Exploring Riboflavin Quantification in Honey via Spectrofluorimetry: A Statistical Examination of Influential Extrinsic Variables

Exploring Riboflavin Quantification in Honey via Spectrofluorimetry: A Statistical Examination of Influential Extrinsic Variables | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Exploring Riboflavin Quantification in Honey via Spectrofluorimetry: A Statistical Examination of Influential Extrinsic Variables Shahab Khan, Maaz Khan, Hamayun Khan, Hameed Ur Rahman, Mohamed Ragab AbdelGawwwad, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3875508/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract The present study is focused on the collection of honey samples from the different geographical and climatic conditions of Khyber Pakhtunkhwa and analyzing them for determination of riboflavin. Quantification of riboflavin being natively fluorescent, was accomplished using spectrofluorimetric method. Riboflavin has characteristic fluorescence spectra with maximum excitation at 464 nm followed by an emission peak at 525 nm. The procedure followed in this work comprised the construction of a calibration curve by plotting the fluorescence intensity of a series of riboflavin solutions versus concentration. This curve was used for quantification of riboflavin in the collected honey samples. The effect of several external factors such as the altitude of the sampling area, type of honey bee, type of flowers from which the nectar was collected, and sampling season on the concentration of riboflavin in the honey samples was statistically evaluated. It was concluded that the samples collected from lower altitudes have high concentration (1.156±0.08 μg g -1 ) of riboflavin. Similarly, the samples collected in autumn were found to have a with a maximum average riboflavin concentration of 1.37±0.06 μg g -1 , which was higher in comparison to the samples collected in other seasons of the year. Likewise, the effect of flora on the concentration of riboflavin was also investigated and it was found that honey samples collected from areas where the nectar was collected from Ziziphus contains maximum riboflavin concentration averaged at 1.383±0.1 μg g -1 . Based on the size of the honey bees the samples collected from hives of small honey bees were found to have maximum riboflavin concentration of 1.176±0.07 μg g -1 . This study suggests that beside the studied vitamin, the rest of the vitamins and other nutritional components may vary in the honey samples depending upon external factors. Riboflavin/vitamin B2 Spectrofluorometer Calibration curve Standard solution Statistical analysis Effect of altitude flower type season bee size. Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 1. Introduction Honey, often referred to as "liquid gold," is a natural product renowned for its diverse composition and numerous health benefits. Among the myriad of bioactive compounds found in honey, riboflavin (vitamin B2) plays a vital role in various metabolic processes within the human body.[ 1 ] Riboflavin is not only essential for our health but also serves as a potential marker for the quality and authenticity of honey. Spectrofluorimetric analysis is a powerful analytical technique that can be employed to quantify riboflavin levels in honey samples. However, the accuracy and reliability of such analyses can be influenced by a range of external factors, including environmental conditions and sample preparation methods. This study aims to explore the application of spectrofluorimetry for the quantification of riboflavin in honey and to statistically evaluate the impact of these external factors, shedding light on the robustness and precision of this analytical approach. In doing so, we aim to contribute to the advancement of honey quality control methods and provide valuable insights for both the honey industry and consumers. According to Pearson (1976), "Honey is the saccharine product gathered by the bees from the nectar of flowers" is how honey is defined. Honey is a naturally sweet material that honey bees make by regurgitating and evaporating floral nectar.[ 2 ] It can also be made from the flowers of plants, their secretions, or the excretions of plant-eating insects.[ 3 ] Honeybees, however, gather nectar, alter it into other substances of their own, store it in the honeycomb, and allow it to develop and mature there.[ 4 ] Bees make nectar-based blossom honey and honeydew honey from honeydew. Small insects that feed on plants produce honeydew.[ 5 ] Honey offers enticing chemical characteristics for baking and is noticeably sweeter than table sugar. Honey has almost the same relative sweetness as granulated sugar and derives its sweetness from the monosaccharide’s fructose and glucose. Some people prefer honey over sugar and other sweeteners because of its particular flavor.[ 6 ] Honey is a food that contains about 200 different chemical compounds.[ 7 , 8 ] These include sugars, water, proteins (enzymes), organic acids, vitamins (especially vitamin B6, thiamine, niacin, riboflavin, and pantothenic acid), minerals (including calcium, copper, iron, magnesium, manganese, phosphorus, potassium, sodium, and zinc)[ 9 , 10 ], pigments, phenolic compounds, a wide range of volatile compounds, and solid particles derived from various sources.[ 11 ] These nutrients play important roles in the functions of nucleic acids[ 12 ], due their intermolecular interactions.[ 13 ] Factors affecting composition of honey: According to a study of the literature on honey composition, the composition of the honey depends on the source of the nectar.[ 14 ] Additionally, the internal and external variables that affect nectar production are significant contributors to the composition of honey.[ 15 ] Some of the internal elements have been proposed to include the size of the bloom and the nectary surface, the age and maturity of the flower on the plant, the species, variety, or cultivated races to which the plant belongs.[ 16 ] Additionally significant outside variables are soil moisture, soil type, fertilizer application, temperature, wind, season, length of day, and sunlight. Similar investigations described the internal and environmental elements that affect nectar secretion. On the composition of honey, the impact of beekeeping practices, honey extraction, and processing by beekeepers or commercial producers have been discussed.[ 17 ] Additional aspects that were involved in the commercial processing have also been discussed.[ 18 ] The storage circumstances have a significant impact on the composition of honey, and storage-related alterations have been documented.[ 19 ] 2. Experimental Section Instrument: Spectroflourophotometer (RF-5301 PC, Shimadzu, Japan) having 150-watt Xenon lamp as excitation source with 1.0 cm quartz cell was used throughout the experimental work for fluorescence measurements. The emission and excitation slit were 4 nm for fluorimetric operation. Required chemicals were weighed using an electrical digital balance (Shimadzu ATY 224) and stirring of the reaction mixture was carried out with the help of magnetic heating stirrer (Heidolph). Borosilicate glassware and apparatus were used for the respective operation. Chemicals used in this study were riboflavin of analytical grade, glacial acetic acid, and distilled water obtained from distillation plant. The honey samples collection was made in the years 2022-2023. A total of twenty (20) honey samples from different agroecological zones were collected by taking into consideration the altitude of the sampling area, the season in which the honey was made, flowers of the area in the respective season, climatic conditions, bees’ types, and size, etc. All the samples were carried in glass stoppered vials from the concerned beekeepers and photodecomposition was prevented by storing the samples in a dark cupboard at ambient temperature until they were needed for laboratory analyses. The spectrofluorimetric method was employed to determine riboflavin in all the collected samples. In which the fluorescence intensity of riboflavin (RF) was measured at 525 nm following excitation at 464 nm. 2.1 Analysis of honey samples for the determination of riboflavin A calibration curve was constructed by plotting Fluorescence Intensity (FI) of a series of standard riboflavin (RF) solutions versus concentration for the analysis of honey samples. 2.2 Preparation of stock solution of RF: Riboflavin Stock Solution (100 ug/ml) was prepared by dissolving 0.01 g of riboflavin in 50 ml of distilled water. A few drops of glacial acetic acid were added because riboflavin is stable in its presence. Then it was transferred to 100 ml of a volumetric flask and diluted up to the mark. This stock solution was stored at 4ºC and was covered with a black plastic bag for prevention from photodecomposition. For the preparation of standard solution of RF for calibration curve, riboflavin working standards in the range of 0.005-0.3 µg ml -1 solution were prepared using the dilution formula C 1 V 1 = C 2 V 2 and FI of each solution was measured using a spectrofluorometer. The range of 0.005-0.3 µg ml -1 . These solutions were protected from light by covering them with a black plastic bag. Then fluorescence intensity was measured through a spectrofluorometer. 