Authentication of Monofloral Yemeni Sidr Honey Using Ultraviolet

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Authentication of the Monofloral Yemeni Sidr Honey Using UV Spectroscopy and Chemometric Analysis Abdul-Rahman A Roshan, Haidy A Gad, Sherweit H El-Ahmady, Mohamed S Khanbash, Mohamed I Abou-Shoer, and Mohamed M Al-Azizi J. Agric. Food Chem., Just Accepted Manuscript • DOI: 10.1021/jf402280y • Publication Date (Web): 09 Jul 2013 Downloaded from http://pubs.acs.org on July 13, 2013

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Journal of Agricultural and Food Chemistry

Authentication of the Monofloral Yemeni Sidr Honey Using UV Spectroscopy and Chemometric Analysis

Abdul-Rahman A. Roshan,1 Haidy A. Gad,1 Sherweit H. El-Ahmady,1* Mohamed S. Khanbash,3 Mohamed I. Abou-Shoer,2 and Mohamed M. Al-Azizi1

1

Department of Pharmacognosy, Faculty of Pharmacy, Ain Shams University, Abbassia, Cairo, Egypt 2 Department of Pharmacognosy, Faculty of Pharmacy, Alexandria University, Alexandria, Egypt 3 Honey and Date Center, Hadramout University of Science and Technology, Hadramout, Yemen

Corresponding author: Sherweit H. El-Ahmady Department of Pharmacognosy Faculty of Pharmacy Ain Shams University Abbassia Cairo-11566 Egypt e mail: [email protected] Tel: +2 0122 4604855 Fax: +202 2 405 1107

1 ACS Paragon Plus Environment


1

ABSTRACT

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In this work, a simple model developed for the authentication of monofloral Yemeni Sidr honey

3

using UV spectroscopy together with chemometric techniques of Hierarchical Cluster Analysis

4

(HCA), Principal Component Analysis (PCA), and Soft Independent Modeling of Class Analogy

5

(SIMCA) is described. The model was constructed using 13 genuine Sidr honey samples and

6

challenged with 25 honey samples of different botanical origins. HCA and PCA were

7

successfully able to present a preliminary clustering pattern to segregate the genuine Sidr

8

samples from the lower priced local polyfloral and non-Sidr samples. The SIMCA model

9

presented a clear demarcation of the samples and was used to identify genuine Sidr honey

10

samples as well as detect admixture with lower priced polyfloral honey by detection limits higher

11

than 10%. The constructed model presents a simple and efficient method of analysis and may

12

serve as a basis for the authentication of other honey types worldwide.

13 14

KEYWORDS

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Ziziphus spina-christi, Sidr, honey, UV spectroscopy, chemometrics, multivariate analysis


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INTRODUCTION

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Honey is defined as “the natural sweet substance produced by Apis mellifera bees from the

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nectar of plants, or from secretions of living parts of plants, or excretions of plant-sucking insects

19

on the living parts of plants, which the bees collect, transform by combining with specific

20

substances of their own, deposit, dehydrate, store and leave in the honeycomb to ripen and

21

mature”.1 Bees forage different plants, and due to different proportions of the possible sources of

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nectar, honey is always a mixture of different sources.2 Monofloral honey is produced from

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nectar that mainly originates from a single plant species and possesses distinctive organoleptic

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characteristics. These honey types of distinct botanical origin are often traded at a higher price

25

than honeys from mixed botanical origins and can thus be considered premium products.3

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Honey production and beekeeping are very old professions in Yemen, dating back to the

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tenth century B.C.4 Yemen produces, annually, up to 5,600 tons of several natural varieties, of

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which the most famous is the Sidr or Elb honey (Ziziphus spina-christi L. Desf. Rhamnaceae),

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which is mainly exported to the Gulf States with annual total gross revenues of US$40 million.5

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The natural specifications of the Sidr honey produced from Dawan and Gerdan valleys in

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Hadramout and Shabwa governorates respectively are well preserved since the bees build their

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hives without any human interference, making this particular honey one of the finest and best

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quality monofloral honey worldwide.6,7 Similar to many other well reputable honey types,

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limited availability, and high pricing of Sidr honey are probably the biggest temptations for its

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adulteration or admixture with other local polyfloral honey types. Hence, identification of

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authenticity is important for financial reasons in addition to consumer and producer protection.

