Food Chemistry 170 (2015) 234–240

Contents lists available at ScienceDirect

Food Chemistry journal homepage: www.elsevier.com/locate/foodchem

Differentiation of Anatolian honey samples from different botanical origins by ATR-FTIR spectroscopy using multivariate analysis Seher Gok a, Mete Severcan b, Erik Goormaghtigh c, Irfan Kandemir d, Feride Severcan a,⇑ a

Department of Biological Sciences, Middle East Technical University, 06531 Ankara, Turkey Department of Electrical and Electronic Engineering, Middle East Technical University, 06531 Ankara, Turkey c Center for Structural Biology and Bioinformatics, Laboratory for the Structure and Function of Biological Membranes, Université Libre de Bruxelles, Brussels, Belgium d Department of Biology, Ankara University, Ankara, Turkey b

a r t i c l e

i n f o

Article history: Received 27 March 2014 Received in revised form 8 August 2014 Accepted 10 August 2014 Available online 20 August 2014 Keywords: Honey Botanical origin ATR-FTIR spectroscopy Multivariate analysis Hierarchical Cluster Analysis Principal Component Analysis

a b s t r a c t Botanical origin of the nectar predominantly affects the chemical composition of honey. Analytical techniques used for reliable honey authentication are mostly time consuming and expensive. Additionally, they cannot provide 100% efficiency in accurate authentication. Therefore, alternatives for the determination of floral origin of honey need to be developed. This study aims to discriminate characteristic Anatolian honey samples from different botanical origins based on the differences in their molecular content, rather than giving numerical information about the constituents of samples. Another scope of the study is to differentiate inauthentic honey samples from the natural ones precisely. All samples were tested via unsupervised pattern recognition procedures like hierarchical clustering and Principal Component Analysis (PCA). Discrimination of sample groups was achieved successfully with hierarchical clustering over the spectral range of 1800–750 cm 1 which suggests a good predictive capability of Fourier Transform Infrared (FTIR) spectroscopy and chemometry for the determination of honey floral source. Ó 2014 Elsevier Ltd. All rights reserved.

1. Introduction Honey characteristics depend primarily on the botanical origin of nectar. Floral source of the nectar predominantly affects the chemical composition of honey in terms of its protein, carbohydrate, enzyme, mineral and organic acid content. It is known that there are more than 100 different botanical origins for the honey. According to the Codex Alimentarius Standard for Honey and the European Union Council Directive related to honey; ‘‘The use of a botanical designation of honey is allowed if it originates predominantly from the indicated floral source’’. Botanical denomination is used for the presentation of more than 50% of honey products. Particularly, unifloral honey has a higher demand and commercial value in the market. However, 60% of indications related to floral origin made by beekeepers are incorrect. Therefore reliable authentication by analytical techniques is important for certification and quality control of honey (Bryant & Jones, 2001). In the European Union the composition, manufacture and marketing of honey is regulated by the Community Directive 74/409/EEC. As a community standard, information referring to honey’s geographical and floral origin must be supplemented (Radovic, Goodacre, & ⇑ Corresponding author. Tel.: +90 0312 210 5157; fax: +90 0312 210 7970. E-mail address: [email protected] (F. Severcan). http://dx.doi.org/10.1016/j.foodchem.2014.08.040 0308-8146/Ó 2014 Elsevier Ltd. All rights reserved.

Anklam, 2001). Authentication of honey has primary importance for both industries and consumers. Demand for ‘‘natural’’ honey has been increasing especially in medical market due to its therapeutic effects. In addition, from the economic perspective, authentication is needed to avoid unfair competition which can lead to a destabilization in market (Cordella, Moussa, Martel, Sbirrazzuoli, & Lizzani-Cuvelier, 2002). Turkey has suitable geographical and climatic conditions for apiculture where approximately 6,600,000 hives resided and lead to a production of 94,694 tones of honey in the year 2013 (TUIK, 2013) and one of the most important honey producer and exporter in the worldwide. Anatolian honeys are rich in pollen types per sample and 85% of the world’s floral types can be found in Turkish honeys. Despite the great diversity of honeys produced in Anatolia, there have been limited studies for the characterisation and classification by botanical or geographical origin. Also these publications are limited to compositional analysis (Kayacier & Karaman, 2008; Kucuk et al., 2007; Senyuva et al., 2009; Silici & Gokceoglu, 2007). With this study, for the first time we have classified the wide range of different authenticated floral types of honey from Anatolia using spectroscopic and chemometric methods. Many analytical techniques have been applied for reliable authenticity testing of honey like high performance liquid chromatography (HPLC) (Swallow & Low, 1990), nuclear magnetic

