Trace-element composition and stable-isotope ratio for discrimination of foods with Protected Designation of Origin

Support vector regression for prediction of stable isotopes and trace elements using hyperspectral imaging on coffee for origin verification

The potential of using rapid and non-destructive near-infrared - hyperspectral imaging (HSI-NIR) for the prediction of an integrated stable isotope and multi-element dataset was explored for the first time with the help of support vector regression. Speciality green coffee beans sourced from three continents, eight countries, and 22 regions were analysed using a push-broom HSI-NIR (700-1700 nm), together with five isotope ratios (δ13C, δ15N, δ18O, δ2H, and δ34S) and 41 trace elements. Support vector regression with the radial basis function kernel was conducted using X as the HSI-NIR data and Y as the geochemistry markers. Model performance was evaluated using root mean squared error, coefficient of determination, and mean absolute error. Three isotope ratios (δ18O, δ2H, and δ34S) and eight elements (Zn, Mn, Ni, Mo, Cs, Co, Cd, and La) had an R2predicted 0.70 - 0.99 across all origin scales (continent, country, region). All five isotope ratios were well predicted at the country and regional levels. The wavelength regions contributing the most towards each prediction model were highlighted, including a discussion of the correlations across all geochemical parameters. This study demonstrates the feasibility of using HSI-NIR as a rapid and non-destructive method to estimate traditional geochemistry parameters, some of which are origin-discriminating variables related to altitude, temperature, and rainfall differences across origins.

Near-infrared reflectance spectroscopy accurately predicted isotope and elemental compositions for origin traceability of coffee

Stable isotope ratios and trace elements are well-established tools that act as signatures of the product's environmental conditions and agricultural processes; but they involve time, money, and environmentally destructive chemicals. In this study, we tested for the first time the potential of near-infrared reflectance spectroscopy (NIR) to estimate/predict isotope and elemental compositions for the origin verification of coffee. Green coffee samples from two continents, 4 countries, and 10 regions were analysed for five isotope ratios (δ¹³C, δ¹⁵N, δ¹⁸O, δ²H, and δ³⁴S) and 41 trace elements. NIR (1100-2400 nm) calibrations were developed using pre-processing with extended multiplicative scatter correction (EMSC) and mean centering and partial-least squares regression (PLS-R). Five elements (Mn, Mo, Rb, B, La) and three isotope ratios (δ¹³C, δ¹⁸O, δ²H) were moderately to well predicted by NIR (R²: 0.69 to 0.93). NIR indirectly measured these parameters by association with organic compounds in coffee. These parameters were related to altitude, temperature and rainfall differences across countries and regions and were previously found to be origin discriminators for coffee.

Application of Stable Isotope Analysis for Detecting the Geographical Origin of the Greek Currants "Vostizza": A Preliminary Study

There is a plethora of food products with geographical indications registered in the European Union without any study about their discrimination from other similar products. This is also the case for Greek currants. This paper aims to analyze if stable isotope analysis of C, N, and S could discriminate the Greek currants "Vositzza", registered as a product of Protected Designation of Origin, from two other currants registered as products of Protected Geographical Indication coming from neighboring areas. The first results show that the stable isotope ratio of sulfur is not detectable due to the very low sulfur content in the samples, and the analysis should be based on the stable isotope ratios of carbon and nitrogen to discriminate these products. The mean value of δ¹⁵N (1.38‱) of PDO "Vostizza" currants is lower than that of currants grown outside the PDO zone (2.01‱), while the mean value of δ¹³C of PDO "Vostizza" currants is higher (-23.93‱) in comparison to that of currants grown outside the PDO zone (-24.83‱). Nevertheless, the results indicate that with only two isotopic ratios, discrimination could not be achieved, and further analysis is required.

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.

Determining the Authenticity of Spirulina Dietary Supplements Based on Stable Isotope and Elemental Composition

While the demand for Spirulina dietary supplements continues to grow, product inspection in terms of authenticity and safety remains limited. This study used the stable isotope ratios of light elements (C, N, S, H, and O) and the elemental composition to characterize Spirulina dietary supplements available on the Slovenian market. Forty-six samples were labelled as originating from the EU (1), non-EU (6), Hawaii (2), Italy (2), Japan (1), Portugal (2), Taiwan (3), India (4), and China (16), and nine products were without a declared origin. Stable isotope ratio median values were -23.9‱ (-26.0 to -21.8‱) for δ¹³C, 4.80‱ (1.30-8.02‱) for δ¹⁵N, 11.0‱ (6.79-12.7‱) for δ³⁴S, -173‱ (- 190 to -158‱) for δ²H, and 17.2‱ (15.8-18.8‱) for δ¹⁸O. Multivariate statistical analyses achieved a reliable differentiation of Hawaiian, Italian, and Portuguese (100%) samples and a good separation of Chinese samples, while the separation of Indian and Taiwanese samples was less successful, but still notable. The study showed that differences in isotopic and elemental composition are indicative of sample origins, cultivation and processing methods, and environmental conditions such that, when combined, they provide a promising tool for determining the authenticity of Spirulina products.

