The use of advanced chemometric techniques and trace element levels for controlling the authenticity of organic coffee

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.

Enhanced food authenticity control using machine learning-assisted elemental analysis

With the increasing attention being paid to the authenticity of food, efficient and accurate techniques that can solve relevant problems are crucial for improving public trust in food. This review explains two main aspects of food authenticity, namely food traceability and food quality control. More explicitly, they are the traceability of food origin and organic food, detection of food adulteration and heavy metals. It also points out the limitations of the commonly used morphology and organic compound detection methods, and highlights the advantages of combining the elements in food as detection indicators using machine learning technology to solve the problem of food authenticity. Taking elements as detection objects has the significant advantages of stability, machine learning technology can combine large data samples, ensuring both the accuracy and efficiency. In addition, the most suitable algorithm can be found by comparing their accuracy.

Improving the geographical traceability of tea in China based on stable isotope ratios

The potential of improving the classification of tea samples from different regions was studied by using stable isotope ratios analysis. The stable isotope ratios of 44 elements in tea samples were determined (p < 0.05).The results showed that 34 stable isotopes ratios were statistically significant, and tea in the four regions had their own characteristic variables. PCA, HCA, PLS-DA, BP-ANN and LDA were used to analyze the stable isotope ratio data in tea. Six key variables were identified to provide the greatest difference between the samples. The overall correct classification rate, cross validation rate and blind validation rate given by LDA are all 100%, and the result is the best. This study has proved that the stable isotope ratio analysis method could improve the geographical origin traceability of Chinese tea.

Supplementary Information

The online version contains supplementary material available at 10.1007/s13197-024-05970-w.

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 %).

Evaluation of Trace Elements in Coffee and Mixed Coffee Samples Using ICP-OES Method

This research examines the concentration of 10 trace elements including arsenic (As), lead (Pb), chromium (Cr), zinc (Zn), iron (Fe), cobalt (Co), cadmium (Cd), nickel (Ni), manganese (Mn), and aluminum (Al) from among 36 different samples of coffee (3 brands and 2 types of simple and instant) and mixed coffee (3 brands and 4 types of simple, creamy, chocolate and sugar free) collected from market of Iran's and analyzed by using ICP-OES (inductively coupled plasma-optical emission spectrometry). The recovery, limit of quantification (LOQ), and limit of detection (LOD) ranged from 93.4 to 103.1%, 0.06 to 7.22, and 0.018 to 2.166 µg/kg, respectively. The findings showed that the highest and lowest average concentrations were 498.72 ± 23.07 μg/kg (Fe) and 3.01 ± 1.30 μg/kg (As) in coffee and mixed coffee samples. Also, in all samples, the maximum concentration of trace elements was related to Fe (1353.61 µg/kg) and the minimum concentration was related to Al, As, Co, Cr, Ni, Pb, and Zn that were not detected (ND). The samples of mixed coffee had highest levels of trace elements compared to coffee samples. In coffee samples, type of instant coffee had highest levels of trace elements compared to simple coffee and mixed coffee samples. The type of creamy mixed coffee had highest levels of trace elements (except Ni and Cr) compared to other type of mixed coffee samples. Finally, trace elements were less than the standard levels of Iran and other countries (in all samples); therefore, it does not threaten Iranian consumers.

1H NMR-based approach to determine the geographical origin and cultivation method of roasted coffee

A comprehensive study of 603 roasted arabica coffee samples using NMR fingerprinting and multivariate data analysis was performed to differentiate coffee samples according to their geographical origin and cultivation method. Both lipophilic and hydrophilic coffee metabolites were recorded using 1H NMR spectroscopy, and principal component analysis followed by linear discriminant analysis (PCA-LDA) was applied. Coffee samples were fist differentiated according to their continents of origin followed by discrimination of coffee samples from Brazil, Ethiopia, and Colombia from coffee samples originating from another continent. Discrimination of coffee samples according to their continent of origin and additional assignment to the countries Brazil and Ethiopia were successful. However, an unambiguous separation of Colombian coffee samples from coffee samples of another continent (other than South America) was not possible. Also, differentiation of organically and conventionally produced coffee samples by using 1H NMR and PCA-LDA was not achieved.