S. Gok et al. / Food Chemistry 170 (2015) 234–240

resonance (NMR) (Lindner, Bermann, & Gamarnik, 1996), gas chromatography (Low & South, 1995) and carbon isotope ratio analysis (White, Winters, Martin, & Rossmann, 1998). These techniques used for reliable honey authentication are mostly time consuming and expensive. Additionally, they cannot provide 100% success in authentication. Infrared (IR) spectroscopy has been preferred as a rapid, non-destructive, reagent-free, operator independent and cheap technique in food industry for the quantification of various food samples (Chalmers & Griffiths, 2002; Li-Chan, Chalmers, & Griffiths, 2010). IR spectroscopy was applied in different honey samples for the determination of botanical or geographical origin, detection of adulteration and for the quantification of fructose, glucose, sucrose, maltose, pH value and electrical conductivity (Chung, Ku, & Lee, 1999; Lichtenberg-Kraag, Hedtke, & Bienefeld, 2002; Ruoff, 2006; Tewari & Irudayaraj, 2004). Chemometric methods based on Fourier Transform Infrared (FTIR) spectroscopy were also used for honey adulteration (Rios-Corripio, Rojas-López, & Delgado-Macuil, 2012; Subari, Saleh, Shakaff, & Zakaria, 2012) and characterisation with limited number of Mexican honeys (Rios-Corripio, Rios-Leal, Rojas-López, & Delgado-Macuil, 2011). Etzold and Lichtenberg-Kraag (2008) have developed FTIR based PCA calibration models with German honeys. Although, there have been many attempts for searching alternative methods for authentication of honey, some of these studies have been limited with certain number of unifloral honey sources and have not been tested sufficiently with polyfloral samples (Ruoff, 2006) and with Anatolian honeys specifically. In the current study, it was aimed to estimate botanical origin of honey samples that are specific to Anatolia by applying two different multivariate analysis techniques to the Attenuated Total Reflectance (ATR)-FTIR spectroscopic data. With this work, it was intended to exemplify the usage and success of ATR-FTIR spectroscopy coupled with multivariate analysis in botanical origin assignment with a high number of sample groups. This work will also provide basis to honey adulteration determination studies. 2. Experimental 2.1. Samples A total of 144 honey samples were collected from different geographical regions of Turkey. The majority of samples used in this study were procured from well known certificated honey brands which have BRC (British Retail Consortium) certificate and officially declare that honeys are subjected to all chemical and physical analysis to detect quality and purity in addition to descriptive organoleptic and microscopic analysis to determine floral and regional origins. Some samples were collected directly from the primary producers. The region and origin of production were known for all samples. Flower originated (polyfloral (n = 30), anzer (n = 3), organic (n = 13), Taurus flower (n = 6)), tree originated (pine (n = 22), chestnut (n = 10), cedar (n = 6)) and rhododendron honey (n = 30) samples in addition to fake (adulterated) honey (n = 6), maple syrup (n = 6), fructose syrup (n = 6) and grape molasses (n = 6) samples were included in the study. The sample size of each group is indicated as ‘‘n’’. Honey samples were grouped as tree and flower originated ones basically. Flower originated group is composed of polyfloral honeys, collected from different regions of Turkey, which are Anzer honey, organic flower honey and Taurus flower honey. Anzer honey, composed of nectar, mainly collected from Anzer plant (thymus species) in the narrow region located in Rize/Ikizdere/ Anzer. It has been largely studied in terms of its medicinal properties that cause its market price to be 10 times higher than other honeys. Organic honeys are collected from the well known brands