The Pattern of Rare Earth Elements Like a Possible Helpful Tool in Traceability and Geographical Characterization of the Soil-Olive System (Olea europaea L.)

The identification of a product, with its geographical origin, is a guaranty of the value of the foodstuff and protection from potential fraud. Extra virgin olive oil is produced or marketed as a single variety or a blend of two or more cultivars, often of different geographic origins. Therefore, to study a possible link between the soil and olive oil, we accounted crucial to analyse the behaviour of olive of different cultivars. We studied Rare Earth Elements (REE) amounts and their relationship to trace their distribution from soil to the olive pulp (Olea europea L.). The results obtained pointed out that the different cultivars of Olea did not drive significant differences in reciprocal ratios of REE in the uptake from the soil up to olive (except for Eu). However soil-plant Rare Earth relationships depend exclusively on the soil REE composition. This method can be the starting point to enforcing the laws, in fact, it is important to develop analytical methods to measure the authenticity of the samples, and to verify the geographical origin even when olive oil is blended.

Stable Isotope Analysis for the Discrimination of the Geographical Origin of Greek Bottarga ‘Avgotaracho Messolongiou’: A Preliminary Research

Consumers are increasingly interested in the geographical origin of the foodstuff they consume as an important characteristic of food authenticity and quality. To assure the authenticity of the geographical origin, various methods have been proposed. Stable isotope analysis is a method that has been extensively used for products such as wine, oil, meat, while only a few studies have been conducted for the discrimination of seafood origin and especially for mullet roes or bottarga products. Analysis of the stable isotopes of C, N and S of Bottarga samples from four different origins were carried out. The values of δ¹⁵N (5.45‰) and δ³⁴S (4.66‰) for the Greek Bottarga Product named ‘Avgotaracho Messolongiou’, from Messolongi lagoon were lower than other areas while δ¹³C values were higher (−14.84‰). The first results show that the stable isotopes ratios of carbon, nitrogen and sulphur could be used to discriminate the Greek Protected Designations of Origin Bottarga product ‘Avgotaracho Messolongiou’ from other similar products.

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.

Geographical origin discrimination of Agaricus bisporus produced by the complete medium: A pilot study in South Korea

The complete medium used for mushroom cultivation is important for reliable crop production. We aimed to identify how the origin of Agaricus bisporus grown in Korea was affected by complete media produced in different geographical regions using stable isotope ratios (SIRs). We found that the SIR features of A. bisporus significantly depended on the complete media origin used; in particular, it appeared the high δ³⁴S in the Chinese complete medium, low δ³⁴S in the Dutch complete medium, and high δ¹⁵N in the Korean complete medium (P < 0.05). The support vector machine method appeared better geo-origin classification of A. bisporus by the complete media compared to a linear discriminant analysis. A large-scale study should be conducted to establish a reliable origin identification model for A. bisporus grown in complete media to improve the global mushroom marketplace.

Constraining parameter uncertainty for predicting oxygen and hydrogen isotope values in fruit

Understanding δ18O and δ2H values of agricultural products like fruit is of particular scientific interest in plant physiology, ecology, and forensic studies. Applications of mechanistic stable isotope models to predict δ18O and δ2H values of water and organic compounds in fruit, however, are hindered by a lack of empirical parameterizations and validations. We addressed this lack of data by experimentally evaluating model parameter values required to model δ18O and δ2H values of water and organic compounds in berries and leaves from strawberry and raspberry plants grown at different relative humidities. Our study revealed substantial differences between leaf and berry isotope values, consistent across the different relative humidity treatments. We demonstrated that existing isotope models can reproduce water and organic δ18O and δ2H values for leaves and berries. Yet, these simulations require organ-specific model parameterization to accurately predict δ18O and δ2H values of leaf and berry tissue and water pools. We quantified these organ-specific model parameters for both species and relative humidity conditions. Depending on the required model accuracy, species- and environment-specific model parameters may be justified. The parameter values determined in this study thus facilitate applications of stable isotope models where understanding δ18O and δ2H values of fruit is of scientific interest.

