Profiling versus fingerprinting analysis of sesquiterpene hydrocarbons for the geographical authentication of extra virgin olive oils

Ground-breaking comparison of target stable isotope ratios vs. emerging sesquiterpene fingerprinting for authenticating virgin olive oil origin

This study presents a pioneering comparison of target stable isotope ratios analysis and sesquiterpene (SH) fingerprinting for authenticating virgin olive oil (VOO) geographical origin. Both methods were selected for being among the most promising targeted and untargeted approaches, respectively. These methods were applied to the same sample set of nearly 400 VOO samples, covering diverse harvest years, cultivars and producers. PLS-DA classification models were developed to differentiate between Italian and non-Italian VOOs, as well as VOOs from three closely located Italian regions. Isotopic models based on bulk δ13C, δ18O and δ2H achieved over 75 % classification accuracy in distinguishing Italian from non-Italian VOOs, while SH fingerprinting outperformed with over 90 % accuracy and greater sensitivity to regional differences, as assessed in external validation. This systematic comparison provides insights into the strengths and weaknesses of each method, and the results will guide future research to enhance their reliability in VOO geographical authentication.

Mono- and sesquiterpenoid fingerprinting: A powerful and streamlined solution for pine nut authentication

This study proposes a novel authentication method for pine nut geographical and botanical origin, using mono- and sesquiterpene fingerprints (extracted ion chromatograms from specific ions) analysed via solid-phase microextraction coupled with gas chromatography-mass spectrometry, combined with chemometrics (partial least squares - discriminant analysis). It was tested on 253 samples from China, Russia (major producers of Pinus koraiensis and Pinus sibirica), Spain and Turkey (supplying Pinus pinea), across harvest years. The method achieved 100 % accuracy in external validation when distinguishing Spanish from non-Spanish pine nuts, and 99 % accuracy in differentiating Pinus pinea samples from two distinct Spanish regions. This simple, affordable, and automatable approach proves to be an effective screening tool. It could support official controls in preventing pine nut counterfeiting, as these highly valued nuts have sensory and nutritional characteristics influenced by their species and origin, which, in turn, affect their price.

Prospective exploration of hazelnut's unsaponifiable fraction for geographical and varietal authentication: A comparative study of advanced fingerprinting and untargeted profiling techniques

This study compares two data processing techniques (fingerprinting and untargeted profiling) to authenticate hazelnut cultivar and provenance based on its unsaponifiable fraction by GC-MS. PLS-DA classification models were developed on a selected sample set (n = 176). As test cases, cultivar models were developed for "Tonda di Giffoni" vs other cultivars, whereas provenance models were developed for three origins (Chile, Italy or Spain). Both fingerprinting and untargeted profiling successfully classified hazelnuts by cultivar or provenance, revealing the potential of the unsaponifiable fraction. External validation provided over 90 % correct classification, with fingerprinting slightly outperforming. Analysing PLS-DA models' regression coefficients and tentatively identifying compounds corresponding to highly relevant variables showed consistent agreement in key discriminant compounds across both approaches. However, fingerprinting in selected ion mode extracted slightly more information from chromatographic data, including minor discriminant species. Conversely, untargeted profiling acquired in full scan mode, provided pure spectra, facilitating chemical interpretability.

HS-SPME-GC-MS and Chemometrics for the Quality Control and Clustering of Monovarietal Extra Virgin Olive Oil: A 3-Year Study on Terpenes and Pentene Dimers of Italian Cultivars

Terpenes and pentene dimers are less studied volatile organic compounds (VOCs) but are associated with specific features of extra virgin olive oils (EVOOs). This study aimed to analyze mono- and sesquiterpenes and pentene dimers of Italian monovarietal EVOOs over 3 years (14 cultivars, 225 samples). A head space-solid-phase microextraction-gas chromatography-mass spectrometry (HS-SPME-GC-MS) method recently validated was used for terpene and pentene dimer quantitation. The quantitative data collected were used for both the characterization and clustering of the cultivars. Sesquiterpenes were the molecules that most characterized the different cultivars, ranging from 3.908 to 38.215 mg/kg; different groups of cultivars were characterized by different groups of sesquiterpenes. Pentene dimers (1.336 and 3.860 mg/kg) and monoterpenes (0.430 and 1.794 mg/kg) showed much lower contents and variability among cultivars. The application of Kruskal-Wallis test-PCA-LDA-HCA to the experimental data allowed defining 4 clusters of cultivars and building a predictive model to classify the samples (94.3% correct classification). The model was further tested on 33 EVOOs, correctly classifying 91% of them.