Development of a coffee bean certified reference material (KRISS CRM 108-10-023) for elemental analysis

The development of a novel coffee bean matrix certified reference material (CRM) for elemental analysis is described. The CRM was prepared by processing green coffee beans into a dry homogeneous powder. Mass fractions of elements in the CRM were measured using double isotope dilution inductively coupled plasma mass spectrometry (double ID-ICP-MS), and measurement results for eight elements (Mg, Ca, Fe, Cu, Zn, Cd, Hg, and Pb) of sufficient quality were certified. The mass fraction range was from 0.09476 mg/kg (Cd) to 1908 mg/kg (Mg), with relative expanded uncertainty range of 0.66% (Cd) to 12% (Pb). Measurement results of two elements (Cr and Ni) with insufficient quality were provided for information only. During characterization, an effective approach for the measurement of isotopic abundances and molar masses of elements with high natural isotopic variations for double ID-ICP-MS was developed and applied. The CRM developed in the present study is expected to be a useful measurement standard for assuring the quality of measurement procedures for coffee beans or related materials.

Authentication of beef cuts by multielement and machine learning approaches

BACKGROUND

Brazil has consolidated a relevant position in the world market, being the largest exporter and second producer of beef. Genetics, feeding system, geographic origin and climate influence the multielement profile of beef. The feasibility of combining classification algorithms with major and trace elements was evaluated as a tool for authentication of beef cuts.

METHODS

Animals of Angus, Nelore and Wagyu crossbreeds, raised in a vertically integrated system, were sampled at the slaughterhouse for chuck steak, rump cap and sirloin steak. Supervised learning algorithms i.e. Classification and Regression Tree (CART), Multilayer Perceptron (MLP), Naïve Bayes (NB), Random Forest (RF) and Sequential Minimal Optimization (SMO) were used to build classification models based on the multielement profile of beef determined by neutron activation analysis.

RESULTS

Br, Co, Cs, Fe, K, Na, Rb, Se and Zn were determined in the beef samples. The classification accuracy values obtained for the beef cuts were 96% (MLP), 95% (SMO), 91% (RF), 86% (NB) and 70% (CART).

CONCLUSION

The Multilayer Perceptron algorithm provided the best classification performance towards authentication of beef cuts on basis of major and trace element mass fractions.

Use of Microscopic Characteristics and Multielemental Fingerprinting Analysis to Trace Three Different Cultivation Modes of Medicinal and Edible Dendrobium officinale in China

The traceability of different cultivation modes is critical for ensuring the commercial viability of high-value Dendrobium officinale. In this study, by means of polarizing microscopy, SEM-EDX, ICP-MS and ICP-AES, the possibility of combining microscopic characteristics, multielemental analysis and multivariate statistical authenticity analysis was realized to determine the origins of the fresh stem and dried stem powder of D. officinale derived from three different cultivation modes from six provinces of China. The microscopic structure, chemical elements on the surface of the main microstructures and concentrations of Ca, K, Ba, Cs, As and Cu varied among specimens derived from different cultivation modes. The fresh stems of D. officinale derived from different cultivation modes can be effectively and quickly identified by various microscopic characteristics and different contents of Ca on the surface of the parenchyma, phloem and xylem. Meanwhile, linear discriminant analysis showed that 98.1% of the dried stem powder samples were correctly classified, and the accuracy of cross-validation was 95.3%. This study facilitated an effective integrated method for determining the traceability of the fresh stem and dried stem powder of D. officinale derived from three different cultivation modes. This approach offers a potential method for identifying the origins of medicinal plants derived from different cultivation modes.

The concentration of potentially toxic elements (PTEs) in the coffee products: a systematic review and meta-analysis

Coffee is one of the most consumed products globally, and its contamination with potentially toxic elements (PTEs) occurs throughout the production chain and production. Therefore, the current meta-analysis study aimed to estimate the concentration of essential elements (Cu and Co) and the contamination of PTEs (Ni, Cr, Pb, As, and Cd) in coffee. The recommended databases, including PubMed, Scopus, and ScienceDirect, were investigated to collect data regarding the contamination of PTEs in coffee products from 2010 to 2021. Among 644 retrieved citations in the identification step, 34 articles were included in the meta-analysis. The pooled mean concentration of essential elements in coffee products is much higher than that of toxic elements (Co (447.106 µg/kg, 95% CI: 445.695-448.518 µg/kg) > Ni (324.175 µg/kg, 95% CI: 322.072-326.278 µg/kg) > Cu (136.171 µg/kg, 95% CI: 134.840-137.503 µg/kg) > Cr (106.865 µg/kg, 95% CI: 105.309-108.421 µg/kg) > Pb (21.027 µg/kg, 95% CI: 20.824-21.231 µg/kg) > As (3.158 µg/kg, 95% CI: 3.097-3.219 µg/kg) > Cd (0.308 µg/kg; 95% CI: 0.284-0.332 µg/kg)). Results showed high differences between pooled concentrations of all PTEs in coffee products of different countries.

