ISSN 1313 - 8820 Volume 5, Number 4 December 2013
2013
Editor-in-Chief Tsanko Yablanski Faculty of Agriculture Trakia University, Stara Zagora Bulgaria Co-Editor-in-Chief Radoslav Slavov Faculty of Agriculture Trakia University, Stara Zagora Bulgaria Editors and Sections Genetics and Breeding Atanas Atanasov (Bulgaria) Ihsan Soysal (Turkey) Max Rothschild (USA) Stoicho Metodiev (Bulgaria) Nutrition and Physiology Nikolai Todorov (Bulgaria) Peter Surai (UK) Zervas Georgios (Greece) Ivan Varlyakov (Bulgaria) Production Systems Dimitar Pavlov (Bulgaria) Dimitar Panaiotov (Bulgaria) Banko Banev (Bulgaria) Georgy Zhelyazkov (Bulgaria) Agriculture and Environment Georgi Petkov (Bulgaria) Ramesh Kanwar (USA) Product Quality and Safety Marin Kabakchiev (Bulgaria) Stefan Denev (Bulgaria) Vasil Atanasov (Bulgaria) English Editor Yanka Ivanova (Bulgaria)
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Volume 5, Number 4 December 2013
ISSN 1313 - 8820
2013
AGRICULTURAL SCIENCE AND TECHNOLOGY, VOL. 5, No 4, pp 455 - 458, 2013
Product Quality and Safety
Study of bee honey by spectral analysis in the near infrared spectrum 1
2
I. Zhelyazkova *, S. Atanasova , K. Elencheva – Karaneycheva
3
1
Department of Animal Science – Non-ruminants and other animals, Faculty of Agriculture, Trakia University, 6000 Stara Zagora, Bulgaria Department of Biochemistry Microbiology and Physics, Faculty of Agriculture, Trakia University, 6000 Stara Zagora, Bulgaria 3 Institute of Animal Science, 2232 Kostinbrod, Bulgaria 2
Abstract. The objective of the present study is to examine samples of bee honey of different origin and create models for their classification through spectral analysis in the near infrared spectrum. Representative samples unifloral (sunflower, acacia, lime and coriander), multifloral and honeydew honey are used. The origin of the above honey samples has been determined in advance by pollen analysis. The spectral measurement (NIR spectroscopy) of each sample was done non-destructively, by measuring the diffuse reflection of the honey samples using the spectral device NIRQuest 512 within the range 900–1700 nm. The spectral differences between the different types of bee honey have been studied. For classification of the samples based on their spectra the SIMCA method (Soft Independent Modeling of Class Analogy) was used. Models for distinguishing different types of bee honey – unifloral, multifloral and honeydew – have been obtained. The results show 100% accuracy of classification of the samples from the three groups based on the received spectral information. The most compact is the group of honeydew honey.
Keywords: bee honey, near infrared (NIR) spectroscopy
Introduction The establishment of bee honey as valuable food product and the increase of its consumption require good knowledge on the conditions of manufacture and storage and the possible changes of the composition. Its quality as a subject of international trade is determined by two key regulatory documents: Council Directive 2001/110/ EC of 20 December 2001 relating to Honey (Kirillov, 2007). The regulations about our country stating the methods of analysis of bee honey are BDS 3050-80 and Ordinance № 48 of 2003. The traditional methods for determining the chemical composition of food products, including bee honey, are quite labor intensive and time consuming, require special skills, consumables and good analytical skills. This can be saved by using rapid analytical technique to detect easily any non-compliance with the regulatory requirements. As an alternative to the traditional methods the spectroscopic analysis in the near infrared spectrum can be used. Foley et al. (1998) think that the method has a number of advantages over the standard methods for conducting environmental studies. It is fast, inexpensive, does not require anything but a spectrophotometer, a software for processing the spectral data and a sample, preserves the integrity of the sample and it can be used for the other assays as well. Studies so far associated with the use of infrared spectroscopy for the analysis of bee honey can be divided into two groups. Some authors studied the potential of that method to determine the chemical composition (glucose, fructose, moisture, etc. content), and some physical properties such as electrical conductivity, optic activity, etc. (Garcia-Alvarez et al., 2000; Lichtenberg-Kraag et al.,2002; Chen et al., 2011). Another part of the studies is related to the ability to distinguish bee honey according to floral origin,
geographical origin and the existence of fake products (Drash et al., 2002; Corbella and Cozzolino, 2005; Kelly et al., 2006; Ruoff et al., 2006; Chen et al., 2012; Li and Yang, 2012). The objective of the present study is to examine samples of honey of different origin and to create models for their classification through spectral analysis in the near infrared spectrum.
