Authentication of monofloral Yemeni Sidr honey using ultraviolet spectroscopy and chemometric analysis

Fourier transform infrared spectroscopy and chemometrics for the rapid screening of economically motivated adulteration of honey spiked with corn or rice syrup

Due to its high price, increased consumption, and limited production, honey has been a main target for economically motivated adulteration (EMA). An approach combining Fourier-Transform infrared spectroscopy (FTIR) and chemometrics was evaluated to develop a rapid screening tool to detect potential EMA of honey with either rice or corn syrup. A single-class soft independent modeling of class analogy (SIMCA) model was developed using a diverse set of commercial honey products and an authentic set of honey samples collected at four different U.S. Department of Agriculture (USDA) honey sample collection locations. The SIMCA model was externally validated with a set of calibration-independent authentic honey, typical commercial honey control samples, and those spiked with rice and corn syrups in the 1-16% concentration range. The authentic honey and typical commercial honey test samples were correctly predicted with an 88.3% classification rate. High accuracy was found in predicting the rice and corn syrup spiked samples above the 7% concentration range, yielding 97.6% and 94.8% correct classification rates, respectively. This study demonstrated the potential for a rapid and accurate infrared and chemometrics method that can be used to rapidly screen for either rice or corn adulterants in honey in less than 5 min.

UV Fingerprinting Approaches for Quality Control Analyses of Food and Functional Food Coupled to Chemometrics: A Comprehensive Analysis of Novel Trends and Applications

(1) Background: Ultraviolet-visible (UV-Vis) spectroscopy is a common analytical tool to detect chromophore in compounds by monitoring absorbance spectral wavelengths. Further, it could provide spectral information about complex conjugated systems in mixtures aided by chemometric tools to visualize large UV-Vis datasets as typical in food samples. This review provides novel insight on UV-Vis applications in the last 20 years, as an advanced analytical tool in the quality control of food and dietary supplements, as well as several other applications, including chemotaxonomy, authentication, fingerprinting, and stability studies. (2) Conclusions: A critical assessment of the value of UV application and its novel trends in the quality control (QC) of nutraceuticals reveals the advantages and limitations, focusing on areas where future advancements are in need. Although simple, UV and its novel trends present potential analytical tools with an acceptable error for QC applications from a non-targeted perspective compared to other expensive spectral tools.

Breaking the challenge of polyherbal quality control using UV and HPLC fingerprints combined with multivariate analysis

INTRODUCTION

Traditional herbal medicines are mostly composed of more than one herb which act synergistically; hence, there is high demand for proper quality control methods to ensure the consistent quality of polyherbal formulations.

AIMS

Proposing a simple, reliable, and efficient method for the qualitative and quantitative analysis of a polyherbal product using multivariate analysis of ultraviolet-visible (UV-Vis) spectroscopy or HPLC-PDA data.

METHODOLOGY

An antiobesity formula consisting of equal proportions of Trachyspermum ammi, Cuminum cyminum, and Origanum majorana was prepared as well as spiked with one of each herb simultaneously, representing accepted and unaccepted samples. Full factorial design (2^k ) was applied to study the effect of temperature, sonication, and stirring time for extraction optimisation. The HPLC and UV spectral fingerprints were separately subjected to multivariate analysis. The soft independent modelling of class analogy (SIMCA) and partial least squares (PLS) models were developed to segregate the accepted from the unaccepted samples and to predict the herbal composition in addition to the thymol content in each sample.

RESULTS

The SIMCA^uv and SIMCA^hplc models showed correct discrimination between the accepted and unaccepted samples with excellent selectivity and sensitivity. The PLS^uv , PLS^hplc , and PLS^thym models showed excellent linearity and accuracy with R²  > 0.98, slope close to 1, intercept close to 0, low root mean square error of calibration (RMSEC), and root mean square error of prediction (RMSEP) (close to 0). On validation, the PLS models correctly predicted the quantity of the three herbs and thymol content with ±5% accuracy.

CONCLUSION

This study demonstrates the reliability and efficiency of HPLC and UV spectroscopy coupled with multivariate statistical analysis (MVA) for ensuring the consistency of polyherbal preparations.

