The Use of SPME-GC-MS IR and Raman Techniques for Botanical and Geographical Authentication and Detection of Adulteration of Honey

The Effect of Honey Powder Addition on Chosen Quality Properties of Model Chicken Products

The objective of our paper was to evaluate the effect of honey powder addition on the quality of model chicken products over 14 days of refrigerated storage. Three model chicken product variants were produced: C-control, HP1%, HP2%-with 1 or 2% of honey powder addition. The cooking loss, basic chemical composition, water activity, texture, color, lipid oxidation (TBARS and PDSC), microbiological and sensory quality, and volatile compounds profile were determined. The adverse changes in lipids were slower in products with honey powder added compared to control product, revealing lower TBARS index values and longer oxidation induction times. After 14 days of storage, HP2% products showed significantly lower (up to 50%) TBARS values than control products. Furthermore, honey powder addition reduced the growth of psychrotrophic and lactic acid bacteria for up to 14 days of storage in comparison to the control products. However, deterioration of the volatile compounds profile (presence of alcohols and sulfur compounds) and occurrence of storage odor and flavor had an impact on the poorer sensory desirability of the control and HP1% products. Additional research is necessary aiming to improve the sensory quality of products with honey powder addition.

Detecting Honey Adulteration: Advanced Approach Using UF-GC Coupled with Machine Learning

This article introduces a novel approach to detecting honey adulteration by combining ultra-fast gas chromatography (UF-GC) with advanced machine learning techniques. Machine learning models, particularly support vector regression (SVR) and least absolute shrinkage and selection operator (LASSO), were applied to predict adulteration in orange blossom (OB) and sunflower (SF) honeys. The SVR model achieved R2 values above 0.90 for combined honey types. Treating OB and SF honeys separately resulted in a significant accuracy improvement, with R2 values exceeding 0.99. LASSO proved especially effective when honey types were treated individually. The integration of UF-GC with machine learning not only provides a reliable method for detecting honey adulteration, but also sets a precedent for future research in the application of this technique to other food products, potentially enhancing food authenticity across the industry.

Examining the Use of Polyphenols and Sugars for Authenticating Honey on the U.S. Market: A Comprehensive Review

The rise in honey production and imports into the United States necessitates the need for robust methods to authenticate honey origin and ensure consumer safety. This review addresses the scope of honey authentication, with a specific focus on the exploration of polyphenols and sugar markers to evaluate honeys in the U.S. In the absence of comprehensive federal standards for honey in the United States, challenges related to authenticity and adulteration persist. Examining the global landscape of honey authentication research, we observed a significant gap in the literature pertaining to U.S. honeys. While honeys from Europe, Australia, New Zealand, and Asia have been extensively studied, the decentralized nature of the U.S. honey market and the lack of comprehensive standards have limited the number of investigations conducted. This review consolidates the findings of global honey studies and emphasizes the need for further research studies on honey authenticity markers within the United States. We also explore previous studies on the U.S. that focused on identifying potential markers for honey authenticity. However, the inherent variability in polyphenol profiles and the lack of extensive studies of the sugar contents of honey on a global scale pose challenges to establishing universal markers. We conclude that by addressing these challenges, the field of research on polyphenols and sugars in honey can move toward more reliable and standardized methods. This advancement will enhance the use of polyphenols and other constituents like sugars as authenticity markers, ultimately benefiting both researchers and the honey industry in ensuring honey quality.

Exploring the role of NIR spectroscopy in quantifying and verifying honey authenticity: A review

Honey, recognized for its diverse flavors and nutritional benefits, confronts challenges in maintaining authenticity and quality due to factors like adulteration and mislabelling. This review undertakes a comprehensive exploration of the utility of Near-Infrared (NIR) spectroscopy as a non-destructive analytical method for concurrently evaluating both honey quantity and authenticity. The primary purpose of this investigation is to delve into the various applications of NIR spectroscopy in honey analysis, with a specific focus on its capability to identify and quantify significant quality parameters such as sugar content, moisture levels, 5-HMF, and proline content. Results from the study underscore the effectiveness of NIR spectroscopy, especially when integrated with advanced chemometrics models. This combination not only facilitates quantification of diverse quality parameters but also enhances the classification of honey based on geographical and botanical origin. The technology emerges as a potent tool for detecting adulteration, addressing critical challenges in preserving the authenticity and quality of honey products. The impact of this critical analysis extends to shedding light on the current state, challenges, and future prospects of applying NIR spectroscopy in the honey industry. This analysis outlines the current challenges and future prospects of NIR spectroscopy in the honey industry. Emphasizing its potential to improve consumer confidence and food safety, the research has broader implications for authenticity and quality assurance in honey. Integrating NIR spectroscopy into industry practices could establish stronger quality control measures, benefiting both producers and consumers globally.

