

2 Champhai latitude 23.4454° N

and longitude

93.178° E

7

(HB004,

HB005,

HB006,

HB007,

HB008,

HB010 and

HB013)

Atropa belladonna

Oryza sativa, Nicotiana

tabacum, Datura

strammonium Combretum

indicum (Syn. Quisqualis

indicum),

Cucumis sativus

3 Lawngtlai latitude 22.58° N 5 Macaranga indica, Strychno

www.mizenvis.nic.in

www.bsienvis.nic.in

www.bsi.gov.in

Singh et al. (2002).

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and longitude 92.8°

E

(HB011,

HB014,

HB015,

HB016 and

HB017)

serichsonii, Amaranthus

tricolor, Macaranga tessellate

4 Mamit latitude 23.2559° N

and longitude

92.2624° E

7

(HB023,

HB024,

HB025,

HB026,

HB027,

HB028 and

HB029)

Macaranga pachyphylla,

Macaranga umbrosa,

Mikania scandens, Mikania

micrantha Syzygium cumini,

Macaranga denticulata,

Jasminum sambac

5 Siaha latitude 22.3527° N

and longitude

93.0576° E

5

(HB018,

HB019,

HB020,

HB021 and

HB022)

Schima crenata

Strychno serichsonii

Macaranga pachyphylla

Macaranga umbrosa,

Macaranga denticulate

Cucumis sativus

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Table 2. Species-level identification through melissopalynology and DNA barcode analysis.

Melissopalynology Species

identified

through

DNA

barcodingSample ID and Plant Species

Number of

pollen

grains

observed

Pollen

grain

assigned

to species

(%)

Species

identified

HB001

Combretum indicum (Syn. Quisqualis indicum)

Cucumis sativus, Mikania micrantha, Schima crenata*

343 100 4 3

HB002


Cucumis sativus, Olmeca reflexa*

371 983

2

HB003


Cucumis sativus, Mikania micrantha, Schima crenata*

383 100 4 3

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HB004

Nicotiana tabacum, Atropa belladonna*, Oryza sativa376 98 3 2

HB005

Mikania micrantha,

Oryza sativa, Nicotiana tabacum*

309 100 3 2

HB006

Nicotiana tabacum, Combretum indicum(Syn.

Quisqualis indicum)*

291 100 2 1

HB007

Nicotiana tabacum, Datura stramonium, Combretum

indicum (Syn. Quisqualis indicum)*

323 98 3 2

HB008

Cucumis sativus, Nicotiana tabacum*

Oryza sativa*

225 99 3 1

HB009

Combretum indicum (Syn. Quisquali sindicum),

Nicotiana tabacum*

Oryza sativa

353 99 3 2

HB010

Cucumis sativus, Nicotiana tabacum*343 100 2 1

HB011

Mikania scandens, Amaranthus tricolor

Nicotiana tabacum*

265 98 3 2

HB012

Nicotiana tabacum, Amaranthus tricolor,

Cucumis sativus*Combretum indicum(Syn. Quisqualis

indicum) *

374 100 4 2

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HB013

Cucumis sativus*, Amaranthus tricolor, Nicotiana

tabacum*, Datura stramonium

324 98 3 2

HB014

Macaranga indica, Cucumis sativus, Amaranthus

tricolor*

386 99 3 2

HB015

Macaranga indica, Strychnos erichsonii, Amaranthus

tricolor*

347 98 3 2

HB016

Macaranga pachyphylla, Macaranga tessellate*

Schima crenata

353 98 3 2

HB017

Macaranga pachyphylla, Schima crenata, Cucumis

sativus*

372 98 3 2

HB018

Schima crenata, Cucumis sativus332 99 2 2

HB019

Cucumis sativus ,Strychnos erichsonii*348 98 2 1

HB020

Cucumis sativus, Macaranga pachyphylla*321 99 2 1

HB021

Macaranga umbrosa, Macaranga denticulate,

Cucumis sativus*

343 98 3 2

HB022

Macaranga pachyphylla, Schima crenata, Cucumis 321 98 3 2

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sativus*

HB023

Mikania scandens, Syzygium cumini, Schima crenata*359 98 3 2

HB024

Macaranga pachyphylla, Macaranga umbrosa,

Mikania scandens* , Syzygium cumini*, Macaranga

denticulata

274 99 3 3

HB025

Macaranga pachyphylla, Macaranga umbrosa,

Mikania scandens* Macaranga denticulata, Syzygium

cumini*

236 99 3 3

HB026

Mikania scandens, Syzygium cumini, Schima crenata*268 98 3 2

HB027

Mikania scandens*, Syzygium cumini, Cucumis sativus*346 98 3 1

HB028

Macaranga pachyphylla, Syzygium cumini*, Cucumis

sativus* Jasminum sambac

347 98 3 2

HB029

Macaranga pachyphylla*, Syzygium cumini Cucumis

sativus.

342 99 3 2

Sp *species that were identified only from melissopalynology.

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Table 3. Identification of species by TA cloning of ITS2 DNA barcode amplicons from the samples

HB0012 and HB0013.

Sample IDNo. of total clones

obtained

No. of colonies

sequencedSpecies Identified

86 Nicotiana tabacum

6 Amaranthus tricolorHB0012 94

2 Cucumis sativus

58 Cucumis sativus

4 Datura stramoniumHB0013 64

2 Amaranthus tricolor

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Figure 1. Schematic representation of the various methods used to detect the floral origin of natural honey by different approaches

153x166mm (150 x 150 DPI)

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Figure 2. Honey sample collection sites in Mizoram, Northeast India.

266x195mm (300 x 300 DPI)

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244x115mm (150 x 150 DPI)

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DNA Barcoding Detects Floral Origin of Indian Honey Samples

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