Probabilistic Machine Learning for the Authentication of the Protected Designation of Origin of Greek Bottarga from Messolongi: A Generic Methodology to Cope with Very Small Number of Samples

Consumers are increasingly interested in the geographical origin of foodstuff, as an important characteristic of food authenticity and quality. To assure the authenticity of the geographical origin, various methods have been proposed. Stable isotope analysis is a method that has been extensively used for products like wine, oil, and meat by using large datasets and analysis. On the other hand, only few studies have been conducted for the discrimination of seafood origin and especially for mullet roes or bottarga products, and even fewer investigate a small number of samples and datasets. Stable isotopes of Carbon (C), Nitrogen (N), and Sulfur (S) analysis of bottarga samples from four different origins were carried out. The first results show that the stable isotopes ratios of C, N, and S could be used to discriminate the Greek PDO Bottarga (Messolongi) from other similar products by using a probabilistic machine learning methodology. That could use limited sample data to fit/estimate their parameters, while, at the same time, being capable of describing accurately the population and discriminate individual samples regarding their origin.

Integration of Innovative Technologies in the Agri-Food Sector: The Fundamentals and Practical Case of DNA-Based Traceability of Olives from Fruit to Oil

Several socio-economic problems have been hidden by the COVID-19 pandemic crisis. Particularly, the agricultural and food industrial sectors have been harshly affected by this devastating disease. Moreover, with the worldwide population increase and the agricultural production technologies being inefficient or obsolete, there is a great need to find new and successful ways to fulfill the increasing food demand. A new era of agriculture and food industry is forthcoming, with revolutionary concepts, processes and technologies, referred to as Agri-food 4.0, which enables the next level of agri-food production and trade. In addition, consumers are becoming more and more aware about the origin, traceability, healthy and high-quality of agri-food products. The integration of new process of production and data management is a mandatory step to meet consumer and market requirements. DNA traceability may provide strong approach to certify and authenticate healthy food products, particularly for olive oil. With this approach, the origin and authenticity of products are confirmed by the means of unique nucleic acid sequences. Selected tools, methods and technologies involved in and contributing to the advance of the agri-food sector are presented and discussed in this paper. Moreover, the application of DNA traceability as an innovative approach to authenticate olive products is reported in this paper as an application and promising case of smart agriculture.

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

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

Multi-chemical analysis combined with chemometrics to characterize PDO and PGI Italian apples

BACKGROUND

The use of PDO (protected designation of origin) and PGI (protected geographical indication) labels allows to protect and promote agricultural products characterized by unique features related to the place of origin and traditional know-how. However, the presence of non-authentic products in the market represents a fraud that can be tackled applying analytical techniques combined with chemometric analysis. In this study, we applied multi-element and multi-isotope analysis to characterize PDO and PGI apples cultivated in northern Italy, comparing them with Italian apples without labels of geographical indications.

RESULTS

The multi-element and multi-isotope approach allowed to characterize the apples cultivated in northern Italy. Despite a significant effect of the sampling sites on the apple composition, the comparison of the multi-chemical fingerprint of the apples significantly varied among cultivation areas. Results of this characterization were used to classify samples according to their cultivation area applying a linear discriminant analysis (LDA). Outputs of the LDA showed that correct sample classification can be successfully achieved (balanced accuracy > 96%). Moreover, using a selection of variables, it was possible to correctly classify samples also at regional level.

CONCLUSION

The presented evidences indicate that the multi-element and multi-isotope fingerprint can be successfully applied to traceability studies. The combination of this characterization with chemometric tools allows the classification of Italian apples based on their origin both on a national and regional scale. This approach represents an interesting tool to enhance and protect PDO and PGI Italian products. © 2021 The Authors. Journal of The Science of Food and Agriculture published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry.

Using plant physiological stable oxygen isotope models to counter food fraud

Fraudulent food products, especially regarding false claims of geographic origin, impose economic damages of $30-$40 billion per year. Stable isotope methods, using oxygen isotopes (δ18O) in particular, are the leading forensic tools for identifying these crimes. Plant physiological stable oxygen isotope models simulate how precipitation δ18O values and climatic variables shape the δ18O values of water and organic compounds in plants. These models have the potential to simplify, speed up, and improve conventional stable isotope applications and produce temporally resolved, accurate, and precise region-of-origin assignments for agricultural food products. However, the validation of these models and thus the best choice of model parameters and input variables have limited the application of the models for the origin identification of food. In our study we test model predictions against a unique 11-year European strawberry δ18O reference dataset to evaluate how choices of input variable sources and model parameterization impact the prediction skill of the model. Our results show that modifying leaf-based model parameters specifically for fruit and with product-independent, but growth time specific environmental input data, plant physiological isotope models offer a new and dynamic method that can accurately predict the geographic origin of a plant product and can advance the field of stable isotope analysis to counter food fraud.