Enhancing Odor Analysis with Gas Chromatography-Olfactometry (GC-O): Recent Breakthroughs and Challenges

Gas chromatography-olfactometry (GC-O) has made significant advancements in recent years, with breakthroughs in its applications and the identification of its limitations. This technology is widely used for analyzing complex odor patterns. The review begins by explaining the principles of GC-O, including sample preparation, separation methods, and olfactory evaluation techniques. It then explores the diverse range of applications where GC-O has found success, such as food and beverage industries, environmental monitoring, perfume and aroma development, and forensic analysis. One of the major breakthroughs in GC-O analysis is the improvement in separation power and resolution of odorants. Techniques like rapid GC, comprehensive two-dimensional GC, and multidimensional GC have enhanced the identification and quantification of odor-active chemicals. However, GC-O also has limitations. These include the challenges in detecting and quantifying trace odorants, dealing with matrix effects, and ensuring the repeatability and consistency of results across laboratories. The review examines these limitations closely and discusses potential solutions and future directions for improvement in GC-O analysis. Overall, this review presents a comprehensive overview of the recent advances in GC-O, covering breakthroughs, applications, and limitations. It aims to promote the wider usage of GC-O analysis in odor analysis and related industries. Researchers, practitioners, and anyone interested in leveraging the capabilities of GC-O in analyzing complex odor patterns will find this review a valuable resource. The article highlights the potential of GC-O and encourages further research and development in the field.

Volatile profile of bee bread

Bee bread is one of the least studied bee products. In this study, ten bee bread samples were characterized using palynology and HS-SPME-GC-MS (headspace solid-phase microextraction gas chromatography-mass spectrometry). In total, over one hundred different volatile components were identified, belonging to different chemical groups. Only ten common components were detected in all the samples. These volatiles were ethanol, ethylene chloride, ethyl acetate, acetic acid, α-pinene, furfural, nonane, nonanal, n-hexane and isovaleric acid. Several other components were commonly shared among various bee bread samples. Over sixty detected compounds have not been previously reported in bee bread. The analysis required a mild extraction temperature of 40 °C, as higher temperatures resulted in the Maillard reaction, leading to the production of furfural. The profile of volatile compounds of the tested bee pollen samples was complex and varied. Some relationships have been shown between botanical origin and volatile organic compound profile.

Geographical Origin Authentication-A Mandatory Step in the Efficient Involvement of Honey in Medical Treatment

Nowadays, in people's perceptions, the return to roots in all aspects of life is an increasing temptation. This tendency has also been observed in the medical field, despite the availability of high-level medical services with many years of research, expertise, and trials. Equilibrium is found in the combination of the two tendencies through the inclusion of the scientific experience with the advantages and benefits provided by nature. It is well accepted that the nutritional and medicinal properties of honey are closely related to the botanical origin of the plants at the base of honey production. Despite this, people perceive honey as a natural and subsequently a simple product from a chemical point of view. In reality, honey is a very complex matrix containing more than 200 compounds having a high degree of compositional variability as function of its origin. Therefore, when discussing the nutritional and medicinal properties of honey, the importance of the geographical origin and its link to the honey's composition, due to potential emerging contaminants such as Rare Earth Elements (REEs), should also be considered. This work offers a critical view on the use of honey as a natural superfood, in a direct relationship with its botanical and geographical origin.

Simultaneous qualitative and quantitative analyses of volatile components in Chinese honey of six botanical origins using headspace solid-phase microextraction and gas chromatography-mass spectrometry

BACKGROUND

Honey aroma is one of its most important properties and it depends on the qualitative and quantitative composition of the volatile compounds. The volatile profile of honey could reveal its botanical origin to avoid a false characterization. Thus, it is of great significance to honey authentication. This study developed and validated a headspace solid-phase microextraction and gas chromatography-mass spectrometry (HS-SPME-GC-MS) method for simultaneous qualitative and quantitative analyses of 34 volatile components in honey. The developed method was applied to 86 honey samples from six different botanical origins, including linden honey, rape honey, jujube honey, vitex honey, lavender honey and acacia honey.

