Use of elemental profiles to verify geographical origin and botanical variety of Spanish honeys with a protected denomination of origin

Identifying bee species origins of Philippine honey using X-ray fluorescence elemental analysis coupled with machine learning

Stingless bee honey is emerging as a superfood, given its enhanced health and therapeutic benefits. In this paper, we used handheld X-ray fluorescence spectroscopy (hXRF) with machine learning techniques to classify Philippine honey based on its entomological origin. Honey samples from three different bee species were analyzed, specifically European honeybee (Apis mellifera), Philippine giant honeybees (Apis breviligula and Apis dorsata), and Philippine stingless bee (Tetragonula biroi). Random forest and logistic regression models were used on the hXRF dataset for entomological origin classification. The optimized random forest model classified entomological origin with 85.2 % (225 out of 264) overall accuracy. The logistic regression model confirmed the entomological origin of Philippine stingless bees with 94.1 % accuracy and 100.0 % specificity. As such, honey that passes this model's test is undoubtedly made by Philippine stingless bees, making it an excellent screening tool for authenticating Philippine stingless bee honey.

Exploring the Nutritional Value and Health Benefits of Honey from the Portuguese Protected Area of Montesinho Natural Park

The main objective of this study was to provide an overview of the potential health-promoting properties of honeys collected at specific apiary locations from the protected area of Montesinho Natural Park (MNP), by analyzing their amino acid and polyphenolic profiles, and their biological properties, and comparing them with the renowned Manuka honey. The results revealed differences in total phenolic content among the samples examined and between sampling campaigns, with values ranging from 55.6 to 225 mg gallic acid equivalents (GAE) per 100 g of honey, compared to 57.2 mg GAE/100 g for Manuka honey. Gallic acid, neochlorogenic acid, and catechin were the key phenolics of MNP honey samples. MNP honey exhibited high levels of essential amino acids (phenylalanine, lysine, and valine), strong antioxidant and antimicrobial activities, moderate enzyme inhibition, and high proline content in some locations. These results highlight the exceptional nutritional and therapeutic potential of MNP honey.

Use of energy dispersive X-ray fluorescence to authenticate European wines with protected designation of origin. Challenges of a successful control system based on modelling

Consumers are willing to pay a higher price for food with geographical origin labels such as Protected Designation of Origin and Protected Geographical Indication. In this work, the elemental profile of wine obtained by XRF, combined with multivariate analyses, is used to authenticate 111 Croatian, Italian and Spanish red and white wines, 102 of them from 20 Protected Designations of Origin, reproducing the circumstances faced by control laboratories, using commercially available wines without traceability records. Wines that shared origin clustered together and separated from those of other regions following multivariate statistical tests. Classifications made using Soft Independent Modelling by Class Analogy were characterised by poor sensitivity and specificity. An alternative approach based on successive Partial Least Square Discriminant Analyses with consecutive classifications at country, region and finally, Protected Designation of Origin level, was developed and implemented with good accuracy results. In total, 88 % of the samples were correctly classified.

Tracing the origin and authenticity of Spanish PDO honey using metagenomics and machine learning

The Protected Designation of Origin (PDO) indication for foods intends to guarantee the conditions of production and the geographical origin of regional products within the European Union. Honey products are widely consumed due to their health-promoting properties and there is a general interest in tracing their authenticity. In this regard, metagenomics sequencing and machine learning (ML) have been proposed as complementary technologies to improve the traceability methods of foods. Therefore, the aim of this study was to analyze the metagenomic profiles of Spanish honeys from three different PDOs (Granada, Tenerife and Villuercas-Ibores), and compare them with non-PDO honeys using ML models (PLS, RF, LOGITBOOST, and NNET). According to the results obtained, non-PDO honeys and Granada PDO showed higher beta diversity values than Tenerife and Villuercas-Ibores PDOs. ML classification of honey products allowed the identification of different microbial biomarkers of the geographical origin of honeys: Lactobacillus kunkeei, Parasaccharibacter apium and Lactobacillus helsingborgensis for PDO honeys and Paenibacillus larvae, Lactobacillus apinorum and Klebsiella pneumoniae for non-PDO honeys. In addition, potential microbial biomarkers of some honey varieties including L. kunkeei for Albaida and Retama del Teide varieties, and P. apium for Tajinaste variety, were identified. ML models were validated on an independent set of samples leading to high accuracy rates (above 90 %). This work demonstrates the potential of ML to differentiate different types of honey using metagenome-based methods, leading to high performance metrics. In addition, ML models discriminate both the geographical origin and variety of products corresponding to different PDOs and non-PDO products. Results here presented may contribute to develop enhanced traceability and authenticity methods that could be applied to a wide range of foods.

