Geographical origin and botanical type honey authentication through elemental metabolomics via chemometrics

New Insights on Quality, Safety, Nutritional, and Nutraceutical Properties of Honeydew Honeys from Italy

Honeydew honey is less studied than nectar honey, although it is characterized by peculiar nutritional properties. This is mainly due to its challenging production, which leads to easy counterfeiting and difficult valorization. This contribution aims to provide a comprehensive characterization of the physico-chemical, palynological, functional, and food safety properties of a large sampling of honeydew honeys collected throughout Italy. The honeydew elements, conductivity, color, antioxidant properties, total polyphenol content, hydroxymethylfurfural, major and trace elements, toxic and rare earth elements, and pesticide residues were measured in 59 samples of honeydew honey from forest, eucalyptus, fir, oak, and citrus sources. Physico-chemical and antioxidant properties were unable to differentiate the botanical origin of Italian honeydew honeys. Similarly, the mineral composition did not vary significantly, whereas rare earth elements appeared to be promising markers for classifying their origin. Multivariate analysis allowed discriminating fir honeydews from the other varieties. Concerning safety aspects, pesticide residues were detected in 90% of the samples, with fir honeydews exhibiting the lowest contamination levels, probably due to its production in less industrialized areas. Acetamiprid and imidacloprid were the most prevalent pesticide residues, but their concentrations were below the limit indicated by the EFSA. These findings suggest the need for a continuous monitoring program for contaminants to ensure safety and to assess risk.

Ultrasound-Assisted Extraction Followed by Inductively Coupled Plasma Mass Spectrometry and Multivariate Profiling of Rare Earth Elements in Coffee

A rapid and efficient ultrasound-assisted extraction (UAE) procedure followed by inductively coupled plasma mass spectrometry (ICP-MS) was developed for the determination of 14 rare earth elements (REEs) (La, Ce, Pr, Nd, Sm, Eu, Gd, Tb, Dy, Ho, Er, Tm, Yb, Lu), along with yttrium (Y) and scandium (Sc), in coffee samples. The method was validated using certified reference material (NIST SRM 1547), recovery tests at four fortification levels, and comparisons with microwave-assisted digestion (MAD). Excellent accuracy and precision were achieved, with recovery rates ranging from 80.1% to 112% and relative standard deviations (RSD%) below 14%. Limits of detection (LODs) ranged from 0.2 ng/kg (Yb) to 0.16 µg/kg (Nd). Total REE concentrations varied between 8.3 µg/kg and 1.1 mg/kg, with the highest individual mean concentrations (µg/kg) observed for Ce (11.7), La (6.0), and Sc (4.7). The lowest individual mean concentrations (µg/kg) were for Ho (0.16), Lu (0.066), and Tm (0.063). Multivariate analysis of REE profiles from 92 coffee samples collected in Serbia revealed clear distinctions between ground roasted and instant coffees, as well as between different surrogate blends. This study indicated that the determination of coffee's geographical origin was not possible due to the diverse types, blends, and additives. However, differences in REE profiles suggest potential classification based on variety. REEs pose a negligible health risk to coffee consumers, with HI values ranging from 4.7 × 10-8 to 6.3 × 10-6 and TCR ranging from 2.6 × 10-14 to 3.5 × 10-12.

Portable NIR Spectroscopy to Simultaneously Trace Honey Botanical and Geographical Origins and Detect Syrup Adulteration

Fraudulent practices concerning honey are growing fast and involve misrepresentation of origin and adulteration. Simple and feasible methods for honey authentication are needed to ascertain honey compliance and quality. Working on a robust dataset and simultaneously investigating honey traceability and adulterant detection, this study proposed a portable FTNIR fingerprinting approach combined with chemometrics. Multifloral and unifloral honey samples (n = 244) from Spain and Sardinia (Italy) were discriminated by botanical and geographical origin. Qualitative and quantitative methods were developed using linear discriminant analysis (LDA) and partial least squares (PLS) regression to detect adulterated honey with two syrups, consisting of glucose, fructose, and maltose. Botanical and geographical origins were predicted with 90% and 95% accuracy, respectively. LDA models discriminated pure and adulterated honey samples with an accuracy of over 92%, whereas PLS allows for the accurate quantification of over 10% of adulterants in unifloral and 20% in multifloral honey.