Quality assessment of leaf extracts of 12 olive cultivars and impact of seasonal variation based on UV spectroscopy and phytochemcial content using multivariate analyses

INTRODUCTION

Recently, focus has been made on the health-oriented uses of olive leaves, a byproduct of olive production, as a potential source of antioxidants. Oleuropein is one of the phenolic components in olive leaves known for its high antioxidant value.

OBJECTIVE

The main aim of the current study was constructing a model for the quality assessment of olive leaves and their potential phytochemical content and hence biological value as well. The phytochemical variation in olive leaves in both flowering (spring) and fruiting seasons (autumn) was also investigated.

METHODS

In this study, the leaves of 12 different olive cultivars from different geographical origins growing in Egypt were assessed for their oleuropein content, total flavonoid (TF) content and total polyphenol (Pph) content in spring and autumn via ultraviolet (UV) spectroscopy and high-performance liquid chromatography (HPLC) coupled to multivariate data analyses. The antioxidant activity of olive leaf extracts was assessed using 2,2'-diphenyl-1-picrylhydrazyl (DPPH) assay.

RESULTS

Higher levels of oleuropein, TF and Pph content were found in spring with the highest oleuropein content in the Spanish cultivar; Manzanillo, followed by the Italian cultivar Coratina and the Egyptian Agizi Okasi (218.94, 151.58 and 122.18 mg/100 g of dried leaf extract, respectively). UV spectra was also measured and the collected data were coupled to multivariate analyses showing clustering of cultivars with common geographical origin.

CONCLUSION

Our findings emphasised the influence of collection time and type of cultivar on the chemical profile of olive leaves. The model presented herein, serves for the quality assessment of olive leaves based on their phytochemical profile.

Optical spectroscopy methods combined with multivariate statistical analysis for the classification of Cretan thyme, multi-floral and honeydew honey

BACKGROUND

The botanical origin of honey attracts both commercial and research interest. Consumers' preferences and medicinal uses of particular honey types drive the demand for the determination of their authenticity with regard to their botanical origin. This study presents the discrimination of thyme, multi-floral. and honeydew honeys by Fourier-transform infrared (FTIR) and ultraviolet (UV) absorption spectroscopy combined with multivariate statistical analysis. UV absorption spectroscopy was applied without any dilution of the sample using a custom-made cuvette. FTIR and UV absorption spectroscopic data were processed by means of the orthogonal partial least squares discriminant analysis.

RESULTS

The optimal classification of floral and honeydew honeys was accomplished with UV spectroscopy with a successful estimation of 92.65% for floral honey and 91.30% for honeydew honey. The discrimination of thyme versus the multi-floral honey was best achieved with FTIR, with a correct classification of 95.56% and 100% for multi-floral and thyme honey respectively. Furthermore, our findings revealed the region of 2400-4000 cm^-1 of the FTIR spectra as the most significant for this discrimination.

CONCLUSION

This work demonstrates that optical spectroscopic techniques in combination with multivariate statistical analysis can be a rapid, low-cost, easy-to-use approach for the determination of the botanical origin of honey without sample pretreatment. © 2021 Society of Chemical Industry.

Honey authentication in terms of its adulteration with sugar syrups using UV-Vis spectroscopy and one-class classifiers

This work proposes the use of UV-Vis spectroscopy and one-class classifiers to authenticate honey in terms of their individual and simultaneous adulterations with corn syrup, agave syrup, and sugarcane molasses. Then, spectra of aqueous authentic (n = 73) and adulterated (n = 162) honey samples were recorded. Before the construction of OC-PLS and DD-SIMCA models, different pre-processing techniques were used to removed baseline shifts. The best result obtained by DD-SIMCA using offset correction, correctly classifying all the samples in the test set. Therefore, the proposed methodology can be used as a promising tool to authenticate honey and prevent fraudulent labeling, affording security to consumers and providing an alternative to regulatory agencies. Moreover, it avoids laborious sample preparation and additional operational costs, since the analytical information is acquired using a routine instrumental technique, without the need for any sample preparation step, other than dilution of the samples in water alone.

