Food authentication: Techniques, trends & emerging approaches

Integrated analytical assets aid botanical authenticity and adulteration management

This article reviews and develops a perspective for the meaning of authenticity in the context of quality assessment of botanical materials and the challenges associated with discerning adulterations vs. contaminations vs. impurities. Authentic botanicals are by definition non-adulterated, a mutually exclusive relationship that is confirmed through the application of a multilayered set of analytical methods designed to validate the (chemo)taxonomic identity of a botanical and certify that it is devoid of any adulteration. In practice, the ever-increasing sophistication in the process of intentional adulteration, as well as the growing number of botanicals entering the market, altogether necessitate a constant adaptation and reinforcement of authentication methods with new approaches, especially new technologies. This article summarizes the set of analytical methods - classical and contemporary - that can be employed in the authentication of botanicals. Particular emphasis is placed on the application of untargeted metabolomics and chemometrics. An NMR-based untargeted metabolomic model is proposed as a rapid, systematic, and complementary screening for the discrimination of authentic vs. potentially adulterated botanicals. Such analytical model can help advance the evaluation of botanical integrity in natural product research.

Influence of Chemical and Physical Variables on 87Sr/86Sr Isotope Ratios Determination for Geographical Traceability Studies in the Oenological Food Chain

This study summarizes the results obtained from a systematic and long-term project aimed at the development of tools to assess the provenance of food in the oenological sector. 87Sr/86Sr isotope ratios were measured on a representative set of soils, branches, and wines sampled from the Chianti Classico wine production area. In particular, owing to the high spatial resolution of the 87Sr/86Sr ratio in the topsoil, the effect of two mill techniques for soil pretreatment was investigated to verify the influence of the particle dimension on the measured isotopic ratios. Samples with particle sizes ranging from 250 to less than 50 µm were investigated, and the extraction was performed by means of the DIN 19730 procedure. For each sample, the Sr isotope ratio was determined as well. The obtained results showed that the 87Sr/86Sr ratio is not influenced by soil particle size and may represent an effective tool as a geographic provenance indicator for the investigated product.

Meat provenance: Authentication of geographical origin and dietary background of meat

The authenticity of meat is now an important consideration in the multi-step food chain from production of animals on farm to consumer consumption of the final meat product. A range of techniques, involving analysis of elemental and molecular constituents of meat, fingerprint profiling and multivariate statistical analysis exists and these techniques are evolving in the quest to provide robust methods of establishing the dietary background of animals and the geographical origin of the meat derived from them. The potential application to meat authentication of techniques such as stable isotope ratio analysis applied to different animal tissues, measurement in meat of compounds directly derived from the diet of animals, such as fatty acids and fat soluble vitamins, and spectroscopy is explored. Challenges pertaining to the interpretation of data, as they relate to assignment of dietary background or geographical origin, are discussed.

Differentiation of Apple Varieties and Investigation of Organic Status Using Portable Visible Range Reflectance Spectroscopy

Food fraud, the sale of goods that have in some way been mislabelled or tampered with, is an increasing concern, with a number of high profile documented incidents in recent years. These recent incidents and their scope show that there are gaps in the food chain where food authentication methods are not applied or otherwise not sufficient and more accessible detection methods would be beneficial. This paper investigates the utility of affordable and portable visible range spectroscopy hardware with partial least squares discriminant analysis (PLS-DA) when applied to the differentiation of apple types and organic status. This method has the advantage that it is accessible throughout the supply chain, including at the consumer level. Scans were acquired of 132 apples of three types, half of which are organic and the remaining non-organic. The scans were preprocessed with zero correction, normalisation and smoothing. Two tests were used to determine accuracy, the first using 10-fold cross-validation and the second using a test set collected in different ambient conditions. Overall, the system achieved an accuracy of 94% when predicting the type of apple and 66% when predicting the organic status. Additionally, the resulting models were analysed to find the regions of the spectrum that had the most significance. Then, the accuracy when using three-channel information (RGB) is presented and shows the improvement provided by spectroscopic data.