Identifying Key Markers for Monofloral (Eucalyptus, Rosemary, and Orange Blossom) and Multifloral Honey Differentiation in the Spanish Market by UHPLC-Q-Orbitrap-High-Resolution Mass Spectrometry Fingerprinting and Chemometrics

Honey differentiation based on the botanical origin is crucial to guarantee product authenticity, especially considering the increasing number of fraud cases. This study assessed the metabolomic differences arising from various botanical origins in honey products sold in Spanish markets, focusing on two goals: (1) discrimination within monofloral samples (eucalyptus, rosemary, and orange blossom honey) and (2) differentiation between multifloral vs. monofloral honey samples. An omics strategy based on ultra-high-performance liquid chromatography coupled with quadrupole-Orbitrap-high-resolution mass spectrometry (UHPLC-Q-Orbitrap-HRMS) was applied for the reliable identification of specific honey markers selected by orthogonal partial least squares discriminant analysis (OPLS-DA) (R2Y = 0.929-0.981 and Q2 = 0.868-0.952), followed by the variable importance in projection (VIP) approach. Key amino acid, alkaloid, and trisaccharide markers were identified to distinguish between honey samples. Some Amadori compounds were highlighted as eucalyptus honey markers, suggesting their potential use for honey aging and botanical origin differentiation. L-phenylalanine and raffinose were markers of rosemary honey. Four markers (e.g., trigonelline, L-isoleucine, and N-(1-deoxy-1-fructosyl)isoleucine) were found in higher levels in multifloral samples, indicating a greater availability of amino acids, potentially increasing the Maillard reaction. This research is the first to address the botanical origin's impact on honey by identifying novel markers not previously described.

Mineral Concentrations in Different Types of Honey Originating from Three Regions of Continental Croatia

Honey has been recognized as a reliable indicator of environmental quality because of honeybees' intense foraging activity, which brings them into contact with many persistent organic pollutants around the hive. In this study, four types of honey (meadow, acacia, chestnut, and honey in comb) collected at three different locations were analyzed for Co, Cr, Cu, Fe, Mn, Pb, and Zn levels. The highest levels of Fe and Cu in chestnut honey, Co and Zn in meadow honey, and Pb in honey in comb were observed in Varaždin County. The lowest levels of Pb in meadow honey and Co in comb honey were found from apiaries in Sisak-Moslavina County. Significant differences in the mean concentrations of Cr, Cu, Mn, and Fe were observed among the four honey types. Conversely, no significant differences in Co, Pb, and Zn levels were found. Most of the significant differences between the elements are related to chestnut honey. While sampling location (Fe) and type of honey (Pb), or both (Cr and Zn), significantly influenced the concentrations of some elements, these factors were found to be irrelevant for Mn, Co, and Cu. The results showed varying degrees of similarities and differences in mineral levels in honey samples, depending on floral and geographical origin.

Metabolomics applications for plant-based foods origin tracing, cultivars identification and processing: Feasibility and future aspects

Metabolomics, the systematic study of metabolites, is dedicated to a comprehensive analysis of all aspects of plant-based food research and plays a pivotal role in the nutritional composition and quality control of plant-based foods. The diverse chemical compositions of plant-based foods lead to variations in sensory characteristics and nutritional value. This review explores the application of the metabolomics method to plant-based food origin tracing, cultivar identification, and processing methods. It also addresses the challenges encountered and outlines future directions. Typically, when combined with other omics or techniques, synergistic and complementary information is uncovered, enhancing the classification and prediction capabilities of models. Future research should aim to evaluate all factors affecting food quality comprehensively, and this necessitates advanced research into influence mechanisms, metabolic pathways, and gene expression.

Identification of lentils (Lens culinaris Medik) from Eglouvi (Lefkada, Greece) based on rare earth elements profile combined with chemometrics

An analytical approach has been developed to verify the authenticity of premium lentils originating from Eglouvi, Lefkada, Greece. The method relies on the digestion of samples followed by the analysis of their rare earth elements (REEs) content. Lentils originating from Eglouvi exhibit higher content in most REEs compared to lentils from other regions as well as distinct Sc/Y and Sc/Yb concentration ratios. Principal component analysis effectively segregates "Eglouvi" lentils into a distinct cluster. Soft Independent Modelling of Class Analogy (SIMCA) successfully models "Eglouvi" lentils. Significant enhancement in model specificity was achieved upon inclusion of Sc/Y and Sc/Yb concentration ratios as additional variables. The model is capable of detecting adulteration in blends of Eglouvi lentils, with a minimum rejection threshold of 4.6% w/w for Greek lentil adulterants and 6.0% w/w for imported lentil adulterants.