Targeted and Non-Targeted HPLC Analysis of Coffee-Based Products as Effective Tools for Evaluating the Coffee Authenticity

Coffee is a very popular beverage worldwide. However, its composition and characteristics are affected by a number of factors, such as geographical and botanical origin, harvesting and roasting conditions, and brewing method used. As coffee consumption rises, the demands on its high quality and authenticity naturally grows as well. Unfortunately, at the same time, various tricks of coffee adulteration occur more frequently, with the intention of quick economic profit. Many analytical methods have already been developed to verify the coffee authenticity, in which the high-performance liquid chromatography (HPLC) plays a crucial role, especially thanks to its high selectivity and sensitivity. Thus, this review summarizes the results of targeted and non-targeted HPLC analysis of coffee-based products over the last 10 years as an effective tool for determining coffee composition, which can help to reveal potential forgeries and non-compliance with good manufacturing practice, and subsequently protects consumers from buying overpriced low-quality product. The advantages and drawbacks of the targeted analysis are specified and contrasted with those of the non-targeted HPLC fingerprints, which simply consider the chemical profile of the sample, regardless of the determination of individual compounds present.

Analysis of Volatile Compounds, Composition, and Thermal Behavior of Coffee Beans According to Variety and Roasting Intensity

This study aimed to investigate the ways in which the thermal behavior, composition, and volatile compound contents of roasted coffee beans depend on variety and roasting intensity. The thermal analysis revealed various transformations in coffee composition, namely, drying, water loss, and decomposition of polysaccharides, lipids, amino acids, and proteins. The results showed that volatile compounds are released differently in coffee depending on coffee type and degree of roasting. The most abundant volatile compounds present in the samples were 2-butanone, furan, 2-methylfuran, methyl formate, 2.3-pentanedione, methylpyrazine, acetic acid, furfural, 5-methyl furfural, and 2-furanmethanol. The total polyphenol contents ranged between 13.3 and 18.9 g gallic acid/kg, being slightly higher in Robusta than in Arabica varieties and in more intensely roasted beans compared to medium-roasted beans. The Robusta variety has higher mineral contents than Arabica, and the contents of most minerals (K, Ca, Mg, Fe, Cu, P, N, and S) increased with roasting intensity. Discrimination between coffee varieties and roasting intensities is possible based on mineral and polyphenol contents.

Assessment of the Authenticity of Whisky Samples Based on the Multi-Elemental and Multivariate Analysis

Two hundred and five samples of whisky, including 170 authentic and 35 fake products, were analyzed in terms of their elemental profiles in order to distinguish them according to the parameter of their authenticity. The study of 31 elements (Ag, Al, B, Ba, Be, Bi, Cd, Co, Cr, Cu, Li, Mn, Mo, Ni, Pb, Sb, Sn, Sr, Te, Tl, U, V, Ca, Fe, K, Mg, P, S, Ti and Zn) was performed using the Inductively Coupled Plasma Mass Spectrometry (ICP-MS), Inductively Coupled Plasma Optical Emission Spectrometry (ICP-OES) and Cold Vapor-Atomic Absorption (CVAAS) techniques. Additionally, the pH values of all samples were determined by pH-meter, and their isotopic ratios of ⁸⁸Sr/⁸⁶Sr, ⁸⁴Sr/⁸⁶Sr, ⁸⁷Sr/⁸⁶Sr and ⁶³Cu/⁶⁵Cu were assessed, based on the number of counts by ICP-MS. As a result of conducted research, elements, such as Mn, K, P and S, were identified as markers of whisky adulteration related to the age of alcohol. The concentrations of manganese, potassium and phosphorus were significantly lower in the fake samples (which were not aged, or the aging period was much shorter than legally required), compared to the original samples (in all cases subjected to the aging process). The observed differences were related to the migration of these elements from wooden barrels to the alcohol contained in them. On the other hand, the sulfur concentration in the processed samples was much higher in the counterfeit samples than in the authentic ones. The total sulfur content, such as that of alkyl sulfides, decreases in alcohol with aging in the barrels. Furthermore, counterfeit samples can be of variable origin and composition, so they cannot be characterized as one group with identical or comparable features. Repeatedly, the element of randomness dominates in the production of these kinds of alcohols. However, as indicated in this work, the extensive elemental analysis supported by statistical tools can be helpful, especially in the context of detecting age-related adulteration of whisky. The results presented in this paper are the final part of a comprehensive study on the influence of selected factors on the elemental composition of whisky.