235

which have ‘‘organic’’ certificate. Taurus flower honey is collected from the Taurus Mountains located at the south Mediterranean region of Anatolia. Rhododendron honey, locally called as ‘‘mad honey’’ or ‘‘toxic honey’’, is made up of spring flowers of Rhododendron ponticum (rhododendron plant). Rhododendrons mainly grow in the eastern Black Sea Region of Turkey. Their phenolic content and antimicrobial activities are quite different from the other honey plant species. Nectar contains andromedotoxin, which causes various physiological effects in humans (Onat, Yegen, Lawrence, Oktay, & Oktay, 1991). Chestnut honey is produced from both nectar and secretum collection by honey bee. These are collected from various regions of Anatolia. Pine honey is a kind of honeydew honey. It is produced via using the secretum of an insect (Marchalina hellenica) living in the trunk of pine tree and collected by bees. Pine honey is a specific endemic product, and can be found only in Turkey and Greece. Cedar honey, used in this study, was collected from the Taurus Mountains in the Mediterranean region of Turkey, and is mainly originated from Cedar trees. Fake (adulterated) honey used in this study was collected from Apis mellifera. Hives were fed with sugar (sucrose) syrup thus sugar was incorporated into the honey via bee-feeding. Study in this field has shown that adulteration is also possible via bee-feeding syrups and this can cause chemical modifications of the honey quality similar to artificial adulteration via direct syrup incorporation to honey (Cordella, Militão, Clément, Drajnudel, & Cabrol-Bass, 2005). Maple syrup produced from the xylem sap of maple tree and contains primarily sucrose and water. Maple syrups retrieved to the study are Canadian origin and were used as a non-honey control group. Fructose syrup was directly purchased from the market. Grape molasses (pekmez) is traditional syrup produced by boiling of the pressed grape juice and special grape soil mixture or cream of lime. It is rich in both carbohydrates and minerals. 2.2. Instrumentation and sample analysis Spectra from all samples were collected in the one-bounce ATR mode in a Spectrum 100 FTIR spectrometer (Perkin-Elmer Inc., Norwalk, CT, USA) equipped with a Universal ATR accessory. Samples were placed on Diamond/ZnSe crystal plate (PerkinElmer) and scanned from 4000 to 650 cm 1 for 50 scans with resolution of 4 cm 1 at room temperature. Each sample was replicated three times. Identical spectra were obtained in each case. This process was done to see the accuracy of the absorbance values, which might be affected from intra-sample variability and from variation in experimental conditions. Average spectra were used for further analysis. Data manipulations were carried out via Spectrum 100 software (Perkin-Elmer). 2.3. Chemometrics Cluster and Principal Component Analysis were applied to classify the samples based on spectral differences. For the determination of spectral differentiation among studied groups, cluster analysis was performed via OPUS 5.5 software (Bruker Optics, GmbH). Vector normalised, first derivative of each spectrum in the range of 1800–750 cm 1 was used as an input data. Spectral distances were calculated between pairs of spectra as Pearson’s correlation coefficients and Euclidean distance was used to calculate the sample similarities and to indicate the complete linkage clustering by Ward’s algorithm. Principle Component Analyses (PCA) was used as a data reduction method where each spectrum, which consists of hundreds of

236

S. Gok et al. / Food Chemistry 170 (2015) 234–240

absorbance values, is represented by a point in a multidimensional space using a linear transformation. In this work, PCA was conducted on the ATR-FTIR spectra over 4000–650 cm 1 1700–1600 cm 1, 1175–940 cm 1 and 940– 700 cm 1 range using by ‘‘Kinetics’’, a custom made program running under MATLAB (Matlab, Mathworks Inc.). 3. Results & discussion In the current study, ATR-FTIR spectroscopy has been used to compare honey samples based on their spectral differences in the 4000–650 cm 1 spectral region. A representative ATR-FTIR spectrum of honey is given in Fig. 1. Table 1 presents the band assignments along with the corresponding modes of vibrations in the ATR-FTIR spectrum of honey, based on the literature (GallardoVelázquez, Osorio-Revilla, Loa, & Rivera-Espinoza, 2009; Kelly, Downey, & Fouratier, 2004; Movasaghi, Rehman, & Rehman, 2008; Sivakesava & Irudayaraj, 2001; Subari et al., 2012; Tewari & Irudayaraj, 2004, 2005). Fig. 2 shows comparative infrared spectra of all samples in the 4000–650 cm 1 region. In this figure, spectral differences between the groups were clearly seen. Based on the spectral differences Hierarchical Cluster Analysis (HCA) and PCA have been applied to different spectral regions. Use of chemometrics together with classical methods for the

classification of different honey samples has been proposed in previous researches. In 1960, discriminant functions of monosaccharide and ash content in addition to pH values were used for classification of honey samples (Kirkwood, Mitchell, & Smith, 1960). Linear discriminant analysis was employed to select most useful measurands by evaluating different sugars, water, pH value, colour, diastase enzyme activity conductivity and hyroxymethylfurfural content. Later, by using pH value, free acidity, electrical conductivity, fructose, glucose and raffinose contents, botanical origins of honeys were estimated perfectly (Devillers, Morlot, Pham-Delegue, & Dore, 2004). In addition, flower honey was characterised by high concentration values for glucose and fructose and low free acidity, polyphenol content, lactone quantity and electrical conductivity; whereas honeydew honeys have low concentration glucose and fructose while showing high free acidity, polyphenol content, lactone quantity and electrical conductivity (Sanz, Gonzalez, de Lorenzo, Sanz, & Martinez-Castro, 2005). The algorithms behind the cluster and Principal Component Analysis that were used in the current study are quite different. PCA-like techniques can be preferred primarily for the determination of general relationship among data (Gasper et al., 2010). However, if one wants to show the grouping of similar data gathered from different samples, cluster analysis must be performed (Wang & Mizaikoff, 2008). Similar samples tend to be classified in the same cluster and the level of difference between the clusters