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The adulteration techniques of honey are based on various principles including extension as well



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as bee feeding with sugar and / or syrups, and mixing with honey originating from different

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floral or geographical origins.8

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Many different techniques are employed in authenticity testing of honey including pollen

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analysis (melissopalynology) which was used as the traditional method to determine the honey’s

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botanical origin.9 In the last few decades, new analytical techniques were implemented for the

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determination of the honey’s botanical origin in an effort to find alternative methods for honey

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authentication. These methods were based on statistical evaluation of their physicochemical

45

data,10,11 or the determination of certain chemical constituents to be used as biomarkers by

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applying various chromatographic and spectroscopic techniques.12-14 Recently, analytical

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techniques in conjunction with multivariate analysis and chemometrics have been widely

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implemented in the quality control of various foods and herbal drugs.15,16 Reports on the

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authentication of honey samples using this approach have also received wide recognition where

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various honey characteristics were associated with chemometrics for botanical and geographical

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classification. Analytical methods used included the measurement of physicochemical

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parameters of honey,17-19 application of GC-MS for the characterization of volatile compounds,

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20,21

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flavonoids using LC-DAD-ESI/MS.23 In addition, a commonly used spectroscopic method was

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the Near Infrared (NIR) spectroscopy which was used to classify floral origins,24 to detect honey

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adulteration by addition of fructose, glucose and corn syrup25,26 and to develop calibrations for

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the quantitative prediction of physicochemical measurements of honey as protein, fat and

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moisture.9 Fourier-transform mid-infrared (FT-MIR) spectroscopy was established for analyzing

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physicochemical parameters of honey27,28 while MIR-ATR spectroscopy was shown to offer a

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valuable approach to honey authentication.29,30

quantitation and characterization of sugars using Raman spectroscopy and HPLC22 and


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The application of UV spectroscopy in the analysis of food products has increased during

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the last decade, probably due to its advantage of being simple, quick, non-destructive and

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relatively inexpensive to carry out.31-34 While there has been a steady growth in applications of

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honey authentication using IR spectroscopy as well as other spectroscopic methods, little

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research has been carried out using Ultraviolet (UV) spectroscopy for this purpose despite its

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many advantages of simplicity, rapidity and low cost.35 A study using size exclusion

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chromatography (SEC) coupled to UV diode array detection (UV-DAD) for botanical and

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geographical classification36 and another applying Front face fluorescence for the botanical

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classification of honey samples in Switzerland,37 both combined with chemometrics were the

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closest to the presented approach.

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The work presented in this study aims at developing and establishing a simple model,

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based on chemometric analysis of UV spectroscopic data, to confirm any botanical claim made

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on Yemeni Sidr honey. The designed model will allow for the detection of adulteration resulting

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from admixture with honeys of lower quality and price as well as the authentication of genuine

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Sidr honey. This model may also serve as a basis for implementing this technique to other honey

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types worldwide.

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MATERIALS AND METHODS

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Sample Collection. A total of 38 honey samples were used in this study including 13

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samples that represented genuine monofloral Sidr honey (Ziziphus spina-christi) samples (SG1-

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13), 14 samples marketed and claimed to be Sidr honey (SM1-14), including three imported

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samples; two from Kashmeer, India and one sample from KSA (Kingdom of Saudi Arabia), five

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polyfloral samples (PF1-5) and six non-Sidr samples of different botanical origins (NS1-6). All

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samples were collected from different regions of the Republic of Yemen. Genuine Sidr honey



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and marketed sample codes, botanical and geographical origins, price and collection season are

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presented in Tables 1 and 2 respectively. A map of Yemen showing collection sites is presented

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in Figure 1. Regarding the genuine Side samples, a thorough field survey was conducted prior to

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collection in areas abundant with Ziziphus trees and the honey was collected directly from the

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hives (by help of the bee keepers) in Dawan valley of Hadramout governorate, during the

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flowering season of Ziziphus trees (October to November, 2010) to ensure the authenticity of the

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samples. These samples were certified as genuine Sidr honey samples by Professor M.