Trends in Chemometrics: Food Authentication, Microbiology, and Effects of Processing

In the last decade, the use of multivariate statistical techniques developed for analytical chemistry has been adopted widely in food science and technology. Usually, chemometrics is applied when there is a large and complex dataset, in terms of sample numbers, types, and responses. The results are used for authentication of geographical origin, farming systems, or even to trace adulteration of high value-added commodities. In this article, we provide an extensive practical and pragmatic overview on the use of the main chemometrics tools in food science studies, focusing on the effects of process variables on chemical composition and on the authentication of foods based on chemical markers. Pattern recognition methods, such as principal component analysis and cluster analysis, have been used to associate the level of bioactive components with in vitro functional properties, although supervised multivariate statistical methods have been used for authentication purposes. Overall, chemometrics is a useful aid when extensive, multiple, and complex real-life problems need to be addressed in a multifactorial and holistic context. Undoubtedly, chemometrics should be used by governmental bodies and industries that need to monitor the quality of foods, raw materials, and processes when high-dimensional data are available. We have focused on practical examples and listed the pros and cons of the most used chemometric tools to help the user choose the most appropriate statistical approach for analysis of complex and multivariate data.

Feasibility Assessment of Synchronous Fluorescence Spectral Fusion by Application to Argan Oil for Adulteration Analysis

Synchronous fluorescence spectroscopy (SFS) is used for quantitative analysis as well as for qualitative analysis, such as with classification methods. With SFS, determination of a useful wavelength interval between the excitation and emission wavelengths (Δλ) is required. There are a multitude of Δλ intervals that can be evaluated and optimization of the best one is complex. Presented here is a fusion approach for combining Δλ intervals, thereby negating the need to perform the selection by a skilled operator. To demonstrate the feasibility of omitting selection of the best Δλ interval, adulterated argan oil samples are studied. Argan oil is made from the argan tree, endemic to southwestern Morocco, and is well-known for its cosmetic, pharmaceutical, and nutritional applications. It is considered a luxury product and exported from Morocco around the world. Consequently, detection of argan oil adulteration followed by quantitative analysis of the adulterant concentration is important. This study uses fusion of SFS spectra obtained at ten Δλ intervals to first detect adulteration of argan oil by corn oil and then determination of the corn oil content. For detection of adulteration, 15 one-class classification methods were used simultaneously over the ten Δλ sets of SFS spectra. For tuning parameter dependent classifiers such as Mahalanobis distance, non-optimized classifiers are used. Raw classification values are used, removing the need to set classifier-dependent threshold values, albeit, ultimately, a fusion decision rule is needed for classification. For quantitative analysis, two calibration approaches are evaluated with fusion of these ten Δλ SFS spectral data sets. One is multivariate calibration by partial least squares (PLS). The second approach is a univariate calibration process where the SFS spectra are summed over respective SFS spectral ranges, also known as the area under the curve (AUC). For adulteration detection and quantitation of the corn oil, prediction errors decrease with fusion compared to individually using the ten Δλ interval SFS specific data sets. For this argan oil data set, the AUC method generally provides equivalent prediction errors to PLS.

Timber isoscapes. A case study in a mountain area in the Italian Alps

BACKGROUND

Local timber is still one of the main sources of work and income for mountain communities. However, illegal logging is a major cause of deforestation in many countries and has significant impacts on local communities and biodiversity. Techniques for tracing timber would provide a useful tool to protect local timber industries and contribute to the fight against illegal logging. Although considerable progress has been made in food traceability, timber provenance is still a somewhat neglected research area. Stable isotope ratios in plants are known to reflect geographical variations. This study reports accurate spatial distribution of δ18O and δ2H in timber from north-eastern Italy (Trentino) in order to trace geographical origin.