Fig. 1. Representative ATR-FTIR spectrum of honey in the 4000–650 cm

1

spectral region.

Table 1 General band assignment of ATR-FTIR spectrum of honey. The related references are indicated in the parenthesis. Region 1

3000–2800 cm

1

Region 2

1700–1600 cm

1

Region 3

1540–1175 cm

1

Region 4

1175–940 cm

Region 5

940–700 cm

1

1

C–H stretching (carbohydrates) (Gallardo-Velázquez et al., 2009) O–H stretching (carboxylic acids) (Movasaghi et al., 2008) NH3 stretching (free amino acids) (Gallardo-Velázquez et al., 2009; Sivakesava & Irudayaraj, 2001) O–H stretching/bending (water) (Cai & Singh, 2004; Stuart, 1997) C@O stretching (mainly from carbohydrates) (Gallardo-Velázquez et al., 2009) N–H bending of amide I (mainly proteins) (Philip, 2009) O–H stretching/bending (Gallardo-Velázquez et al., 2009; Tewari & Irudayaraj, 2004) C–O stretching (carbohydrates) (Tewari & Irudayaraj, 2004) C–H stretching (carbohydrates) (Tewari & Irudayaraj, 2005) C@O stretching of ketones (Tewari & Irudayaraj, 2004) C–O & C–C stretching (carbohydrates) (Subari et al., 2012; Tewari & Irudayaraj, 2005) Ring vibrations (mainly from carbohydrates) (Gallardo-Velázquez et al., 2009; Tewari & Irudayaraj, 2004) Anomeric region of carbohydrates (Mathlouthi & Koenig, 1986; Subari et al., 2012) C–H bending (mainly from carbohydrates) (Gallardo-Velázquez et al., 2009; Kelly et al., 2004; Tewari & Irudayaraj, 2004) Ring vibrations (mainly from carbohydrates) (Tewari & Irudayaraj, 2004)

237

S. Gok et al. / Food Chemistry 170 (2015) 234–240

Fig. 2. Comparative ATR-FTR spectra of all samples in the 4000–650 cm

1

spectral region. Spectra were normalised to the band located at 3300 cm

Fig. 3. Hierarchical clustering of all samples in the 1800–750 cm

is indicated with heterogeneity values (Ward, 1963). Ward’s algorithm was previously reported to give one of the best predictions, among the different methods used in cluster analysis (Lasch, Haensch, Naumann, & Diem, 2004; Severcan, Bozkurt, Gurbanov, & Gorgulu, 2010). As opposed to other methods, algorithm tries to find groups which are as homogeneous as possible. This means

1

1

.

(fingerprint) spectral region.

that only two groups, which show the smallest growth in heterogeneity factor H, are merged. Detailed information about this method was reported in Severcan et al. (2010). In this study, in order to reduce the number of variables prior to performing cluster analysis, we used the PCA. This was conducted on four different regions. Depending on the PCA outputs, the

238

S. Gok et al. / Food Chemistry 170 (2015) 234–240

Fig. 4. PCA scatter plots for all of the samples over 4000–650 cm confidence factor of 0.8.)

1

(A), 1700–1600 cm

regions having the highest principal component (PC) values were selected for HCA. Other spectral regions, used for PCA analysis, were also tried for hierarchical clustering of the sample groups. However, the best differentiation was achieved only in the 1800–750 cm 1 region. This region includes the anomeric region at 950–750 cm 1 which was frequently preferred for the spectral analysis of carbohydrates in IR spectroscopy. Analysis in this range makes it possible to distinguish bands characteristic for a and b conformers or pyranoid and furanoid ring vibrations of mono and polysaccharides (Mathlouthi & Koenig, 1986). In addition to alpha and beta conformers, the fingerprint region (1800– 750 cm 1) contains other contributions that arise from different molecules. Especially water (around 1640 cm 1) and minute amount of protein molecules give bands in the indicated region. Also the differences among honey samples can be related not only to different water content in the different honeys but also to the interaction between water molecules and carbohydrates, depending on their structure. The precise assignment of bands in this region cannot be stated unequivocally. However fingerprint region provides a unique spectrum for each compound where the position and intensity of the bands are specific for every polysaccharide (Filippov, 1992; Li-Chan et al., 2010). Therefore,