RESULTS

The volatile fingerprints and quantitative results were simultaneously obtained by using the full scan and selected ion monitoring (SCAN+SIM) MS scanning mode. The limits of quantification (LOQs) and limits of detection (LODs) of 34 volatile compounds were in the ranges of 1-10 ng/g and 0.3-3 ng/g, respectively. And the spiked recoveries ranged between 70.6% and 126.2%, with the relative standard deviations (RSDs) not higher than 45.4%. A total of 98 volatile compounds were found with relative contents determined, and the 34 volatile compounds were determined with absolute concentrations. Based on the volatile fingerprints and the contents of volatile compounds, honey samples from six botanical origins were well classified by principal component analysis and orthogonal partial least-squares discrimination analysis.

CONCLUSIONS

The HS-SPME-GC-MS method was successfully applied to achieve the volatile fingerprints of six types of honey and to quantitatively analyze 34 volatile compounds with satisfying sensitivity and accuracy. Chemometrics analysis showed significant correlations between honey types and volatiles. These results reveal the characteristics of volatile compounds in six types of unifloral honey and provide some supports for honey authentication. © 2023 Society of Chemical Industry.

Rapid and Automated Method for Detecting and Quantifying Adulterations in High-Quality Honey Using Vis-NIRs in Combination with Machine Learning

Honey is one of the most adulterated foods, usually through the addition of sweeteners or low-cost honeys. This study presents a method based on visible near infrared spectroscopy (Vis-NIRs), in combination with machine learning (ML) algorithms, for the correct identification and quantification of adulterants in honey. Honey samples from two botanical origins (orange blossom and sunflower) were evaluated and adulterated with low-cost honey in different percentages (5%, 10%, 15%, 20%, 25%, 30%, 35%, 40%, 45%, and 50%). The results of the exploratory analysis showed a tendency to group the samples according to botanical origin, as well as the presence of adulteration. A supervised analysis was performed to detect the presence of adulterations. The best performance with 100% accuracy was achieved by support vector machines (SVM) and random forests (RF). A regression study was also carried out to quantify the percentage of adulteration. The best result was obtained by support vector regression (SVR) with a coefficient of determination (R²) of 0.991 and a root mean squared error (RMSE) of 1.894. These results demonstrate the potential of combining ML with spectroscopic data as a method for the automated quality control of honey.

On the Traceability of Honey by Means of Lanthanide Distribution

Honey is a natural food appreciated all over the world since antiquity due to its well-recognised beneficial properties. However, it is also considered among the most counterfeited foods. Therefore, analytical methods are currently being developed to allow the verifying of its geographic provenance and its botanical origin. Trace- and ultra-trace elements are usually exploited as chemical descriptors in authentication studies, as they allow the properties declared in the label to be verified. A different matter is to trace a food by means of traceability, that is, to find the link between a food and the soil in which this food originates. For traceability, it has been demonstrated in several studies that the lanthanides are particularly useful to find this link. In the present study, the traceability of the honey chain has been studied by means of ICP-MS and ICP-OES analysis, by comparing the lanthanide distributions of 17 different monofloral honey chains, each one composed of honey, flowers and soil in which such flowers grew. The results show that, while the fingerprint of soil, described by the lanthanide distribution, is transmitted unaltered from soil to flowers, a slight fractionation on the heavier lanthanides (from Dy to Lu) occurs in the passage from flowers to honey.

Optical Rotation-A Reliable Parameter for Authentication of Honey?

The controversial question of whether optical rotation data can be used to distinguish floral from honeydew honey was investigated. Specific optical rotation angles were determined for 41 honey samples, including floral, honeydew, and adulterated honey, indicating that moderate to high positive optical rotation angles were found for all adulterated samples measured. A strong correlation between the sugar profile and the specific optical rotation angle of honey was confirmed, and a method based on ¹³C NMR metabolomics was proposed to calculate specific optical rotation angles with good correlation with the experimental values. The results indicate that optical rotation is not a reliable method for distinguishing the origin of honey but could indicate adulteration.