Material and methods Representative samples of unifloral (sunflower, acacia, lime and coriander), multifloral and honeydew honey were used in the study. The origin of the above honey samples was determined in advance by pollen analysis. The spectral measurement (NIR spectroscopy) of each sample was performed non-destructively, by measuring the diffuse reflection of the honey samples with the spectral device NIRQuest 512. NIRQuest 512 of the company Ocean Optics is a portable scanning spectrophotometer operating in the range 900–1700 nm. It is a new generation of spectrophotometers using fiber optic and a diode ruler with 512 pixels as a detector. The spectrophotometer is connected to a computer via a USB port and is controlled through the software package SpectraSuite of the company Ocean Optics. Through the software you can choose the mode of measuring, the data format, to control measuring – time of measuring, number of scans, number of mean values, include correction of scattering, etc. The time for each measurement can vary from microseconds to seconds. NIRQuest spectral data are recorded as a text file and then opened in the software Pirouette 4.5 (Infometrix, Inc., USA), which is further used for processing the spectral data. To clarify the structure of the data analysis of the key components is used (Principal Component Analysis). In it spectral
* e-mail:
[email protected]
455
data are converted into new factors describing in a specific way the information in the spectral data. The first factor describes the maximum of the spectral information, the second one - maximum of the rest of the information, etc. Thus all the spectral information about the samples can be described by several factors (Principal components). To classify the samples based on their spectra the SIMCA
0.0002
Coriander Sunflower
Second derivative spectral data
0.00015
method (Soft Independent Modeling of Class Analogy) has been used. With this method samples are divided into classes on the basis of a known parameter. In our study samples are divided into three classes – unifloral, multifloral and honeydew honey. Afterwards a model for each class is made through analysis of the key components.
Lime Honeydew
Acacia Multifloral
0.0001 5E-05 0 1124
-5E-05
1254-1320 1186
0.0001
1400-1440
987 -0.00015 -0.0002 950
1050
1150
1250
1350
1450
1550
Wavelength, nm Figure 1. Mean values of the spectral data of the samples of bee honey of the studied types transformed as second derivative
Second derivative spectral data
0.00008
0.00003
0.00002
1188
1438 1280-1294
-0.00007 Polyfloral
Honeydew
-0.00012 950
1050
1150
1250
1350
1450
1550
Wavelength, nm Figure. 2. Mean values of the spectral data of the samples of multifloral and honeydew bee honey transformed as second derivative
456
Table 1. Parameter "Interclass distance" of SIMCA classification
Results and discussion Analysis of the spectra of samples of different types of bee honey The average values of the spectral data of the bee honey samples of the studied types transformed as the second derivative, are presented in Figure 1. There are differences both among unifloral, multiloral and honey dew honew, and among the different types of monofloral honey. The largest differences are observed around 987, 1124, 1186, the range 1254–1320 and 1400–1440 nm. The absorption of these wavelengths is related mainly to the O-H and C-H groups in carbohydrates. The differences are mainly related to the intensity of absorption, not the position of the absorption maxima, and can be accounted for by the different ratio of sugars in the different types of honey. Of the unifloral types of honey, a different spectrum was observed for lime honey. Differences in the absorption spectra between honeydew and other types of honey were observed predominantly in 2 spectral ranges – between 1280 and 1294 nm and around 1438 nm, as shown on Figure 2, which presents the average values of the spectral data for the samples of honeydew and multifloral honey. Classification of bee honey Models for quality distinction of bee honey of different types – unifloral, multifloral and honeydew – by the SIMCA method have been obtained. For each class a model has been constructed on the basis of spectral data for the samples forming the relevant class, and their transformation through analysis of the main components. A graphic illustration of the results obtained is presented in Figure 3. The results show 100% accuracy of the classification of the samples of bee honey from the three classes through the resulting SIMCA models based on the spectral information obtained. The most compact is the group of honeydew honey. The good distinction between the different types of honey based on the spectral values is confirmed by the parameter "Interclass distance", which indicates the distance between classes (Table 1). The biggest is the distance
Unifloral honey Multifloral honey Honeydew honey 0.00 2.42 5.46 Unifloral honey 2.42 0.00 2.78 Multifloral honey 2.78 0.00 Honeydew honey 5.46
CS2@12
CS1@15
CS3@8 - unifloral,
- multifloral,
– honeydew honey
Figure 3. SIMCA classification monofloral, polyfloral and honeydew honey.