1

(B), 1175–940 cm

1

(C), and 940–700 cm

1

(D) spectral region. (Ellipses have a

1800–750 cm 1 spectral region was selected for successful discrimination of clusters. For the calculation of sample similarities, the Euclidean distance was used indicating the complete linkage clustering values. The results obtained are represented in Fig. 3 in the form of dendrograms. Clear cut classes were gathered over the range of 1800– 750 cm 1 with high heterogeneity values (up to 10). All of the tree originated samples (chestnut, cedar, pine) are aggregated in one cluster on the left arm. As the maple syrup is also the maple tree originated sample, it shows more similarity to tree originated group than to the flower originated ones. One arm of the second cluster is composed of flower originated honey samples including polyfloral, anzer, organic, Taurus flower and rhododendron honeys. Anzer, organic and Taurus flower honeys are region specific samples, it is known that the purity of their botanical origins is higher than polyfloral honey group. So they were clustered in the same arm. As the rhododendron honey is collected from the Black Sea Region mountains, it was clustered closer to that group than polyfloral ones. Fructose syrup, grape molasses and the fake (adulterated) honey were agglomerated on the far right arm of the second cluster in that they differ from the natural samples in terms of their carbohydrate content significantly.

S. Gok et al. / Food Chemistry 170 (2015) 234–240

Based on spectral differences a mean-centered PCA was conducted to all samples on the infrared spectra over the range of 4000–650 cm 1 (Fig. 4A), 1700–1600 cm 1 (Fig. 4B), 1175– 940 cm 1 (Fig. 4C) and 940–700 cm 1 (Fig. 4D). Distinct segregation and clustering between the samples were apparent in all figures. Samples were grouped close together creating uniform clusters for each of the analysed honey types in the PCA scatter plot. PCA is a data reduction method where each spectrum, which consists of hundreds of absorbance values, is represented by a point in a multidimensional space using a linear transformation. The coordinates of the point are the principal components (PC) and the plot obtained is called the scores plot. The transformation matrix in PCA consists of the eigenvectors of the covariance matrix of the whole set of spectra. Thus, each spectrum can be represented as the sum of a number of weighted orthonormal eigenvectors, where the weights are the PCs. In PCA the eigenvectors are ordered such that corresponding eigenvalues appear in decreasing order. Since the eigenvalues represent the variance of the corresponding eigenvectors, the first eigenvector describes the highest part of variability; the second eigenvector describes the second largest source of variability, and so on. Therefore, the corresponding scores defining a specific spectrum indicate the amount of contribution of each eigenvector to this spectrum. An important property of PCA is that no other orthogonal transformation can give smaller error (in mean squared sense) than that of PCA if the number of transform coefficients is truncated at some value. In practice, the first 3 or 4 PCs are sufficient to represent an FTIR spectrum and observe the significant differences among spectra. Therefore, scores of similar spectra in a multidimensional score plot are clustered. On the other hand, as the dissimilarity of clusters increases the clusters are separated from each other. PCA was applied to FTIR spectra of all groups, obtaining an evident discrimination (score plot) of the different pure honey samples with respect to the fake honey, grape molasses, fructose and maple syrups (Fig. 4A). In practice, it is more convenient to plot 2-D plots of any two of the PCs. Such a plot is the projection of a multidimensional plot onto a 2-D space. Therefore it may be possible to have overlaps of clusters in one plot and one has to check other combinations of PCs in other plots to observe cluster separations. Here, successful significant differentiation of all investigated groups has been achieved in a single 2D plot. Only rhododendron samples make penetrations to both tree and flower originated groups. This result is in consistence with the fact that rhododendron can be classified as brier, therefore, located closer to both tree and flower originated samples. Infrared modes of water are very intense and may overlap with the carbohydrate modes. The major infrared bands of water located at 3920 cm 1, 3490 cm 1 and 3280 cm 1 for O–H stretching and 1645 cm 1 for H–O–H bending vibrations (Stuart, 1997). In the scope of this study, primary goal is the discrimination of honey samples coming from different floral origins rather than giving numerical information about the constituents of samples. Also as it can be seen in the general honey spectrum (Fig. 1), vibrations coming from bulk water at 2250 cm 1 are negligible. So the water in the samples is mainly bounded water. Water content itself can be used as a parameter for honey characterisation (Manikis & Thrasyvoulou, 2001; Persano Oddo & Piro, 2004). Thus, vibrations coming from water may also contribute the discrimination success in 4000–650 cm 1 region, in addition the differences in carbohydrate content and structure of samples. A clear splitting of the data can be observed as depicted in Fig. 4B, by the first two principal components in the 1700– 1600 cm 1 region. This describes, 97.1% of the total variance for PC1 and 2.4% for PC2. Studies revealed that pollen proteins can be used as a marker for botanical classification of honey. At least nineteen different protein bands were visualised by SDS–PAGE experiments in honeys of different botanical origin; molecular