Mass Spectrometry Characterization of Honeydew Honey: A Critical Review

Honeydew honey is produced by bees (Apis mellifera) foraging and collecting secretions produced by certain types of aphids on various parts of plants. In addition to exhibiting organoleptic characteristics that distinguish them from nectar honey, these honeys are known for their functional properties, such as strong antioxidant and anti-inflammatory activities. Despite their importance, they remain poorly characterized in comparison with flower honeys, as most studies on this subject are not only carried out on too few samples but also still focused on traditional chemical-physical parameters, such as specific rotation, major sugars, or melissopalynological information. Since mass spectrometry has consistently been a primary tool for the characterization and authentication of honeys, this review will focus on the application of these methods to the characterization of the minor fraction of honeydew honey. More specifically, this review will attempt to highlight what progress has been made so far in identifying markers of the authenticity of the botanical and/or geographical origin of honeydew honeys by mass spectrometry-based approaches. Furthermore, strategies devoted to the determination of contaminants and toxins in honeydew honeys will be addressed. Such analyses represent a valuable tool for establishing the level of food safety associated with these products. A critical analysis of the presented studies will identify their limitations and critical issues, thereby describing the current state of research on the topic.

Analytical insights for ensuring authenticity of Greek agriculture products: Unveiling chemical marker applications

Food authentication, including the differentiation of geographical or botanical origin, the method of production i.e. organic vs. conventional farming as well as the detection of food fraud/adulteration, has been a rapidly growing field over the past two decades due to increasing public awareness regarding food quality and safety, nutrition, and health. Concerned parties include consumers, producers, and legislators. Thus, the development of rapid, accurate, sensitive, and reproducible analytical methods to guarantee the authenticity of foods is of primary interest to scientists and technologists. The aim of the present article is to summarize research work carried out on the authentication of Greek agricultural products using spectroscopic (NIR, FTIR, UV-Vis, Raman and fluorescence spectroscopy, NMR, IRMS, ICP-OES, ICP-MS) and chromatographic (GC, GC/MS, HPLC, HPLC/MS, etc.) methods of analysis in combination with chemometrics highlighting the chemical markers that enable product authentication. The review identified a large number of chemical markers including volatiles, phenolic substances, natural pigments, elements, isotopes, etc. which can be used for (i) the differentiation of botanical/geographical origin; conventional from organic farming; production procedure and vintage year, etc. and (ii) detection of adulteration of high quality plant and animal origin foods with lower value substitutes. Finally, the constant development of reliable analytical techniques in combination with law enforcement authorities will ensure authentic foods in terms of quality and safety for consumers.

A one-class classification approach for authentication of specialty coffees by inductively coupled plasma mass spectroscopy (ICP-MS)

Due to the lucrative nature of specialty coffees, there have been instances of adulteration where low-cost materials are mixed in to increase the overall volume, resulting in illegal profit. A widely used and recommended approach to detect possible adulteration is the application of one-class classifiers (OCC), which only require information about the target class to build the models. Thus, this work aimed to identify adulterations in specialty coffees with low-quality coffee using multielement analysis determined by ICP-MS and to evaluate the performance of one-class classifiers (dd-SIMCA, OCRF, and OCPLS). Therefore, authentic specialty coffee samples were adulterated with low-quality coffee in 25 % to 75 % (w/w) proportions. Samples were subjected to acid decomposition for analysis by ICP-MS. OCPLS method presented the best performance to detect adulterations with low-quality coffee in specialty coffees, showing higher specificity (SPE = 100 %) and reliability rate (RLR = 94.3 %).

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.