Combination of fingerprint and chemometric analytical approaches to identify the geographical origin of Qinghai-Tibet plateau rapeseed oil

Verification of the geographical origin of rapeseed oil is essential to protect consumers from fraudulent products. A prospective study was conducted on 45 samples from three rapeseed oil-producing areas in Qinghai Province, which were analyzed by GC-FID and GC-MS. To assess the accuracy of the prediction of origin, classification models were developed using PCA, OPLS-DA, and LDA. It was found that multivariate analysis combined with PCA separate 96% of the samples, and the correct sample discrimination rate based on the OPLS-DA model was over 98%. The predictive index of the model was Q2 = 0.841, indicating that the model had good predictive ability. The LDA results showed highly accurate classification (100%) and cross-validation (100%) rates for the rapeseed oil samples, demonstrating that the model had strong predictive capacity. These findings will serve as a foundation for the implementation and advancement of origin traceability using the combination of fatty acid, phytosterol and tocopherol fingerprints.

Analysis of Volatile Hydrocarbons (Pentene Dimers and Terpenes) in Extra Virgin Olive Oil: Optimization by Response Surface Methodology and Validation of HS-SPME-GC-MS Method

A head space-solid phase microextraction-gas chromatography-mass spectrometery (HS-SPME-GC-MS) method for the simultaneous analysis of pentene dimers from lipoxygenase (LOX) pathway, monoterpenes, and sesquiterpenes in extra virgin olive oil (EVOO) was proposed. A Doehlert design was performed; the conditions of the HS-SPME preconcentration step (extraction temperature, extraction time, sample amount, and desorption time) were optimized by response surface methodology, allowing defining the method operable design region. A quantitative method was set up using the multiple internal standard normalization approach: four internal standards were used, and the most suitable one was selected for area normalization of each external standard. The quantitative method was successfully validated and applied to a series of monocultivar EVOOs. This is the first paper in which a quantitative method using commercial standards has been proposed for the analysis of an important class of molecules of EVOO such as pentene dimers. The optimized method is suitable for routine analysis aimed at characterizing high quality EVOOs.

Rare Earth Element Variability in Italian Extra Virgin Olive Oils from Abruzzo Region

Extra virgin olive oil is a food product from the Mediterranean area that is particularly and continuously experiencing to increasing instances of fraudulent geographical labeling. Therefore, origin protection must be improved, mainly based on its intrinsic chemical composition. This study aimed to perform a preliminary chemical characterization of Abruzzo extra virgin olive oils (EVOOs) using rare earth elements (REEs). REEs were evaluated in EVOO samples of different varieties produced in different geographical origins within the Abruzzo region (Italy) in three harvest years using ICP-MS chemometric techniques. Principal component, discriminant, and hierarchical cluster analyses were conducted to verify the influence of the variety, origin, and vintage of the REE composition. The results of a three-year study showed a uniform REE pattern and a strong correlation in most EVOOs, in particular for Y, La, Ce, and Nd. However, europium and erbium were also found in some oil samples. Compared with cultivar and origin, only the harvest year slightly influenced the REE composition, highlighting the interactions of the olive system with the climate and soil chemistry that could affect the multielement composition of EVOOs.

Determination of adulteration, geographical origins, and species of food by mass spectrometry

Food adulteration, mislabeling, and fraud, are rising global issues. Therefore, a number of precise and reliable analytical instruments and approaches have been proposed to ensure the authenticity and accurate labeling of food and food products by confirming that the constituents of foodstuffs are of the kind and quality claimed by the seller and manufacturer. Traditional techniques (e.g., genomics-based methods) are still in use; however, emerging approaches like mass spectrometry (MS)-based technologies are being actively developed to supplement or supersede current methods for authentication of a variety of food commodities and products. This review provides a critical assessment of recent advances in food authentication, including MS-based metabolomics, proteomics and other approaches.