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Khanbash; expert of Sidr honey at the Honey and Date Research Center in Hadramout, Yemen;

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after sensory and physicochemical analyses (Refractive Index, Water content, pH, Free acid

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content and Electric Conductivity) were conducted. The remaining 25 samples were collected

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from the market and their botanical origin identified by sensory as well as physicochemical

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analyses. An overview of the physicochemical measurements for all samples (unpublished data)

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is presented in Table 1 of the Supporting Information. Accordingly, the physicochemical data

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obtained from most of the marketed claimed to be Sidr samples were within the genuine Sidr

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criteria except for five samples: SM1, SM4, SM8, SM9 and SM13.

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Sample Preparation. Each honey sample (2.50 g) was dissolved in 20 ml of 80%

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ethanol solution (Carlo Erba anhydrous HPLC grade ethanol diluted with distilled water) which

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provided a solvent of expanded polarity range to dissolve most of the honey components. The

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samples were then filtered (using Grade 1 Whatman filter paper), transferred into a 25 ml

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volumetric flask and completed to volume with 80% ethanol to be kept as stock solution. For the

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preparation of admixed samples, two sets of samples representing admixture with two polyfloral

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honey samples; PF3 for the first set and PF4 for the second set were prepared. The admixed

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samples were prepared by diluting four different genuine Sidr honey samples (SG2-4 and SG12),


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chosen at random with different concentrations (10%, 20%, 30% and 40%) of each of PF3 for

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the first set and PF4 for the second set to amount to 16 adulterated samples in each set.

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Ultraviolet Spectroscopy. A dilution of 1 ml was taken from the stock solution for each

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sample into a 10 ml volumetric flask and completed with 80% ethanol. All samples were

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analyzed by UV Spectroscopy using UV-1601 PC, UV-visible spectrophotometer, Shimadzu,

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Japan equipped with a quartz cell with an optical path of 1 cm, and spectral resolution of 1 nm in

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the range 200-400 nm. The obtained absorption readings over the spectral points of all the

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samples were converted into data matrix using Microsoft® Excel 2010 (Microsoft, Washington,

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USA) with the spectral points as variables represented by columns and their corresponding

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spectral absorption measurements of different samples represented by rows. For each of the 13

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genuine Sidr samples, 20 replicates (to be used as the training set in the model construction) and

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five replicates for each of the 25 marketed samples were prepared.

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Multivariate Data Analysis. The data measured was represented in a matrix consisting

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of the total number of samples and their replicates multiplied by 200 variables in MS Excel® and

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exported to the appropriate software for chemometric analysis of the spectra. The UV spectral

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data were subjected to unsupervised recognition techniques of data analyses performed by

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applying Hierarchical Cluster Analysis (HCA) using Hierarchical Clustering Explorer 3.5

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(Human computer interaction lab. University of Maryland, College Park, USA) and Principal

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Component Analysis (PCA) using Unscrambler®9.7 (CAMO SA, Oslo, Norway). The UV data

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matrix preprocessing was performed prior to data analysis by mean centering of the raw spectral

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data matrix of all the samples, a default option in the software. Both HCA and PCA methods aim

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at reducing the multivariate space in which objects (samples) are distributed but are

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complementary in their ability to present results. HCA was used to sort the sample into groups



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using average linkage method for cluster building and the distance between clusters was

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computed by Pearson’s correlation method as measure of similarity. PCA was utilized as a data

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reduction technique to generate a visual plot of the samples and their distribution on a score plot

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often showing trends that, in spite of having to be interpreted and explained, constitute a first

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step in subsequent modeling for samples classification. In the model construction, HCA and