Validation and analysis of the geographical origin of Angelica sinensis (Oliv.) Diels using multi-element and stable isotopes

Background

Place of origin is an important factor when determining the quality and authenticity of Angelica sinensis for medicinal use. It is important to trace the origin and confirm the regional characteristics of medicinal products for sustainable industrial development. Effectively tracing and confirming the material’s origin may be accomplished by detecting stable isotopes and mineral elements.

Methods

We studied 25 A. sinensis samples collected from three main producing areas (Linxia, Gannan, and Dingxi) in southeastern Gansu Province, China, to better identify its origin. We used inductively coupled plasma mass spectrometry (ICP-MS) and stable isotope ratio mass spectrometry (IRMS) to determine eight mineral elements (K, Mg, Ca, Zn, Cu, Mn, Cr, Al) and three stable isotopes (δ¹³C, δ¹⁵N, δ¹⁸O). Principal component analysis (PCA), partial least square discriminant analysis (PLS-DA) and linear discriminant analysis (LDA) were used to verify the validity of its geographical origin.

Results

K, Ca/Al, δ¹³C, δ¹⁵N and δ¹⁸O are important elements to distinguish A. sinensis sampled from Linxia, Gannan and Dingxi. We used an unsupervised PCA model to determine the dimensionality reduction of mineral elements and stable isotopes, which could distinguish the A. sinensis from Linxia. However, it could not easily distinguish A. sinensis sampled from Gannan and Dingxi. The supervised PLS-DA and LDA models could effectively distinguish samples taken from all three regions and perform cross-validation. The cross-validation accuracy of PLS-DA using mineral elements and stable isotopes was 84%, which was higher than LDA using mineral elements and stable isotopes.

Conclusions

The PLS-DA and LDA models provide a theoretical basis for tracing the origin of A. sinensis in three regions (Linxia, Gannan and Dingxi). This is significant for protecting consumers’ health, rights and interests.

Classification of organic and ordinary kiwifruit by chemometrics analysis of elemental fingerprint and stable isotopic ratios

Elemental fingerprint, stable isotopic analysis, and chemometrics were combined to identify organic kiwifruit from ordinarily cultivated kiwifruit. Samples of organic (n1 = 78) and ordinary kiwifruit (n2 = 85) were collected from neighboring areas. For elemental fingerprint, the contents of 15 elements in fresh fruits, including Al, Cr, Mg, Pb, Zn, Ca, Cu, Mn, Se, Cd, Fe, Na, Sr, Co, and K, were determined by inductively coupled plasma optical emission spectrometry (ICP-OES). Three stable isotopes, including δ¹³ C, δ¹⁵ N, and δ¹⁸ O, were analyzed using an isotope-ratio mass spectrometer (IRMS). Different classification methods including soft independent modeling of class analogy (SIMCA), partial least squares discriminant analysis (PLSDA), and least squares support vector machines (LS-SVM), were used to discriminate the organic and ordinary kiwifruits by fusion of elemental and stable isotopic. As a result, the sensitivity, specificity, and overall accuracy of SIMCA model were 0.885, 0.857, and 0.864, respectively. PLSDA and LS-SVM obtained 0.950 and 0.983 classification accuracy of organic and ordinary kiwifruits, respectively. It was demonstrated that elemental fingerprint and stable isotopic analysis would provide useful chemical information for the identification of organic fruits, and the capacity of these methods could be enhanced by chemometrics. PRACTICAL APPLICATION: The classification of kiwifruit usually relies on the label assigned by the merchant, which is prone to deceive consumers. This research has developed an accurate and effective classification method based on stable isotopes and mineral elements for the identification of ordinary kiwifruit and organic kiwifruit, providing a tool for the quality monitoring of organic food.

Investigation of inorganic elemental content of honey from regions of North Island, New Zealand

Determination of geographical origin of honey is important to consumers to confirm authenticity. This study investigated the elemental fingerprint of 181 honey samples collected from apiary sites in six regions of North Island, New Zealand to determine if differences were observed due to region of collection or land use surrounding the hive (e.g. agricultural, rural, urban). Using principal component analysis, soil related elements (Ca, K, Mg, Mn, Na) provided 75.2% discrimination of samples in the first two principal components. Overall, low concentrations of heavy metals were observed; lead was present in close proximity to highly trafficked roads (28.1% of samples; 9.50-76.5 µg kg^-1) and cadmium was primarily present in honey collected from agricultural land in the Waikato (<51.6 µg kg^-1). The use of an elemental fingerprint of New Zealand honey may be advantageous to determine the geographical origin compared to honey produced from other countries.