Revealing adulterated olive oils by triacylglycerol screening methods: Beyond the official method

Official control methods to detect olive oil (OO) adulteration fail to provide satisfactory consumer protection. Thus, faster and more sensitive screening tools are needed to increase their effectiveness. Here, the official method for adulterant detection in OO was compared with three untargeted screening methods based on triacylglycerol analysis using high-throughput (FIA-HESI-HRMS; HT-GC-MS; HPLC-RID) and pattern recognition techniques (PLS-DA). They were assayed on a set of genuine and adulterated samples with a high natural variability (n = 143). The sensitivity of the official method was 1 for high linoleic (HL) blends at ≥2 % but only 0.39 for high oleic (HO) blends at ≥5 %, while specificity was 0.96. The sensitivity of the screening methods in external validation was 0.90-0.99 for the detection of HL and 0.82-0.88 for HO blends. Among them, HT-GC-MS offered the highest sensitivity (0.94) and specificity (0.76), proving to be the most suitable screening tool for OO authentication.

Rapid authentication of Chaenomeles species by visual volatile components fingerprints based on headspace gas chromatography-ion mobility spectrometry combined with chemometric analysis

INTRODUCTION

Chaenomeles, including Chaenomeles speciosa (ZP), Chaenomeles sinensis (GP), Chaenomeles tibetica (XZ), and Chaenomeles japonica (RB), has been widely used as food in China for thousands of years. However, only ZP, was recorded to be the authentic medicinal Chaenomeles. Therefore, the rapid and accurate method for the authenticity identification of Chaenomeles species is urgently needed.

OBJECTIVE

To develop a method for rapid differentiation of Chaenomeles species.

METHODS

The visual volatile components fingerprints based on headspace gas chromatography-ion mobility spectrometry (HS-GC-IMS) combined with chemometric analysis, including principal component analysis (PCA), linear discriminant analysis (LDA) and partial least-squares discriminant analysis (PLS-DA), were utilised for the authentication of Chaenomeles species.

RESULTS

The visual volatile components fingerprints by the GC-IMS intuitively showed the distribution features of the volatile components for different Chaenomeles samples. The LDA and PLS-DA models successfully discriminated Chaenomeles species with original discrimination accuracy of 100%. Fifteen volatile compounds (VOCs) (peaks 9, 12, 13, 19, 23, 24, 35, 48, 57, 65, 67, 76, 79, 80, 83) were selected as the potential species-specific markers of Chaenomeles via variable importance of projection (VIP > 1.2) and one-way analysis of variance (P < 0.05).

CONCLUSIONS

This study showed that the visual volatile components fingerprints by HS-GC-IMS combined with chemometric analysis is a meaningful method in the Chaenomeles species authentication.

Nuclear magnetic resonance spectroscopy in extra virgin olive oil authentication

Extra virgin olive oil (EVOO) is a high-quality product that has become one of the stars in the food fraud context in recent years. EVOO can encounter different types of fraud, from adulteration with cheaper oils to mislabeling, and for this reason, the assessment of its authenticity and traceability can be challenging. There are several officially recognized analytical methods for its authentication, but they are not able to unambiguously trace the geographical and botanical origin of EVOOs. The application of nuclear magnetic resonance (NMR) spectroscopy to EVOO is reviewed here as a reliable and rapid tool to verify different aspects of its adulteration, such as undeclared blends with cheaper oils and cultivar and geographical origin mislabeling. This technique makes it possible to use both targeted and untargeted approaches and to determine the olive oil metabolomic profile and the quantification of its constituents.