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PCA were followed by the supervised pattern recognition technique of Soft Independent

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Modeling of Class Analogy (SIMCA) using Unscrambler®9.7 which is considered a key

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chemometric approach for classification. This technique allows for the classification of the

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samples into an already existing group, assigning new objects to the class to which they show the

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largest similarity. SIMCA is strongly based on PCA, because each class is defined by an

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independent PCA. In our study, both PCA models (genuine Sidr and polyfloral) were developed

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using full cross validation, and the number of PCs used in each model were automatically

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selected by the software using the predicted residual error sum-of-squares (PRESS) function.

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The choice of the SIMCA technique in contrast to other supervised pattern recognition

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techniques is based on the modeling properties of SIMCA, which provides approaches more

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versatile than those obtained using discriminant techniques.38

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RESULTS AND DISCUSSION

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The UV absorption bands of the presented samples are usually associated with the

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presence of different chromophores exemplified in various components as phenolics, flavonoids,

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conjugated systems as well as other UV absorbing systems39 and recently these compounds have

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been used as markers for the determination of the botanical origin of honey.23 Concerning the

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studied honey samples, UV spectrum was recorded for each sample (20 replicates for the 13

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genuine Sidr samples and five replicates for each of the 25 marketed samples) versus 200


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variables representing the absorbencies in the region between 200 - 400 nm. The UV spectrum

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for all the samples is presented as Figure 1 of the Supporting Information.

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Hierarchical Cluster Analysis (HCA). The search for natural groupings among the

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samples is one preliminary way to study data sets. Because of its unsupervised character, HCA

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was used to perform a preliminary data scan and to uncover the structure residing in the data.

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The dendogram in Figure 2a shows the clustering pattern of the data set constituting the 13

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genuine Sidr, 5 polyfloral and 6 non-Sidr honey samples. The model revealed a rational

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segregation of the samples into three clusters; clusters A and B representing genuine Sidr while

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cluster C included most of the non-Sidr samples in addition to 3 polyfloral samples. The reason

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behind the splitting of genuine Sidr samples into two clusters may be related to the difference in

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collection altitude, since the samples in cluster B were collected from the mountainous area of

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Dawan valley while those in cluster A were collected from the low land of the valley as indicated

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in Table 1. The chemical compositions of honeys will vary according to the contaminant nectars

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which in consequence are influenced by the variation in the chemical composition of the same

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plant species growing at different altitude.12 In the spring season, if it rains, a large number of

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plants, including Ziziphus, will flower in Dawan valley where honey production takes place and

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hence, the constituents of the polyfloral honey produced may resemble that of genuine Sidr

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samples. In autumn (September to November), the flowering season of Ziziphus trees, only a few

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other plants will flower at the same time,40 and therefore, the honey produced is considered of

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premium quality (Bagheah Sidr honey), while that produced in spring is called Marbaee and is

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mostly polyfloral and hence of less quality. Accordingly, this can explain the resemblance of PF2

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and PF3 to Sidr samples and their grouping in cluster A.



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The only non-Sidr sample grouped with the Sidr samples in cluster A was NS4 collected

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from Suqatra island. The UV absorbance spectrum of this sample was strikingly different from

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the rest of the samples and it is worth mentioning that this sample showed a distinctive flavor in

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the sensory analysis and different physicochemical characters than any of the samples. The

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anomalous grouping of this sample in cluster A is however sometimes observed in HCA models,

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and has been reported in similar studies.41 The addition of the 14 marketed samples to the data

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set resulted in the same three clusters as shown in Figure 2b with minor but meaningful changes.

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Sample PF3 was regrouped with cluster C while PF2 remained in cluster A due to its geographic

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and somewhat similarity in botanical origin of the latter to Sidr samples as indicated in Table 2.

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Cluster A originally constituting Sidr samples collected from the valleys also grouped SM1 from

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Nakhal valley in addition to all non-Hadramout samples including Kashmeer and KSA marketed

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samples. Cluster B compiled 7 marketed samples (SM4-10), all of which originated from the

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Hadramout governate except for SM4 in addition to the 3 Sidr samples collected from the

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mountainous area of Dawan valley.