2.3 Preparation of sample solution and measurement of FI Preparation of sample solution: In a small beaker 5.0 g of each sample was taken. Dissolved it in 50 ml of distilled water added a few drops of Glacial acetic acid and diluted up to the mark in 100 ml of volumetric flask. Covered the sample solution with a black plastic bag to prevent photodecomposition of RF. Fluorescence measurement: Each sample was taken in a quartz cuvette and placed in the sample holder of the pre-calibrated spectrofluorometer and FI was measured at an emission wavelength of 525 nm following an excitation wavelength at 464 nm. The measurement was done in triplicates. Calculation of concentration of RF: The concentration of RF in each sample was calculated from the average FI using the straight-line equation of the calibration curve which is given below. Y = 2513.2x + 13.17 (1) Here Y is the fluorescence intensity of sample X in the above equation 1 is concentration which is calculated by rearranging equation 1 as X = (Y -13.17) / 2513.2 (2) To calculate the concentration of RF in µg g -1 of honey the following equation was used. Conc (µg g -1 ) = (X (µg mL -1 )) (100 ml)/5g (3) Conc (µg g -1 ) = X (µg g -1 ) (4) 3. Results and Discussion 3.1 Calibration curve for quantification of RF in honey samples In order to quantify the concentration of Riboflavin (RF) in honey samples, a crucial step was the construction of a calibration curve, also known as a working curve. This curve was meticulously constructed by plotting the Fluorescence Intensity (FI) against the concentration of standard Riboflavin solutions, spanning the range of 0.005 to 0.3 µg ml -1 . The results, which provide a clear insight into the relationship between FI and RF concentration, are presented in Table 1 and visually depicted in Figure 1. Table 1 outlines the calibration curve, detailing the corresponding FI values at various RF concentrations. Notably, as the RF concentration increases, so does the FI. This direct correlation between the two parameters is vital for the accurate quantification of Riboflavin in honey samples. Figure 1 complements the tabulated data, offering a graphical representation of this relationship, enabling an even more intuitive understanding of the calibration curve. Such a well-constructed calibration curve forms the cornerstone of precise analytical methods, allowing for the accurate determination of Riboflavin levels in honey samples, a fundamental aspect of quality control and authenticity assessment in the honey industry. Table 1 Calibration curve for quantification of RF in honey samples Concentration (µg ml -1 ) FI 0.005 15.153 0.008 22.795 0.01 29.497 0.03 92.374 0.05 139.844 0.08 228.754 0.1 276.864 0.2 533.608 0.3 747.449 3.2 Quantification of RF concentration in honey samples For the purpose of quantification of RF concentration, all the collected samples were analyzed by spectrofluorimetric measurement and subsequent calculation of RF in (µg g -1 ). Honey composition and its relationship with various environmental and seasonal factors are examined here. The data given in Table 2, present information about 20 honey samples, with altitude from sea surface (in feet), flower type, season of collection, bee size is considered as variable factor, and the concentration of riboflavin (RF) in each sample along with standard deviations. For instance, the concentration of riboflavin (µg g -1 ) varies considerably across the samples, ranging from as high as 2.74±0.02 in a Ziziphus honey collected at an altitude of 1497 feet, to as low as 0.08±0.02 in Brassica honey collected at an altitude of 4194 feet. The diverse flower types, seasons, and bee sizes also appear to have a potential influence on riboflavin content in honey. This dataset beckons further exploration and analysis, offering valuable insights into the multifaceted nature of honey composition and its dependence on external factors. It opens the door to questions regarding the significance of altitude, flower type, and bee size in determining the nutritional content and quality of honey. Such investigations can provide critical knowledge for honey producers and enthusiasts alike, contributing to a deeper understanding of this beloved natural sweetener. Table 2 Concentration of RF (µg g -1 ) in honey samples Sample No Altitude from sea surface (ft) Flower s Season B ee size Conc of RF (µg g -1 ) ±SD 1 1497 Ziziphus Jan-18 Small 2.74±0.02 2 2478 Ziziphus Nov17 Medium 1.34±0.04 3 3487 Multifloral Jul-17 Small 1.2±0.1 4 3407 Multifloral Apr-18 Small 1.32±0.02 5 5433 Commercial Dec-17 Unknown 0.26±0.02 6 4194 Brassica Apr-18 Medium 0.08±0.02 7 2353 Ziziphus Oct-18 Medium 1.4±0.02 8 3655 Brassica Apr-18 Medium 0.313±0.04 9 2310 Multifloral Apr-18 Medium 0.25±0.01 10 1805 Ziziphus Dec-17 Small 0.826±0.01 11 3655 Acacia Jun-17 Medium 1.167±0.04 12 2310 Acacia Jun-17 Medium 0.92±0.04 13 1897 Ziziphus Dec-17 Medium 1.74±0.02 14 1869 Acacia Jul-17 Small 0.67±0.05 15 1890 Acacia Jun-17 Medium 1.38±0.02 16 1517 Ziziphus Dec-17 Big 1.32±0.02 17 1880 Acacia Jun-17 Big 0.51±0.05 18 1805 Multifloral Apr-18 Medium 0.22±0.02 19 1880 Acacia Jun-17 Medium 1.04±0.02 20 4190 Ziziphus Jan-18 Small 0.34±0.04 3.3 Statistical analysis For statistical evaluation of the individual effect of different variables like altitude of sampling site, season of honey formation, Bee size, and flower type on the concentrationof RF (µg g -1 ), SPSS V. 23 was used. 3.3.1 Effect of altitude on RF concentration in honey The effect of the altitude of the sampling site on RF concentration in honey was investigated. Herethe dependent variable is RF concentration (µg g -1 ) whereas altitude is considered as theindependent variable. The results are given in Table 3 and shown in Figure 2 . In the results given in Table 3 , the coefficient of altitude = -0.271 indicates that there is negative effect of altitude on RF concentration i.e. for one thousand ft increase in altitude from sea level, an average of 0.271 µg g -1 decrease will occur in RF concentration. The p value = 0.044 indicates that the effect of altitude on the RF concentration in honey is statistically significant. Table 3 Effect of altitude on RF concentration in honey Model Coefficients t-value P-value β Std. Error (Constant) 1.674 0.360 4.654 0.000 Altitude -0.271 0.125 -2.170 0.044 a. Dependent variable: RF Concentration (µg g -1 ) b. Independent variable: Altitude 3.3.2 Effect of season of honey formation on RF concentration To study the effect of season on RF concentration in honey, RF concentration was taken asthe dependent variable while the season (i.e. winter, spring, summer and autumn) was considered as the independent variable. Since season is a categorical variable therefore, the concept of dummy variable is used in regression. As there are four categories of the season therefore three dummy variables, winter, spring, and summer were included in the regression and the left-over category i.e. autumn was taken as the base category. The results are given in Table 4 and shown in Figure 3. From the results given in Table 4 it can be observed that the value of left-over category of season (i.e. autumn) = 1.370 indicating that on the average RF concentration in honey is 1.370 (µg g -1 ) in autumn season. And P-value indicates that the result is statistically significant. The coefficients of winter season = -0.171 shows that RF concentration in winter season decreases by 0.171 (µg g -1 ) from autumn season, but is not statistically significant. Whereas the coefficient of spring season = -0.937 shows that RF concentration in spring season decreases by 0.937 (µg g -1 ) from autumn season and P-value = 0.048 indicates that the result is statistically significant. Similarly, the coefficient of summer season = -0.386 shows that RF concentration in summer season decreases by 0.386 (µg g -1 ) from autumn season and P-value = 0.044 indicates that the result is statistically significant. Table 4 Effect of sampling season on RF concentration in honey Model Coefficients t Significance β Std. Error (Constant) 1.370 0.436 3.141 0.006 Winter -0.171 0.504 -0.339 0.739 Spring -0.937 0.516 -2.316 0.048 Summer -0.386 0.495 -2.780 0.044 Dependent variable: Rf concentration (µg g -1 ) Independent variables: seasons 3.3.3 Effect of bee size on RF concentration in honey Investigating the effect of bee size on RF concentration in honey, the dependent variable is RF concentration whereas bee size (i.e. small, medium, big) is considered as the independent variable. Since bee size is a categorical variable therefore, the concept of dummy variable is used in regression. As there are three categories of the bee size therefore, 2 dummy variables middle and big are included in the regression and the left-over category i.e. small is taken as the base category. The results are given in table 5 and shown in Figure 4 . While the Table 5 shows the regression results of RF concentration on bee size. From the results it can be observed from the value of left-over category of bee size (i.e. small) = 1.176 indicating that on the average RF concentration in honey is 1.176 (µg g -1 ) from small size bee. The P-value indicates that the result is statistically significant. The coefficients of medium size bee = -0.333 which shows that RF concentration from medium size bee decreases by 0.333 (µg g -1 ) from small size bee and the results are statistically significant as the P-value = 0.033. Whereas, the coefficient of big size bee = -0.263 shows that RF concentration from big size bee decreases by 0.937 (µg g -1 ) from small size bee but the results are not statistically significant as clear from the P-value = 0.638. Table 5 Effect of bee size on RF concentration in honey Model Coefficients t P-value β Std. Error (Constant) 1.176 0.275 4.277 0.001 Medium -0.333 0.337 -0.990 0.033 Large -0.263 0.550 -0.479 0.638 Dependent variable: RF concentration (µg g -1 ) Independent variable: Bee size 3.3.4 Effect of flower type on RF concentration in honey The effect of flower type on RF concentration in honey was studied in which the dependent variable is RF concentration whereas flower type (i.e. Ziziphus, Multi floral , Brassica and Acacia flowers) is considered as the independent variable. Since flower type is a categorical variable therefore, the concept of dummy variable is used in regression. As there are four categories of the flower type therefore 3 dummy variables multi floral, brassica and acacia are included in the regression and the left-over category i.e. Ziziphus is taken as the base category. The results are given in the table 6 and shown in Figure 5 . While Table 6 shows the results of regression RF concentration on flower type. From the results it can be observed that the value of left-over category of flower type (i.e. Ziziphus ) = 1.242 indicating that on the average RF concentration in honey is 1.242 (µg g -1 ) from Ziziphus flower and P-value indicates that the result is statistically significant. The coefficients of multi floral samples = -0.497 which shows that RF concentration from multiflora flower is less than Ziziphus flower by 0.497 (µg g -1 ), and from P-value = 0.021 the result is statistically significant. Whereas, the coefficient of brassica flower = -1.052 which shows that RF concentration from brassica flower is less than Ziziphus flower by 1.052 (µg g -1 ) and P-value = 0.048 indicates that the result is statistically significant. Similarly, the coefficient of acacia flower = -0.294 which shows that RF concentration from acacia flower is less than Ziziphus flower by 0.294 (µg g -1 ). P-value indicates that the result is not statistically significant. Table 6 Effect of flower type on RF concentration in honey Model Coefficients t value Significance β Std. Error (Constant) 1.242 0.219 5.663 0.000 Multifloral -0.497 0.380 -1.307 0.021 Brassica -1.052 0.490 -2.144 0.048 Acacia -0.294 0.335 -0.876 0.394 Dependent variable: RF concentration (µg g -1 ) Independent variable: flower type 4. Conclusions The primary objective of this study was to gather honey samples from diverse geographical and climatic regions within Khyber Pakhtunkhwa and subsequently analyze them to determine their riboflavin content. The quantification of riboflavin, which inherently exhibits fluorescence, was achieved through the use of spectrofluorimetry. Riboflavin exhibits distinct fluorescence