250
909
Discriminating Power
200
925
150
1562 1073
1620
1264
100 1188 50
0 900
1000
1100
1200
1300
1400
1500
1600
1700
Wavelength, nm Figure 4. Graph of the parameter “Discriminating power” of SIMCA classification
457
between unifloral and honeydew honey. The graph of the parameter "Discriminating power" of the SIMCA classification is presented in Figure 4. The higher this parameter, the more significant the absorption of the respective wavelength for distinguishing the three groups of honey. The graph shows these significant wavelengths. The absorption of these wavelengths can be associated with the following chemical bonds: C-H at 909, 925, 1188 and 1620 nm; O-H at 1073 nm; and N-H at 1562 nm. The main part are ones with spectral differences observed between the different types of honey, as shown in Figure 1 and 2. The main differences between nectar and honeydew honey are related to the difference in the content of certain sugars, the mineral composition and the content of certain amino acids. For example, in honeydew honey dextrins are about 4–6 times more than in nectar honey and this accounts for its difficult crystallization. Honeydew honey is richer in minerals – their concentration is 1 to 1.5%, while in nectar honey they are about 0.4%. In honey there is a large number of free or bound amino acids and depending on their botanical origin, differences in the composition of amino acids contained in honey are observed. The most significant wavelengths to distinguish the three groups of honey can be explained by the differences in the composition of honeydew and nectar honey, as well as the differences among the different types of unifloral and multifloral honey.
Conclusion Differences have been found between the spectra in the near infrared area of the honey samples of the studied types both between unifloral, multifloral and honeydew honey and between the different types of unifloral honey. Of the unifloral types of honey, a more different spectrum is observed for lime honey. The results obtained from the SIMCA models show 100% accuracy of the classification of the samples of bee honey from the three groups. The most compact is the group of honeydew honey. The good distinction among the different types of honey based on the spectral values is confirmed by the parameter "Interclass distance", which indicates the distance between the classes.
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Determination of Chinese honey adulterated with high fructose corn syrup by near infrared spectroscopy. Food Chemistry, 128, 11101114. Chen L, Wang J, Ye Z, Zhao J, Xue X, Heyden Y and Sun Q, 2012. Classification of Chinese honeys according to their floral origin by near infrared spectroscopy. Food Chemistry, 135, 338-342. Corbella E and Cozzolino D, 2005. The use of visible and near infrared spectroscopy to classify the floral origin of honey samples produced in Uruguay. Journal of near infrared spectroscopy, 13, 6368. Council Directive 2001/ 110/ EC of 20 December 2001 Cozzolino D, Corbella E and Smyth H. 2011: Quality Control of Honey Using Infrared Spectroscopy: A Review, Applied Spectroscopy Reviews, 46, 7, 523-538. Drash L, Afik O, Shafik S, Schaffer A, Yeselson Y, Dag A and Landau S, 2002. Determination by near infrared spectroscopy of perseitol used as a marker for the botanical origin of avocado (Persea Americana Mill) honey. Journal of Agricultural and Food Chemistry, 50, 5283-5287. Foley WJ, McIlwee A, Lawler I, Aragones L, Woolnough AP and Berdig N, 1998. Ecological applications of near infrared reflectance spectroscopy: A tool for rapid, cost-effective prediction of the composition of plant and animal tissues and aspects of animal performance. Ecology, 116,293-305. Garcia-Alvarez M, Huidobro JF, Hermida M and RodrıguezOtero JL, 2000, Major Components of Honey Analysis by NearInfrared Transflectance Spectroscopy. Journal of Agriculture and Food Chemistry, 48, 5154-5158. Kelly JD, Petisco C and Downey G, 2006. Potential of near infrared transflectance spectroscopy to detect adulteration of Irish honey by beet invert syrup and high fructose corn syrup. Journal Near-Infrared Spectroscopy, 14, 139-146. Kirilov N, 2007. Bee products food and healing power. Enyovche Publishing House, Sofia (Bg). Li Y and Yang H, 2012. Honey Discrimination Using Visible and Near-Infrared Spectroscopy. ISRN Spectroscopy, 2012, Article ID 487040, 4 pages,doi:10.5402/2012/487040. Lichtenberg-Kraag B, Hedtke C and Bienefeld K, 2002. Infrared spectroscopy in routine quality analysis of honey. Apidologie, 33, 327-337. Regulation N 48/ 2003 – on the rules and methods of sample taking and methods used for analysis of bee honey, SG N 103/ 25 November 2003 (Bg). Ruoff K, Luginbuhl W, Bogdanov S, Bosset JO, Estermann B, Ziolko T and Amaro R, 2006. Authentication of the Botanical Origin of Honey by Near-Infrared Spectroscopy. Journal of Agriculture and Food Chemistry, 54, 6867-6872.