Graphical Discrimination of New Zealand Honey from International Honey Using Elemental Analysis

This study investigated the potential of utilising the elemental fingerprinting of honey to differentiate New Zealand (NZ) honey from that of international origin. Twenty elements were analysed by ICP-MS in 352 honeys from 34 various countries. Of these, 323 honeys (245 New Zealand honeys, 78 international) and two subsets of data (NZ and European origin, n = 306, and, NZ and Denmark/Germany, n = 280) were visualised using principal component analysis (PCA). For the NZ/Europe subset, 42.2% of data was explained in the first two principal components. Statistical classification rules were also derived using linear discriminant analysis (LDA) and decision tree analysis. Various combinations of elements were explored for classification, considering the effect of soil-derived elements and those from anthropogenic sources. A high degree of accuracy (at least 90%) for the characterisation of New Zealand honey was observed for all statistical models, showing the robustness of these analyses. When using decision tree analysis to distinguish New Zealand samples from international samples, a tree with five terminal nodes (using Cs, Ba and Rb) was created with 92.4% accuracy. This work has demonstrated that elemental fingerprints of honey are a promising tool for categorising New Zealand honey from other geographical locations.

Use of elemental profiles determined by energy-dispersive X-ray fluorescence and multivariate analyses to detect adulteration in Ceylon cinnamon

The price of Cinnamomum verum (Ceylon cinnamon) is around twice as high as that of the other cinnamon varieties commonly grouped under the name cassia cinnamon, making the former spice an attractive target for fraudsters. This work demonstrates that elemental profiles obtained by energy-dispersive X-ray fluorescence in combination with multivariate analyses can be used as a screening method to detect Ceylon cinnamon adulteration. Thirty-six elements were analysed in 52 commercially available cinnamon samples, 29 Ceylon, 8 cassia, and 15 for which no indication about variety was provided. Fifty-eight percent of the samples were either adulterated or did not meet international quality criteria. Four of the ground cinnamon samples labelled as Ceylon cinnamon were found to be pure cassia or a mixture with a high cassia content, and 26 samples were suspected of other types of adulteration including replacement of bark with other parts of the cinnamon tree. Headspace gas chromatography-mass spectrometry and ash determination by thermogravimetric analysis confirmed the conclusions reached by elemental analysis. Only one sample labelled as Ceylon cinnamon and that according to its volatile composition was cassia cinnamon was not flagged as suspicious by elemental analysis.

XRF analysis searching for fingerprint elemental profile in south-eastern Sicily tomatoes

The implementation of analytical techniques able to certify food quality and origin in a fast and non-destructive way is becoming a widespread need in the agri-food sector. Among the physical non-destructive techniques, X-ray fluorescence (XRF) spectrometry is often used to analyze the elemental composition of biological samples. In this study, X-ray fluorescence (XRF) elemental profiles were measured on tomato samples belonging to different geographical areas in Sicily (Italy). The purpose of this investigation was aiming to establish a protocol for in-situ measurement and analysis able to provide quality assessment and traceability of PGI agri-food products, specifically sustaining health safety and self qualifying bio-chemical signature. In detail, sampling was performed in one of the most tomato productive area of south-eastern Sicily (Pachino district), characterised by a relative higher amount of Organic Carbon and Cation Exchange Capacity, and compared with samples from other growing areas of Sicily, falling in Ragusa province and Mt. Etna region. Experimental data were analyzed in the framework of multivariate analysis by using principal component analysis and further validated by discriminant analysis. The results show the presence of specific elemental signatures associated to several characterizing elements. This methodology establishes the possibility to disentangle a clear fingerprint pattern associated to the geographical origin of an agri-food product.