Multielement and chemometric analysis for the traceability of the Pachino Protected Geographical Indication (PGI) cherry tomatoes

To prevent PGI (Protected Geographical Indication) cherry tomato of Pachino (Sicily, Italy) from frauds, an alternative method, which includes chemometric treatments, was proposed. The content of 32 inorganic elements (macro-micronutrients and lanthanides) present in 16 PGI and 24 not PGI cherry tomato samples cv Naomy, and in 16 PGI and 8 not PGI soil samples, was determined by Inductively Coupled Plasma - Mass Spectrometer (ICP-MS). To identify the elements able to differentiate PGI and not PGI cherry tomato samples, Principal Components Analysis (PCA) and Canonical discriminant analysis (CDA) were performed. The first two principal components (PC1-PC2) explain a total variance of 71,41% between PGI and not PGI group, whereas CDA showed Zn, Cd, Mn and Ca as inorganic markers able to correctly classify the 100% of samples. Furthermore, with a translocation factor (K), evaluated in soil/plant chain, the comparison of absorption trends for PGI and not PGI samples was realized.

Comparative investigation of the stems, leaves, flowers, and roots of Centipeda minima based on fingerprinting-multivariate classification techniques

BACKGROUND

Centipeda minima (L.) A. Br. et Aschers, known as Ebushicao (EBSC) in Chinese, has long been used in traditional Chinese medicine for dispelling wind, clearing orifices, detoxification and swelling. Although the traditional use of EBSC involves the whole plant, during harvesting and processing, separation of the stems, leaves, flowers and roots often occurs. However, there are few studies on its medicinal parts.

OBJECTIVE

A strategy combining high performance liquid chromatography (HPLC) fingerprinting and multivariate classification techniques are here proposed for the comparison of roots, stems, leaves, and flowers of EBSC.

METHOD

The roots, stems, leaves, and flowers of EBSC samples were analyzed and compared based on HPLC fingerprints combined with chemometrics, including hierarchical cluster analysis (HCA), principal component analysis (PCA), partial least squares-discriminant analysis (PLS-DA), and back propagation artificial neural network (BP-ANN). Chemical markers were screened using PLS-DA, and the contents of representative ingredients were determined by an HPLC method.

RESULTS

The HCA and PCA provided clear discrimination of roots, stems, leaves and flowers. Moreover, the PLS-DA model and BP-ANN were established to verify the classification results and showed a greater ability to predict new samples. Four representative chemical markers were screened out, and the content of these markers in flowers and leaves was higher than that in stems and roots, and the difference was significant.

CONCLUSION

Combining HPLC fingerprinting and multi-component chemical pattern recognition technology can be used to distinguish different parts of EBSC. The results indicated that brevilin A, quercetin, rutin and chlorogenic acid, the important active components of EBSC, were mainly present in the leaves and flowers. This is of great significance for the differentiation and identification of the different medicinal parts of EBSC, as well as for the effectiveness of drug usage in clinical practice.

Provenance discrimination of Sorrento lemon with Protected Geographical indication (PGI) by multi-elemental fingerprinting

Multielement analysis and chemometric methods were proposed to discriminate the Sorrento lemon (PGI) juices according to geographical origin. In 2018 and 2019, 169 fruits from three farms in PGI area and two in not-PGI area were collected and analysed for essential and not-essential elements by ICP-MS. The PCA of multielement fingerprinting grouped lemon juices from PGI farms revealing a strong differentiation at small geographical scale. The S-LDA discriminated lemon juices for Mo, Ba, Rb, Mg, Co, Ca, Fe, Sr on the two production years, giving 97.7% correct classification, 98.5% accuracy and 93.8% external validation. The good correlation lemon juice vs cultivation soil and the soil discrimination by not-essential elements suggested the use of these elements as reliable indicators of lemon juice provenances. Despite lowering the number of variables, constituted by not-essential elements Ba, Rb, Ti, Co, the use of S-QDA discriminated the lemons juices with 87.5% accuracy and 83.9% validation.