Elemental Fingerprinting Combined with Machine Learning Techniques as a Powerful Tool for Geographical Discrimination of Honeys from Nearby Regions

Discrimination of honey based on geographical origin is a common fraudulent practice and is one of the most investigated topics in honey authentication. This research aims to discriminate honeys according to their geographical origin by combining elemental fingerprinting with machine-learning techniques. In particular, the main objective of this study is to distinguish the origin of unifloral and multifloral honeys produced in neighboring regions, such as Sardinia (Italy) and Spain. The elemental compositions of 247 honeys were determined using Inductively Coupled Plasma Mass Spectrometry (ICP-MS). The origins of honey were differentiated using Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA), and Random Forest (RF). Compared to LDA, RF demonstrated greater stability and better classification performance. The best classification was based on geographical origin, achieving 90% accuracy using Na, Mg, Mn, Sr, Zn, Ce, Nd, Eu, and Tb as predictors.

Enabling honey quality and authenticity with NMR and LC-IRMS based platform

Honey has been known for economically motivated adulteration around the world, because of its high demand and short supply. As consequence increasing honey production using the deliberate addition of sugar syrups while claiming a fictitious origin and diversifying it to increase its value. Generally, honey testing is supervised by a set of guidelines and quality parameters to ensure its quality and authenticity. As per the many regulatory bodies, current honey scams have been challenging to identify with conventional methods, so quality control labs require sophisticated technology. With these paradigm shifts, the aim of the present review is focused on the authenticity of honey through two important cutting-edge methods viz LC-IRMS and NMR. The LC-IRMS aids in the detection of added C₃ and C₄ sugars. Whereas NMR has provided a potent solution by allowing the classification of botanical varieties and geographical origin along with the quantification of a set of quality parameters in a single experiment.

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.

Insight into the Recent Application of Chemometrics in Quality Analysis and Characterization of Bee Honey during Processing and Storage

The application of chemometrics, a widely used science in food studies (and not only food studies) has begun to increase in importance with chemometrics being a very powerful tool in analyzing large numbers of results. In the case of honey, chemometrics is usually used for assessing honey authenticity and quality control, combined with well-established analytical methods. Research related to investigation of the quality changes in honey due to modifications after processing and storage is rare, with a visibly increasing tendency in the last decade (and concentrated on investigating novel methods to preserve the honey quality, such as ultrasound or high-pressure treatment). This review presents the evolution in the last few years in using chemometrics in analyzing honey quality during processing and storage. The advantages of using chemometrics in assessing honey quality during storage and processing are presented, together with the main characteristics of some well-known chemometric methods. Chemometrics prove to be a successful tool to differentiate honey samples based on changes of characteristics during storage and processing.

Multi-element Analysis of Honey from Amhara Region-Ethiopia for Quality, Bioindicator of Environmental Pollution, and Geographical Origin Discrimination

Honey is a widely utilized sweetener containing mainly sugars with many other minor ingredients such as metallic elements. The analysis aimed to develop a chemometric model for tracing the geographical origin, evaluating nutritional quality, assessing pollution effect, and searching for marker metals for the region's honey. Forty-seven honey samples were collected directly from the apiarists at seven administrative zones. The contents of 14 metals were analyzed using inductively coupled plasma optical emission spectrometry after standard sample digestion. The findings showed us the major elements ranged from 24.8 to 1996 mg/kg of the honey sample with K > Ca > Na > Mg. The minimum and maximum values for the trace metals were 2.35 mg/kg and 163 mg/kg, respectively, in the order of Fe > Cr > Zn > Ni > Mn > Cu > Co. From this data, the region's honey has its own contribution as a source of major and trace elements. Furthermore, mean values for the toxic heavy metals were 0.57 to 1.85 for Pb, 1.03 to 1.21 for Cd, and 2.85 to 6.21 for As in mg/kg. Thus, the pollution level in the environment seems to be at an alarming rate. Using principal components analysis (PCA), the first four principal components explained 80.16% of the total variation. The region's honey was best classified into five major clusters using linear discriminant analysis (LDA) with an average discrimination power of 89.91%. The LDA sorting model was verified by the cross-validation method. The verification revealed that the model has 92.11% recognition power and 93.33% prediction ability.