ATR-FTIR Spectroscopy, HPLC Chromatography, and Multivariate Analysis for Controlling Bee Pollen Quality in Some Algerian Regions

Bee pollen collected by honeybees (Apis mellifera) is one of the bee products, and it is as valuable as honey, propolis, royal jelly, or beebread. Its quality varies according to its geographic location or plant sources. This study aimed to apply rapid, simple, and accurate analytical methods such as attenuated total reflectance Fourier transform infrared spectroscopy (ATR-FTIR) and high-performance liquid chromatography (HPLC) along with chemometrics analysis to construct a model aimed at discriminating between different pollen samples. In total, 33 samples were collected and analyzed using principal component analysis (PCA), hierarchical clustering analysis (HCA), and partial least squares regression (PLS) to assess the differences and similarities between them. The PCA score plot based on both HPLC and ATR-FTIR revealed the same discriminatory pattern, and the samples were divided into four major classes depending on their total content of polyphenols. The results revealed that spectral data obtained from ATR-FTIR acquired in the region (4000-500 cm^-1) were further subjected to a standard normal variable (SNV) method that removes scattering effects from spectra. However, PCA, HCA, and PLS showed that the best PLS model was obtained with a regression coefficient (R ²) of 0.9001, root-mean-square estimation error (RMSEE) of 0.0304, and root-mean-squared error cross-validation (RMSEcv) of 0.036. Discrimination between the three species has also been possible by combining the pre-processed ATR-FTIR spectra with PCA and PLS. Additionally, the HPLC chromatograms after pre-treatment (SNV) were subjected to unsupervised analysis (PCA-HCA) and supervised analysis (PLS). The PLS model confers good results by factors (R ² = 0.98, RMSEE = 8.22, and RMSEcv = 27.86). Prospects for devising bee pollen quality assessment methods include utilizing ATR-FTIR and HPLC in combination with multivariate methods for rapid authentication of the geographic location or plant sources of bee pollen.

The Use of UV Spectroscopy and SIMCA for the Authentication of Indonesian Honeys According to Botanical, Entomological and Geographical Origins

As a functional food, honey is a food product that is exposed to the risk of food fraud. To mitigate this, the establishment of an authentication system for honey is very important in order to protect both producers and consumers from possible economic losses. This research presents a simple analytical method for the authentication and classification of Indonesian honeys according to their botanical, entomological, and geographical origins using ultraviolet (UV) spectroscopy and SIMCA (soft independent modeling of class analogy). The spectral data of a total of 1040 samples, representing six types of Indonesian honey of different botanical, entomological, and geographical origins, were acquired using a benchtop UV-visible spectrometer (190-400 nm). Three different pre-processing algorithms were simultaneously evaluated; namely an 11-point moving average smoothing, mean normalization, and Savitzky-Golay first derivative with 11 points and second-order polynomial fitting (ordo 2), in order to improve the original spectral data. Chemometrics methods, including exploratory analysis of PCA and SIMCA classification method, was used to classify the honey samples. A clear separation of the six different Indonesian honeys, based on botanical, entomological, and geographical origins, was obtained using PCA calculated from pre-processed spectra from 250-400 nm. The SIMCA classification method provided satisfactory results in classifying honey samples according to their botanical, entomological, and geographical origins and achieved 100% accuracy, sensitivity, and specificity. Several wavelengths were identified (266, 270, 280, 290, 300, 335, and 360 nm) as the most sensitive for discriminating between the different Indonesian honey samples.

Use of visible spectrophotometric fingerprint and chemometric approaches for the differentiation of Mimosa scabrella Bentham honeydew honey

Honeydew honeys, including bracatinga (Mimosa scabrella Bentham) honeydew honey, are highly appreciated due to its nutritional and sensory properties. Therefore, fast and inexpensive methods must be developed to differentiate this distinct class of honey. Although methods using the visible spectrophotometric fingerprint (VSF) have never been used to differentiate bracatinga honeydew honey and blossom honey, this technique appears to be a viable alternative, successfully used in the differentiation and fraud detection of various foods, including honeys from other botanical sources. In this sense, the present study aimed to verify the existence of a specific VSF for bracatinga honeydew honey and blossom honey and differentiate both type of honeys using the VSF associated with chemometric analysis. The VSF of 30 bracatinga honeydew honeys harvested in three different years (2014, 2016, and 2018) and 21 blossom honeys (harvested in 2016) were evaluated. The existence of a different VSF for bracatinga honeydew honeys and blossom honeys allowed the satisfactory differentiation of both types of honeys. Additionally, bracatinga honeydew honey presented a unique VSF, independent of the year of harvest, which can be exploited as a fingerprint of this type of honey, contributing to its authenticity.

