1H NMR Spectroscopy for Determination of the Geographical Origin of Hazelnuts

Comparative analysis of spectroscopic methods for rapid authentication of hazelnut cultivar and origin

Hazelnut market prices fluctuate significantly based on cultivar and provenance, making them susceptible to counterfeiting. To develop an accurate authentication method, we compared the performances of three spectroscopic methods: near infrared (NIR), handheld near infrared (hNIR), and medium infrared (MIR), on over 300 samples from various origins, cultivars, and harvest years. Spectroscopic fingerprints were used to develop and externally validate PLS-DA classification models. Both cultivar and origin models showed high accuracy in external validation. The hNIR model effectively distinguished cultivars but struggled with geographic distinctions due to lower sensitivity. NIR and MIR models showed over 93 % accuracy, with NIR slightly outperforming MIR for geographic origin. NIR proved to be a fast and suitable tool for hazelnut authentication. This study is the first to systematically compare spectroscopic tools for authenticating hazelnut cultivar and origin using the same dataset, offering valuable insights for future food authentication applications.

Meeting the challenge of varietal and geographical authentication of hazelnuts through lipid metabolite fingerprinting

Hazelnuts are high-quality products with significant economic importance in many European countries. Their market price depends on their qualitative characteristics, which are driven by cultivar and geographical origin, making hazelnuts susceptible to fraud. This study systematically compared two lipidomic fingerprinting strategies for the simultaneous authentication of hazelnut cultivar and provenance, based on the analysis of the unsaponifiable fraction (UF) and triacylglycerol (TAG) profiles by gas chromatography-mass spectrometry coupled with chemometrics. PLS-DA classification models were developed using a large sample set with high natural variability (n = 309) to discriminate hazelnuts by cultivar and origin. External validation results demonstrated the suitability of the UF fingerprint as a hazelnut authentication tool, both tested models showing a high efficiency (>94 %). The correct classification rate of the TAG fingerprinting method was lower (>80 %), but due to its faster analysis time, it is recommended as a complementary screening tool to UF fingerprinting.

Geographical origin determination of the PDO hazelnut (cv. Giresun Tombul) by chemometric analysis of FT-NIR and Raman spectra acquired from shell and kernel

Turkey is the leading producer of hazelnuts, contributing to 62% of the total global production. Among 18 distinct local hazelnut cultivars, Giresun Tombul is the only cultivar that has received Protected Designation of Origin denomination from the European Comission (EC). However, there is currently no practical objective method to ensure its geographic origin. Therefore, in this study NIR and Raman spectroscopy, along with chemometric methods, such as principal component analysis, PLS-DA (partial least squares-discriminant analysis), and SVM-C (support vector machine-classification), were used to determine the geographical origin of the Giresun Tombul hazelnut cultivar. For this purpose, samples from unique 118 orchards were collected from eight different regions in Turkey during the 2021 and 2022 growing seasons. NIR and Raman spectra were obtained from both the shell and kernel of each sample. The results indicated that hazelnut samples exhibited distinct grouping tendencies based on growing season regardless of the spectroscopic technique and sample type (shell or kernel). Spectral information obtained from hazelnut shells demonstrated higher discriminative power concerning geographical origin compared to that obtained from hazelnut kernels. The PLS-DA models utilizing FT-NIR (Fourier transform near-infrared) and Raman spectra for hazelnut shells achieved validation accuracies of 81.7% and 88.3%, respectively, while SVM-C models yielded accuracies of 90.9% and 86.3%. It was concluded that the lignocellulosic composition of hazelnut shells, indicative of their geographic origin, can be accurately assessed using FT-NIR and Raman spectroscopy, providing a nondestructive, rapid, and user-friendly method for identifying the geographical origin of Giresun Tombul hazelnuts. PRACTICAL APPLICATION: The proposed spectroscopic methods offer a rapid and nondestructive means for hazelnut value chain actors to verify the geographic origin of Giresun Tombul hazelnuts. This could definitely enhance consumer trust by ensuring product authenticity and potentially help in preventing fraud within the hazelnut market. In addition, these methods can also be used as a reference for future studies targeting the authentication of other shelled nuts.