Geographical Origin Determination of Cigar at Different Spatial Scales Based on C and N Metabolites and Mineral Elements Combined with Chemometric Analysis

In this paper, five C and N metabolites and eighteen mineral elements were used to identify the cigar's geographical origin on a country scale (Dominica, Indonesia, and China) and on a prefecture scale (Yuxi, Puer, and Lincang in China). The results show that the best origin traceability method is the combination of C and N metabolites and mineral elements method. Its. Its accuracy of cross-validation can achieve 95% on a country scale and 94% on a prefecture scale. Determination accuracy is ranked as identification by combination > mineral elements > C and N metabolites. For geo-origin determination of cigars, mineral element identification is better than that metabolite identification. The algorithm and factors for origin determination are selected. The results can be used to guide cigar agricultural practices and monitor and regulate the cigar in production and circulation.

Inductively Coupled Plasma-Mass Spectrometry (ICP-MS), a Useful Tool in Authenticity of Agricultural Products' and Foods' Origin

Fraudulent practices are the first and foremost concern of food industry, with significant consequences in economy and human's health. The increasing demand for food has led to food fraud by replacing, mixing, blending, and mislabeling products attempting to increase the profits of producers and companies. Consequently, there was the rise of a multidisciplinary field which encompasses a large number of analytical techniques aiming to trace and authenticate the origins of agricultural products, food and beverages. Among the analytical strategies have been developed for the authentication of geographical origin of foodstuff, Inductively Coupled Plasma Mass Spectrometry (ICP-MS) increasingly dominates the field as a robust, accurate, and highly sensitive technique for determining the inorganic elements in food substances. Inorganic elements are well known for evaluating the nutritional composition of food products while it has been shown that they are considered as possible tracers for authenticating the geographical origin. This is based on the fact that the inorganic component of identical food type originating from different territories varies due to the diversity of matrix composition. The present systematic literature review focusing on gathering the research has been done up-to-date on authenticating the geographical origin of agricultural products and foods by utilizing the ICP-MS technique. The first part of the article is a tutorial about food safety/control and the fundaments of ICP-MS technique, while in the second part the total research review is discussed.

Glucose Oxidase and Catalase Activities in Honey Samples from the Southwestern Region of Saudi Arabia

The activity of honey enzymes are affected by floral and geographical origins, climate conditions, honeybee species, health and nutrition. This article investigated the effect of floral and geographical origins on the activity of glucose oxidase (GOx) and catalase (CAT) enzymes in honey samples from the southwestern region of Saudi Arabia. Moreover, the moisture, total sugars, pH and conductivity were measured as quality parameters. The floral origin of the honey samples was determined microscopically while the quality parameters were measured according to the methods of international honey commission. The activity of the honey enzyme was determined following the instructions of the Megazyme International kits. The obtained results were statistically analyzed by the statistical Package for Social Sciences (SPSS, v.20). The GOx activity of the Acacia, Ziziphus and polyfloral honey samples of the Asir region were (5.19 ± 2.33 U/g), (4.01 ± 1.17 U/g) and (5.69 ± 1.67 U/g), respectively. The Acacia, Ziziphus and polyfloral honey samples from the Jazan region had GOx activities of (6.85 ± 0.47 U/g), (10.48 ± 9.22 U/g) and (5.31 ± 2.7 U/g), respectively. The geographical origin significantly affected the GOx activity of Ziziphus honey (p-value = 0.005) and the GOx activity of the Ziziphus honey was significantly more than that of the polyfloral honey of the Jazan region (p-value = 0.009). With regard to the CAT activity in Asir region honey samples, the mean values of the Acacia, Ziziphus and polyfloral honeys were (2.89 ± 1.08 U/g), (3.58 ± 1.59 U/g) and (2.84 ± 1.24 U/g), respectively. The mean values of the CAT activity in the Jazan honey samples were Acacia (4.35 ± 1.01 U/g), Ziziphus (3.94 ± 0.04 U/g) and polyfloral (3.43 ± 0.67 U/g). The geographical origin significantly affected the CAT activity in Acacia honey (p-value = 0.014). The geographical and floral origins had significant effects on the activity of the honey GOx and CAT enzymes.