spectra, characterized by its maximum excitation at 464 nm, followed by an emission peak at 525 nm. Furthermore, we investigated the influence of various external factors on the riboflavin concentration in the collected honey samples. These factors included the species of honey bees, the altitude of the sampling locations, the types of flowers from which the nectar was sourced, and the seasons during which the samples were collected. Statistically, we evaluated how these factors affected the riboflavin levels in the honey. Our findings revealed that honey samples collected from lower altitudes exhibited a notably higher riboflavin concentration, averaging at 1.156 ± 0.08 µg g − 1 . Similarly, samples gathered during the autumn season consistently demonstrated the highest average riboflavin concentration, reaching 1.37 ± 0.06 µg/g − 1 , surpassing those collected during other seasons. Additionally, we delved into the influence of the floral source of nectar and discovered that honey samples obtained from regions where nectar was derived from Ziziphus plants boasted the highest riboflavin concentration, averaging at 1.383 ± 0.1 µg g − 1 . Moreover, we explored the connection between the size of honey bees and riboflavin concentration, finding that samples collected from hives of smaller honey bees displayed the maximum riboflavin concentration, measuring 1.176 ± 0.07 µg g − 1 . This study underscores, the composition of honey may vary significantly based on external factors, encompassing an array of vitamins and other nutritional constituents. Declarations Acknowledgment: The authors extend their appreciation to the Researchers supporting project number (RSP2023R349) King Saud University, Riyad, Saudi Arabia Declaration of interest: Not applicable Funding: The current study was supported by Researchers supporting project number (RSP2023R349) King Saud University, Riyad, Saudi Arabia References Li, S., Synthetic Bioactive Substances 33. Handbook of Food Chemistry 2015, 1061. Anumba, I.A., C.E. Akunne, B.U. Ononye, C.A. 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Cedon, Development barriers of stingless bee honey industry in Bicol, Philippines. International Journal on Advanced Science Engineering and Information Technology 2020, 10, Bhandari, P.L., R.R. Kattel, Value chain analysis of honey sub-sector in Nepal. International Journal of Applied Sciences and Biotechnology 2020, 8, 83-95. Aguiar, D., A.C. Pereira, J.C. Marques, The influence of transport and storage conditions on beer stability—a systematic review. Food and Bioprocess Technology 2022, 15, 1477-1494. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-3875508","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":268401573,"identity":"0896bcdf-116e-4884-9161-1755973b0052","order_by":0,"name":"Shahab 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Malakand","correspondingAuthor":false,"prefix":"","firstName":"Maaz","middleName":"","lastName":"Khan","suffix":""},{"id":268401575,"identity":"d1fa2bd9-66b6-44f5-ab92-146325002253","order_by":2,"name":"Hamayun Khan","email":"","orcid":"","institution":"University Lahore","correspondingAuthor":false,"prefix":"","firstName":"Hamayun","middleName":"","lastName":"Khan","suffix":""},{"id":268401576,"identity":"e9605f65-779e-4f6c-88b6-19ab0bad2fd8","order_by":3,"name":"Hameed Ur Rahman","email":"","orcid":"","institution":"University of Malakand","correspondingAuthor":false,"prefix":"","firstName":"Hameed","middleName":"Ur","lastName":"Rahman","suffix":""},{"id":268401577,"identity":"ed9ca834-875c-46c8-8057-fef48e7f5937","order_by":4,"name":"Mohamed Ragab AbdelGawwwad","email":"","orcid":"","institution":"International University of Sarajevo","correspondingAuthor":false,"prefix":"","firstName":"Mohamed","middleName":"Ragab","lastName":"AbdelGawwwad","suffix":""},{"id":268401578,"identity":"5fdd73fa-b1a8-4ed7-b67a-1c58711c091e","order_by":5,"name":"Mohamed Farouk Elsadek","email":"","orcid":"","institution":"King Saud University","correspondingAuthor":false,"prefix":"","firstName":"Mohamed","middleName":"Farouk","lastName":"Elsadek","suffix":""}],"badges":[],"createdAt":"2024-01-18 10:32:48","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3875508/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3875508/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":49961999,"identity":"a03ffd85-a274-4fe4-8aeb-7235d3747a30","added_by":"auto","created_at":"2024-01-22 10:11:34","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":34420,"visible":true,"origin":"","legend":"\u003cp\u003eCalibration curve of FI vs. RF concentration\u003c/p\u003e","description":"","filename":"1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3875508/v1/101805bb33043c77095bbec0.jpg"},{"id":49961816,"identity":"1459f09b-379e-4763-ba96-77d0424ce482","added_by":"auto","created_at":"2024-01-22 10:03:34","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":30357,"visible":true,"origin":"","legend":"\u003cp\u003eEffect of altitude on RF concentration in honey\u003c/p\u003e","description":"","filename":"2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3875508/v1/e5d65a8677a9a37afa27a2ca.jpg"},{"id":49962382,"identity":"873b9f56-0b44-491d-9995-5b536f690e54","added_by":"auto","created_at":"2024-01-22 10:19:34","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":34175,"visible":true,"origin":"","legend":"\u003cp\u003eEffect of sampling season on RF concentration in honey\u003c/p\u003e","description":"","filename":"3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3875508/v1/636c2dd7b7b0a85c87beb63a.jpg"},{"id":49962001,"identity":"72a2c905-5aa8-49c9-873a-4b6edcd18f25","added_by":"auto","created_at":"2024-01-22 10:11:34","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":31565,"visible":true,"origin":"","legend":"\u003cp\u003eEffect of bee type on RF concentration in honey\u003c/p\u003e","description":"","filename":"4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3875508/v1/ea7a01e044ee859b56f49e39.jpg"},{"id":49961819,"identity":"ec07ea16-5b09-44ce-b91e-13bb17dc5749","added_by":"auto","created_at":"2024-01-22 10:03:34","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":30779,"visible":true,"origin":"","legend":"\u003cp\u003eEffect of flower type on RF concentration in honey\u003c/p\u003e","description":"","filename":"5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3875508/v1/2566d92b1e984501efdfaf5a.jpg"},{"id":53995268,"identity":"300e6c11-1a00-46f0-a957-331403f110e3","added_by":"auto","created_at":"2024-04-03 07:05:33","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":480387,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3875508/v1/8eebcd03-614d-4cd4-b0a5-acf53e046d94.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Exploring Riboflavin Quantification in Honey via Spectrofluorimetry: A Statistical Examination of Influential Extrinsic Variables","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eHoney, often referred to as \"liquid gold,\" is a natural product renowned for its diverse composition and numerous health benefits. Among the myriad of bioactive compounds found in honey, riboflavin (vitamin B2) plays a vital role in various metabolic processes within the human body.[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e] Riboflavin is not only essential for our health but also serves as a potential marker for the quality and authenticity of honey. Spectrofluorimetric analysis is a powerful analytical technique that can be employed to quantify riboflavin levels in honey samples. However, the accuracy and reliability of such analyses can be influenced by a range of external factors, including environmental conditions and sample preparation methods. This study aims to explore the application of spectrofluorimetry for the quantification of riboflavin in honey and to statistically evaluate the impact of these external factors, shedding light on the robustness and precision of this analytical approach. In doing so, we aim to contribute to the advancement of honey quality control methods and provide valuable insights for both the honey industry and consumers. According to Pearson (1976), \"Honey is the saccharine product gathered by the bees from the nectar of flowers\" is how honey is defined. Honey is a naturally sweet material that honey bees make by regurgitating and evaporating floral nectar.[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e] It can also be made from the flowers of plants, their secretions, or the excretions of plant-eating insects.[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e] Honeybees, however, gather nectar, alter it into other substances of their own, store it in the honeycomb, and allow it to develop and mature there.[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e] Bees make nectar-based blossom honey and honeydew honey from honeydew. Small insects that feed on plants produce honeydew.[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e] Honey offers enticing chemical characteristics for baking and is noticeably sweeter than table sugar. Honey has almost the same relative sweetness as granulated sugar and derives its sweetness from the monosaccharide\u0026rsquo;s fructose and glucose. Some people prefer honey over sugar and other sweeteners because of its particular flavor.[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e] Honey is a food that contains about 200 different chemical compounds.[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e] These include sugars, water, proteins (enzymes), organic acids, vitamins (especially vitamin B6, thiamine, niacin, riboflavin, and pantothenic acid), minerals (including calcium, copper, iron, magnesium, manganese, phosphorus, potassium, sodium, and zinc)[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e], pigments, phenolic compounds, a wide range of volatile compounds, and solid particles derived from various sources.[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e] These nutrients play important roles in the functions of nucleic acids[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e], due their intermolecular interactions.[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e] Factors affecting composition of honey: According to a study of the literature on honey composition, the composition of the honey depends on the source of the nectar.[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e] Additionally, the internal and external variables that affect nectar production are significant contributors to the composition of honey.[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e] Some of the internal elements have been proposed to include the size of the bloom and the nectary surface, the age and maturity of the flower on the plant, the species, variety, or cultivated races to which the plant belongs.