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.

Exploring the potential of high-resolution LC-MS in combination with ion mobility separation and surrogate minimal depth for enhanced almond origin authentication

Almonds (Prunus dulcisMill.) are consumed worldwide and their geographical origin plays a crucial role in determining their market value. In the present study, a total of 250 almond reference samples from six countries (Australia, Spain, Iran, Italy, Morocco, and the USA) were non-polar extracted and analyzed by UPLC-ESI-IM-qToF-MS. Four harvest periods, more than 30 different varieties, including both sweet and bitter almonds, were considered in the method development. Principal component analysis showed that there are three groups of samples with similarities: Australia/USA, Spain/Italy and Iran/Morocco. For origin determination, a random forest achieved an accuracy of 88.8 %. Misclassifications occurred mainly between almonds from the USA and Australia, due to similar varieties and similar external influences such as climate conditions. Metabolites relevant for classification were selected using Surrogate Minimal Depth, with triacylglycerides containing oxidized, odd chained or short chained fatty acids and some phospholipids proven to be the most suitable marker substances. Our results show that focusing on the identified lipids (e. g., using a QqQ-MS instrument) is a promising approach to transfer the origin determination of almonds to routine analysis.

Spectral data fusion in nondestructive detection of food products: Strategies, recent applications, and future perspectives

In recent years, the food industry has shown a growing interest in the development of rapid and nondestructive analytical methods. However, the utilization of a solitary nondestructive detection technique offers only a constrained extent of physical or chemical insights regarding the sample under examination. To overcome this limitation, the amalgamation of spectroscopy with data fusion strategies has emerged as a promising approach. This comprehensive review delves into the fundamental principles and merits of low-level, mid-level, and high-level data fusion strategies within the domain of food analysis. Various data fusion techniques encompassing spectra-to-spectra, spectra-to-machine vision, spectra-to-electronic nose, and spectra-to-nuclear magnetic resonance are summarized. Moreover, this review also provides an overview of the latest applications of spectral data fusion techniques (SDFTs) for classification, adulteration, quality evaluation, and contaminant detection within the purview of food safety analysis. It also addresses current challenges and future prospects associated with SDFTs in real-world applications. Despite the extant technical intricacy, the ongoing evolution of online data fusion platforms and the emergence of smartphone-based multi-sensor fusion detection technology augur well for the pragmatic realization of SDFTs, endowing them with formidable capabilities for both qualitative and quantitative analysis in the realm of food analysis.

Opening the Random Forest Black Box of 1H NMR Metabolomics Data by the Exploitation of Surrogate Variables

The untargeted metabolomics analysis of biological samples with nuclear magnetic resonance (NMR) provides highly complex data containing various signals from different molecules. To use these data for classification, e.g., in the context of food authentication, machine learning methods are used. These methods are usually applied as a black box, which means that no information about the complex relationships between the variables and the outcome is obtained. In this study, we show that the random forest-based approach surrogate minimal depth (SMD) can be applied for a comprehensive analysis of class-specific differences by selecting relevant variables and analyzing their mutual impact on the classification model of different truffle species. SMD allows the assignment of variables from the same metabolites as well as the detection of interactions between different metabolites that can be attributed to known biological relationships.

A geographical origin assessment of Italian hazelnuts: Gas chromatography-ion mobility spectrometry coupled with multivariate statistical analysis and data fusion approach