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Principal Component Analysis (PCA). PCA was applied to the matrix formed by the

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total spectral data corresponding to all the different honey samples. The maximum number of

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PCs was set at 6, however the first two components explained almost all the data variance. As

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shown in the score plot of the first two PCs in Figure 3, the genuine Sidr honey samples (SG1-13

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x 20) were clustered together on the horizontal (PC1) axis, and well separated from the polyfloral

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samples (PF1-5 x 5) which were clustered in the lower left quarter of the score plot. The

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remaining non-Sidr honey samples (NS1-6 x 5) were scattered in the lower right quarter of the

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score plot in a distance away from the two former clusters. However some of the marketed


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claimed to be Sidr honey samples were scattered in a distance closer to the genuine Sidr cluster

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showing some similarity.

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PCA is among the most versatile of all chemometric methods; it involves a mathematical

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procedure that reduces data dimensionality by performing a covariance analysis between factors,

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and visualizes the hidden trends in a data matrix without much loss of information.37 Even

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though the results obtained from the PCA revealed obvious clustering of the samples according

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to their botanical origin which indicated differences in the honey samples composition, and was

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in fact mostly coherent with the HCA results, yet a more precise demarcation was required to be

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achieved between the genuine Sidr honey samples and its blends collected from the market. As a

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consequence, it was necessary to approach the problem with the SIMCA technique which

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utilized principal component per category to further confirm the classification obtained by the

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PCA and HCA.

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Soft Independent Modeling of Class Analogy (SIMCA). Development of the

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classification model. The SIMCA classification process consists of two stages, namely: the

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training stage, in which the individual models of the data classes are developed, and the testing

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or validation stage, in which new samples (not used in the training stage) are classified within the

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established class models to evaluate the model’s efficiency. Classes corresponding to genuine

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Sidr honey and polyfloral honey were developed using independent principal component

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analysis. The training set for the class of genuine Sidr consisted of 10 samples, while the

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polyfloral training set was composed of 4 samples. External validation was carried out by

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samples that were not used for the model’s development which consisted of 3 genuine Sidr

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samples (SG1, SG6 and SG8), 1 polyfloral sample (PF1) and all non-Sidr samples except for

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NS4 which was excluded due to its high leverage effect apparent from the PCA score plot.



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Validation of the model. Cooman’s plots were used to evaluate the results of

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classification where in case an object belonged to a certain class, it should fall within the

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membership limit, on the left of the vertical line or below the horizontal line. The validation

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samples corresponding to genuine Sidr and polyfloral honey were all within the limit of each

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membership indicating the perfect predictability and sensitivity of the model (Figure 2 of

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Supporting Information). Also the model showed good specificity as all non-Sidr honey samples

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were not classified into either class. It is worth noting that the developed SIMCA model provided

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interclass distances of about 380, and all variables showed a discrimination power higher than 3,

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and a modelling power higher than 0.8 which reflected the discrimination capability of the

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developed model to discriminate the spectral signals of genuine Sidr honey samples with respect

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to polyfloral samples, with 95% confidence limits. For the above reasons, the constructed

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SIMCA model was employed in the classification of formulated admixed samples and later for

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the authentication of genuine Sidr samples.

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Challenging of the model using formulated admixed Sidr honey samples. The results

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in Figure 4a showed that the Sidr samples admixed with 10% and 20% polyfloral honey (PF3)

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fell within the class membership limit of the genuine Sidr model, while samples adulterated with

236

30% and 40% did not. In case of admixture with polyfloral sample PF4, only the 10% admixed

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samples were classified as genuine Sidr honey (Figure 4b). These results are quite promising

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since both polyfloral samples used to adulterate the genuine samples shared the same geographic

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location as that of genuine Sidr honey samples which was confirmed by the HCA (Figure 2a) and

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in many cases such polyfloral honey may have originated from Ziziphus species and have

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common constituents to that of genuine Sidr honey as explained previously. The fact that the

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model was able to allow admixture of genuine Sidr with up to 20% of PF3 and only 10% of PF4


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may be was in coherence with HCA results showing a higher resemblance of PF3 to genuine Sidr

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samples than PF4.