Automatic soft independent modeling for class analogies

Soft independent modeling of class analogy (SIMCA) is an important method for authentication. The key parameters for SIMCA, the number of principal components and the decision threshold, determine the model's performance. In this report, a self-optimizing SIMCA that automatically determines these two parameters is devised and referred to as automatic SIMCA (aSIMCA). An efficient optimization is obtained by incorporating response surface modeling (RSM) and bootstrapped Latin partitions with the model-building dataset. A set of design points over the ranges of the two parameters are evaluated with respect to sensitivity and specificity by using the model-building data from target and non-target classes. Averages of the sensitivity and specificity are used as responses for the design points. A 2-dimensional interpolation and a bivariate cubic polynomial were used to model the response surface. As a control method, a grid search that evaluates all combinations of the two parameters over the same ranges was performed in parallel to determine the best conditions for SIMCA and the modeling performance was compared to aSIMCA with RSM. The developed aSIMCA methods were evaluated by authenticating two botanical extracts sets, i.e., marijuana and hemp, with spectral datasets collected from various spectroscopic techniques, including nuclear magnetic resonance, high-resolution mass, and ultraviolet spectrometry. Results of a paired t-test indicated that the aSIMCA with the RSM had similar performance with the one optimized by the grid search for modeling marijuana and hemp, while the RSM was more computationally efficient. The 2-dimensional interpolation is preferred because the better efficiency and the fit to the response surface is more precise.

Establishing authenticity of honey via comprehensive Romanian honey analysis

Assessing the authenticity of honey is a serious problem that has gained much interest internationally because honey has frequently been subject to various fraudulent practices, including mislabelling of botanical and geographical origin and mixing with sugar syrups or honey of lower quality. To protect the health of consumers and avoid competition, which could create an unstable market, consumers, beekeepers and regulatory bodies are interested in having reliable analytical methodologies to detect non-compliant honey. This paper gives an overview of the different approaches used to assess the authenticity of honey, specifically by the application of advanced instrumental techniques, including spectrometric, spectroscopic and chromatographic methods coupled with chemometric interpretation of the data. Recent development in honey analysis and application of the honey authentication process in the Romanian context are highlighted, and future trends in the process of detecting and eliminating fraudulent practices in honey production are discussed.

Authentication and discrimination of green tea samples using UV-vis, FTIR and HPLC techniques coupled with chemometrics analysis

Green tea is a popular beverage consumed worldwide. Its quality should be controlled adequately as the quality is influenced by several factors in addition to adulterations. This study aimed to develop a simple method for assessing the quality of green tea samples obtained from the South and the East Asian regions. The UV-vis, FTIR and HPLC data from 38 samples were subjected to multivariate analyses using the unsupervised recognition techniques comprising Hierarchical Cluster Analysis (HCA) and Principal Component Analysis (PCA). The model for their authentication was constructed and validated by applying the supervised recognition techniques as Soft Independent Modeling of Class Analogy (SIMCA) and Partial Least Square Discriminant Analysis (PLS-DA). The percentages of caffeine in the identified samples were determined using a validated HPLC assay in addition to in vitro determination of their antioxidant activity using DPPH radical-scavenging capacity assay. HCA and PCA based on UV data successfully distributed the tested samples into informative clusters. However, that obtained from visible data could only differentiate samples with respect to their powdered condition. On the contrary, PCA from FTIR and HPLC data could hardly discriminate any of the samples. The models constructed using SIMCA and PLS-DA showed a good class separation between the South and the East Asian samples. The percentages of caffeine in the identified samples and the IC₅₀ in DPPH assay are greatly diverse among all the tested samples. Thus, UV spectroscopy and chemometrics have provided a simple and quick tool for the quality control of commercial green tea samples.

Validation of botanical origins and geographical sources of some Saudi honeys using ultraviolet spectroscopy and chemometric analysis

This study aims at distinguishing honey based on botanical and geographical sources. Different floral honey samples were collected from diverse geographical locations of Saudi Arabia. UV spectroscopy in combination with chemometric analysis including Hierarchical Cluster Analysis (HCA), Principal Component Analysis (PCA), and Soft Independent Modeling of Class Analogy (SIMCA) were used to classify honey samples. HCA and PCA presented the initial clustering pattern to differentiate between botanical as well as geographical sources. The SIMCA model clearly separated the Ziziphus sp. and other monofloral honey samples based on different locations and botanical sources. The results successfully discriminated the honey samples of different botanical and geographical sources validating the segregation observed using few physicochemical parameters that are regularly used for discrimination.