Hazelnut is a commodity that has gained interest in the food science community concerning its authenticity. The quality of the Italian hazelnuts is guaranteed by Protected Designation of Origin and Protected Geographical Indication certificates. However, due to their modest availability and the high price, fraudulent producers/suppliers blend, or even substitute, Italian hazelnuts with others from different countries, having a lower price, and often a lower quality. To contrast or prevent these illegal activities, the present work investigated the application of the Gas Chromatography-Ion mobility spectrometry (GC-IMS) technique on the hazelnut chain (fresh, roasted, and paste of hazelnuts). The raw data obtained were handled and elaborated using two different ways, software for statistical analysis, and a programming language. In both cases, Principal Component Analysis and Partial Least Squares-Discriminant Analysis models were exploited, to study how the Volatile Organic Profiles of Italian, Turkish, Georgian, and Azerbaijani products differ. A prediction set was extrapolated from the training set, for a preliminary models' evaluation, then an external validation set, containing blended samples, was analysed. Both approaches highlighted an interesting class separation and good model parameters (accuracy, precision, sensitivity, specificity, F1-score). Moreover, a data fusion approach with a complementary methodology, sensory analysis, was achieved, to estimate the performance enhancement of the statistical models, considering more discriminant variables and integrating at the same time further information correlated to quality aspects. GC-IMS could be a key player as a rapid, direct, cost-effective strategy to face authenticity issues regarding the hazelnut chain.

Analysis of Hazelnuts (Corylus avellana L.) Stored for Extended Periods by 1H NMR Spectroscopy Monitoring Storage-Induced Changes in the Polar and Nonpolar Metabolome

Storage is a critical step in the post-harvest processing of hazelnuts, as it can lead to mold, rancidity, and off-flavor. However, there is a lack of analytical methods to detect improper or extended storage. To comprehensively investigate the effects of hazelnut storage, samples were stored under five different conditions for up to 18 months. Subsequently, the polar and nonpolar metabolome were analyzed by ¹H NMR spectroscopy and chemometric approaches for classification as well as variable selection. Increases in hexanoic, octanoic, and nonanoic acid, all products of lipid oxidation and responsible for quality defects, were found across all conditions. Furthermore, the concentration of free long-chain fatty acids increased in samples stored at high temperatures. Harsh short-term storage resulted in an increase in fumaric and lactic acid, glucose, fructose, and choline and a decrease in acetic acid.

Lipids in a Nutshell: Quick Determination of Lipid Content in Hazelnuts with NIR Spectroscopy

Hazelnuts (Corylus avellana L.) are among the most consumed dry fruits all over the world. Their commercial quality is defined, above all, by origin and dimension, as well as by lipid content. Evaluation of this parameter is currently performed with chemical methods, which are expensive, time consuming, and complex. In the present work, the near-infrared (NIR) spectroscopy, using both a benchtop research spectrometer and a retail handheld instrument, was evaluated in comparison with the traditional chemical approach. The lipid content of hazelnuts from different growing regions of origin (Italy, Chile, Turkey, Georgia, and Azerbaijan) was determined with two NIR instruments: a benchtop FT-NIR spectrometer (Multi Purpose Analyser-MPA, by Bruker), equipped with an integrating sphere and an optic fibre probe, and the pocket-sized, battery-powered SCiO molecular sensor (by Consumer Physics). The Randall/Soxtec method was used as the reference measurement of total lipid content. The collected NIR spectra were inspected through multivariate data analysis. First, a Principal Component Analysis (PCA) model was built to explore the information contained in the spectral datasets. Then, a Partial Least Square (PLS) regression model was developed to predict the percentage of lipid content. PCA showed samples distributions that could be linked to their total crude fat content determined with the Randall/Soxtec method, confirming that a trend related to the lipid content could be detected in the spectral data, based on their chemical profiles. PLS models performed better with the MPA instrument than SCiO, with the highest R2 of prediction (R2PRED = 0.897) achieved by MPA probe, while this parameter for SCiO was much lower (R2PRED = 0.550). Further analyses are necessary to evaluate if more acquisitions may lead to better performances when using the SCiO portable spectrometer.

NMR-Based Approaches in the Study of Foods

In this review, the three different NMR-based approaches usually used to study foodstuffs are described, reporting specific examples. The first approach starts with the food of interest that can be investigated using different complementary NMR methodologies to obtain a comprehensive picture of food composition and structure; another approach starts with the specific problem related to a given food (frauds, safety, traceability, geographical and botanical origin, farming methods, food processing, maturation and ageing, etc.) that can be addressed by choosing the most suitable NMR methodology; finally, it is possible to start from a single NMR methodology, developing a broad range of applications to tackle common food-related challenges and different aspects related to foods.