Geographical authentication of virgin olive oil by GC-MS sesquiterpene hydrocarbon fingerprint: Verifying EU and single country label-declaration

According to the last report from the European Union (EU) Food Fraud Network, olive oil tops the list of the most notified products. Current EU regulation states geographical origin as mandatory for virgin olive oils, even though an official analytical method is still lacking. Verifying the compliance of label-declared EU oils should be addressed with the highest priority level. Hence, the present work tackles this issue by developing a classification model (PLS-DA) based on the sesquiterpene hydrocarbon fingerprint of 400 samples obtained by HS-SPME-GC-MS to discriminate between EU and non-EU olive oils, obtaining an 89.6% of correct classification for the external validation (three iterations), with a sensitivity of 0.81 and a specificity of 0.95. Subsequently, multi-class discrimination models for EU and non-EU countries were developed and externally validated (with three different validation sets) with successful results (average of 92.2% of correct classification for EU and 96.0% for non-EU countries).

Olive Oil Traceability Studies Using Inorganic and Isotopic Signatures: A Review

The olive oil industry is subject to significant fraudulent practices that can lead to serious economic implications and even affect consumer health. Therefore, many analytical strategies have been developed for olive oil’s geographic authentication, including multi-elemental and isotopic analyses. In the first part of this review, the range of multi-elemental concentrations recorded in olive oil from the main olive oil-producing countries is discussed. The compiled data from the literature indicates that the concentrations of elements are in comparable ranges overall. They can be classified into three categories, with (1) Rb and Pb well below 1 µg kg⁻¹; (2) elements such as As, B, Mn, Ni, and Sr ranging on average between 10 and 100 µg kg⁻¹; and (3) elements including Cr, Fe, and Ca ranging between 100 to 10,000 µg kg⁻¹. Various sample preparations, detection techniques, and statistical data treatments were reviewed and discussed. Results obtained through the selected analytical approaches have demonstrated a strong correlation between the multi-elemental composition of the oil and that of the soil in which the plant grew. The review next focused on the limits of olive oil authentication using the multi-elemental composition method. Finally, different methods based on isotopic signatures were compiled and critically assessed. Stable isotopes of light elements have provided acceptable segregation of oils from different origins for years already. More recently, the determination of stable isotopes of strontium has proven to be a reliable tool in determining the geographical origin of food products. The ratio ⁸⁷Sr/⁸⁶Sr is stable over time and directly related to soil geology; it merits further study and is likely to become part of the standard tool kit for olive oil origin determination, along with a combination of different isotopic approaches and multi-elemental composition.

A Bayesian Approach to Predict Food Fraud Type and Point of Adulteration

Primary and secondary food processing had been identified as areas vulnerable to fraud. Besides the food processing area, other stages within the food supply chain are also vulnerable to fraud. This study aims to develop a Bayesian network (BN) model to predict food fraud type and point of adulteration i.e., the occurrence of fraudulent activity. The BN model was developed using GeNie Modeler (BayesFusion, LLC) based on 715 notifications (1979-2018) from Food Adulteration Incidents Registry (FAIR) database. Types of food fraud were linked to six explanatory variables such as food categories, year, adulterants (chemicals, ingredients, non-food, microbiological, physical, and others), reporting country, point of adulteration, and point of detection. The BN model was validated using 80 notifications from 2019 to determine the predictive accuracy of food fraud type and point of adulteration. Mislabelling (20.7%), artificial enhancement (17.2%), and substitution (16.4%) were the most commonly reported types of fraud. Beverages (21.4%), dairy (14.3%), and meat (14.0%) received the highest fraud notifications. Adulterants such as chemicals (21.7%) (e.g., formaldehyde, methanol, bleaching agent) and cheaper, expired or rotten ingredients (13.7%) were often used to adulterate food. Manufacturing (63.9%) was identified as the main point of adulteration followed by the retailer (13.4%) and distribution (9.9%).

Geographical Origin Assessment of Extra Virgin Olive Oil via NMR and MS Combined with Chemometrics as Analytical Approaches

Geographical origin assessment of extra virgin olive oil (EVOO) is recognised worldwide as raising consumers’ awareness of product authenticity and the need to protect top-quality products. The need for geographical origin assessment is also related to mandatory legislation and/or the obligations of true labelling in some countries. Nevertheless, official methods for such specific authentication of EVOOs are still missing. Among the analytical techniques useful for certification of geographical origin, nuclear magnetic resonance (NMR) and mass spectroscopy (MS), combined with chemometrics, have been widely used. This review considers published works describing the use of these analytical methods, supported by statistical protocols such as multivariate analysis (MVA), for EVOO origin assessment. The research has shown that some specific countries, generally corresponding to the main worldwide producers, are more interested than others in origin assessment and certification. Some specific producers such as Italian EVOO producers may have been focused on this area because of consumers’ interest and/or intrinsic economical value, as testified also by the national concern on the topic. Both NMR- and MS-based approaches represent a mature field where a general validation method for EVOOs geographic origin assessment could be established as a reference recognised procedure.