Authenticity Assessment and Fraud Quantitation of Coffee Adulterated with Chicory, Barley, and Flours by Untargeted HPLC-UV-FLD Fingerprinting and Chemometrics

Coffee, one of the most popular drinks around the world, is also one of the beverages most susceptible of being adulterated. Untargeted high-performance liquid chromatography with ultraviolet and fluorescence detection (HPLC-UV-FLD) fingerprinting strategies in combination with chemometrics were employed for the authenticity assessment and fraud quantitation of adulterated coffees involving three different and common adulterants: chicory, barley, and flours. The methodologies were applied after a solid–liquid extraction procedure with a methanol:water 50:50 (v/v) solution as extracting solvent. Chromatographic fingerprints were obtained using a Kinetex^® C18 reversed-phase column under gradient elution conditions using 0.1% formic acid aqueous solution and methanol as mobile phase components. The obtained coffee and adulterants extract HPLC-UV-FLD fingerprints were evaluated by partial least squares regression-discriminants analysis (PLS-DA) resulting to be excellent chemical descriptors for sample discrimination. One hundred percent classification rates for both PLS-DA calibration and prediction models were obtained. In addition, Arabica and Robusta coffee samples were adulterated with chicory, barley, and flours, and the obtained HPLC-UV-FLD fingerprints subjected to partial least squares (PLS) regression, demonstrating the feasibility of the proposed methodologies to assess coffee authenticity and to quantify adulteration levels (down to 15%), showing both calibration and prediction errors below 1.3% and 2.4%, respectively.

An origin identification model for labeling of shiitake (Lentinula edodes)

With the increasing globalization of the food trade across countries and continents, reliable identification of the geographical origin of products is critical. In this study, we describe the limitations of the current origin labeling system for non-soil-based agricultural products and suggest alternative strategies for the identification of the geographical origin of such products. An origin identification model based on stable isotope ratio analysis combined with discriminant analysis is used to evaluate the similarities and dissimilarities between domestic and foreign shiitake mushrooms, including Chinese inoculated sawdust blocks and Chinese origin. The results show a classification sensitivity of 92.0%, classification specificity of 91.5%, and overall accuracy of 93.5%. In particular, δ¹⁵N was the most important isotope marker for the identification of the origin of shiitake mushrooms. Hence, the current origin labeling system for mushroom species has to be revised to establish fair trade and avoid improper origin labeling in the global shiitake market.

Nondestructive Detection of Authenticity of Thai Jasmine Rice Using Multispectral Imaging

The detection of authenticity is essential to the development and management of Thai jasmine rice industry. In this study, the multispectral imaging system (405–970 nm) was used for the detection of adulteration in Thai jasmine rice combined with chemometric methods including principal component analysis (PCA), partial least squares (PLS), least squares-support vector machines (LS-SVM), and backpropagation neural network (BPNN). Three varieties of rice that were similar to Thai jasmine rice in appearance were selected to perform the classification and quantitative prediction experiments by multispectral images. For the classification experiment, four varieties of rice samples could be easily classified with accuracy achieved to 92% by the BPNN model. For the quantitative prediction of adulteration proportion experiments, the results showed that, among the different chemometric methods, LS-SVM achieved the best prediction performance comparing the results of coefficient of determination, root-mean-square error (RMSEP), bias, and residual predictive deviation (RPD). It can be concluded that multispectral imaging technology with chemometric methods can be applied in the rapid and nondestructive detection of authenticity of Thai jasmine rice.

High-performance liquid chromatography with fluorescence detection fingerprints as chemical descriptors to authenticate the origin, variety and roasting degree of coffee by multivariate chemometric methods

BACKGROUND

Coffee is one of the most popular beverages around the world, consumed as an infusion of ground roasting coffee beans with a characteristic taste and flavor. Two main varieties, Arabica and Robusta, are produced worldwide. Furthermore, interest of consumers in quality attributes related to coffee production region and varieties is increasing. Thus, it is necessary to encourage the development of simple methodologies to authenticate and guarantee the coffee origin, variety and roasting degree, aiming to prevent fraudulent practices.

RESULTS

C18 high-performance liquid chromatography with fluorescence detection (HPLC-FLD) fingerprints obtained after brewing coffees without any sample treatment other than filtration (i.e. considerably reducing sample manipulation) were employed as sample chemical descriptors for subsequent coffee characterization and classification by principal component analysis (PCA) and partial least squares regression-discriminant analysis (PLS-DA). PLS-DA showed good classification capabilities regarding coffee origin, variety and roasting degree when employing HPLC-FLD fingerprints, although overlapping occurred for some sample groups. However, the discrimination power increased when selecting HPLC-FLD fingerprinting segments richer in discriminant features, which were deduced from PLS-DA loading plots. In this case, excellent separation was observed and 100% classification rates for both PLS-DA calibrations and predictions were obtained (all samples were correctly classified within their corresponding groups).