Food Authentication of Almonds (Prunus dulcis Mill.). Fast Origin Analysis with Laser Ablation Inductively Coupled Plasma Mass Spectrometry and Chemometrics

Food fraud is a growing problem, especially misdeclaration due to regional price differences offering a wide field. Fast, powerful, and cost-effective analytical methods are therefore essential to counteract food fraud. The isotopolome is suitable for origin discrimination and was analyzed in this study using laser ablation inductively coupled plasma mass spectrometry (ICP-MS). A total of 250 almond samples from six countries and four crop years were analyzed and evaluated by chemometric methods. By using a ratio-based assessment, calibration problems were avoided and an origin predictive accuracy of 85.2 ± 1.2% was achieved. Compared to ICP-MS with solution nebulization, the analysis time could be reduced to about one-fifth.

Opening the Random Forest Black Box of the Metabolome by the Application of Surrogate Minimal Depth

For the untargeted analysis of the metabolome of biological samples with liquid chromatography–mass spectrometry (LC-MS), high-dimensional data sets containing many different metabolites are obtained. Since the utilization of these complex data is challenging, different machine learning approaches have been developed. Those methods are usually applied as black box classification tools, and detailed information about class differences that result from the complex interplay of the metabolites are not obtained. Here, we demonstrate that this information is accessible by the application of random forest (RF) approaches and especially by surrogate minimal depth (SMD) that is applied to metabolomics data for the first time. We show this by the selection of important features and the evaluation of their mutual impact on the multi-level classification of white asparagus regarding provenance and biological identity. SMD enables the identification of multiple features from the same metabolites and reveals meaningful biological relations, proving its high potential for the comprehensive utilization of high-dimensional metabolomics data.

Glass authentication: Laser ablation-inductively coupled plasma mass spectrometry (LA-ICP-MS) for origin discrimination of glass bottles

Counterfeiting is an omnipresent issue, among others in the cosmetics industry or on the art market. Particularly in the case of very expensive perfumes or very valuable art objects, counterfeits are strongly represented and are steadily increasing. Typically, the content of perfumes is analyzed, but the bottle offers another level of authentication, as it is an essential part of the product. For art objects made of glass, glass is an essential part of the artwork and thus provides an important contribution to the authenticity of the object. In the present pilot study, we developed a laser ablation-inductively coupled plasma mass spectrometry (LA-ICP-MS) method to classify glass using perfume bottles manufactured at different production facilities, Germany, India, Peru and Poland as an example. Using minimally invasive laser ablation invisible to the eye, we were able to detect counterfeit flacons without having to open the vials. A total of 63 elements could be recorded during method development. After statistical evaluation (t-test, ANOVA, principal component analysis (PCA)), 15 (Li, Na, Al, Ti, V, Co, Rb, Sr, Mo, Ba, La, Ce, Pr, Er and Pb) significant marker elements were extracted from the data sets to differentiate the different glass origins. By using LDA, six different production sites from four different countries could be differentiated over a sample period of six months with a prediction accuracy of 100%.

NMR Tracing of Food Geographical Origin: The Impact of Seasonality, Cultivar and Production Year on Data Analysis

The traceability of typical foodstuffs is necessary to protect high quality of traditional products. It is well-known that several factors could influence metabolites content in certified foods, but soil composition, altitude, latitude and coded production protocols constitute the territorial conditions responsible for the peculiar organoleptic and nutritional properties of labelled foods. Instead, regardless of origin, seasonality, cultivar, collection year can affect all agricultural products, so it is appropriate to include them in data analysis in order to obtain a correct interpretation of the differences linked to growing areas alone. Therefore, it is useful to use a flexible all-round technique, and NMR spectroscopy coupled with multivariate statistical analysis is considered a powerful means of assessing food authenticity. The purpose of this review is to investigate the relevance of year, cultivar, and seasonal period in the determination of food geographical origin using NMR spectroscopy. The strategy for testing these three factors may differ from author to author, but a preliminary study of cultivar or collection year effects on NMR spectra is the most popular method before starting the geographical characterization of samples. In summary, based on the available literature, the most significant influence is due to cultivar, followed by harvesting year, however seasonality is not considered a source of variability in data analysis.