239

Fig. 5. The first 4 eigenvectors (PCA loading spectra) for the spectral region 1175– 940 cm 1.

weights of proteins in honey can vary depending on the honeybee species (Won, Lee, Ko, Kim, & Rhee, 2008). Immunological characterisation of honey major protein and its application have been reported by Won, Li, Kim, and Rhee (2009). The bands in the region 1700–1600 cm 1 had been previously assigned as amide I vibrations of the honey proteins (Philip, 2009). However, water molecules give strong absorption between 1640 and 1650 cm 1 (Cai & Singh, 2004) so the discrimination in this region can be explained by the difference in water content, protein content and water– carbohydrate interactions between sample groups. PCA results of the regions lying between the 1175–940 cm 1 and 940–700 cm 1 are given in Fig. 4C and D, respectively. Here highly successful discrimination of all samples is achieved due to differences in their carbohydrate content and structure. In addition, tree originated samples were separated from flower originated samples clearly, in both figures. The peaks observed in the loading spectra explain the chemical basis of the discrimination between different types of honey (Fig. 5). As mentioned above, PCA provides a decomposition of an FTIR spectrum X in terms of a set of eigenvectors (or PCA loading P vectors/spectra) Vi, as in X = ixiVi, where xi are the score values. This implies that for positive score values of xi positive peaks, and for negative score values of xi negative peaks of the PCA loading spectra Vi have significant contribution to the spectrum X. For example, using the PCA results for the interval 1175–940 cm 1 and the corresponding eigenvectors shown in Fig. 5, it can be seen that chestnut honey is characterised by a high score value on PC1 (which explains 79.7% of the total variance). PC1 has a strong contribution from the line at around 1020 cm 1 (carbohydrate band). This contribution is particularly high in chestnut honey. However, using only two PCs in general cannot provide a complete representation and contributions of other loading spectra may need to be taken into account to see how significant the observed contribution of the peak is. 4. Conclusion The results from the current study point out that there are many considerable variations in the spectral parameters of honey samples which come from different botanical origins. Especially