Discrimination of geographical origin and species of China's cattle bones based on multi-element analyses by inductively coupled plasma mass spectrometry

Consumers have an increasing concern in the provenance of the foods they consume. Methods for discriminating geographical origins and species of cattle bone product are essential to provide veracious information for consumers and avoid the adulteration and inferior problems. In this study, 50 element contents of a total of 143 cattle bone samples from eight producing regions in China, were determined by inductively coupled plasma mass spectrometry (ICP-MS). Element contents were used as chemical indicators to discriminate species and geographical origins of cattle bone samples by multivariate data analysis, including hierarchical cluster analysis (HCA) and linear discriminant analysis (LDA). The K-fold cross validation accuracy for species and geographical origin discrimination was 99.3% and 94.5%, respectively. This study reveals that multi-element analysis accompanied by LDA is an effective technique to ensure the information reliability of cattle bone samples, and this strategy may be a potential tool for standardizing market.

Macro-, micro-, and trace element distributions in areca nut, husk, and soil of northeast India

In areca nut and husk, 14 elements (As, Ca, Cd, Cl, Co, Cu, K, Mg, Mn, Na, Rb, Sb, and Zn) were determined, while 34 elements including rare earth elements were detected in the corresponding soil samples using instrumental neutron activation analysis and atomic absorption spectrometry methods, whereas the concentration levels of Hg in tested samples are negligible, perhaps, below the detection limits. No rare earth elements were detected in edible areca nut. The concentration levels of various essential elements and heavy elements such as As, Cd, and Cu present in areca nut are within the permissible levels, whereas Pb content is relatively higher than FAO/WHO's permissible levels. The order of bioaccumulation index for heavy metals in areca nut was Cd > Sb > Cu > Zn ≥ Mn ≥ Co > Pb ≥ As. Bioaccumulation index values are indicating that areca palm may not be able to accumulate other heavy elements in the edible areca nut, except for Cd. On the basis of pollution indices, Northeast Indian soil may be relatively unpolluted.

Geographic origin discrimination of pork from different Chinese regions using mineral elements analysis assisted by machine learning techniques

Porkis thelargest-producedandmost-consumedmeat intheworld, and the food market globalization has increased public attention to food origin. Therefore, advanced techniques are required to determine the geographical origin of pork. This study investigated the prospects of using fingerprint analysis of mineral elements and machine learning to facilitate the traceability of pork origin in China. The results showed that each of seven regions had a characteristic element content profile. To improve the performance of the origin traceability model, popular machine learning techniques in food authenticity were introduced. This resulted in a high-performance origin tracing model. Comparing various machine learning algorithms, the feedforward neural network achieved superior performance with an overall accuracy of 95.71% and area under the curve close to one. Thus, this study proves that mineral elements analysis assisted by machine learning can be applied to distinguish pork samples within a country.

Comparison of Chemometric Problems in Food Analysis Using Non-Linear Methods

Food analysis is a challenging analytical problem, often addressed using sophisticated laboratory methods that produce large data sets. Linear and non-linear multivariate methods can be used to process these types of datasets and to answer questions such as whether product origin is accurately labeled or whether a product is safe to eat. In this review, we present the application of non-linear methods such as artificial neural networks, support vector machines, self-organizing maps, and multi-layer artificial neural networks in the field of chemometrics related to food analysis. We discuss criteria to determine when non-linear methods are better suited for use instead of traditional methods. The principles of algorithms are described, and examples are presented for solving the problems of exploratory analysis, classification, and prediction.

Improved geographical origin discrimination for tea using ICP-MS and ICP-OES techniques in combination with chemometric approach

BACKGROUND

There is an urgent need to strengthen the testing and certification of geographically iconic foods, as well as to use discriminatory science and technology for their regulation and verification. Multi-element and stable isotope analyses were combined to provide a new chemometric approach for improving the discrimination tea samples from different geographical origins. Different stoichiometric methods [principal component analysis (PCA), hierarchical cluster analysis (HCA), partial least squares-discriminant analysis (PLS-DA), back propagation based artificial neural network (BP-ANN) and linear discriminant analysis (LDA)] were used to demonstrate this discrimination approach using Yongchuanxiuya tea samples in an experimental test.

RESULTS

Multi-element and stable isotope analyses of tea samples using inductively coupled plasma mass spectrometry and inductively coupled plasma optical emission spectrometry easily distinguished the geographical origins. However, the clustering ability of the two unsupervised learning methods (PCA and HCA) were worse compared to that of the three supervised learning methods (PLS-DA, BP-ANN and LDA). BP-ANN and LDA, with 100% recognition and prediction abilities, were found to be better than PLS-DA. ⁸⁶ Sr and ¹¹² Cd were the markers enabling the successful classification of tea samples according to their geographical origins. Under the validation by 'blind' dataset, the prediction accuracies of the BP-ANN and LDA methods were all greater than 90%. The LDA method showed the best performance, with an accuracy of 100%.