Geographical discrimination of dried chili peppers using femtosecond laser ablation-inductively coupled plasma-mass spectrometry (fsLA-ICP-MS)

This study presents a method for discriminating the geographical origin of dried chili peppers using femtosecond laser ablation-inductively coupled plasma-mass spectrometry (fsLA-ICP-MS) and multivariate analysis, such as orthogonal partial least squares discriminant analysis (OPLS-DA), heatmap analysis, and canonical discriminant analysis (CDA). Herein, 102 samples were analyzed for the content of 33 elements using optimized conditions of 200 Hz (repetition rate), 50 μm (spot size), and 90% (energy). Significant differences in count per second (cps) values of the elements were observed between domestic and imported peppers, with variations of up to 5.66 times (¹³³Cs). The OPLS-DA model accuracy achieved an R² of 0.811 and a Q² of 0.733 for distinguishing dried chili peppers of different geographical origins. The variable importance in projection (VIP) and s-plot identified elements 10 and 3 as key to the OPLS-DA model, and in the heatmap, six elements were estimated to be significant in discriminating between domestic and imported samples. Furthermore, CDA showed a high accuracy of 99.02%. This method can ensure food safety for consumers, and accurately determine the geographic origin of agricultural products.

Applications of Delayed Luminescence for tomato fruit quality assessment across varied Sicilian cultivation zones

The food industry places significant emphasis on ensuring quality and traceability as key components of a healthy diet. To cater to consumer demands, researchers have prioritized the development of analytical techniques that can rapidly and non-invasively provide data on quality parameters. In this study, we propose to use the Delayed Luminescence (DL), an ultra-weak and photo-induced emission of optical photons, as a tool for a rapid evaluation of quality profile associated with fruit ripening, in support of traditional analysis methods. Delayed Luminescence measurements have been performed on cherry tomatoes, with and without the PGI "Pomodoro di Pachino" certification, harvested from two different growing areas of south-eastern Sicily (Italy). Then, DL emissions were correlated with soluble solid content and titratable acidity values, which are known to affect the flavor, the commerciality and the maturity degree of tomato fruits. In addition, we evaluated the changes in the DL parameters with respect to the geographical origin of the cherry tomatoes, with the aim of testing the possibility of applying the technique for identification purposes. The signals of Delayed Luminescence appeared to be good indicators of the macromolecular structure of the biological system, revealing structural changes related to the content of total soluble solids present in the juice of tomatoes analyzed, and they appeared unsuitable for authenticating vegetable crops, since the differences in the photon yields emitted by tomato Lots were not related to territory of origin. Thus, our results suggest that DL can be used as a nondestructive indicator of important parameters linked to tomato fruit quality.

A review of conventional and rapid analytical techniques coupled with multivariate analysis for origin traceability of soybean

Soybean has developed a reputation as a superfood due to its nutrient profile, health benefits, and versatility. Since 1960, its demand has increased dramatically, going from a mere 17 MMT to almost 358 MMT in the production year 2021/22. These extremely high production rates have led to lower-than-expected product quality, adulteration, illegal trade, deforestation, and other concerns. This necessitates the development of an effective technology to confirm soybean's provenance. This is the first review that investigates current analytical techniques coupled with multivariate analysis for origin traceability of soybeans. The fundamentals of several analytical techniques are presented, assessed, compared, and discussed in terms of their operating specifics, advantages, and shortcomings. Additionally, significance of multivariate analysis in analyzing complex data has also been discussed.

Feasibility study of tomato fruit characterization by fast XRF analysis for quality assessment and food traceability

Food product nutritional and sensory characteristics are often deeply linked to its territory of origin; therefore, its authentication by means of elemental composition becomes crucial for traceability and fighting food fraud. This study aims to establish a fast and reproducible procedure for origin and quality assessment of Sicilian tomato fruits, including PGI "Pomodoro di Pachino", by using the X-ray fluorescence (XRF) technique. Measurements were performed on different parts of PGI Pachino tomatoes belonging to the same production lot. Principal Component and Cluster Analyses show that the samples cluster accordingly with the production lot, disentangling the different parts of the fruit. This procedure, which uses XRF yield elemental pattern and statistical analysis, establishes a solid basis for characterizing elemental profiles by a fast XRF in-situ campaign, supporting the traceability system. The reliability of XRF results was confirmed by comparing elemental concentrations with ICP-MS measurements, performed for comparison, and tomato literature values.