240

S. Gok et al. / Food Chemistry 170 (2015) 234–240

the variations in the water, carbohydrates and protein dominantly determine the specific spectral pattern of each sample. ATR-FTIR spectroscopy in combination with multivariate analysis enables the extraction of useful quantitative information via single rapid measurement for classification of botanical origin of honey samples. Based on spectral variations, successful differentiation was obtained with HCA and PCA. Results of this study exposed the potential power of ATR-FTIR spectroscopy in automated and highly sensitive botanical origin estimation of honey. It should be emphasized that some of the natural honeys (i.e., chestnut and Anzer) are relatively expensive. For instance in Turkey, the price of pine, flower, chestnut, rhododendron and Anzer honeys increase in the given order. As an example, the cost of pine honey is 20–30 $/kg and Anzer is 700 $/kg. In developing counties, due to the high cost of such honeys, adulterated honeys or fake honeys are put on the markets or even open markets with or without a brand. It is therefore very important to discriminate between natural and adulterated and/or fake honey types in developing countries, as in Turkey. Acknowledgement We would like to thank Mrs. Enise Gulsum Su, for supplying the Taurus flower honey samples (from Fethiye region) used in this study. References Bryant, V. M., Jr., & Jones, G. D. (2001). The R-values of honey: Pollen coefficients. Palynology, 25, 11–28. Cai, S., & Singh, R. B. (2004). A distinct utility of the amide III infrared band for secondary structure estimation of aqueous protein solutions using partial least squares methods. Biochemistry, 43, 2541–2549. Chalmers, J. M., & Griffiths, P. R. (2002). Handbook of vibrational spectroscopy (Vol. 5). Chichester: John Wiley & Sons. Chung, H., Ku, M. S., & Lee, J. S. (1999). Comparison of near-infrared and midinfrared spectroscopy for the determination of distillation property of kerosene. Vibrational Spectroscopy, 20(2), 155–163. Cordella, C., Militão, J. S. L. T., Clément, M. C., Drajnudel, P., & Cabrol-Bass, D. (2005). Detection and quantification of honey adulteration via direct incorporation of sugar syrups or bee-feeding: Preliminary study using high-performance anion exchange chromatography with pulsed amperometric detection (HPAEC-PAD) and chemometrics. Analytica Chimica Acta, 531(2), 239–248. Cordella, C., Moussa, I., Martel, A. C., Sbirrazzuoli, N., & Lizzani-Cuvelier, L. (2002). Recent developments in food characterization and adulteration detection: Technique-oriented perspective. Journal of Agricultural Food Chemistry, 50, 1751–1764. Devillers, J., Morlot, M., Pham-Delegue, M. H., & Dore, J. C. (2004). Classification of monofloral honeys based on their quality control data. Food Chemistry, 86, 305–312. Etzold, E., & Lichtenberg-Kraag, B. (2008). Determination of the botanical origin of honey by Fourier-transformed infrared spectroscopy: An approach for routine analysis. European Food Research and Technology, 227, 579–586. Filippov, M. P. (1992). Practical infrared spectroscopy of pectic substances. Food Hydrocolloids, 6(1), 115–142. Gallardo-Velázquez, T., Osorio-Revilla, G., Loa, M. Z., & Rivera-Espinoza, Y. (2009). Application of FTIR-HATR spectroscopy and multivariate analysis to the quantification of adulterants in Mexican honeys. Food Research International, 42, 313–3318. Gasper, R., Mijatovic, T., Bénard, A., Derenne, A., Kiss, R., & Goormaghtigh, E. (2010). FTIR spectral signature of the effect of cardiotonic steroids with antitumoral properties on a prostate cancer cell line. Biochimica et Biophysica Acta (BBA) – Molecular Basis of Disease, 1802(11), 1087–1094. Kayacier, A., & Karaman, S. (2008). Rheological and some physicochemical characteristics of selected Turkish honeys. Journal of Texture Studies, 39, 17–27. Kelly, J. F. D., Downey, G., & Fouratier, V. (2004). Initial study of honey adulteration by sugar solutions using mid-infrared (MIR) spectroscopy and chemometrics. Journal of Agricultural Food Chemistry, 52, 33–39. Kirkwood, K. C., Mitchell, T. J., & Smith, D. (1960). An examination of the occurrence of honeydew in honey. Analyst, 85, 412–416. Kucuk, M., Kolayli, S., Karaoglu, S., Ulusoy, E., Baltaci, C., & Candan, F. (2007). Biological activities and chemical composition of three honeys of different types from Anatolia. Food Chemistry, 100(2), 526–534.