CONCLUSION

HPLC-FLD fingerprinting segments were3 found to be suitable chemical descriptors for discriminating the origin (country of production), variety (Arabica and Robusta) and roasting degree of coffee. Therefore, HPLC-FLD fingerprinting can be proposed as a feasible, simple and cheap methodology to address coffee authentication, especially for developing coffee production countries. © 2020 Society of Chemical Industry.

NMR in the Service of Wine Differentiation

NMR is a swift and highly reproducible spectrometric technique that makes it possible to obtain spectra containing a lot of information about the sample analyzed. This approach helps major components be described in complex mixtures such as wine in just one analysis. Analysis of wine metabolites is very often used to understand the impact of geographical origin or variety on wine quality. NMR is often used for tracing the geographical origin of wine. Research on NMR metabolic effects of geographical origin is of great importance as the high added value of wines results from compliance with state legislation on the protected denomination of origin (PDO) and protected geographical indication (PGI) for the administration of the appellation of wines. A review of NMR with emphasis on SNIF-NMR in the analysis of wine authenticity is given. SNIF-NMR remains a method of choice for the detection of wine chaptalization as it is the only approach which provides position-specific information on the origin of sugar in wine. However, the sample preparation step, which lacks major improvements since its conception, is strenuous and expensive, and suffers from drawbacks in terms of low sample throughput. Mainstream 1D and 2D NMR experiments provide a fast and affordable way to authenticate wine based on the geographical origin, vintage, and variety discrimination, and include a simple and non-destructive sample preparation step. With this approach, spectral data processing often represents a crucial step of the analysis. With properly performed NMR experiments good to excellent differentiation of wines from different vintages, regions, and varieties was achieved recently.

Prospect on Rare Earth Elements and Metals Fingerprint for the Geographical Discrimination of Commercial Spanish Wines

This paper presents a novel tool for Spanish commercial wine discrimination according to their designation of origin (PDO). A total of 65 commercial wines from different Spanish designation of origin (Alicante, Bullas, Campo de Borja, Jumilla, Castilla la Mancha, Ribeiro, Ribera de Duero, Rioja, Rueda, Utiel-Requena, Valdepeñas and Valencia) were characterized. The rare earth elements (REEs) content was determined by a high-temperature torch integrated sample introduction system (hTISIS) coupled to inductively coupled plasma mass spectrometry (ICP-MS). The REE content was used to draw characteristic PDOs radar charts. Results indicated that the REEs fingerprint provides a good prospect to discriminate the different Spanish PDOs, except for Alicante, Castilla la Mancha, Jumilla, Utiel-Requena and Valdepeñas. Finally, for those PDOs that were not properly distinguished, a second fingerprint obtained from Ba, Co, Cr, Mn, Ni, Pb and V content was used for discrimination purposes.

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

Honey with Protected Denomination of Origin (PDO) could be an attractive target for fraudsters. Elemental profiles by Energy Dispersive-X Ray Fluorescence were processed by multivariate methods to classify 183 PDO honeys produced in three regions of Spain (Liébana, Granada, Tenerife). Additional honey samples (18) produced in a fourth region without PDO (El Bierzo) separated well from the PDO clusters. The manganese content was a discriminant marker of Liébana PDO and El Bierzo, that could also be differentiated from each other. Within each region, distinct clusters revealed differences between dark vs light varieties, multi- vs uni-floral honey and producers of the same PDO. The developed models were validated with 131 samples produced outside the PDO regions and El Bierzo. The proposed classification approach could be implemented as a fast screening tool to support pollen analysis in honey authentication. The reduced number of observations in some light honey models affected their performance.

Monitoring the authenticity of pu'er tea via chemometric analysis of multielements and stable isotopes

Mineral elements and stable isotopes combined with stoichiometric methods were used as a potential tool for first authenticating Chinese tea according to it's production year. A total of 86 mineral elements and stable isotope compositions were determined from the Xiangzhujing Pu'er tea in five different production years using ICP-MS and ICP-OES. On the basis of 78 statistically significant mineral elements and stable isotopes, HCA, PCA, PLS-DA, BP-ANN, and LDA were employed to build authentication models for predicting the Pu'er tea with different production years. The clustering results of the HCA and PCA were worse than that of PLS-DA, BP-ANN, and LDA. The PLS-DA model displayed a perfect model performance (R²X = 0.86, R²Y = 0.974, and Q² = 0.922). The authentication performance of LDA and BP-ANN revealed their 100% recognition sensitivity and prediction ability and was thus better than that of PLS-DA. Mn, ⁶⁸Zn, and ²⁰³Tl were the markers for enabling the successful authentication of Pu'er tea with different production years. This study contributes toward generalizing the use of mineral element and stable isotope fingerprinting combined with LDA and BP-ANN as a promising tool for authentication of tea worldwide.