Chromatographic Fingerprinting Enables Effective Discrimination and Identitation of High-Quality Italian Extra-Virgin Olive Oils

The challenging process of high-quality food authentication takes advantage of highly informative chromatographic fingerprinting and its identitation potential. In this study, the unique chemical traits of the complex volatile fraction of extra-virgin olive oils from Italian production are captured by comprehensive two-dimensional gas chromatography coupled to time-of-flight mass spectrometry and explored by pattern recognition algorithms. The consistent realignment of untargeted and targeted features of over 73 samples, including oils obtained by different olive cultivars (n = 24), harvest years (n = 3), and processing technologies, provides a solid foundation for sample identification and discrimination based on production region (n = 6). Through a dedicated multivariate statistics workflow, identitation is achieved by two-level partial least-square (PLS) regression, which highlights region diagnostic patterns accounting between 58 and 82 of untargeted and targeted compounds, while sample classification is performed by sequential application of soft independent modeling for class analogy (SIMCA) models, one for each production region. Samples are correctly classified in five of the six single-class models, and quality parameters [i.e., sensitivity, specificity, precision, efficiency, and area under the receiver operating characteristic curve (AUC)] are equal to 1.00.

The use of analytical techniques coupled with chemometrics for tracing the geographical origin of oils: A systematic review (2013-2020)

The global market for imported, high-quality priced foods has grown dramatically in the last decade, as consumers become more conscious of food originating from around the world. Many countries require the origin label of food to protect consumers need about true characteristics and origin. Regulatory authorities are looking for an extended and updated list of the analytical techniques for verification of authentic oils and to support law implementation. This review aims to introduce the efforts made using various analytical tools in combination with the multivariate analysis for the verification of the geographical origin of oils. The popular analytical tools have been discussed, and scientometric assessment that underlines research trends in geographical authentication and preferred journals used for dissemination has been indicated. Overall, we believe this article will be a good guideline for food industries and food quality control authority to assist in the selection of appropriate methods to authenticate oils.

Instrumental Techniques to Classify Olive Oils according to Their Quality

This review includes an update of the publications on quality classification of olive oils into extra, virgin or lampante olive oil categories. Nowadays, the official method to carry out this classification is time-consuming and, sometimes, it is not systematic and/or objective. It is based on conventional physicochemical analysis and on a sensorial tasting of olive oils carried out by a panel of experts. The aim of this review was to explore and give value to the alternative techniques reported in the bibliography to complement the current official methods established for that classification of olive oils. Specifically considered were non-separation and separation analytical techniques which could contribute to correctly classify olive oils according to their physicochemical and/or sensorial characteristics. An in-depth description has been written on the methods used to differentiate these three types of olive oils and the main advantages and disadvantages of the proposed procedures. The techniques here reviewed could be a real and fast option to complement or even substitute some of the analysis included in the official method. Finally, general trends and detected difficulties found to address this issue have been discussed throughout the article.

Authentication of the Botanical and Geographical Origin and Detection of Adulteration of Olive Oil Using Gas Chromatography, Infrared and Raman Spectroscopy Techniques: A Review

Olive oil is among the most popular supplements of the Mediterranean diet due to its high nutritional value. However, at the same time, because of economical purposes, it is also one of the products most subjected to adulteration. As a result, authenticity is an important issue of concern among authorities. Many analytical techniques, able to detect adulteration of olive oil, to identify its geographical and botanical origin and consequently guarantee its quality and authenticity, have been developed. This review paper discusses the use of infrared and Raman spectroscopy as candidate tools to examine the authenticity of olive oils. It also considers the volatile fraction as a marker to distinguish between different varieties and adulterated olive oils, using SPME combined with gas chromatography technique.