[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e] Additionally significant outside variables are soil moisture, soil type, fertilizer application, temperature, wind, season, length of day, and sunlight. Similar investigations described the internal and environmental elements that affect nectar secretion. On the composition of honey, the impact of beekeeping practices, honey extraction, and processing by beekeepers or commercial producers have been discussed.[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e] Additional aspects that were involved in the commercial processing have also been discussed.[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e] The storage circumstances have a significant impact on the composition of honey, and storage-related alterations have been documented.[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]\u003c/p\u003e"},{"header":"2. Experimental Section","content":"\u003cp\u003eInstrument: Spectroflourophotometer (RF-5301 PC, Shimadzu, Japan) having 150-watt Xenon lamp as excitation source with 1.0 cm quartz cell was used throughout the experimental work for\u003cbr\u003e\u0026nbsp;fluorescence measurements. The emission and excitation slit were 4 nm for fluorimetric\u003cbr\u003e\u0026nbsp;operation. Required chemicals were weighed using an electrical digital balance (Shimadzu\u003cbr\u003e\u0026nbsp;ATY 224) and stirring of the reaction mixture was carried out with the help of magnetic\u003cbr\u003e\u0026nbsp;heating stirrer (Heidolph). Borosilicate glassware and apparatus were used for the respective\u003cbr\u003e\u0026nbsp;operation. Chemicals used in this study were riboflavin of analytical grade, glacial acetic acid, and distilled water obtained from distillation plant. The honey samples collection was made in the years 2022-2023. A total of twenty (20) honey samples from different agroecological zones were collected by taking into consideration the altitude of the sampling area, the season in which the honey was made, flowers of the area in the respective season, climatic conditions, bees\u0026rsquo; types, and size, etc. All the samples were carried in glass stoppered vials from the concerned beekeepers and photodecomposition was prevented by storing the samples in a dark cupboard at ambient temperature until they were needed for laboratory analyses. The spectrofluorimetric method was employed to determine riboflavin in all the collected samples. In which the fluorescence intensity of riboflavin (RF) was measured at 525 nm following excitation at 464 nm.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.1 Analysis of honey samples for the determination of riboflavin\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA calibration curve was constructed by plotting Fluorescence Intensity (FI) of a series of standard riboflavin (RF) solutions versus concentration for the analysis of honey samples.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.2 Preparation of stock solution of RF:\u0026nbsp;\u003c/strong\u003eRiboflavin Stock Solution (100 ug/ml) was prepared by dissolving 0.01 g of riboflavin in\u0026nbsp;50 ml of distilled water. A few drops of glacial acetic acid were added because riboflavin is stable in its presence. Then it was transferred to 100 ml of a volumetric flask and diluted up to the mark. This\u0026nbsp;stock solution was stored at 4\u0026ordm;C and was covered with a black plastic bag for prevention\u0026nbsp;from photodecomposition. For the preparation of standard solution of RF for calibration\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003ecurve,\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eriboflavin working standards in the range of 0.005-0.3 \u0026micro;g ml\u003csup\u003e-1\u003c/sup\u003e solution were prepared using the dilution formula C\u003csub\u003e1\u003c/sub\u003eV\u003csub\u003e1\u003c/sub\u003e = C\u003csub\u003e2\u003c/sub\u003eV\u003csub\u003e2\u003c/sub\u003e and FI of each solution was measured using a spectrofluorometer. The range of 0.005-0.3 \u0026micro;g ml\u003csup\u003e-1\u003c/sup\u003e. These solutions were protected from light by covering them with a black plastic bag. Then fluorescence intensity was measured through a spectrofluorometer.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.3 Preparation of sample solution and measurement of FI\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePreparation of sample solution: In a small beaker 5.0 g of each sample was taken. Dissolved it in 50 ml of distilled water added a few drops of Glacial acetic acid and diluted up to the mark in 100 ml of volumetric flask. Covered the sample solution with a black plastic bag to prevent photodecomposition of RF. Fluorescence measurement: Each sample was taken in a quartz cuvette and placed in the sample holder of the pre-calibrated spectrofluorometer and FI was measured at an emission wavelength of 525 nm following an excitation wavelength at 464 nm. The measurement was done in triplicates. \u0026nbsp;Calculation of concentration of RF: The concentration of RF in each sample was calculated from the average FI using the straight-line equation of the calibration curve which is given below.\u003c/p\u003e\n\u003cp\u003eY = 2513.2x + 13.17 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; (1)\u003c/p\u003e\n\u003cp\u003eHere Y is the fluorescence intensity of sample X in the above equation 1 is concentration which is calculated by rearranging equation 1 as\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eX = (Y -13.17) / 2513.2 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;(2)\u003c/p\u003e\n\u003cp\u003eTo calculate the concentration of RF in \u0026micro;g g\u003csup\u003e-1\u003c/sup\u003e of honey the following equation was used.\u003cbr\u003e Conc (\u0026micro;g g\u003csup\u003e-1\u003c/sup\u003e) = (X (\u0026micro;g mL\u003csup\u003e-1\u003c/sup\u003e)) (100 ml)/5g (3)\u003cbr\u003e Conc (\u0026micro;g g\u003csup\u003e-1\u003c/sup\u003e) = X (\u0026micro;g g\u003csup\u003e-1\u003c/sup\u003e) \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;(4)\u003c/p\u003e"},{"header":"3. Results and Discussion","content":"\u003cp\u003e\u003cstrong\u003e3.1 Calibration curve for quantification of RF in honey samples\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn order to quantify the concentration of Riboflavin (RF) in honey samples, a crucial step was the construction of a calibration curve, also known as a working curve. This curve was meticulously constructed by plotting the Fluorescence Intensity (FI) against the concentration of standard Riboflavin solutions, spanning the range of 0.005 to 0.3 \u0026micro;g ml\u003csup\u003e-1\u003c/sup\u003e. The results, which provide a clear insight into the relationship between FI and RF concentration, are presented in \u003cstrong\u003eTable 1\u003c/strong\u003e and visually depicted in Figure 1. \u003cstrong\u003eTable 1\u003c/strong\u003e outlines the calibration curve, detailing the corresponding FI values at various RF concentrations. Notably, as the RF concentration increases, so does the FI. This direct correlation between the two parameters is vital for the accurate quantification of Riboflavin in honey samples. \u003cstrong\u003eFigure 1\u003c/strong\u003e complements the tabulated data, offering a graphical representation of this relationship, enabling an even more intuitive understanding of the calibration curve. Such a well-constructed calibration curve forms the cornerstone of precise analytical methods, allowing for the accurate determination of Riboflavin levels in honey samples, a fundamental aspect of quality control and authenticity assessment in the honey industry.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1\u0026nbsp;\u003c/strong\u003eCalibration curve for quantification of RF in honey samples\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"52.56410256410256%\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Concentration (\u0026micro;g ml\u003csup\u003e-1\u003c/sup\u003e)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"47.43589743589744%\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;FI\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"52.56410256410256%\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; 0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"47.43589743589744%\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;15.153\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"52.56410256410256%\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; 0.008\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"47.43589743589744%\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;22.795\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"52.56410256410256%\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; 0.01\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"47.43589743589744%\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;29.497\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"52.56410256410256%\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; 0.03\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"47.43589743589744%\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;92.374\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"52.56410256410256%\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; 0.05\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"47.43589743589744%\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;139.844\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"52.56410256410256%\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; 0.08\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"47.43589743589744%\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;228.754\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"52.56410256410256%\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; 0.1\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"47.43589743589744%\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;276.864\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"52.56410256410256%\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; 0.2\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"47.43589743589744%\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;533.608\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"52.56410256410256%\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; 0.3\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"47.43589743589744%\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;747.449\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.2 Quantification of RF concentration in honey samples\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFor the purpose of quantification of RF concentration, all the collected samples were analyzed by spectrofluorimetric measurement and subsequent calculation of RF in (\u0026micro;g g\u003csup\u003e-1\u003c/sup\u003e). Honey composition and its relationship with various environmental and seasonal factors are examined here. \u0026nbsp;The data given in Table 2, present information about 20 honey samples, with altitude from sea surface (in feet), flower type, season of collection, bee size is considered as variable factor, and the concentration of riboflavin (RF) in each sample along with standard deviations. For instance, the concentration of riboflavin (\u0026micro;g g\u003csup\u003e-1\u003c/sup\u003e) varies considerably across the samples, ranging from as high as 2.74\u0026plusmn;0.02 in a Ziziphus honey collected at an altitude of 1497 feet, to as low as 0.08\u0026plusmn;0.02 in Brassica honey collected at an altitude of 4194 feet. The diverse flower types, seasons, and bee sizes also appear to have a potential influence on riboflavin content in honey. This dataset beckons further exploration and analysis, offering valuable insights into the multifaceted nature of honey composition and its dependence on external factors. It opens the door to questions regarding the significance of altitude, flower type, and bee size in determining the nutritional content and quality of honey. Such investigations can provide critical knowledge for honey producers and enthusiasts alike, contributing to a deeper understanding of this beloved natural sweetener.