Multi-Element Analysis and Origin Discrimination of Panax notoginseng Based on Inductively Coupled Plasma Tandem Mass Spectrometry (ICP-MS/MS)

Panax notoginseng is an important functional health product, and has been used worldwide because of a wide range of pharmacological activities, of which the taproot is the main edible or medicinal part. However, the technologies for origin discrimination still need to be further studied. In this study, an ICP-MS/MS method for the accurate determination of 49 elements was established, whereby the instrumental detection limits (LODs) were between 0.0003 and 7.716 mg/kg, whereas the quantification limits (LOQs) were between 0.0011 and 25.7202 mg/kg, recovery of the method was in the range of 85.82% to 104.98%, and the relative standard deviations (RSDs) were lower than 10%. Based on the content of multi-element in P. notoginseng (total of 89 mixed samples), the discriminant models of origins and cultivation models were accurately determined by the neural networks (prediction accuracy was 0.9259 and area under ROC curve was 0.9750) and the support vector machine algorithm (both 1.0000), respectively. The discriminant models established in this study could be used to support transparency and traceability of supply chains of P. notoginseng and thus avoid the fraud of geographic identification.

An overview on multi-elemental profile integrated with chemometrics for food quality assessment: toward new challenges

Food products, especially those with high value-added, are commonly subjected to strict quality controls, which are of paramount importance, especially for attesting to some peculiar features related, for instance, to their geographical origin and/or the know-how of their producers. However, the sophistication of fraudulent practices requires a continuous update of analytical platforms. Different analytical techniques have become extremely appealing since the instrumental analysis tools evolution has substantially improved the capability to reveal and understand the complexity of food. In light of this, multi-elemental composition has been successful implemented solving a plethora of food authentication and traceability issues. In the last decades, it has existed an ever-increasing trend in analysis based on spectrometry analytical platforms in order to obtain a multi-elemental profile that combined with chemometrics have been noteworthy analytical methodologies able to solve these problems. This review provides an overview of published reports in the last decade (from 2011 to 2021) on food authentication and quality control from their multi-element composition in order to evaluate the state-of-the-art of this field and to identify the main characteristics of applied analytical techniques and chemometric data treatments that have permit achieve accurate discrimination/classification models, highlighting the strengths and the weaknesses of these methodologies.

Multi-Elemental Analysis as a Tool to Ascertain the Safety and the Origin of Beehive Products: Development, Validation, and Application of an ICP-MS Method on Four Unifloral Honeys Produced in Sardinia, Italy

Despite unifloral honeys from Sardinia, Italy, being appreciated worldwide for their peculiar organoleptic features, their elemental signature has only partly been investigated. Hence, the principal aim of this study was to measure the concentration of trace and toxic elements (i.e., Ag, As, Ba, Be, Bi, Cd, Co, Cr, Cu, Fe, Hg, Li, Mn, Mo, Ni, Pb, Sb, Sn, Sr, Te, Tl, V, and Zn) in four unifloral honeys produced in Sardinia. For this purpose, an original ICP-MS method was developed, fully validated, and applied on unifloral honeys from asphodel, eucalyptus, strawberry tree, and thistle. Particular attention was paid to the method’s development: factorial design was applied for the optimization of the acid microwave digestion, whereas the instrumental parameters were tuned to minimize the polyatomic interferences. Most of the analytes’ concentration ranged between the relevant LoDs and few mg kg⁻¹, while toxic elements were present in negligible amounts. The elemental signatures of asphodel and thistle honeys were measured for the first time, whereas those of eucalyptus and strawberry tree honeys suggested a geographical differentiation if compared with the literature. Chemometric analysis allowed for the botanical discrimination of honeys through their elemental signature, whereas linear discriminant analysis provided an accuracy level of 87.1%.

Unraveling the Complex Composition of Produced Water by Specialized Extraction Methodologies