AGRICULTURAL SCIENCE AND TECHNOLOGY, VOL. 5, No 4, 2013
CONTENTS
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Genetics and Breeding Investigation on the possibility to efficiently use Ukrainian cultivars for developing of early winter wheat lines I. Grain productivity N. Tsenov, T. Petrova, E. Tsenova
351
Combinig ability for grain yield of late maize lines N. Petrovska
358
Use of recurrent selection in middle late synthetic maize population I. Results of the first cycle in synthetic “1/2005” N. Petrovska, V. Valkova
362
Genetic diversity and distance between two Bulgarian local sheep breeds assessed by microsatellite markers S. Georgieva, E. Todorovska, D. Hristova, I. Dimitrova, N. Stancheva, Ts. Yablanski
367
Testing of new Bulgarian sunflower hybrids under the conditions of North-East Bulgaria I. Productivity and traits related to productivity G. Georgiev, P. Peevska, E. Penchev
371
Comparative morphological study of new Burley tobacco lines T. Radoukova, Y.Dyulgerski
376
Effect of genotypic and environmental factors on the inheritance of the main characters in chickpea and relationships between them R. Sturzu, T. Nistot, Cr. Melucă, Fl. Bodescu, A. Stoilova
380
Evaluation of double haploid lines of winter malting barley using selection indices B. Dyulgerova, D. Valcheva
384
Evaluation of the combining ability of grain yield of mutant maize lines M. Ilchovska
388
Comparative study of some biochemical indicators in Karakachan and Copper-Red Shumen sheep breeds G. Angelov, I.Dimitrova, T. Mehmedov, P. Stamberov, N. Stancheva, S. Georgieva, Zh. Nakev
391
Nutrition and Physiology Impaired pancreatic function in mulard ducks with experimental aflatoxicosis I. Valchev, N. Grozeva, D. Kanakov, Ts. Hristov, L. Lazarov, R. Binev, Y. Nikolov
394
Comparative investigations on feeding efficiency in growing and fattening DanBred and Topigs hybrid pigs G. Ganchev, A. Ilchev
400
Blood parameters in yearling sheep fed Paulownia (Paulownia spp.) leaves I. Varlyakov, V. Radev, T. Slavov, G. Ganchev
405
AGRICULTURAL SCIENCE AND TECHNOLOGY, VOL. 5, No 4, 2013
CONTENTS
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Changes in some blood parameters in yearling rams fed diets with different protein and lipid levels V. Radev, T. Slavov, I. Varlyakov
410
Production Systems Effect of the sowing norm and nitrogen fertilization on the yield from dry bean (Phaseolus vulgaris L.) cultivar Beslet G. Milev
415
Evapotranspiration of corn crop for silage R. Bazitov, A. Stoyanova
420
Productivity and economic traits of winter oilseed rape (Brassica napus var. biennis) under the conditions of Dobrudzha G. Georgiev, G. Georgiev, P. Chamurliyski
424
Feasibility of the use of heat energy from alternative sources for air conditioning in sows facility K. Peichev, R. Georgiev
428
Productivity of green beans, irrigated at different pre-irrigation soil moisture R. Petrova, A. Matev, K. Koumanov, B. Harizanova-Petrova
432
Agriculture and Environment Comparative assessment of plant resources as substrates for bioshlam production Z. Shindarska, V. Kirov, G. Kostadinova, B. Baykov
438
The influence of organic carbon on bioremediation process of wastewater originate from aquaculture with use of microalgae from genera Botryococcus and Scenedesmus I. Sirakov, K. Velichkova, G. Beev, Y. Staykov
443
Sanitary hygienic assessment of drinking water from underground source at a pig farm G. Kostadinova
448
Product Quality and Safety Study of bee honey by spectral analysis in the near infrared spectrum I. Zhelyazkova, S. Atanasova , K. Elencheva – Karaneycheva
455
Comparative GC/MS analysis of lavender (Lavandula angustifolia Mill.) inflorescence and essential oil volatiles T. Zagorcheva, S. Stanev, K. Rusanov, I. Atanassov
459
Influence of key factors on the time of initial coagulation of cow's milk using milk-clotting enzyme of camel origin P. Panayotov, K. Yoanidu, P. Boyanova, B. Milenkov
463