CONCLUSION

In summary, determination of mineral elements and stable isotopes using inductively coupled plasma mass spectrometry and inductively coupled plasma optical emission spectrometry techniques coupled with chemometric methods, especially the LDA method, is a good approach for improving the authentication of a diverse range of tea. The present study contributes toward generalizing the use of fingerprinting mineral elements and stable isotopes as a promising tool for testing the geographic roots of tea and food worldwide. © 2020 Society of Chemical Industry.

Evaluation of the influence of cultivation on the total magnesium concentration and infusion extractability in commercial arabica coffee

Coffee is considered an important source of organic nutrients and minerals, and these resources are strongly affected by agricultural management. Among the minerals, the element Mg is important, which is essential for both plants and humans. In this work, the effects of agricultural management on the absorption and storage of Mg by commercial, ground, roasted Arabica coffee were investigated. For this purpose, some Mg and P fractions were evaluated. It was observed that Mg stored in the grain was concentrated in the inorganic fraction, with an average extraction of 102% and in conventional samples and 119% in organic samples. These results suggest that in these samples Mg is probably largely presented as different inorganic salts. Phytate and organic acid salts are two possibilities discussed in this work that could explain this hypothesis. This can be corroborated by the extraction of Mg in the infusion of hot water.

Linking growing conditions to stable isotope ratios and elemental compositions of Costa Rican bananas (Musa spp.)

Traceability of agricultural produce is getting increasingly important for numerous reasons including marketing, certification, and food safety. Globally, banana (Musa spp.) with its high nutritional value and easy accessibility, is a popular fruit among consumers. Bananas are produced throughout the (sub-)tropics under a wide range of environmental conditions. Environmental conditions could influence the composition of bananas. Understanding the effect of these conditions on fruit composition provides a way of increasing the fruit's traceability and linking it to its origin - a crucial aspect for the increasing global supply chain. In this study, we examined the influence of growing conditions on the isotopic and elemental composition of bananas produced in 15 Costa Rican farms. A total of 88 bananas (peel and pulp) were collected from the farms and analysed for isotopic signatures (δ¹³C, δ¹⁵N, and δ¹⁸O) and elemental compositions. The growing conditions were characterized in terms of climate, topography and soil conditions. The isotopic ratios differed significantly between groups of farms. The δ¹³C and δ¹⁵N values were mainly influenced by soil types, while rainfall and temperatures related more to the δ¹⁸O values. The elemental compositions of the bananas were primarily influenced by the local rainfall and soil types, while the geographical origin could be distinguished using principal component analysis. The overall results link the growing conditions to the isotopic and elemental compositions of bananas, thereby also providing a way to trace its origin.

Physico-elemental analysis of roasted organic coffee beans from Ethiopia, Colombia, Honduras, and Mexico using X-ray micro-computed tomography and external beam particle induced X-ray emission

The physico-elemental profiles of commercially attained and roasted organic coffee beans from Ethiopia, Colombia, Honduras, and Mexico were compared using light microscopy, X-ray micro-computed tomography, and external beam particle induced X-ray emission. External beam PIXE analysis detected P, S, Cl, K, Ca, Ti, Mn, Fe, Cu, Zn, Br, Rb, and Sr in samples. Linear discriminant analysis showed that there was no strong association between elemental data and production region, whilst a heatmap combined with hierarchical clustering showed that soil-plant physico-chemical properties may influence regional elemental signatures. Physical trait data showed that Mexican coffee beans weighed significantly more than beans from other regions, whilst Honduras beans had the highest width. X-ray micro-computed tomography qualitative data showed heterogeneous microstructural features within and between beans representing different regions. In conclusion, such multi-dimensional analysis may present a promising tool in assessing the nutritional content and qualitative characteristics of food products such as coffee.