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Authentication of Marketed Sidr Honey Samples. The 14 marketed samples of

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claimed to be Sidr honey were tested using the constructed and validated SIMCA model for their

247

classification into Sidr or non-Sidr honey as shown in Figure 5. Samples SM6, SM7, SM10 and

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SM14 were classified as Sidr honey which was in coherence with the HCA which grouped the

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former three samples with cluster B and the latter with cluster A, all classified as genuine Sidr

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honey. These results were also in agreement with the data from the physicochemical analyses

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showing characters of genuine Sidr including higher pH and electrical conductivity as well as

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lower free acid content. In addition, these results were compatible with our expectations as these

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samples were collected from four highly reputable honey sellers in the honey market, and the

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prices of these samples ranked the top most when compared to the other marketed samples as

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shown in Table 2. It is also worth noting that the two samples; SM2 and SM5 closest in distance

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on the plot to the membership limit for genuine Sidr, had the source, physicochemical

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characteristics and price criteria comparable to the samples classified as genuine Sidr honey. The

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claimed Sidr honey samples from Kashmeer (SM11 and SM12) were placed at a further distance

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from the genuine Sidr class model, while the sample from KSA (SM13) projected within the

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Yemeni claimed Sidr honey near 0.2 distances to the Sidr model. These results may be explained

261

by the similarity of the latter sample to the Yemeni samples due to the closer geographic location

262

of the KSA Southern border to Yemen and hence the increase in the similarity of botanical origin

263

than that present in Kashmeer. Table 3 presents the classification of the 14 marketed samples in

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accordance with the authentication results obtained.



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In conclusion, considering the many variables involved in the production of Yemeni Sidr

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honey as geographic location, botanical origins and collection season, the approach of using UV

267

spectroscopy coupled with chemometric analysis methods was confidently able to segregate

268

genuine Sidr samples from non-Sidr and relatively inferior polyfloral samples available in

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Yemen and even accomplish classification within the genuine Sidr samples. Applying HCA to

270

the spectroscopic data resulted in the successful segregation of genuine Sidr honey from

271

polyfloral and non-Sidr honey as well as the distinction of Sidr honey according to collection

272

altitude. Both HCA and PCA showed obvious grouping patterns and high resemblance of most

273

of the 14 marketed samples to the Sidr samples. The SIMCA model was successfully able to

274

classify the authentic Sidr honey from non-Sidr honey samples with a clear demarcation. Also,

275

the model was able to detect admixture of genuine Sidr honey with closely related polyfloral

276

honey by detection limits of higher than 10% which potentially serves the trading of honey in

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Yemen. Finally, the model was capable of distinguishing genuine Sidr honey collected in

278

Hadramout from that collected in other regions as far as KSA and India. Further studies on the

279

under investigated Yemeni Sidr honey involving the incorporation of considerably more

280

samples, of different botanical and geographical origins into the model as well as compositional

281

analysis of the honey would definitely accentuate the model and is considered for future work.

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Finally, adopting the model for the authentication of other honey types worldwide is

283

recommended as a simple and inexpensive approach.

284

Supporting Information Available: The UV absorbance spectra of all the 38 samples is

285

presented as Figure 1. Cooman’s plot for the validation of the SIMCA model is presented as

286

Figure 2.