Produced water (PW), a waste byproduct of oil and gas extraction, is a complex mixture containing numerous organic solubles and elemental species; these constituents range from polycyclic aromatic hydrocarbons to naturally occurring radioactive materials. Identification of these compounds is critical in developing reuse and disposal protocols to minimize environmental contamination and health risks. In this study, versatile extraction methodologies were investigated for the untargeted analysis of PW. Thin-film solid-phase microextraction with hydrophilic-lipophilic balance particles was utilized for the extraction of organic solubles from eight PW samples from the Permian Basin and Eagle Ford formation in Texas. Gas chromatography-mass spectrometry analysis found a total of 266 different organic constituents including 1,4-dioxane, atrazine, pyridine, and PAHs. The elemental composition of PW was evaluated using dispersive solid-phase extraction followed by inductively coupled plasma-mass spectrometry, utilizing a new coordinating sorbent, poly(pyrrole-1-carboxylic acid). This confirmed the presence of 29 elements including rare earth elements, as well as hazardous metals such as Cr, Cd, Pb, and U. Utilizing chemometric analysis, both approaches facilitated the discrimination of each PW sample based on their geochemical origin with a prediction accuracy above 90% using partial least-squares-discriminant analysis, paving the way for PW origin tracing in the environment.

Levels of trace elements and rare earth elements in honey from Jordan

Honey is a common sweetener in the Jordanian diet with an annual consumption of approximately one thousand tons, two-thirds of which are imported. It is believed that the elemental profile of honey is an indicator of safety and botanical and geographic origin. In the literature, there are a lack of studies concerning the levels of major and trace elements in honey in Jordan. A total of 46 elements, including 15 rare earth elements (REEs), were analyzed by inductively coupled plasma-mass spectrometry (ICP-MS) in 18 monofloral and multifloral imported honey samples and 12 multifloral local samples. Regarding monofloral samples, Black Forest samples had the highest total metal content, while acacia samples had the lowest total metal content. Local multifloral honey had the largest Sr and total REE levels, while it had the lowest Mn levels. Very low levels of toxic elements were found in all samples, indicating the safety of honey in Jordan for human consumption. The results of this study showed that a large number of samples (> 100) and the application of advanced statistical models are required to discriminate between multifloral imported and local honey.

Recent techniques for the authentication of the geographical origin of tea leaves from camellia sinensis: A review

Tea is one of the most important beverages worldwide, is produced in several distinct geographical regions, and is traded on the global market. The ability to determine the geographical origin of tea products helps to ensure authenticity and traceability. This paper reviews the recent research on authentication of tea using a combination of instrumental and chemometric methods. To determine the production region of a tea sample, instrumental methods based on analyzing isotope and mineral element contents are suitable because they are less affected by tea variety and processing methods. Chemometric analysis has proven to be a valuable method to identify tea. Principal component analysis (PCA) and linear discriminant analysis (LDA) are the most preferred methods for processing large amounts of data obtained through instrumental component analysis.

A New Approach for Determination of the Botanical Origin of Monofloral Bee Honey, Combining Mineral Content, Physicochemical Parameters, and Self-Organizing Maps

A new approach for the botanical origin determination of monofloral bee honey is developed. The methodology combines mineral content and physicochemical parameters determination with intelligent statistics such as self-organizing maps (SOMs). A total of 62 monofloral bee honey samples were analysed, including 31 linden, 14 rapeseed, 13 sunflower, and 4 acacia. All of them were harvested in 2018 and 2019 from trusted beekeepers, after confirming their botanical origin, using melissopalynological analysis. Nine physicochemical parameters were determined, including colour, water content, pH, electrical conductivity, hydroxymethylfurfural content, diastase activity, specific optical rotation, invertase activity, and proline. The content of thirty chemical elements (Ag, Al, As, B, Ba, Bi, Ca, Cd, Co, Cr, Cs, Cu, Fe, Ga, In, K, Li, Mg, Mn, Na, Ni, P, Pb, Rb, S, Se, Sr, Te, V, and Zn) was measured using ICP-OES, ICP-MS, and FAAS as instrumental techniques. The visualisation of the SOMs shows an excellent separation of honey samples in five well-defined clusters—linden, rapeseed, acacia, sunflower, and polyfloral honey—using the following set of 16 descriptors: diastase activity, hydroxymethylfurfural content, invertase activity, pH, specific optical rotation, water content, Al, B, Cr, Cs, K, Na, Ni, Rb, V, and Zn.