Detecting Admixture to Mango Purée of the Alphonso Cultivar (Mangifera indica L. cv. Alphonso) by 1H-NMR Spectroscopy

Food authenticity is becoming increasingly important but challenges existing analytical methods. In this study, we analyze the mango cultivar Alphonso with regard to authenticity using 1H-NMR spectroscopy. This cultivar has been termed “the king of mangoes” due to its unique flavor. Regarding its metabolites however, little is known about unique constellations that allow for differentiation of the Alphonso cultivar. We find that the Alphonso cultivar is distinguished by high levels of niacin, trigonelline, and histidine but features relatively low levels of alanine. Furthermore, we develop a model based on the local outlier factor algorithm that effectively detects admixture of non-Alphonso cultivars to Alphonso purée. This task is highly challenging because we identified no metabolites that are unique or uniquely absent in the Alphonso cultivar compared to other mango cultivars analyzed in this study. Our model shows promising results on a test set: Admixtures consisting of 35% non-Alphonso and 65% Alphonso mango purée were uncovered with a sensitivity of 88%. At the same time, our model verified Alphonso samples with a good specificity of 86%.

Metabolomics as a marketing tool for geographical indication products: a literature review

Geographical indication (GI) is used to identify a product's origin when its characteristics or quality are a result of geographical origin, which includes agricultural products and foodstuff. Metabolomics is an “omics” technique that can support product authentication by providing a chemical fingerprint of a biological system, such as plant and plant-derived products. The main purpose of this article is to verify possible contributions of metabolomic studies to the marketing field, mainly for certified regions, through an integrative review of the literature and maps produced by VOSviewer software. The results indicate that studies based on metabolomics approaches can relate specific food attributes to the region’s terroir and know-how. The evidence of this connection, marketing of GIs and metabolomics methods, is viewed as potential tool for marketing purposes (e.g., to assist communication of positive aspects and quality), and legal protection. In addition, our results provide a taxonomic categorization that can guide future marketing research involving metabolomics. Moreover, the results are also useful to government agencies to improve GIs registration systems and promotion strategies.

Simple and Rapid (Extraction) Protocol for NMR Metabolomics-Direct Measurement of Hydrophilic and Hydrophobic Metabolites Using Slice Selection

Investigating the metabolic profiles of solid sample materials with solution nuclear magnetic resonance (NMR) spectroscopy requires the extraction of these metabolites. This is commonly done by using two immiscible solvents such as water and chloroform for extraction. Subsequent solvent removal makes these extraction procedures very time-consuming. To shorten the preparation time of the NMR sample, the following protocol is proposed: the metabolites from a solid or liquid sample are extracted directly in the NMR tube, the NMR tube is centrifuged, and the metabolite profiles in the aqueous and organic phases are determined by using slice-selective proton NMR experiments. This protocol was tested with 11 black teas and 11 green teas, which can be easily distinguished by their metabolic profiles in the aqueous phase. As a test case for liquid samples, 29 milk samples were investigated. The geographical origin of the diaries where the milk was processed could not be determined unequivocally from the metabolic profiles of the hydrophilic metabolites; however, this was easily seen in the lipid profiles. As shown for the different test samples, the extraction protocol in combination with slice-selection NMR experiments is suitable for metabolic investigations. Because samples are rapidly processed, this approach can be used to explore different extraction strategies for metabolite isolation.