Evaluation of emeralds by their origin: Multivariate analysis - Infrared spectra

Colombian emeralds, 26 from Palo Arañado (eastern emerald zone, Chivor district, Boyacá, Colombia), and 28 from Santo Domingo - La Pava mine (western emerald zone, Muzo district, Boyacá, Colombia), together with 30 from Kafubu - Zambia, were studied by reflectance Fourier transform infrared spectroscopy, principal component analysis, clustering, and partial least squares – discriminant analysis, in order to differentiate them by geographical origin. The spectra were smoothed and a baseline correction was made. The principal component analysis showed that the wavenumbers 2,474; 2,640; 2,686; 2,818; 5,448, and 6,815 cm-1 are the most significant in the first principal component and the most valuable in separating the emeralds by their geographical origin. This allowed us to completely discriminate emeralds from Santo Domingo and Zambia, while only five emeralds from Palo Arañado were 100 % differentiable from the other two groups of emeralds.

Antioxidative, Antibacterial and Antiproliferative Properties of Honey Types from the Western Balkans

This paper presents the physicochemical characteristics and antioxidative, antibacterial and antiproliferative effects of nineteen samples of different honey types (acacia, linden, heather, sunflower, phacelia, basil, anise, sage, chestnut, hawthorn, lavender and meadow) collected from different locations in the Western Balkans (Republic of Serbia, Kosovo, Bosnia and Herzegovina, and Northern Macedonia). Physicochemical parameters (moisture, pH, electrical conductivity, free acidity, and hydroxymethylfurfural [HMF]) were analysed. Based on the obtained results, all tested honey samples were in agreement with EU regulation. The antioxidant potential of honey samples was assessed by determination of total phenolic content (TPC) and evaluation of scavenging activity towards diphenilpicrylhydrazyl radicals (DPPH·). The highest phenolic content was found in basil honey (101 ± 2.72 mg GAE/100 g), while the lowest was registered in rapeseed honey (11.5 ± 0.70 mg GAE/100 g). Heather, anise, phacelia, sage, chestnut and lavender honey samples were also rich in TP, containing 80−100 mg GAE/100 g. DPPH scavenging activity varied among the samples being the highest for lavender honey (IC50 = 88.2 ± 2.11 mg/mL) and the lowest for rapeseed honey (IC50 = 646 ± 8.72 mg/mL). Antibacterial activity was estimated in vitro using agar diffusion tests and measuring minimal inhibitory concentration (MIC). Among investigated bacterial strains following resistant potencies were determined: Escherichia coli > Escherichia coli ATCC 8739 > Enterococcus faecalis > Proteus mirabilis > Staphylococcus aureus > Staphylococcus epidermidis. The linden honey from Fruška Gora (MIC values of 3.12% and 6.25% against Staphylococcus aureus and Staphylococcus epidermidis, respectively) and phacelia honey (MIC values of 6.25% and 3.12% against S.Staphylococcus aureus and Staphylococcus epidermidis, respectively) showed the strongest antibacterial activity. Antiproliferative activity was evaluated using the colorimetric sulforhodamine B (SRB) assay. The highest antiproliferative activity was obtained from linden honey sample 1 (IC50MCF7 = 7.46 ± 1.18 mg/mL and IC50HeLa =12.4 ± 2.00 mg/mL) and meadow sample 2 (IC50MCF7 = 12.0 ± 0.57 mg/mL, IC50HeLa = 16.9 ± 1.54 mg/mL and IC50HT−29 = 23.7 ± 1.33 mg/mL) towards breast (MCF7), cervix (HeLa) and colon (HT-29) cancer cells. Active components other than sugars contributed to cell growth activity.

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.

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.