Application of chemometrics in quality control of Turmeric (Curcuma longa) based on Ultra-violet, Fourier transform-infrared and 1H NMR spectroscopy

Turmeric (Curcuma longa L.) belongs to the family Zingiberaceae that is widely used as a spice in food preparations in addition to its biological activities. UV, FT-IR, ¹H NMR in addition to HPLC were applied to construct a metabolic fingerprint for Turmeric in an attempt to assess its quality. 30 samples were analyzed, and then principal component analysis (PCA) and hierarchical clustering analysis (HCA) were utilized to assess the differences and similarities between collected samples. PCA score plot based on both HPLC and UV spectroscopy showed the same discriminatory pattern, where the samples were segregated into four main groups depending on their total curcuminoids content. The results revealed that UV could be utilized as a simple and rapid alternative for HPLC. However, FT-IR failed to discriminate between the same species. By applying ¹H NMR, the metabolic variability between samples was more evident in the essential oils/fatty acid region.

Chemical Profiling of the Essential Oils of Syzygium aqueum, Syzygium samarangense and Eugenia uniflora and Their Discrimination Using Chemometric Analysis

The essential oil compositions of the leaves of three related Myrtaceae species, namely Syzygium aqueum, Syzygium samarangense and Eugenia uniflora, were investigated using GLC/MS and GLC/FID. Altogether, 125 compounds were identified: α-Selinene (13.85%), β-caryophyllene (12.72%) and β-selinene constitute the most abundant constituents in S. aqueum. Germacrene D (21.62%) represents the major compound in S. samarangense whereas in E. uniflora, spathulenol (15.80%) represents the predominant component. Multivariate chemometric analyses were used to discriminate the essential oils using hierarchical cluster analysis (HCA) and principal component analysis (PCA) based on the chromatographic results. The antimicrobial activity of the popularly used E. uniflora essential oil was assessed using broth microdilution method against six Gram-positive, three Gram-negative bacteria and two fungi. The oil showed moderate antimicrobial activity against Bacillus licheniformis exhibiting MIC and MMC of 0.63 mg/ml. The cytotoxic activity of E. uniflora essential oil was investigated against Trypanosoma brucei brucei (T. b. brucei) and MCF-7 cancer cell line using MTT assay. It showed moderate activity against MCF-7 cells with an IC₅₀ value of 76.40 μg/ml. On the other hand, T. brucei was highly susceptible to E. uniflora essential oil with IC₅₀ of 11.20 μg/ml, and a selectivity index of 6.82.

Adulteration Identification of Commercial Honey with the C-4 Sugar Content of Negative Values by an Elemental Analyzer and Liquid Chromatography Coupled to Isotope Ratio Mass Spectroscopy

According to the AOAC 998.12 method, honey is considered to contain significant C-4 sugars with a C-4 sugar content of >7%, which are naturally identified as the adulteration. However, the authenticity of honey with a C-4 sugar content of <0% calculated by the above method has been rarely investigated. A new procedure to determine δ(13)C values of honey, corresponding extracted protein and individual sugars (sucrose, glucose, and fructose), δ(2)H and δ(18)O values, sucrose content, and reducing sugar content of honey using an elemental analyzer and liquid chromatography coupled to isotope ratio mass spectroscopy, was first developed to demonstrate the authenticity of honey with a C-4 sugar content of <0%. For this purpose, 800 commercial honey samples were analyzed. A quite similar pattern on the pentagonal radar plot (isotopic compositions) between honey with -7 < C-4 sugar content (%) < 0 and 0 < C-4 sugar content (%) < 7 indicated that honey with -7 < C-4 sugar content (%) < 0 could be identified to be free of C-4 sugars as well. A very strong correlation is also observed between δ(13)C honey values and δ(13)C protein values of both honey groups. For the δ(18)O value, the C-4 sugar content (%) < -7 group has lower (p < 0.05) values (16.30‰) compared to other honey, which could be a useful parameter for adulterated honey with a C-4 sugar content (%) < -7. The use of isotopic compositions and some systematic differences permits the honey with a C-4 sugar content of <0% to be reliably detected. The developed procedure in this study first and successfully provided favorable evidence in authenticity identification of honey with a C-4 sugar content of <0%.