Comparison of Regular, Pure Shift, and Fast 2D NMR Experiments for Determination of the Geographical Origin of Walnuts

¹H NMR spectroscopy, in combination with chemometric methods, was used to analyze the methanol/acetonitrile (1:1) extract of walnut (Juglans Regia L.) regarding the geographical origin of 128 authentic samples from different countries (France, Germany, China) and harvest years (2016–2019). Due to the large number of different metabolites within the acetonitrile/methanol extract, the one-dimensional (1D) ¹H NOESY (nuclear Overhauser effect spectroscopy) spectra suffer from strongly overlapping signals. The identification of specific metabolites and statistical analysis are complicated. The use of pure shift ¹H NMR spectra such as PSYCHE (pure shift yielded by chirp excitation) or two-dimensional ASAP-HSQC (acceleration by sharing adjacent polarization-heteronuclear single quantum correlation) spectra for multivariate analysis to determine the geographical origin of foods may be a promising method. Different types of NMR spectra (1D ¹H NOESY, PSYCHE, and ASAP-HSQC) were acquired for each of the 128 walnut samples and the results of the statistical analysis were compared. A support vector machine classifier was applied for differentiation of samples from Germany/China, France/Germany, and France/China. The models obtained by conduction of a repeated nested cross-validation showed accuracies from 58.9% (±1.3%) to 95.9% (±0.8%). The potential of the ¹H-¹³C HSQC as a 2D NMR experiment for metabolomics studies was shown.

Food Profiling: Determination of the Geographical Origin of Walnuts by 1H NMR Spectroscopy Using the Polar Extract

Walnuts, with their health-promoting ingredients, are among the most popular nuts, and practicable methods for determining their geographical origin are needed to tackle food fraud. Authentic walnut samples (235, Juglans Regia L.) from different harvest years (2016-2019) and countries were analyzed by ¹H NMR spectroscopy in combination with chemometric methods to determine their geographical origin. Two sample groups were analyzed at a time with a support vector machine algorithm to obtain two-class classifier models. In total, nine two-class models were built (e.g., Germany/China, France/Germany, and USA/Switzerland), and a repeated nested cross-validation was performed. The models obtained showed high accuracies from 78.0% (±2.3%) to 96.6% (±0.6%). Furthermore, identification of potential chemical markers in the walnut extract was performed.

Mixture Analyses of Air-sampled Pollen Extracts Can Accurately Differentiate Pollen Taxa

The daily pollen forecast provides crucial information for allergic patients to avoid exposure to specific pollen. Pollen counts are typically measured with air samplers and analyzed with microscopy by trained experts. In contrast, this study evaluated the effectiveness of identifying the component pollens using the metabolites extracted from an air-sampled pollen mixture. Ambient air-sampled pollen from Munich in 2016 and 2017 was visually identified from reference pollens and extracts were prepared. The extracts were lyophilized, rehydrated in optimal NMR buffers, and filtered to remove large proteins. NMR spectra were analyzed for pollen associated metabolites. Regression and decision-tree based algorithms using the concentration of metabolites, calculated from the NMR spectra outperformed algorithms using the NMR spectra themselves as input data for pollen identification. Categorical prediction algorithms trained for low, medium, high, and very high pollen count groups had accuracies of 74% for the tree, 82% for the grass, and 93% for the weed pollen count. Deep learning models using convolutional neural networks performed better than regression models using NMR spectral input, and were the overall best method in terms of relative error and classification accuracy (86% for tree, 89% for grass, and 93% for weed pollen count). This study demonstrates that NMR spectra of air-sampled pollen extracts can be used in an automated fashion to provide taxa and type-specific measures of the daily pollen count.

Food Authentication: Species and Origin Determination of Truffles (Tuber spp.) by Inductively Coupled Plasma Mass Spectrometry and Chemometrics

The aim of this study was to develop a protocol for the authentication of truffles using inductively coupled plasma mass spectrometry. The price of the different truffle species varies significantly, and because the visual differentiation is difficult within the white truffles and within the black truffles, food fraud is likely to occur. Thus, in the context of this work, the elemental profiles of 59 truffle samples of five commercially relevant species were analyzed and the resulting element profiles were evaluated with chemometrics. Classification models targeting the species and the origins were validated using nested cross validation and were able to differentiate the most expensive Tuber magnatum from any other examined truffle. For the black truffles, an overall classification accuracy of 90.4% was achieved, and, most importantly, a falsification of the expensive Tuber melanosporum by Tuber indicum could be ruled out. With regard to the geographical origin, for Italy and Spain, one-versus-all classification models were calculated each to differentiate truffle samples from any other origins by 75.0 and 86.7%, respectively. The prediction was still possible according to an internal mathematical normalization scheme using only the element ratios instead of the absolute element concentrations. The established authentication protocol was successfully tested with an external sample set of five fresh truffles. Our results show the high potential of the element profile for the parallel species and origin authentication of truffles.