Phenolic Compounds in Honey and Their Relationship with Antioxidant Activity, Botanical Origin, and Color

Honey has been employed since antiquity due to its sensory, nutritional, and therapeutic properties. These characteristics are related to its physical and chemical composition. For example, phenolic compounds are substances that can determine antioxidant activity, as well as sensory characteristics, and can be employed as biomarkers of floral and geographical origin. This has generated a growing interest in the study of phenolic compounds and their influence in the intrinsic properties of this beekeeping product. This review aims to summarize, analyze, and update the status of the research that demonstrates the role of phenolic compounds in antioxidant activity, botanical-geographical origin, and the sensory characteristics of honey. These phenolic compounds, according to various results reported, have great relevance in honey's biological and functional activity. This leads to research that will link phenolic compounds to their floral, geographical, productive, and territorial origin, as well as some sensory and functional characteristics.

Rapid and Accurate Approach for Honeybee Pollen Analysis Using ED-XRF and FTIR Spectroscopy

Since honeybee pollen is considered a “perfectly complete food” and is characterized by many beneficial properties (anti-inflammatory, antioxidant, anti-bacterial, etc.), it has begun to be used for therapeutic purposes. Consequently, there is a high need to develop methods for controlling its composition. A thorough bee pollen analysis can be very informative regarding its safety for consumption, the variability of its composition, its biogeographical origin, or harvest date. Therefore, in this study, two reliable and non-destructive spectroscopy methods, i.e., ED-XRF and ATR–FTIR, are proposed as a fast approach to characterize bee pollen. The collected samples were derived from apiaries located in west-central Poland. Additionally, some commercially available samples were analyzed. The applied methodology was optimized and combined with sophisticated chemometric tools. Data derived from IR analyses were also subjected to two-dimensional correlation spectroscopy. The developed ED-XRF method allowed the reliable quantification of eight macro- and micro-nutrients, while organic components were characterized by IR spectroscopy. Principal component analysis, cluster analysis, and obtained synchronous and asynchronous maps allowed the study of component changes occurring dependently on the date and location of harvest. The proposed approach proved to be an excellent tool to monitor the variability of the inorganic and organic content of bee pollen.

Feasibility study on the use of elemental profiles to authenticate aromatic rice: the case of Basmati and Thai rice

Among the thousands of existing rice varieties, aromatic rice has increasingly attracted consumer’s preference in recent years. Within aromatic rice, Basmati, cultivated in some regions in Pakistan and India, is highly demanded. Other aromatic rice, cultivated in specific regions, for instance in Thailand (commonly referred to as Jasmine Thai rice), are also highly appreciated by consumers. In this work, the elemental profiles of commercially available rice samples (17 Basmati, 11 Thai, and 7 Long Grain rice) were determined by energy-dispersive X-ray fluorescence (ED-XRF) spectroscopy. The mass fractions of P, Cl, S, K, Fe, Cu, and Zn were significantly different (95% confidence interval) between Basmati and Thai rice and between Thai and Long Grain rice; only Cl, S, and Zn were significantly different between Basmati and Long Grain rice. Multivariate evaluation of the results combining soft independent modelling by class analogy (SIMCA) and partial least square discriminant analysis (PLS-DA) allowed the correct classification (true positives) of 94.1, 85.6, and 100% of the Basmati, Long Grain, and Thai rice, respectively. The specificity (true negatives) of Basmati, Long Grain, and Thai was 94.4, 82.1, and 100%, respectively.

Are the elemental fingerprints of organic and conventional food different? ED-XRF as screening technique

Research has been conducted the last years to assess whether organically grown food is chemically different from produce of conventional agriculture and which markers are appropriate to discriminate between them. Most articles focus on one single food commodity, produced under strict controlled organic farming conditions, leaving open the question whether the difference would be seen when applied to the same commodity under different growing conditions. In this work 118 organic and 151 conventional samples of commercially available paprika powder, cinnamon, coffee, tea, chocolate, rice, wheat flour, cane sugar, coconut water, honey and bovine milk were characterised for their elemental composition using energy dispersive X-ray fluorescence. Resulting profiles were analysed using univariate and multivariate statistical techniques. Organic samples of a given commodity clustered together and were separated from their conventional counterparts. Differences in the elemental composition of food, could be used to develop statistical models for verifying the agronomical production system.