Monitoring Volatile Organic Compounds in Different Pear Cultivars during Storage Using HS-SPME with GC-MS

Aroma, which plays an essential role in food perception and acceptability, depends on various mixture of volatile organic compounds (VOCs). Meanwhile, as a field of metabolomics, VOC analysis is highly important for aroma improvement and discrimination purposes. In this work, VOCs in pear fruits were determined via headspace solid-phase micro-extraction (HS-SPME) combined with gas chromatography-mass spectrometry (GC-MS) to study variations among different cultivars and storage stages. In 12 cultivars of pear fruits, a total of 121 VOCs were quantified, including 40 esters, 32 alcohols, 16 aldehydes, 13 alkenes, 11 ketones, 4 acids, and 5 other compounds. The types and amounts of VOCs in different cultivars varied dramatically, which were in the range of 13-71 and 3.63-55.65 mg/kg FW (fresh weight), respectively. The Dr. Guyot cultivar showed the highest level of VOCs, both in type and amount. After 21 days storage at 4 °C, total concentration of VOCs increased from initial levels of 50.76 to 101.33 mg/kg FW. Storage at 20 °C made a larger contribution to production for VOCs than that at 4 °C, resulting in the maximum content of VOCs (117.96 mg/kg FW) in fruit after 14 days storage at 4 °C plus 7 days at 20 °C. During storage, the content of esters showed a gradual increase, while the content of alcohols and aldehydes decreased. Based on the results presented, related alcohols were recognized as the intermediates of conversion from aldehydes to esters.

Characterization, Classification and Authentication of Spanish Blossom and Honeydew Honeys by Non-Targeted HPLC-UV and Off-Line SPE HPLC-UV Polyphenolic Fingerprinting Strategies

Honey is a highly consumed natural product produced by bees which is susceptible to fraudulent practices, some of them regarding its botanical origin. Two HPLC-UV non-targeted fingerprinting approaches were evaluated in this work to address honey characterization, classification, and authentication based on honey botanical variety. The first method used no sample treatment and a universal reversed-phase chromatographic separation. On the contrary, the second method was based on an off-line SPE preconcentration method, optimized for the isolation and extraction of polyphenolic compounds, and a reversed-phase chromatographic separation optimized for polyphenols as well. For the off-line SPE method, the use of HLB (3 mL, 60 mg) cartridges, and 6 mL of methanol as eluent, allowed to achieve acceptable recoveries for the selected polyphenols. The obtained HPLC-UV fingerprints were subjected to an exploratory principal component analysis (PCA) and a classificatory partial least squares-discriminant analysis (PLS-DA) to evaluate their viability as sample chemical descriptors for authentication purposes. Both HPLC-UV fingerprints resulted to be appropriate to discriminate between blossom honeys and honeydew honeys. However, a superior performance was accomplished with off-line SPE HPLC-UV polyphenolic fingerprints, being able to differentiate among the different blossom honey samples under the study (orange/lemon blossom, rosemary, thyme, eucalyptus, and heather). In general, this work demonstrated the feasibility of HPLC-UV fingerprints, especially those obtained after off-line SPE polyphenolic isolation and extraction, to be employed as honey chemical descriptors to address the characterization and classification of honey samples according to their botanical origin.

Assessing food authenticity through protein and metabolic markers

This chapter aims to address an issue of ancient origins, but more and more topical in a globalized world in which consumers and stakeholders are increasingly aware: the authenticity of food. Foods are systems that can also be very complex, and verifying the correspondence between what is declared and the actual characteristics of the product is often a challenging issue. The complexity of the question we want to answer (is the food authentic?) means that the answer is equally articulated and makes use of many different analytical techniques. This chapter will consider the chemical analyses of foods aimed at guaranteeing their authenticity and will focus on frontier methods that have been developed in recent years to address the need to respond to ever-increasing guarantees of authenticity. Targeted and non-targeted approaches will be considered for verifying the authenticity of foods, through the study of different classes of constituents (proteins, metabolites, lipids, flavors). The numerous approaches available (proteomics, metabolomics, lipidomics) and the related analytical techniques (LC-MS, GC-MS, NMR) are first described from a more general point of view, after which their specific application for the purposes of authentication of food is addressed.

Geographical identification of strawberries based on stable isotope ratio and multi-elemental analysis coupled with multivariate statistical analysis: A Slovenian case study

The geographical classification and authentication of strawberries were attempted using discriminant and class-modelling methods applied to stable isotopes of light elements and elemental composition. The work involved creating a database of 92 authentic Slovenian strawberry samples and 32 imported samples. All samples were harvested between 2018 and 2020. A good geographical classification of Slovenian and non-Slovenian strawberries was obtained despite different production years using discriminant approaches. However, for verifying compliance with a given specification (geographical indications), a class-modelling approach was used to build an unbiased verification model. Class models generated by data-driven soft independent modelling of class analogy (DD-SIMCA) had high sensitivity (96% to 97%) and good specificity (81% to 91%) on a yearly basis, while a more generalised model combining total yearly data gave a lower specificity (63%). Of the 33 commercially available samples (test samples) with declared Slovenian origin, 39% were from outside of Slovenia.