Determination of the Geographical Origin of Asparagus officinalis L. by 1H NMR Spectroscopy

Food authenticity concerning the geographical origin becomes increasingly important for consumers, food industries, and food authorities. In this study, nontargeted ¹H NMR metabolomics combined with machine learning methodologies was applied to successfully distinguish the geographical origin of 237 samples of white asparagus from Germany, Poland, The Netherlands, Spain, Greece, and Peru. Support vector classification of the geographical origin achieved an accuracy of 91.5% for the entire sample set and 87.8% after undersampling the majority class. Important regions of the spectra could be identified and assigned to potential chemical markers. A subset of samples was compared to isotope-ratio mass spectrometry (IRMS), an established method for the determination of origin of white asparagus in Germany. Here, SVM classification led to accuracies of 79.4% for NMR and 70.9% for IRMS. Finally, the classification of asparagus from different German regions was evaluated, and the influence of year and variety was analyzed.

Detection of Peanut Adulteration in Food Samples by Nuclear Magnetic Resonance Spectroscopy

The addition of cheap and also readily available raw materials, such as peanut powder, to visually and chemically similar matrices is a common problem in the food industry. When peanuts are used as an adulterant, there is an additional risk of potential health hazard to consumers as a result of allergy-induced anaphylaxis. In this study, different series of peanut admixtures to visually similar food products, such as powdered hazelnuts, almonds, and walnuts, were prepared and analyzed by ¹H nuclear magnetic resonance (NMR) spectroscopy. For identification, an isolated signal at 3.05 ppm in the ¹H NMR spectrum of polar peanut extract was used as an indicator of peanut adulteration. The chemical marker was identified as (2S,4R)-N-methyl-4-hydroxy-l-proline by resynthesis of the compound and used as an internal standard. The signal-to-noise ratio and the integral of the signal of the marker can both be used to detect peanut impurities. Overall, an approximate limit of detection of 4% admixtures of peanut in various food products was determined using a 400 MHz spectrometer. With regard to food fraud, we present a viable screening method for detection of economic-relevant peanut adulteration.

Mass-Spectrometry-Based Food Metabolomics in Routine Applications: A Basic Standardization Approach Using Housekeeping Metabolites for the Authentication of Asparagus

The low reproducibility of non-targeted liquid chromatography-mass spectrometry-based metabolomics approaches represents a major challenge for their implementation in routine analyses, because it is impossible to compare individual measurements directly with each other, if they were not analyzed in the same batch. This study describes a normalization process based on housekeeping metabolites in plant-based raw materials, which are present in comparatively constant concentrations and are subject to no or only minor deviations as a result of exogenous influences. As a model, an authenticity study was selected to determine the origin of white asparagus (Asparagus officinalis). Using three model data sets and one test data set, we were able to show that samples that have been measured independently of one another can be correctly assigned in terms of origin after the normalization with housekeeping metabolites. The procedure does not require internal standards or the measurements of further reference samples and can also be applied to other matrices and scientific issues.

Origin Determination of Walnuts (Juglans regia L.) on a Worldwide and Regional Level by Inductively Coupled Plasma Mass Spectrometry and Chemometrics

To counteract food fraud, this study aimed at the differentiation of walnuts on a global and regional level using an isotopolomics approach. Thus, the multi-elemental profiles of 237 walnut samples from ten countries and three years of harvest were analyzed with inductively coupled plasma mass spectrometry (ICP-MS), and the resulting element profiles were evaluated with chemometrics. Using support vector machine (SVM) for classification, validated by stratified nested cross validation, a prediction accuracy of 73% could be achieved. Leave-one-out cross validation was also applied for comparison and led to less satisfactory results because of the higher variations in sensitivity for distinct classes. Prediction was still possible using only elemental ratios instead of the absolute element concentrations; consequently, a drying step is not mandatory. In addition, the isotopolomics approach provided the classification of walnut samples on a regional level in France, Germany, and Italy, with accuracies of 91%, 77%, and 94%, respectively. The ratio of the model's accuracy to a random sample distribution was calculated, providing a new parameter with which to evaluate and compare the performance of classification models. The walnut cultivar and harvest year had no observable influence on the origin differentiation. Our results show the high potential of element profiling for the origin authentication of walnuts.