Alternative data mining/machine learning methods for the analytical evaluation of food quality and authenticity - A review

In recent years, the variety and volume of data acquired by modern analytical instruments in order to conduct a better authentication of food has dramatically increased. Several pattern recognition tools have been developed to deal with the large volume and complexity of available trial data. The most widely used methods are principal component analysis (PCA), partial least squares-discriminant analysis (PLS-DA), soft independent modelling by class analogy (SIMCA), k-nearest neighbours (kNN), parallel factor analysis (PARAFAC), and multivariate curve resolution-alternating least squares (MCR-ALS). Nevertheless, there are alternative data treatment methods, such as support vector machine (SVM), classification and regression tree (CART) and random forest (RF), that show a great potential and more advantages compared to conventional ones. In this paper, we explain the background of these methods and review and discuss the reported studies in which these three methods have been applied in the area of food quality and authenticity. In addition, we clarify the technical terminology used in this particular area of research.

Elemental analysis of Brazilian coffee with ion beam techniques: From ground coffee to the final beverage

Brazilian coffee is well known worldwide due to its quality and richness in taste. The aim of the present study is to provide the elemental characterization of Brazilian coffee along different stages of the drip brewing process. To that end, samples from roasted ground coffee, spent coffee, paper filters and the final beverage were analyzed with one single ion beam technique, namely particle-induced X-ray emission (PIXE). In total, over 140 samples from 8 different Brazilian brands of ground coffee were analyzed. Large differences in some elemental concentrations were observed among different brands and among different batches of a single brand, which leads to high variances in the data. Concerning the beverage preparation, the analysis of the spent coffee shows that the transfer ratio from the ground coffee to the beverage differs for each element. Our results indicate that potassium and chlorine have the highest transfer ratio. Moreover, the concentration of rubidium is relatively high in drinking coffee. Finally, there is no influence of the elemental composition of paper filter in the preparation of drinking coffee.

Rare Earth Element Labeling as a Tool for Assuring the Origin of Eggs and Poultry Products

Laying hens were fed terbium and thulium supplemented feed in order to introduce a distinctive rare earth element pattern that allows discrimination of labeled from unlabeled poultry products. Samples of egg yolk, egg shells, meat, bones, liver, blood, and feces were analyzed using either conventional or laser ablation inductively coupled plasma mass spectrometry. Already after a short time of administering supplemented feed, terbium and thulium enrichment could be unambiguously detected in the products, while absolute terbium and thulium contents remained low enough to ensure safety for the customer. This method could potentially be applied to specifically label foodstuffs produced in certain regions or under certain conditions, in order to ensure food authenticity.

Discrimination of three Ephedra species and their geographical origins based on multi-element fingerprinting by inductively coupled plasma mass spectrometry

Discrimination of species and geographical origins of traditional Chinese medicine (TCM) is essential to prevent adulteration and inferior problems. We studied Ephedra sinica Stapf, Ephedra intermedia Schrenk et C.A.Mey. and Ephedra przewalskii Bge. to investigate the relationship between inorganic element content and these three species and their geographical origins. 38 elemental fingerprints from six major Ephedra-producing regions, namely, Inner Mongolia, Ningxia, Gansu, Shanxi, Shaanxi, and Sinkiang, were determined to evaluate the importance of inorganic elements to three species and their geographical origins. The contents of 15 elements, namely, N, P, K, S, Ca, Mg, Fe, Mn, Na, Cl, Sr, Cu, Zn, B, and Mo, of Ephedra samples were measured using inductively coupled plasma mass spectroscopy. Elemental contents were used as chemical indicators to classify species and origins of Ephedra samples using a radar plot and multivariate data analysis, including hierarchical cluster analysis (HCA), principal component analysis (PCA), and discriminant analysis (DA). Ephedra samples from different species and geographical origins could be differentiated. This study showed that inorganic elemental fingerprint combined with multivariate statistical analysis is a promising tool for distinguishing three Ephedra species and their geographical origins, and this strategy might be an effective method for authenticity discrimination of TCM.

Advanced data mining approaches in the assessment of urinary concentrations of bisphenols, chlorophenols, parabens and benzophenones in Brazilian children and their association to DNA damage