Sustainable Palm Oil-The Role of Screening and Advanced Analytical Techniques for Geographical Traceability and Authenticity Verification

Palm oil production from oil palm (Elaeis guineensis Jacq.) is vital for the economy of Malaysia. As of late, sustainable production of palm oil has been a key focus due to demand by consumer groups, and important progress has been made in establishing standards that promote good agricultural practices that minimize impact on the environment. In line with the industrial goal to build a traceable supply chain, several measures have been implemented to ensure that traceability can be monitored. Although the palm oil supply chain can be highly complex, and achieving full traceability is not an easy task, the industry has to be proactive in developing improved systems that support the existing methods, which rely on recorded information in the supply chain. The Malaysian Palm Oil Board (MPOB) as the custodian of the palm oil industry in Malaysia has taken the initiative to assess and develop technologies that can ensure authenticity and traceability of palm oil in the major supply chains from the point of harvesting all the way to key downstream applications. This review describes the underlying framework related to palm oil geographical traceability using various state-of-the-art analytical techniques, which are also being explored to address adulteration in the global palm oil supply chain.

Exploring the Scientific Interest for Olive Oil Origin: A Bibliometric Study from 1991 to 2018

The authenticity and traceability of olive oils have been a growing concern over the past decades, generating numerous scientific studies. This article applies the tools of bibliometric analyses to explore the evolution and strategic orientation of the research focused on olive oil geographical and varietal origins. A corpus of 732 papers published in 178 different journals between 1991 and 2018 was considered. The most productive journals, authors and countries are highlighted, as well as the most cited articles associated with specific analytical techniques. A cluster analysis on the keywords generates 8 main themes of research, each focused on different analytical techniques or compounds of interest. A network between these thematic clusters and the main authors indicates their area of expertise. The metabolomics methods are drawing increasing interest and studies focused on the relationships between the origin and the sensory or nutritional properties provided by minor compounds of olive oils appear to be future lines of research.

Geographical Origin Authentication of Agri-Food Products: Α Review

This study is a systematic literature review of geographical origin authentication by elemental analytical techniques. Authentication and certification of geographic origin of agri-food products is a useful tool toward the protection of the quality for products. The aim of this work was to map the current state of research in the area of agricultural products and food, identifying emerging fields to the geographical origin of products. The article is divided in three parts. The first part of the article deals with the analytical techniques applied in the food authentication. Special mention is made to elemental analysis and multiple isotope ratio. The second section focuses on statistically published data concerning published research for geographical origin authentication for the period 2015-2019. Specific results are presented inter alia: number of articles according to the type of product, articles according to the type of the analytical techniques, and others. The third part contains characteristic results from articles that were published in the period 2015-2019, on certification of geographical origin on specific agricultural products.

Comparison of different classification algorithms to identify geographic origins of olive oils

Research on investigation and determination of geographic origins of olive oils is increased by consumers' demand to authenticated olive oils. Classification algorithms which are machine learning methods can be employed for the authentication of olive oils. In this study, different classification algorithms were evaluated to reveal the most accurate one for authentication of Turkish olive oils. BayesNet, Naive Bayes, Multilayer Perception, IBK, Kstar, SMO, Random Forest, J48, LWL, Logistic Regression, Simple Logistic, LogitBoost algorithms were implemented on 61 chemical analysis parameters of 49 olive oil samples from 6 different locations at Western Turkey. These 61 parameters were obtained from five different chemical analyses which are stable carbon isotope ratio, trace elements, sterol compositions, FAMEs and TAGs. This study is the most comprehensive study to determine the geographical origin of Turkish olive oils in terms of these mentioned features. Classification performances of the algorithms were compared using accuracy, specificity and sensitivity metrics. Random Forest, BayesNet, and LogitBoost algorithms were found as the best classification algorithms for authentication of Turkish olive oils. Using the classification model in this study, geographic origin of an unknown olive oil can be predicted with high accuracy. Besides, similar models can be developed to obtain useful information for authentication of other food products.