Lasch, P., Haensch, W., Naumann, D., & Diem, M. (2004). Imaging of colorectal adenocarcinoma using FTIR microspectroscopy and cluster analysis. Biochimica Biophysica Acta, 1688, 176–178. Li-Chan, E., Chalmers, J. M., & Griffiths, P. (2010). Applications of vibrational spectroscopy in food science. 1 Instrumentation and fundamental applications. Chichester: John Wiley & Sons. Lichtenberg-Kraag, B., Hedtke, C., & Bienefeld, K. (2002). Infrared spectroscopy in routine quality analysis of honey. Apidologie, 33(3), 327–337. Lindner, P., Bermann, E., & Gamarnik, B. (1996). Characterization of citrus honey by deuterium NMR. Journal of Agricultural Food Chemistry, 44, 139–140. Low, N. H., & South, W. (1995). Determination of honey authenticity by capillary gas chromatography. Journal of AOAC International, 78, 1210–1218. Manikis, I., & Thrasyvoulou, A. (2001). The relation of physico-chemical characteristics of honey and the crystallization sensitive parameters. Apiacta, 36, 106–112. Mathlouthi, M., & Koenig, J. L. (1986). Vibrational spectra of carbohydrates. Advances in Carbohydrate Chemistry and Biochemistry, 44, 7–89. Movasaghi, Z., Rehman, S., & Rehman, I. (2008). Fourier transform infrared (FTIR) spectroscopy of biological tissues. Applied Spectroscopy Reviews, 43(2), 134–179. Onat, F. Y., Yegen, B. C., Lawrence, R., Oktay, A., & Oktay, S. (1991). Mad honey poisoning in man and rat. Reviews on Environmental Health, 1, 4–9. Persano Oddo, L., & Piro, R. (2004). Main European unifloral honeys: Descriptive sheets. Apidologie, 35, 38–81 (special issue). Philip, D. (2009). Honey mediated green synthesis of gold nanoparticles. Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy, 73(4), 650–653. Radovic, B. S., Goodacre, R., & Anklam, E. (2001). Contribution of pyrolysis mass spectrometry. (Py-MS) to authenticity testing of honey. Journal of Analytical and Applied Pyrolysis, 60, 79–87. Rios-Corripio, M. A., Rios-Leal, E., Rojas-López, M., & Delgado-Macuil, R. (2011). FTIR characterization of Mexican honey and its adulteration with sugar syrups by using chemometric methods. Journal of Physics: Conference Series, 274, 012098. Rios-Corripio, M. A., Rojas-López, M., & Delgado-Macuil, R. (2012). Analysis of adulteration in honey with standard sugar solutions and syrups using attenuated total reflectance-Fourier transform infrared spectroscopy and multivariate methods. CYTA – Journal of Food, 10(2), 119–122. Ruoff, K. (2006). Authentication of the botanical origin of honey (Doctoral dissertation). Swiss Federal Institute of Technology, Zurich. Sanz, M. L., Gonzalez, M., de Lorenzo, C., Sanz, J., & Martinez-Castro, I. A. (2005). Contribution to the differentiation between nectar honey and honeydew honey. Food Chemistry, 91, 313–317. Senyuva, H. Z., Gilbert, J., Silici, S., Charlton, A., Dal, C., Gurel, N., et al. (2009). Profiling Turkish honeys to determine authenticity using physical and chemical characteristics. Journal of Agricultural and Food Chemistry, 57(9), 3911–3919. Severcan, F., Bozkurt, O., Gurbanov, R., & Gorgulu, G. (2010). FT-IR spectroscopy in diagnosis of diabetes in rat animal model. Journal of Biophotonics, 3(8–9), 621–631. Silici, S., & Gokceoglu, M. (2007). Pollen analysis of honeys from Mediterranean regions of Anatolia. Grana, 46, 57–64. Sivakesava, S., & Irudayaraj, J. (2001). Prediction of inverted cane sugar adulteration of honey by Fourier transform infrared spectroscopy. Journal of Food Science, 66, 972–978. Stuart, B. (1997). Biological applications of infrared spectroscopy. Chichester, UK: ACOL Series, Wiley. Subari, N., Saleh, J. M., Shakaff, A. Y. M., & Zakaria, A. (2012). A hybrid sensing approach for pure and adulterated honey classification. Sensors, 12, 14022–14040. Swallow, K. W., & Low, N. H. (1990). Analysis and quantitation of the carbohydrates in honey using high-performance liquid chromatography. Journal of Agricultural Food Chemistry, 38, 1828–1832. Tewari, J., & Irudayaraj, J. (2004). Quantification of saccharides in multiple floral honeys using Fourier transform infrared microattenuated total reflectance spectroscopy. Journal of Agricultural Food Chemistry, 52, 3237–3243. Tewari, J. C., & Irudayaraj, J. M. K. (2005). Floral classification of honey using midinfrared spectroscopy and surface acoustic wave based z-Nose sensor. Journal of Agricultural Food Chemistry, 53, 6955–6966. _ Göstergeler. TUIK (Turkish Statistical Institute). (2013). Istatistiksel Wang, L., & Mizaikoff, B. (2008). Application of multivariate data-analysis techniques to biomedical diagnostics based on mid-infrared spectroscopy. Analytical and Bioanalytical Chemistry, 391(5), 1641–1654. Ward, J. H. (1963). Hierarchical grouping to optimize an objective function. Journal of American Statistical Association, 58, 236. White, J. W., Winters, K., Martin, P., & Rossmann, A. (1998). Stable carbon isotope ratio analysis of honey: Validation of internal standard procedure for worldwide application. Journal of AOAC International, 81, 610–619. Won, S.-R., Lee, D.-C., Ko, S. H., Kim, J.-W., & Rhee, H.-I. (2008). Honey major protein characterization and its application to adulteration detection. Food Research International, 41, 952–956. Won, S.-R., Li, C.-Y., Kim, J.-W., & Rhee, H.-I. (2009). Immunological characterization of honey major protein and its application. Food Chemistry, 113(4), 1334–1338.