Human exposure to endocrine disrupting chemicals (EDCs) has received considerable attention over the last three decades. However, little is known about the influence of co-exposure to multiple EDCs on effect-biomarkers such as oxidative stress in Brazilian children. In this study, concentrations of 40 EDCs were determined in urine samples collected from 300 Brazilian children of ages 6-14 years and data were analyzed by advanced data mining techniques. Oxidative DNA damage was evaluated from the urinary concentrations of 8-hydroxy-2'-deoxyguanosine (8OHDG). Fourteen EDCs, including bisphenol A (BPA), methyl paraben (MeP), ethyl paraben (EtP), propyl paraben (PrP), 3,4-dihydroxy benzoic acid (3,4-DHB), methyl-protocatechuic acid (OH-MeP), ethyl-protocatechuic acid (OH-EtP), triclosan (TCS), triclocarban (TCC), 2-hydroxy-4-methoxybenzophenone (BP3), 2,4-dihydroxybenzophenone (BP1), bisphenol A bis(2,3-dihydroxypropyl) glycidyl ether (BADGE·2H₂O), 2,4-dichlorophenol (2,4-DCP), and 2,5-dichlorophenol (2,5-DCP) were found in >50% of the urine samples analyzed. The highest geometric mean concentrations were found for MeP (43.1 ng/mL), PrP (3.12 ng/mL), 3,4-DHB (42.2 ng/mL), TCS (8.26 ng/mL), BP3 (3.71 ng/mL), and BP1 (4.85 ng/mL), and exposures to most of which were associated with personal care product (PCP) use. Statistically significant associations were found between urinary concentrations of 8OHDG and BPA, MeP, 3,4-DHB, OH-MeP, OH-EtP, TCS, BP3, 2,4-DCP, and 2,5-DCP. After clustering the data on the basis of i) 14 EDCs (exposure levels), ii) demography (age, gender and geographic location), and iii) 8OHDG (effect), two distinct clusters of samples were identified. 8OHDG concentration was the most critical parameter that differentiated the two clusters, followed by OH-EtP. When 8OHDG was removed from the dataset, predictability of exposure variables increased in the order of: OH-EtP > OH-MeP > 3,4-DHB > BPA > 2,4-DCP > MeP > TCS > EtP > BP1 > 2,5-DCP. Our results showed that co-exposure to OH-EtP, OH-MeP, 3,4-DHB, BPA, 2,4-DCP, MeP, TCS, EtP, BP1, and 2,5-DCP was associated with DNA damage in children. This is the first study to report exposure of Brazilian children to a wide range of EDCs and the data mining approach further strengthened our findings of chemical co-exposures and biomarkers of effect.

Discrimination of geographical origin of cultivated Polygala tenuifolia based on multi-element fingerprinting by inductively coupled plasma mass spectrometry

Inorganic elements are important components of medicinal herbs, and provide valuable experimental evidence for the quality evaluation and control of traditional Chinese medicine (TCM). In this study, to investigate the relationship between the inorganic elemental fingerprint and geographical origin identification of cultivated Polygala tenuifolia, 41 elemental fingerprints of P. tenuifolia from four major polygala-producing regions (Shanxi, Hebei, Henan, and Shaanxi) were evaluated to determine the importance of inorganic elements to cultivated P. tenuifolia. A total of 15 elemental (B, Ca, Cl, Cu, Fe, K, Mg, Mn, Na, N, Mo, S, Sr, P, and Zn) concentrations of cultivated P. tenuifolia were measured using inductively coupled plasma mass spectroscopy (ICP-MS). The element composition samples were classified by radar plot, elemental fingerprint, and multivariate data analyses, such as hierarchical cluster analysis (HCA), principle component analysis (PCA), and discriminant analysis (DA). This study shows that radar plots and multivariate data analysis can satisfactorily distinguish the geographical origin of cultivated P. tenuifolia. Furthermore, PCA results revealed that N, Cu, K, Mo, Sr, Ca, and Zn are the characteristic elements of cultivated P. tenuifolia. Therefore, multi-element fingerprinting coupled with multivariate statistical techniques can be considered an effective tool to discriminate geographical origin of cultivated P. tenuifolia.

Stable Isotope Ratio and Elemental Profile Combined with Support Vector Machine for Provenance Discrimination of Oolong Tea (Wuyi-Rock Tea)

This paper focused on an effective method to discriminate the geographical origin of Wuyi-Rock tea by the stable isotope ratio (SIR) and metallic element profiling (MEP) combined with support vector machine (SVM) analysis. Wuyi-Rock tea (n = 99) collected from nine producing areas and non-Wuyi-Rock tea (n = 33) from eleven nonproducing areas were analysed for SIR and MEP by established methods. The SVM model based on coupled data produced the best prediction accuracy (0.9773). This prediction shows that instrumental methods combined with a classification model can provide an effective and stable tool for provenance discrimination. Moreover, every feature variable in stable isotope and metallic element data was ranked by its contribution to the model. The results show that δ2H, δ18O, Cs, Cu, Ca, and Rb contents are significant indications for provenance discrimination and not all of the metallic elements improve the prediction accuracy of the SVM model.