287

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Table 1. Genuine Sidr (Ziziphus spina-christi) Honey Samples (geographical origins, price per kg and date of collection). Sample code

Geographical origin

Price per kg. (YR)*

SG1

Dawan valley

15,000

Collection Season in 2010 December

SG2

Dawan valley

15,000

December

SG3

Dawan valley

15,000

November

SG4

Dawan valley

17,000

November

SG5

Dawan valley altitude

16,000

November

SG6


15,000

November

SG7

Dawan valley

15,000

November

SG8

Hagr valley

17,000

November

SG9

Hagr valley

15,000

November

SG10

Dawan valley

20,000

November

SG11


16,000

November

SG12

Dawan valley

15,000

December

SG13

Dawan valley

15,000

November

*Exchange rate for Yemeni Riyal in January 2013: 1 USD = 215 YR


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Table 2. Marketed Honey Samples (claimed botanical and geographical origins, price per kg and date of collection). Sample Code

Claimed origin Botanical

Market

Price / kg. (YR)

Collection Season in 2010

Geographical

SM1

Nakhal valley*

Dawan

14,000

November

SM2

Hareeb Shabwa gov. Geradan Shabwa gov.

Aden

13,000

November

Aden

14,000

November

Hareeb Shabwa gov. Saewn* Dawan* Ghyl binyamen* Hadramout gov.* Hadramout gov.* Dawan* Kashmeer, India Kashmeer, India

Aden

10,000

November

Saewn Dawan Saewn Aden Aden Aden Aden Aden

15,000 15,000 16,000 10,000 12,000 18,000 4,000 4,000

July July July August August August November October

KSA Aden

12,000 14,000

October November

Aden

5,000

August

Dawan

4,500

November

Aden Aden Aden Aden

2,500 3,000 3,000 5,000

November September September November

Dawan

5,000

November

Aden Aden

5,000 14,000

November December

Aden

4,000

December

Aden

8,000

November

SM3 SM4 SM5 SM6 SM7 SM8 SM9 SM10 SM11 SM12

Sidr Ziziphus spina-christi

SM13 SM14 PF1

Saudi Arabia (KSA) Hareeb Shabwa gov. Polyfloral with Acacia tortilis

Hadramout gov.*

PF2

Polyfloral with Dawan* Ziziphus spina-christi PF3 Hadramout gov.* Polyfloral PF4 Hadramout gov.* Polyfloral PF5 Hadramout gov.* Polyfloral NS1 Hadramout gov.* Sumar Acacia tortilis NS2 Dawan* Sumar Acacia tortilis NS3 Abyan gov. Marow NS4 Polyfloral with Suqatra Island Dracaena NS5 Taez gov. Dhaba Acacia mellefera NS6 Taez gov. Sal Euphorbia sp *Geographically located in Hadramout governate.



Table 3. Classification of marketed claimed to be Sidr honey samples.

Contaminated with other nectars (impure)

Genuine or Pure Yemeni Sidr SM2

Less contaminated

Highly contaminated

SM3

SM1

SM5

SM8

SM4

SM6

SM9

SM11

SM7

SM13

SM12

SM10 SM14


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Figure 1. Map of Yemen showing collection sites.



Page 24 of 28

a)

A

B

C

b)

A

B

C

Figure 2. a) Clustering dendogram of genuine Sidr, polyfloral and non-Sidr honey. b) Clustering dendogram of the total honey samples from 1a) + 14 marketed samples. A= Genuine Side samples from lowland of Dawan valley. B= Genuine Sidr samples collected from mountainous areas of Dawan valley. C= Polyfloral and non-Sidr samples.


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Genuine Sidr

Polyfloral

Figure 3. PCA of total honey samples. O = Genuine Sidr honey,

● = Polyfloral honey, ●= Claimed to be Sidr honey and ● = Non-

Sidr honey. It should be noted that ellipses are do not denote statistical significance but are rather for better visibility of clusters discussed. The sample NS4 was off scale and was excluded from the PCA model due to its high leverage effect.



a)

b)

Figure 4. Identification of Sidr honey samples admixed with polyfloral honey; PF3 (a) and PF4 (b) using the SIMCA model.


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Figure 5. Authentication of marketed Sidr honey samples using the SIMCA model.



TABLE OF CONTENTS GRAPHIC


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Authentication of Monofloral Yemeni Sidr Honey Using Ultraviolet

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