FoodOmicsGR_RI. A Consortium for Comprehensive Molecular Characterisation of Food Products

The national infrastructure FoodOmicsGR_RI coordinates research efforts from eight Greek Universities and Research Centers in a network aiming to support research and development (R&D) in the agri-food sector. The goals of FoodOmicsGR_RI are the comprehensive in-depth characterization of foods using cutting-edge omics technologies and the support of dietary/nutrition studies. The network combines strong omics expertise with expert field/application scientists (food/nutrition sciences, plant protection/plant growth, animal husbandry, apiculture and 10 other fields). Human resources involve more than 60 staff scientists and more than 30 recruits. State-of-the-art technologies and instrumentation is available for the comprehensive mapping of the food composition and available genetic resources, the assessment of the distinct value of foods, and the effect of nutritional intervention on the metabolic profile of biological samples of consumers and animal models. The consortium has the know-how and expertise that covers the breadth of the Greek agri-food sector. Metabolomics teams have developed and implemented a variety of methods for profiling and quantitative analysis. The implementation plan includes the following research axes: development of a detailed database of Greek food constituents; exploitation of "omics" technologies to assess domestic agricultural biodiversity aiding authenticity-traceability control/certification of geographical/genetic origin; highlighting unique characteristics of Greek products with an emphasis on quality, sustainability and food safety; assessment of diet's effect on health and well-being; creating added value from agri-food waste. FoodOmicsGR_RI develops new tools to evaluate the nutritional value of Greek foods, study the role of traditional foods and Greek functional foods in the prevention of chronic diseases and support health claims of Greek traditional products. FoodOmicsGR_RI provides access to state-of-the-art facilities, unique, well-characterised sample sets, obtained from precision/experimental farming/breeding (milk, honey, meat, olive oil and so forth) along with more than 20 complementary scientific disciplines. FoodOmicsGR_RI is open for collaboration with national and international stakeholders.

Artificial Intelligence in Nutrients Science Research: A Review

Artificial intelligence (AI) as a branch of computer science, the purpose of which is to imitate thought processes, learning abilities and knowledge management, finds more and more applications in experimental and clinical medicine. In recent decades, there has been an expansion of AI applications in biomedical sciences. The possibilities of artificial intelligence in the field of medical diagnostics, risk prediction and support of therapeutic techniques are growing rapidly. The aim of the article is to analyze the current use of AI in nutrients science research. The literature review was conducted in PubMed. A total of 399 records published between 1987 and 2020 were obtained, of which, after analyzing the titles and abstracts, 261 were rejected. In the next stages, the remaining records were analyzed using the full-text versions and, finally, 55 papers were selected. These papers were divided into three areas: AI in biomedical nutrients research (20 studies), AI in clinical nutrients research (22 studies) and AI in nutritional epidemiology (13 studies). It was found that the artificial neural network (ANN) methodology was dominant in the group of research on food composition study and production of nutrients. However, machine learning (ML) algorithms were widely used in studies on the influence of nutrients on the functioning of the human body in health and disease and in studies on the gut microbiota. Deep learning (DL) algorithms prevailed in a group of research works on clinical nutrients intake. The development of dietary systems using AI technology may lead to the creation of a global network that will be able to both actively support and monitor the personalized supply of nutrients.

Lipid Compositions and Geographical Discrimination of 94 Geographically Authentic Wheat Samples Based on UPLC-MS with Non-Targeted Lipidomic Approach

Wheat is the staple food for the world’s major populations. However, chemical characters of geographically authentic wheat samples, especially for the lipids, have not been deeply studied. The present research aimed to investigate lipid compositions of Chinese wheat samples and clarify the major markers that contribute to the geographical differences. A total of 94 wheat samples from eight main wheat-producing provinces in China were evaluated to differentiate their lipid compositions. Based on the data collected from ultra-high-performance-liquid-chromatography tandem time-of-flight mass spectrometry (UPLC-Q/TOF MS), an optimized non-targeted lipidomic method was utilized for analyses. As the results, 62 lipid compounds, including fatty acids, phospholipids, galactolipids, triglycerides, diglycerides, alkylresorcinol, and ceramide were tentatively identified. Partial least squares discriminant analysis (PLS-DA) demonstrated a more satisfying performance in distinguishing wheat samples from different origins compared with principal component analysis (PCA). Further, the abundances of triglycerides and glycerophospholipids with more unsaturated fatty acids were found greater in wheat samples from northern origins of China, while more glycolipids and unsaturated fatty acids arose in southern original wheat samples. These findings describe the lipid profiles of wheat samples in China and could contribute to the quality and safety control for the wheat flour products.