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2\u0026nbsp;\u003c/strong\u003eConcentration of RF (\u0026micro;g g\u003csup\u003e-1\u003c/sup\u003e) in honey samples\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"614\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"10.603588907014682%\"\u003e\n \u003cp\u003e\u003cstrong\u003eSample No\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.270799347471453%\"\u003e\n \u003cp\u003e\u003cstrong\u003eAltitude from sea surface (ft)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.986949429037521%\"\u003e\n \u003cp\u003e\u003cstrong\u003eFlower\u003c/strong\u003e\u003cstrong\u003es\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.681892332789559%\"\u003e\n \u003cp\u003e\u003cstrong\u003eSeason\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.029363784665579%\"\u003e\n \u003cp\u003e\u003cstrong\u003eB\u003c/strong\u003e\u003cstrong\u003eee\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003esize\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.427406199021206%\"\u003e\n \u003cp\u003e\u003cstrong\u003eConc of RF (\u0026micro;g g\u003csup\u003e-1\u003c/sup\u003e) \u0026plusmn;SD\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"10.603588907014682%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.270799347471453%\"\u003e\n \u003cp\u003e1497\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.986949429037521%\"\u003e\n \u003cp\u003e\u003cem\u003eZiziphus\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.681892332789559%\"\u003e\n \u003cp\u003eJan-18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.029363784665579%\"\u003e\n \u003cp\u003eSmall\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.427406199021206%\"\u003e\n \u003cp\u003e\u0026nbsp;2.74\u0026plusmn;0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"10.603588907014682%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.270799347471453%\"\u003e\n \u003cp\u003e2478\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.986949429037521%\"\u003e\n \u003cp\u003e\u003cem\u003eZiziphus\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.681892332789559%\"\u003e\n \u003cp\u003eNov17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.029363784665579%\"\u003e\n \u003cp\u003eMedium\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.427406199021206%\"\u003e\n \u003cp\u003e\u0026nbsp;1.34\u0026plusmn;0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"10.603588907014682%\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.270799347471453%\"\u003e\n \u003cp\u003e3487\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.986949429037521%\"\u003e\n \u003cp\u003eMultifloral\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.681892332789559%\"\u003e\n \u003cp\u003eJul-17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.029363784665579%\"\u003e\n \u003cp\u003eSmall\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.427406199021206%\"\u003e\n \u003cp\u003e\u0026nbsp;1.2\u0026plusmn;0.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"10.603588907014682%\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.270799347471453%\"\u003e\n \u003cp\u003e3407\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.986949429037521%\"\u003e\n \u003cp\u003eMultifloral\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.681892332789559%\"\u003e\n \u003cp\u003eApr-18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.029363784665579%\"\u003e\n \u003cp\u003eSmall\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.427406199021206%\"\u003e\n \u003cp\u003e\u0026nbsp;1.32\u0026plusmn;0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"10.603588907014682%\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.270799347471453%\"\u003e\n \u003cp\u003e5433\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.986949429037521%\"\u003e\n \u003cp\u003eCommercial\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.681892332789559%\"\u003e\n \u003cp\u003eDec-17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.029363784665579%\"\u003e\n \u003cp\u003eUnknown\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.427406199021206%\"\u003e\n \u003cp\u003e\u0026nbsp;0.26\u0026plusmn;0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"10.603588907014682%\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.270799347471453%\"\u003e\n \u003cp\u003e4194\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.986949429037521%\"\u003e\n \u003cp\u003e\u003cem\u003eBrassica\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.681892332789559%\"\u003e\n \u003cp\u003eApr-18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.029363784665579%\"\u003e\n \u003cp\u003eMedium\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.427406199021206%\"\u003e\n \u003cp\u003e\u0026nbsp;0.08\u0026plusmn;0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"10.603588907014682%\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.270799347471453%\"\u003e\n \u003cp\u003e2353\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.986949429037521%\"\u003e\n \u003cp\u003e\u003cem\u003eZiziphus\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.681892332789559%\"\u003e\n \u003cp\u003eOct-18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.029363784665579%\"\u003e\n \u003cp\u003eMedium\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.427406199021206%\"\u003e\n \u003cp\u003e\u0026nbsp;1.4\u0026plusmn;0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"10.603588907014682%\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.270799347471453%\"\u003e\n \u003cp\u003e3655\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.986949429037521%\"\u003e\n \u003cp\u003e\u003cem\u003eBrassica\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.681892332789559%\"\u003e\n \u003cp\u003eApr-18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.029363784665579%\"\u003e\n \u003cp\u003eMedium\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.427406199021206%\"\u003e\n \u003cp\u003e0.313\u0026plusmn;0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"10.603588907014682%\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.270799347471453%\"\u003e\n \u003cp\u003e2310\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.986949429037521%\"\u003e\n \u003cp\u003eMultifloral\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.681892332789559%\"\u003e\n \u003cp\u003eApr-18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.029363784665579%\"\u003e\n \u003cp\u003eMedium\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.427406199021206%\"\u003e\n \u003cp\u003e0.25\u0026plusmn;0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"10.603588907014682%\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.270799347471453%\"\u003e\n \u003cp\u003e1805\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.986949429037521%\"\u003e\n \u003cp\u003e\u003cem\u003eZiziphus\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.681892332789559%\"\u003e\n \u003cp\u003eDec-17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.029363784665579%\"\u003e\n \u003cp\u003eSmall\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.427406199021206%\"\u003e\n \u003cp\u003e0.826\u0026plusmn;0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"10.603588907014682%\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.270799347471453%\"\u003e\n \u003cp\u003e3655\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.986949429037521%\"\u003e\n \u003cp\u003e\u003cem\u003eAcacia\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.681892332789559%\"\u003e\n \u003cp\u003eJun-17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.029363784665579%\"\u003e\n \u003cp\u003eMedium\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.427406199021206%\"\u003e\n \u003cp\u003e1.167\u0026plusmn;0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"10.603588907014682%\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.270799347471453%\"\u003e\n \u003cp\u003e2310\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.986949429037521%\"\u003e\n \u003cp\u003e\u003cem\u003eAcacia\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.681892332789559%\"\u003e\n \u003cp\u003eJun-17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.029363784665579%\"\u003e\n \u003cp\u003eMedium\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.427406199021206%\"\u003e\n \u003cp\u003e0.92\u0026plusmn;0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"10.603588907014682%\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.270799347471453%\"\u003e\n \u003cp\u003e1897\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.986949429037521%\"\u003e\n \u003cp\u003e\u003cem\u003eZiziphus\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.681892332789559%\"\u003e\n \u003cp\u003eDec-17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.029363784665579%\"\u003e\n \u003cp\u003eMedium\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.427406199021206%\"\u003e\n \u003cp\u003e1.74\u0026plusmn;0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"10.603588907014682%\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.270799347471453%\"\u003e\n \u003cp\u003e1869\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.986949429037521%\"\u003e\n \u003cp\u003eAcacia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.681892332789559%\"\u003e\n \u003cp\u003eJul-17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.029363784665579%\"\u003e\n \u003cp\u003eSmall\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.427406199021206%\"\u003e\n \u003cp\u003e0.67\u0026plusmn;0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"10.603588907014682%\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.270799347471453%\"\u003e\n \u003cp\u003e1890\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.986949429037521%\"\u003e\n \u003cp\u003eAcacia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.681892332789559%\"\u003e\n \u003cp\u003eJun-17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.029363784665579%\"\u003e\n \u003cp\u003eMedium\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.427406199021206%\"\u003e\n \u003cp\u003e1.38\u0026plusmn;0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"10.603588907014682%\"\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.270799347471453%\"\u003e\n \u003cp\u003e1517\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.986949429037521%\"\u003e\n \u003cp\u003eZiziphus\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.681892332789559%\"\u003e\n \u003cp\u003eDec-17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.029363784665579%\"\u003e\n \u003cp\u003eBig\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.427406199021206%\"\u003e\n \u003cp\u003e1.32\u0026plusmn;0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"10.603588907014682%\"\u003e\n \u003cp\u003e17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.270799347471453%\"\u003e\n \u003cp\u003e1880\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.986949429037521%\"\u003e\n \u003cp\u003eAcacia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.681892332789559%\"\u003e\n \u003cp\u003eJun-17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.029363784665579%\"\u003e\n \u003cp\u003eBig\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.427406199021206%\"\u003e\n \u003cp\u003e0.51\u0026plusmn;0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"10.603588907014682%\"\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.270799347471453%\"\u003e\n \u003cp\u003e1805\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.986949429037521%\"\u003e\n \u003cp\u003eMultifloral\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.681892332789559%\"\u003e\n \u003cp\u003eApr-18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.029363784665579%\"\u003e\n \u003cp\u003eMedium\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.427406199021206%\"\u003e\n \u003cp\u003e0.22\u0026plusmn;0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"10.603588907014682%\"\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.270799347471453%\"\u003e\n \u003cp\u003e1880\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.986949429037521%\"\u003e\n \u003cp\u003eAcacia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.681892332789559%\"\u003e\n \u003cp\u003eJun-17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.029363784665579%\"\u003e\n \u003cp\u003eMedium\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.427406199021206%\"\u003e\n \u003cp\u003e1.04\u0026plusmn;0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"10.603588907014682%\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.270799347471453%\"\u003e\n \u003cp\u003e4190\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.986949429037521%\"\u003e\n \u003cp\u003eZiziphus\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.681892332789559%\"\u003e\n \u003cp\u003eJan-18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.029363784665579%\"\u003e\n \u003cp\u003eSmall\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.427406199021206%\"\u003e\n \u003cp\u003e0.34\u0026plusmn;0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003e3.3 Statistical analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFor statistical evaluation of the individual effect of different variables like altitude of\u0026nbsp;sampling site, season of honey formation, Bee size, and flower type on the concentrationof RF (\u0026micro;g g\u003csup\u003e-1\u003c/sup\u003e), SPSS V. 23 was used.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.3.1 Effect of altitude on RF concentration in honey\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe effect of the altitude of the sampling site on RF concentration in honey was investigated. Herethe dependent variable is RF concentration (\u0026micro;g g\u003csup\u003e-1\u003c/sup\u003e) whereas altitude is considered as theindependent variable. The results are given in \u003cstrong\u003eTable 3\u003c/strong\u003e and shown in \u003cstrong\u003eFigure 2\u003c/strong\u003e. In the results given in \u003cstrong\u003eTable 3\u003c/strong\u003e, the coefficient of altitude = -0.271 indicates that there is negative effect of altitude on RF concentration i.e. for one thousand ft increase in altitude from sea level, an average of 0.271 \u0026micro;g g\u003csup\u003e-1\u003c/sup\u003e decrease will occur in RF concentration. The p value = 0.044 indicates that the effect of altitude on the RF concentration in honey is statistically significant.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;Table 3\u0026nbsp;\u003c/strong\u003eEffect of altitude on RF concentration in honey\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"477\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"29.140461215932913%\" rowspan=\"2\"\u003e\n \u003cp\u003eModel\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"39.62264150943396%\" colspan=\"2\"\u003e\n \u003cp\u003eCoefficients\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.836477987421384%\" rowspan=\"2\"\u003e\n \u003cp\u003et-value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.40041928721174%\" rowspan=\"2\"\u003e\n \u003cp\u003eP-value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"44.973544973544975%\"\u003e\n \u003cp\u003e\u0026beta;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"55.026455026455025%\"\u003e\n \u003cp\u003eStd. Error\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"29.140461215932913%\"\u003e\n \u003cp\u003e(Constant)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.81970649895178%\"\u003e\n \u003cp\u003e1.674\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.80293501048218%\"\u003e\n \u003cp\u003e0.360\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.836477987421384%\"\u003e\n \u003cp\u003e4.654\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.40041928721174%\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"29.140461215932913%\"\u003e\n \u003cp\u003eAltitude\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.81970649895178%\"\u003e\n \u003cp\u003e-0.271\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.80293501048218%\"\u003e\n \u003cp\u003e0.125\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.836477987421384%\"\u003e\n \u003cp\u003e-2.170\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.40041928721174%\"\u003e\n \u003cp\u003e0.044\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;a. Dependent variable: \u0026nbsp; \u0026nbsp;RF Concentration (\u0026micro;g g\u003csup\u003e-1\u003c/sup\u003e) b. Independent variable: Altitude\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.3.2 Effect of season of honey formation on RF concentration\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo study the effect of season on RF concentration in honey, RF concentration was taken asthe dependent variable while the season (i.e. winter, spring, summer and autumn) was considered as the independent variable. Since season is a categorical variable therefore, the concept of dummy variable is used in regression. As there are four categories of the season therefore three dummy variables, winter, spring, and summer were included in the regression and the left-over category i.e. autumn was taken as the base category. The results are given in \u003cstrong\u003eTable 4\u003c/strong\u003e and shown in \u003cstrong\u003eFigure 3.\u0026nbsp;\u003c/strong\u003eFrom the results given in \u003cstrong\u003eTable 4\u003c/strong\u003e it can be observed that the value of left-over category of season (i.e. autumn) = 1.370 indicating that on the average RF concentration in honey is 1.370 (\u0026micro;g g\u003csup\u003e-1\u003c/sup\u003e) in autumn season. And P-value indicates that the result is statistically\u0026nbsp;significant. The coefficients of winter season = -0.171 shows that RF concentration in winter season decreases by 0.171 (\u0026micro;g g\u003csup\u003e-1\u003c/sup\u003e) from autumn season, but is not statistically significant. Whereas the coefficient of spring season = -0.937 shows that RF concentration in spring season decreases by 0.937 (\u0026micro;g g\u003csup\u003e-1\u003c/sup\u003e) from autumn season and P-value = 0.048 indicates that the result is statistically significant. Similarly, the coefficient of summer season = -0.386 shows that RF concentration in summer season decreases by 0.386 (\u0026micro;g g\u003csup\u003e-1\u003c/sup\u003e) from autumn season and P-value = 0.044 indicates that the result is statistically significant.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 4\u0026nbsp;\u003c/strong\u003eEffect of sampling season on RF concentration in honey\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"492\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.341463414634145%\" rowspan=\"2\"\u003e\n \u003cp\u003eModel\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"42.27642276422764%\" colspan=\"2\"\u003e\n \u003cp\u003eCoefficients\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.447154471544716%\" rowspan=\"2\"\u003e\n \u003cp\u003et\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.934959349593495%\" rowspan=\"2\"\u003e\n \u003cp\u003eSignificance\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"45.67307692307692%\"\u003e\n \u003cp\u003e\u0026beta;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"54.32692307692308%\"\u003e\n \u003cp\u003eStd. Error\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.341463414634145%\"\u003e\n \u003cp\u003e(Constant)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.308943089430894%\"\u003e\n \u003cp\u003e1.370\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.96747967479675%\"\u003e\n \u003cp\u003e0.436\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.447154471544716%\"\u003e\n \u003cp\u003e3.141\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.934959349593495%\"\u003e\n \u003cp\u003e0.006\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.341463414634145%\"\u003e\n \u003cp\u003eWinter\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.308943089430894%\"\u003e\n \u003cp\u003e-0.171\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.96747967479675%\"\u003e\n \u003cp\u003e0.504\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.447154471544716%\"\u003e\n \u003cp\u003e-0.339\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.934959349593495%\"\u003e\n \u003cp\u003e0.739\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.341463414634145%\"\u003e\n \u003cp\u003eSpring\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.308943089430894%\"\u003e\n \u003cp\u003e-0.937\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.96747967479675%\"\u003e\n \u003cp\u003e0.516\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.447154471544716%\"\u003e\n \u003cp\u003e-2.316\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.934959349593495%\"\u003e\n \u003cp\u003e0.048\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.341463414634145%\"\u003e\n \u003cp\u003eSummer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.308943089430894%\"\u003e\n \u003cp\u003e-0.386\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.96747967479675%\"\u003e\n \u003cp\u003e0.495\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.447154471544716%\"\u003e\n \u003cp\u003e-2.780\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.934959349593495%\"\u003e\n \u003cp\u003e0.044\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003col style=\"list-style-type: lower-alpha;\"\u003e\n \u003cli\u003eDependent variable: Rf concentration (\u0026micro;g g\u003csup\u003e-1\u003c/sup\u003e) \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eIndependent variables: seasons\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003e\u003cstrong\u003e3.3.3 Effect of bee size on RF concentration in honey\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eInvestigating the effect of bee size on RF concentration in honey, the dependent variable is RF concentration whereas bee size (i.e. small, medium, big) is considered as the independent variable. Since bee size is a categorical variable therefore, the concept of dummy variable is used in regression. As there are three categories of the bee size therefore, 2 dummy variables middle and big are included in the regression and the left-over category i.e. small is taken as the base category. The results are given in table 5 and shown in\u003cstrong\u003e\u0026nbsp;Figure 4\u003c/strong\u003e. While the \u003cstrong\u003eTable 5\u003c/strong\u003e shows the regression results of RF concentration on bee size. From the results it can be observed from the value of left-over category of bee size (i.e. small) = 1.176 indicating that on the average RF concentration in honey is 1.176 (\u0026micro;g g\u003csup\u003e-1\u003c/sup\u003e) from small size bee. The P-value indicates that the result is statistically significant. The coefficients of medium size bee = -0.333 which shows that RF concentration from medium size bee decreases by 0.333 (\u0026micro;g g\u003csup\u003e-1\u003c/sup\u003e) from small size bee and the results are statistically significant as the P-value = 0.033. Whereas, the coefficient of big size bee = -0.263 shows that RF concentration from big size bee decreases by 0.937 (\u0026micro;g g\u003csup\u003e-1\u003c/sup\u003e) from small size bee but the results are not statistically significant as clear from the P-value = 0.638. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 5\u0026nbsp;\u003c/strong\u003eEffect of bee size on RF concentration in honey \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"462\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.872017353579174%\" rowspan=\"2\"\u003e\n \u003cp\u003eModel\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"47.071583514099785%\" colspan=\"2\"\u003e\n \u003cp\u003eCoefficients\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.43817787418655%\" rowspan=\"2\"\u003e\n \u003cp\u003et\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.61822125813449%\" colspan=\"2\" rowspan=\"2\"\u003e\n \u003cp\u003eP-value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"52.07373271889401%\"\u003e\n \u003cp\u003e\u0026beta;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"47.92626728110599%\"\u003e\n \u003cp\u003eStd. Error\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.872017353579174%\"\u003e\n \u003cp\u003e(Constant)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.5119305856833%\"\u003e\n \u003cp\u003e1.176\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.559652928416487%\"\u003e\n \u003cp\u003e0.275\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.655097613882862%\" colspan=\"2\"\u003e\n \u003cp\u003e4.277\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.401301518438178%\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.872017353579174%\"\u003e\n \u003cp\u003eMedium\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.5119305856833%\"\u003e\n \u003cp\u003e-0.333\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.559652928416487%\"\u003e\n \u003cp\u003e0.337\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.655097613882862%\" colspan=\"2\"\u003e\n \u003cp\u003e-0.990\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.401301518438178%\"\u003e\n \u003cp\u003e0.033\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.872017353579174%\"\u003e\n \u003cp\u003eLarge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.5119305856833%\"\u003e\n \u003cp\u003e-0.263\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.559652928416487%\"\u003e\n \u003cp\u003e0.550\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.655097613882862%\" colspan=\"2\"\u003e\n \u003cp\u003e-0.479\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.401301518438178%\"\u003e\n \u003cp\u003e0.638\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003col start=\"1\" style=\"list-style-type: lower-alpha;\"\u003e\n \u003cli\u003eDependent variable: RF concentration (\u0026micro;g g\u003csup\u003e-1\u003c/sup\u003e)\u003c/li\u003e\n \u003cli\u003eIndependent variable: Bee size\u0026nbsp;\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003e\u003cstrong\u003e3.3.4 Effect of flower type on RF concentration in honey\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe effect of flower type on RF concentration in honey was studied in which the dependent variable is RF concentration whereas flower type (i.e. \u003cem\u003eZiziphus,\u0026nbsp;\u003c/em\u003eMulti floral\u003cem\u003e, Brassica\u003c/em\u003e and\u0026nbsp;\u003cem\u003eAcacia\u003c/em\u003e flowers) is considered as the independent variable. Since flower type is a categorical variable therefore, the concept of dummy variable is used in regression. As there are four categories of the flower type therefore 3 dummy variables multi floral, brassica and acacia are included in the regression and the left-over category i.e. \u003cem\u003eZiziphus\u003c/em\u003e is taken as the base category. The results are given in the table 6 and shown in \u003cstrong\u003eFigure 5\u003c/strong\u003e. While \u003cstrong\u003eTable 6\u003c/strong\u003e shows the results of regression RF concentration on flower type. From the results it can be observed that the value of left-over category of flower type (i.e. \u003cem\u003eZiziphus\u003c/em\u003e) = 1.242 indicating that on the average RF concentration in honey is 1.242 (\u0026micro;g g\u003csup\u003e-1\u003c/sup\u003e) from \u003cem\u003eZiziphus\u003c/em\u003e flower and P-value indicates that the result is statistically significant. The coefficients of multi floral samples = -0.497 which shows that RF concentration from multiflora flower is less than \u003cem\u003eZiziphus\u003c/em\u003e flower by 0.497 (\u0026micro;g g\u003csup\u003e-1\u003c/sup\u003e), and from P-value = 0.021 the result is statistically significant. Whereas, the coefficient of brassica flower = -1.052 which shows that RF concentration from brassica flower is less than \u003cem\u003eZiziphus\u003c/em\u003e flower by 1.052 (\u0026micro;g g\u003csup\u003e-1\u003c/sup\u003e) and P-value = 0.048 indicates that the result is statistically significant. Similarly, the coefficient of acacia flower = -0.294 which shows that RF concentration from acacia flower is less than \u003cem\u003eZiziphus\u0026nbsp;\u003c/em\u003eflower by 0.294 (\u0026micro;g g\u003csup\u003e-1\u003c/sup\u003e). P-value indicates that the\u0026nbsp;result is not statistically significant.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 6\u0026nbsp;\u003c/strong\u003eEffect of flower type on RF concentration in honey\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"438\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.004566210045663%\" rowspan=\"2\"\u003e\n \u003cp\u003eModel\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"40.8675799086758%\" colspan=\"2\"\u003e\n \u003cp\u003eCoefficients\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.296803652968036%\" rowspan=\"2\"\u003e\n \u003cp\u003et value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.831050228310502%\" rowspan=\"2\"\u003e\n \u003cp\u003eSignificance\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"47.486033519553075%\"\u003e\n \u003cp\u003e\u0026beta;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"52.513966480446925%\"\u003e\n \u003cp\u003eStd. Error\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.004566210045663%\"\u003e\n \u003cp\u003e(Constant)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.40639269406393%\"\u003e\n \u003cp\u003e1.242\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.461187214611872%\"\u003e\n \u003cp\u003e0.219\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.296803652968036%\"\u003e\n \u003cp\u003e5.663\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.831050228310502%\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.004566210045663%\"\u003e\n \u003cp\u003e\u003cem\u003eMultifloral\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.40639269406393%\"\u003e\n \u003cp\u003e-0.497\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.461187214611872%\"\u003e\n \u003cp\u003e0.380\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.296803652968036%\"\u003e\n \u003cp\u003e-1.307\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.831050228310502%\"\u003e\n \u003cp\u003e0.021\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.004566210045663%\"\u003e\n \u003cp\u003e\u003cem\u003eBrassica\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.40639269406393%\"\u003e\n \u003cp\u003e-1.052\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.461187214611872%\"\u003e\n \u003cp\u003e0.490\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.296803652968036%\"\u003e\n \u003cp\u003e-2.144\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.831050228310502%\"\u003e\n \u003cp\u003e0.048\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.004566210045663%\"\u003e\n \u003cp\u003e\u003cem\u003eAcacia\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.40639269406393%\"\u003e\n \u003cp\u003e-0.294\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.461187214611872%\"\u003e\n \u003cp\u003e0.335\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.296803652968036%\"\u003e\n \u003cp\u003e-0.876\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.831050228310502%\"\u003e\n \u003cp\u003e0.394\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003col start=\"1\" style=\"list-style-type: lower-alpha;\"\u003e\n \u003cli\u003eDependent variable: RF concentration (\u0026micro;g g\u003csup\u003e-1\u003c/sup\u003e) \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u003c/li\u003e\n \u003cli\u003eIndependent variable: flower type\u0026nbsp;\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"4. Conclusions","content":"\u003cp\u003eThe primary objective of this study was to gather honey samples from diverse geographical and climatic regions within Khyber Pakhtunkhwa and subsequently analyze them to determine their riboflavin content. The quantification of riboflavin, which inherently exhibits fluorescence, was achieved through the use of spectrofluorimetry. Riboflavin exhibits distinct fluorescence spectra, characterized by its maximum excitation at 464 nm, followed by an emission peak at 525 nm. Furthermore, we investigated the influence of various external factors on the riboflavin concentration in the collected honey samples. These factors included the species of honey bees, the altitude of the sampling locations, the types of flowers from which the nectar was sourced, and the seasons during which the samples were collected. Statistically, we evaluated how these factors affected the riboflavin levels in the honey. Our findings revealed that honey samples collected from lower altitudes exhibited a notably higher riboflavin concentration, averaging at 1.156\u0026thinsp;\u0026plusmn;\u0026thinsp;0.08 \u0026micro;g g\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e. Similarly, samples gathered during the autumn season consistently demonstrated the highest average riboflavin concentration, reaching 1.37\u0026thinsp;\u0026plusmn;\u0026thinsp;0.06 \u0026micro;g/g\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, surpassing those collected during other seasons. Additionally, we delved into the influence of the floral source of nectar and discovered that honey samples obtained from regions where nectar was derived from \u003cem\u003eZiziphus\u003c/em\u003e plants boasted the highest riboflavin concentration, averaging at 1.383\u0026thinsp;\u0026plusmn;\u0026thinsp;0.1 \u0026micro;g g\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e. Moreover, we explored the connection between the size of honey bees and riboflavin concentration, finding that samples collected from hives of smaller honey bees displayed the maximum riboflavin concentration, measuring 1.176\u0026thinsp;\u0026plusmn;\u0026thinsp;0.07 \u0026micro;g g\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e. This study underscores, the composition of honey may vary significantly based on external factors, encompassing an array of vitamins and other nutritional constituents.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgment:\u0026nbsp;\u003c/strong\u003eThe authors extend their appreciation to the Researchers supporting project number (RSP2023R349) King Saud University, Riyad, Saudi Arabia\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDeclaration of interest:\u0026nbsp;\u003c/strong\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding:\u0026nbsp;\u003c/strong\u003eThe current study was supported by Researchers supporting project number (RSP2023R349) King Saud University, Riyad, Saudi Arabia\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eLi, S., Synthetic Bioactive Substances 33. Handbook of Food Chemistry 2015, 1061.\u003c/li\u003e\n\u003cli\u003eAnumba, I.A., C.E. Akunne, B.U. Ononye, C.A. Chidi, Assessment on colonization, absconding and honey yield by African honeybee colonies reared in hives with different colours in Awka, South-Eastern Nigeria. Journal of Apiculture 2020, 35, 199-204.\u003c/li\u003e\n\u003cli\u003eChellappan, M., Hand Holding Entrepreneurs in Honey Processing and Value Addition. Entrepreneurship and Skill Development in Horticultural Processing; CRC Press: Boca Raton, FL, USA 2021, 323-343.\u003c/li\u003e\n\u003cli\u003eFranklin, R., S. Niverty, B.A. Harpur, N. Chawla, Unraveling the Mechanisms of the Apis mellifera Honeycomb Construction by 4D X‐ray Microscopy. Adv. Mater. 2022, 34, 2202361.\u003c/li\u003e\n\u003cli\u003eNelson, A.S., K.A. Mooney, The evolution and ecology of interactions between ants and honeydew-producing hemipteran insects. Annual Review of Ecology, Evolution, and Systematics 2022, 53, 379-402.\u003c/li\u003e\n\u003cli\u003eBobroff, L.B., Nutrition for Health and Fitness: Sugar and Other Sweeteners: FSHN20-46/FS406, 10/2020. EDIS 2020, 2020,\u003c/li\u003e\n\u003cli\u003eMračević, S.Đ., M. Krstić, A. Lolić, S. Ražić, Comparative study of the chemical composition and biological potential of honey from different regions of Serbia. Microchem. J. 2020, 152, 104420.\u003c/li\u003e\n\u003cli\u003eda Silva, P.M., C. Gauche, L.V. Gonzaga, A.C.O. Costa, R. Fett, Honey: Chemical composition, stability and authenticity. Food Chem. 2016, 196, 309-323.\u003c/li\u003e\n\u003cli\u003eGul, Z., M. Salman, S. Khan, A. Shehzad, H. Ullah, M. Irshad, M. Zeeshan, S. Batool, M. Ahmed, A.A. Altaf, Single Organic Ligands Act as a Bifunctional Sensor for Subsequent Detection of Metal and Cyanide Ions, a Statistical Approach toward Coordination and Sensitivity. Crit. Rev. Anal. Chem. 2023, 1-17.\u003c/li\u003e\n\u003cli\u003eWang, J., Q.X. Li, Chemical composition, characterization, and differentiation of honey botanical and geographical origins. Advances in food and nutrition research 2011, 62, 89-137.\u003c/li\u003e\n\u003cli\u003eHossain, M.L., L.Y. Lim, K. Hammer, D. Hettiarachchi, C. Locher, Honey-based medicinal formulations: A critical review. Applied Sciences 2021, 11, 5159.\u003c/li\u003e\n\u003cli\u003eKhan, S., F.U. Rahman, M. Zahoor, A.U. Haq, A.B. Shah, M.U. Rahman, H.U. Rahman, The DNA threat probing of some chromophores using UV/VIS spectroscopy. World Journal of Biology and Biotechnology 2023, 8, 19-22.\u003c/li\u003e\n\u003cli\u003eKhan, S., M. Zahoor, M.U. Rahman, Z. Gul, Cocrystals; basic concepts, properties and formation strategies. Zeitschrift f\u0026uuml;r Physikalische Chemie 2023, 237, 273-332.\u003c/li\u003e\n\u003cli\u003eMohammed, M.E.A., Factors affecting the physicochemical properties and chemical composition of bee\u0026rsquo;s honey. Food Reviews International 2022, 38, 1330-1341.\u003c/li\u003e\n\u003cli\u003eRanneh, Y., A.M. Akim, H.A. Hamid, H. Khazaai, A. Fadel, Z.A. Zakaria, M. Albujja, M.F.A. Bakar, Honey and its nutritional and anti-inflammatory value. BMC complementary medicine and therapies 2021, 21, 1-17.\u003c/li\u003e\n\u003cli\u003eZhang, Y., M. Su, L. Wang, S. Huang, S. Su, W.-F. Huang, Vairimorpha (Nosema) ceranae infection alters honey bee microbiota composition and sustains the survival of adult honey bees. Biology 2021, 10, 905.\u003c/li\u003e\n\u003cli\u003eHidalgo, H.A., A.R. Nicolas, R. Cedon, Development barriers of stingless bee honey industry in Bicol, Philippines. International Journal on Advanced Science Engineering and Information Technology 2020, 10,\u003c/li\u003e\n\u003cli\u003eBhandari, P.L., R.R. Kattel, Value chain analysis of honey sub-sector in Nepal. International Journal of Applied Sciences and Biotechnology 2020, 8, 83-95.\u003c/li\u003e\n\u003cli\u003eAguiar, D., A.C. Pereira, J.C. Marques, The influence of transport and storage conditions on beer stability\u0026mdash;a systematic review. Food and Bioprocess Technology 2022, 15, 1477-1494.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Riboflavin/vitamin B2, Spectrofluorometer, Calibration curve, Standard solution, Statistical analysis, Effect of altitude, flower type, season, bee size.","lastPublishedDoi":"10.21203/rs.3.rs-3875508/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3875508/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe present study is focused on the collection of honey samples from the different geographical and climatic conditions of Khyber Pakhtunkhwa and analyzing them for determination of riboflavin. Quantification of riboflavin being natively fluorescent, was accomplished using spectrofluorimetric method. Riboflavin has characteristic fluorescence spectra with maximum excitation at 464 nm followed by an emission peak at 525 nm. The procedure followed in this work comprised the construction of a calibration curve by plotting the fluorescence intensity of a series of riboflavin solutions versus concentration. This curve was used for quantification of riboflavin in the collected honey samples. The effect of several external factors such as the altitude of the sampling area, type of honey bee, type of flowers from which the nectar was collected, and sampling season on the concentration of riboflavin in the honey samples was statistically evaluated. It was concluded that the samples collected from lower altitudes have high concentration (1.156±0.08 μg g\u003csup\u003e-1\u003c/sup\u003e) of riboflavin. Similarly, the samples collected in autumn were found to have a with a maximum average riboflavin concentration of 1.37±0.06 μg g\u003csup\u003e-1\u003c/sup\u003e, which was higher in comparison to the samples collected in other seasons of the year. Likewise, the effect of flora on the concentration of riboflavin was also investigated and it was found that honey samples collected from areas where the nectar was collected from \u003cem\u003eZiziphus\u003c/em\u003e contains maximum riboflavin concentration averaged at 1.383±0.1 μg g\u003csup\u003e-1\u003c/sup\u003e. Based on the size of the honey bees the samples collected from hives of small honey bees were found to have maximum riboflavin concentration of 1.176±0.07 μg g\u003csup\u003e-1\u003c/sup\u003e. This study suggests that beside the studied vitamin, the rest of the vitamins and other nutritional components may vary in the honey samples depending upon external factors. \u0026nbsp;\u003c/p\u003e","manuscriptTitle":"Exploring Riboflavin Quantification in Honey via Spectrofluorimetry: A Statistical Examination of Influential Extrinsic Variables","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-01-22 10:03:29","doi":"10.21203/rs.3.rs-3875508/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"74309115-10c9-4e38-899b-abe131fe5aab","owner":[],"postedDate":"January 22nd, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-04-16T06:18:26+00:00","versionOfRecord":[],"versionCreatedAt":"2024-01-22 10:03:29","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-3875508","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3875508","identity":"rs-3875508","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}