Food Authentication: Truffle (Tuber spp.) Species Differentiation by FT-NIR and Chemometrics

Truffles are certainly the most expensive mushrooms; the price depends primarily on the species and secondly on the origin. Because of the price differences for the truffle species, food fraud is likely to occur, and the visual differentiation is difficult within the group of white and within the group of black truffles. Thus, the aim of this study was to develop a reliable method for the authentication of five commercially relevant truffle species via Fourier transform near-infrared (FT-NIR) spectroscopy as an easy to handle approach combined with chemometrics. NIR-data from 75 freeze-dried fruiting bodies were recorded. Various spectra pre-processing techniques and classification methods were compared and validated using nested cross-validation. For the white truffle species, the most expensive Tuber magnatum could be differentiated with an accuracy of 100% from Tuber borchii. Regarding the black truffle species, the relatively expensive Tuber melanosporum could be distinguished from Tuber aestivum and the Chinese truffles with an accuracy of 99%. Since the most expensive Italian Tuber magnatum is highly prone to fraud, the origin was investigated and Italian T. magnatum truffles could be differentiated from non-Italian T. magnatum truffles by 83%. Our results demonstrate the potential of FT-NIR spectroscopy for the authentication of truffle species.

Food authentication in real life: How to link nontargeted approaches with routine analytics?

In times of increasing globalization and the resulting complexity of trade flows, securing food quality is an increasing challenge. The development of analytical methods for checking the integrity and, thus, the safety of food is one of the central questions for actors from science, politics, and industry. Targeted methods, for the detection of a few selected analytes, still play the most important role in routine analysis. In the past 5 years, nontargeted methods that do not aim at individual analytes but on analyte profiles that are as comprehensive as possible have increasingly come into focus. Instead of investigating individual chemical structures, data patterns are collected, evaluated and, depending on the problem, fed into databases that can be used for further nontargeted approaches. Alternatively, individual markers can be extracted and transferred to targeted methods. Such an approach requires (i) the availability of authentic reference material, (ii) the corresponding high-resolution laboratory infrastructure, and (iii) extensive expertise in processing and storing very large amounts of data. Probably due to the requirements mentioned above, only a few methods have really established themselves in routine analysis. This review article focuses on the establishment of nontargeted methods in routine laboratories. Challenges are summarized and possible solutions are presented.

The Inorganic Component as a Possible Marker for Quality and for Authentication of the Hazelnut's Origin

The inorganic component of hazelnuts was considered as a possible marker for geographical allocation and for the assessment of technological impact on their quality. The analyzed samples were Italian hazelnuts of the cultivar Tonda Gentile Romana and Turkish hazelnuts of the cultivars Tombul, Palaz and Çakildak. The hazelnuts were subjected to different drying procedures and different conservative methods. The concentration of 13 elements, namely Ba, Ca, Cu, Fe, K, Mg, Mn, Na, Ni, P, Sn, Sr and Zn, were quantified by inductively coupled plasma optical emission spectroscopy (ICP-OES). All the samples were previously digested in a microwave oven. Before proceeding with the analysis of the samples, the whole procedure was optimized and tested on a certified reference material. The results show that the inorganic component: (i) can represent a fingerprint, able to identify the geographical origin of hazelnuts, becoming an important quality marker for consumer protection; (ii) is strongly influenced by the treatments undergone by the investigated product during all the processing stages. A pilot study was also carried out on hazelnuts of the cultivar Tonda Gentile Trilobata Piemontese, directly harvested from the plant during early development to maturity and analyzed to monitor the element concentration over time.