Authentication of the Origin, Variety and Roasting Degree of Coffee Samples by Non-Targeted HPLC-UV Fingerprinting and Chemometrics. Application to the Detection and Quantitation of Adulterated Coffee Samples

In this work, non-targeted approaches relying on HPLC-UV chromatographic fingerprints were evaluated to address coffee characterization, classification, and authentication by chemometrics. In general, high-performance liquid chromatography with ultraviolet detection (HPLC-UV) fingerprints were good chemical descriptors for the classification of coffee samples by partial least squares regression-discriminant analysis (PLS-DA) according to their country of origin, even for nearby countries such as Vietnam and Cambodia. Good classification was also observed according to the coffee variety (Arabica vs. Robusta) and the coffee roasting degree. Sample classification rates higher than 89.3% and 91.7% were obtained in all the evaluated cases for the PLS-DA calibrations and predictions, respectively. Besides, the coffee adulteration studies carried out by partial least squares regression (PLSR), and based on coffees adulterated with other production regions or variety, demonstrated the good capability of the proposed methodology for the detection and quantitation of the adulterant levels down to 15%. Calibration, cross-validation, and prediction errors below 2.9%, 6.5%, and 8.9%, respectively, were obtained for most of the evaluated cases.

Distinguishing the region-of-origin of roasted coffee beans with trace element ratios

Determining coffee region-of-origin is most appropriately addressed through analyses of the product available to the consumer. We analyzed the concentrations of 44 trace elements in 53 samples of roasted Arabica coffee beans (Coffea arabica) from 21 different countries. Variations in absolute elemental concentrations of coffee beans arise through varying degrees of roasting (from green through dark roasts). Since trace elements are not volatilized at roasting temperatures, we conducted analyses of element ratios to evaluate concentration-related differences among beans of different origins. We used kernel density estimates to compare the distributions of 1892 element ratios for each of these countries with the combined distribution of coffee samples from the other countries. Using this quantitative approach, we demonstrated that many of the world's coffee-producing regions can be distinguished from other regions of the world on the basis of element ratios.

Sr isotope discrimination of multi species aquaculture productions at a worldwide scale and contribution of the water reservoir in Sr plant input

Aquatic plants commonly used in landscaping or as ornaments are subject to a growing worldwide market that is source of trade between countries which can induce the transfer of unwanted invasive alien plant species. To protect national biodiversity and economy, authorities promote the use of local markets without however providing the method to do so. This study deals with the feasibility of using Sr stable isotopes for discriminating the origin of aquatic plants at a worldwide scale. More than 15 aquatic plant species were collected from main producers in four countries (China, Hungary, Germany, France). Each plant was analysed for its ⁸⁷Sr/⁸⁶Sr ratio and the results are compared within samples from each producer and between countries. For a given produce, significant ⁸⁷Sr/⁸⁶Sr can be measured as a function of the plant species. However, at the scale of Europe, plants from different producers are almost isotopically identical with ⁸⁷Sr/⁸⁶Sr ratios of 0.71228 ± 218.10^-5, 0.71116 ± 178.10^-5 and 0.71066 ± 156.10^-5 for France, Hungary and Germany, respectively. These values are clearly distinguishable from those measured in Chinese aquatic plants, which yield a mean ⁸⁷Sr/⁸⁶Sr ratio of 0.70591 ± 168.10^-5. This Sr isotopic discrimination between European and Chinese aquatic plants is explained by the specific agricultural and hydrogeological environments of the producing countries. Indeed, the cycle of Sr in aquatic plants is studied here for the first time. Natural and anthropic materials of French origin were collected and characterized in terms of ⁸⁷Sr/⁸⁶Sr. Mixing models reveal that irrigation water is the main source of Sr for plants (35-85%). Clays from the substrate (4-38%) and fertilizers (5-19%) can also contribute, in proportions depending on the plant species. This model accounts for the small variations in ⁸⁷Sr/⁸⁶Sr ratios of species from the same producer and allows a discrimination between producers using different agricultural practices.

Tracing the geographical origin of rice by stable isotopic analyses combined with chemometrics

Multivariate stable isotope analysis combined with chemometrics was used to investigate and discriminate rice samples from six rice producing provinces in China (Heilongjiang, Jilin, Jiangsu, Zhejiang, Hunan and Guizhou) and four other Asian rice producing countries (Thailand, Malaysia, Philippines, and Pakistan). The stable isotope characteristics were analyzed for rice of different species cultivated with varied farming methods at different altitudes and latitudes/longitudes. The index groups of δ¹³C, δ¹⁵N, δ¹⁸O, ^207/206Pb and ^208/207Pb were screened and established for the selected samples with different geographical features by means of principal component analysis (PCA) and discriminant analysis (DA), which would provide a sound technical solution for rice traceability and serve as a template for further research on the traceability of other agricultural products, especially plant-derived products.