Classification of Chinese Honeys According to Their Floral Origins Using Elemental and Stable Isotopic Compositions

The objective of this study is to test the feasibility of multi-isotopic and elemental analyses combined with chemometric techniques for differentiating the botanical origins of major honey products in China. The stable isotope and elemental compositions of 57 honey samples from four major floral origins in China (i.e., rape honey, acacia honey, vitex honey, and jujube honey) were analyzed using stable isotope ratio mass spectrometry (IRMS) and inductively coupled plasma mass spectrometry (ICP-MS), respectively. The results showed that hydrogen and oxygen isotopes could be more suitable than the carbon isotope for discriminating the floral origins of major honeys in China. There were significant differences in the contents of most elements between or among different floral origins. The combination of IRMS and ICP-MS methods provides the most effective and accurate approach (in most cases close to 100% accuracy) for classifying Chinese honeys according to their floral origins.

Linking soil, water, and honey composition to assess the geographical origin of argentinean honey by multielemental and isotopic analyses

The objective of this research was to investigate the development of a reliable fingerprint from elemental and isotopic signatures of Argentinean honey to assess its geographical provenance. Honey, soil, and water from three regions (Córdoba, Buenos Aires, and Entre Rı́os) were collected. The multielemental composition was determined by ICP-MS. δ(13)C was measured by isotopic ratio mass spectrometry, whereas the (87)Sr/(86)Sr ratio was determined using thermal ionization mass spectrometry. The data were analyzed by chemometrics looking for the association between the elements, stable isotopes, and honey samples from the three studied areas. Honey samples were differentiated by classification trees and discriminant analysis using a combination of eight key variables (Rb, K/Rb, B, U, (87)Sr/(86)Sr, Na, La, and Zn) presenting differences among the studied regions. The application of canonical correlation analysis and generalized procrustes analysis showed 91.5% consensus between soil, water, and honey samples, in addition to clear differences between studied areas. To the authors' knowledge, this is the first report demonstrating the correspondence between soil, water, and honey samples using different statistical methods, showing that elemental and isotopic honey compositions are related to soil and water characteristics of the site of origin.

Characteristic Fingerprint Based on Low Polar Constituents for Discrimination of Wolfiporia extensa according to Geographical Origin Using UV Spectroscopy and Chemometrics Methods

The fungus species Wolfiporia extensa has a long history of medicinal usage and has also been commercially used to formulate nutraceuticals and functional foods in certain Asian countries. In the present study, a practical and promising method has been developed to discriminate the dried sclerotium of W. extensa collected from different geographical sites based on UV spectroscopy together with chemometrics methods. Characteristic fingerprint of low polar constituents of sample extracts that originated from chloroform has been obtained in the interval 250-400 nm. Chemometric pattern recognition methods such as partial least squares discriminant analysis (PLS-DA) and hierarchical cluster analysis (HCA) were applied to enhance the authenticity of discrimination of the specimens. The results showed that W. extensa samples were well classified according to their geographical origins. The proposed method can fully utilize diversified fingerprint characteristics of sclerotium of W. extensa and requires low-cost equipment and short-time analysis in comparison with other techniques. Meanwhile, this simple and efficient method may serve as a basis for the authentication of other medicinal fungi.

Plausible authentication of manuka honey and related products by measuring leptosperin with methyl syringate

Manuka honey, obtained from Leptospermum scoparium flowers in New Zealand, has strong antibacterial properties. In this study, plausible authentication of the manuka honey was inspected by measuring leptosperin, methyl syringate 4-O-β-D-gentiobiose, along with methyl syringate. Despite a gradual decrease in methyl syringate content over 30 days at 50 °C, even at moderate 37 °C, leptosperin remained stable. A considerable correlation between nonperoxide antibacterial activity and leptosperin content was observed in 20 certified manuka honey samples. Leptosperin and methyl syringate in manuka honey and related products were analyzed using HPLC connected with mass spectrometry. One noncertified brand displayed significant variations in the leptosperin and methyl syringate contents between two samples obtained from different regions. Therefore, certification is clearly required to protect consumers from disguised and/or low-quality honey. Because leptosperin is stable during storage and specific to manuka honey, its measurement may be applicable for manuka honey authentication.