Laser-induced breakdown spectroscopy coupled with machine learning as a tool for olive oil authenticity and geographic discrimination

Olive oil is a basic element of the Mediterranean diet and a key product for the economies of the Mediterranean countries. Thus, there is an added incentive in the olive oil business for fraud through practices like adulteration and mislabeling. In the present work, Laser Induced Breakdown Spectroscopy (LIBS) assisted by machine learning is used for the classification of 139 virgin olive oils in terms of their geographical origin. The LIBS spectra of these olive oil samples were used to train different machine learning algorithms, namely LDA, ERTC, RFC, XGBoost, and to assess their classification performance. In addition, the variable importance of the spectral features was calculated, for the identification of the most important ones for the classification performance and to reduce their number for the algorithmic training. The algorithmic training was evaluated and tested by means of classification reports, confusion matrices and by external validation procedure as well. The present results demonstrate that machine learning aided LIBS can be a powerful and efficient tool for the rapid authentication of the geographic origin of virgin olive oil.

Virgin Olive Oil Volatile Compounds: Composition, Sensory Characteristics, Analytical Approaches, Quality Control, and Authentication

Volatile organic compounds strongly contribute to both the positive and negative sensory attributes of virgin olive oil, and more and more studies have been published in recent years focusing on several aspects regarding these molecules. This Review is aimed at giving an overview on the state of the art about the virgin olive oil volatile compounds. Particular emphasis was given to the composition of the volatile fraction, the analytical issues and approaches for analysis, the sensory characteristics and interaction with phenolic compounds, and the approaches for supporting the Panel Test in virgin olive oil classification and in authentication of the botanical and geographic origin based on volatile compounds. A pair of detailed tables with a total of approximately 700 volatiles identified or tentatively identified to date and tables dealing with analytical procedures, sensory characteristics of volatiles, and specific chemometric approaches for quality assessment are also provided.

Development of Chemometric Models Based on a LC-qToF-MS Approach to Verify the Geographic Origin of Virgin Olive Oil

In the presented study a non-targeted approach using high-performance liquid chromatography coupled to electrospray ionization quadrupole time-of-flight mass spectrometry (HPLC-ESI-qToF-MS) combined with chemometric techniques was used to build a statistical model to verify the geographic origin of virgin olive oils. The sample preparation by means of liquid/liquid extraction of polar compounds was optimized regarding the number of multiple extractions, application of ultrasonic treatment and temperature during concentration of the analytes. The presented workflow for data processing aimed to identify the most predictive features and was applied to a set of 95 olive oils from Spain, Italy, Portugal and Greece. Different strategies for data reduction and multivariate analysis were compared. Stepwise variable selection showed for both applied multivariate models—linear discriminant analysis (LDA) and logit regression (LR)—to be the most suitable variable selection strategy. The 10-fold cross validation of the LDA showed a classification rate of 83.1% for the test set. For the LR models the prediction accuracy of the test set was even higher with values of 90.4% (Portugal), 86.2% (Italy), 93.8% (Greece) and 88.3% (Spain). Moreover, the reduction of features allows an easier following up strategy for identification of the unknowns and defining marker substances.

ROC curves for the optimization of one-class model parameters. A case study: Authenticating extra virgin olive oil from a Catalan protected designation of origin

This paper proposes a ROC curve-based methodology to find optimal classification model parameters. ROC curves are implemented to set the optimal number of PCs to build a one-class SIMCA model and to set the threshold class value that optimizes both the sensitivity and specificity of the model. The authentication of the geographical origin of extra-virgin olive oils of Arbequina botanical variety is presented. The model was developed for samples from Les Garrigues, target class, Samples from Siurana were used as the non-target class. Samples were measured by FT-Raman with no pretreatment. PCA was used as exploratory technique. Spectra underwent pre-treatment and variables were selected based on their VIP score values. ROC curve and others already known criteria were applied to set the threshold class value. The results were better when the ROC curve was used, obtaining performance values higher than 82%, 75% and 77% for sensitivity, specificity and efficiency, respectively.

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.