Instruction for authors Preparation of papers Papers shall be submitted at the editorial office typed on standard typing pages (A4, 30 lines per page, 62 characters per line). The editors recommend up to 15 pages for full research paper ( including abstract references, tables, figures and other appendices) The manuscript should be structured as follows: Title, Names of authors and affiliation address, Abstract, List of keywords, Introduction, Material and methods,Results, Discussion, Conclusion, Acknowledgements (if any), References, Tables, Figures. The title needs to be as concise and informative about the nature of research. It should be written with small letter /bold, 14/ without any abbreviations. Names and affiliation of authors The names of the authors should be presented from the initials of first names followed by the family names. The complete address and name of the institution should be stated next. The affiliation of authors are designated by different signs. For the author who is going to be corresponding by the editorial board and readers, an E-mail address and telephone number should be presented as footnote on the first page. Corresponding author is indicated with *. Abstract should be not more than 350 words. It should be clearly stated what new findings have been made in the course of research. Abbreviations and references to authors are inadmissible in the summary. It should be understandable without having read the paper and should be in one paragraph. Keywords: Up to maximum of 5 keywords should be selected not repeating the title but giving the essence of study. The introduction must answer the following questions: What is known and what is new on the studied issue? What necessitated the research problem, described in the paper? What is your hypothesis and goal ? Material and methods: The objects of research, organization of experiments, chemical analyses, statistical and other methods and conditions applied for the experiments should be described in detail. A criterion of sufficient information is to be possible for others to repeat the experiment in order to verify results. Results are presented in understandable
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Мо, Greek = Gr, Georgian = Geor., Japanese = Jа, Chinese = Ch, Arabic = Аr, etc.) The following order in the reference list is recommended: Journal articles: Author(s) surname and initials, year. Title. Full title of the journal, volume, pages. Example: Simm G, Lewis RM, Grundy B and Dingwall WS, 2002. Responses to selection for lean growth in sheep. Animal Science, 74, 39-50 Books: Author(s) surname and initials, year. Title. Edition, name of publisher, place of publication. Example: Oldenbroek JK, 1999. Genebanks and the conservation of farm animal genetic resources, Second edition. DLO Institute for Animal Science and Health, Netherlands. Book chapter or conference proceedings:
Author(s) surname and initials, year. Title. In: Title of the book or of the proceedings followed by the editor(s), volume, pages. Name of publisher, place of publication. Example: Mauff G, Pulverer G, Operkuch W, Hummel K and Hidden C, 1995. C3variants and diverse phenotypes of unconverted and converted C3. In: Provides of the Biological Fluids (ed. H. Peters), vol. 22, 143-165, Pergamon Press. Oxford, UK. Todorov N and Mitev J, 1995. Effect of level of feeding during dry period, and body condition score on reproductive performance in dairy cows,IXth International Conference on Production Diseases in Farm Animals, Sept.11 – 14, Berlin, Germany, p. 302 (Abstr.). Thesis: Penkov D, 2008. Estimation of metabolic energy and true digestibility of amino acids of some feeds in experiments with muscus duck (Carina moshata, L). Thesis for DSc. Agrarian University, Plovdiv, 314 pp. The Editorial Board of the Journal is not responsible for incorrect quotes of reference sources and the relevant violations of copyrights. Ethics Studies performed on experimental animals should be carried out according to internationally recognized guidelines for animal welfare. That should be clearly described in the respective section “Material and methods”.
Volume 5, Number 4 December 2013