METHODOLOGY AND PRINCIPAL FINDINGS

We tested the accuracy of four kriging methods using an annual resolution of δ18O and δ2H measured in Picea abies. Pearson's correlation coefficients revealed altitude to be the most appropriate covariate for the cokriging model, which has ultimately proved to be the best method due to its low estimation error.

CONCLUSIONS

We present regional maps of interpolated δ18O and δ2H in Picea abies wood together with the 95% confidence intervals. The strong spatial structure of the data demonstrates the potential of multivariate spatial interpolation, even in a highly heterogeneous area such as the Alps. We believe that this geospatial approach can be successfully applied on a wider scale in order to combat illegal logging.

Quantitative analysis of lard in animal fat mixture using visible Raman spectroscopy

Food adulteration is a serious issue that requires verification and strict management due to healthcare, morality, and social value problems. In the context of fat, food manufacturers blend lard with vegetable oils or animal fats for convenience and gaining economic benefits. Thus, we herein report the classification of 4 animal fats, e.g., beef tallow, pork lard, chicken fat, duck oil, using Raman spectroscopy combined with simple calculation of intensity ratios of Raman signal at vibrational modes corresponding to unsaturated fatty acids and total fatty acids. Various calculated values of the species were compared to find a feature that is able to classify each fats using Raman peak ratio. As a result, we suggested "Oil gauge (OG)" as a standard feature for classification of the fats in Raman analysis field. Furthermore, a quantification of the lard in other fat was accomplished with good linear correlation using the OG values.

Authentication of pork in meat mixtures using PRM mass spectrometry of myosin peptides

Adulteration of meat products is a major concern not only for economic fraud, but also for ethical reasons. In this study, we presented a parallel reaction monitoring (PRM) mass spectrometry approach for detection of trace pork in meat mixtures (chicken, sheep, and beef). Specific peptides were identified and screened by a shotgun proteomic approach based on tryptic digests of certain protein. Five surrogate peptides from myosin were screened and then used for pork detection by PRM of Orbitrap MS. When the most sensitive peptide was selected, the LOD in mixed meat can be up to 0.5%. The RSD values between detected and designated pork levels (1%, 5% and 50%) were 4–15%. The targeted method developed can be applied to identify and quantify the pork in meat mixture.

Adulteration of meat products is a major concern not only for economic fraud, but also for ethical reasons.

Real-Time Food Authentication Using a Miniature Mass Spectrometer

Food adulteration is a threat to public health and the economy. In order to determine food adulteration efficiently, rapid and easy-to-use on-site analytical methods are needed. In this study, a miniaturized mass spectrometer in combination with three ambient ionization methods was used for food authentication. The chemical fingerprints of three milk types, five fish species, and two coffee types were measured using electrospray ionization, desorption electrospray ionization, and low temperature plasma ionization. Minimum sample preparation was needed for the analysis of liquid and solid food samples. Mass spectrometric data was processed using the laboratory-built software MS food classifier, which allows for the definition of specific food profiles from reference data sets using multivariate statistical methods and the subsequent classification of unknown data. Applicability of the obtained mass spectrometric fingerprints for food authentication was evaluated using different data processing methods, leave-10%-out cross-validation, and real-time classification of new data. Classification accuracy of 100% was achieved for the differentiation of milk types and fish species, and a classification accuracy of 96.4% was achieved for coffee types in cross-validation experiments. Measurement of two milk mixtures yielded correct classification of >94%. For real-time classification, the accuracies were comparable. Functionality of the software program and its performance is described. Processing time for a reference data set and a newly acquired spectrum was found to be 12 s and 2 s, respectively. These proof-of-principle experiments show that the combination of a miniaturized mass spectrometer, ambient ionization, and statistical analysis is suitable for on-site real-time food authentication.

Game meat authentication through rare earth elements fingerprinting

Accurate labelling of meat (e.g. wild versus farmed, geographical and genetic origin, organic versus conventional, processing treatment) is important to inform the consumers about the products they buy. Meat and meat products declared as game have higher commercial value making them target to fraudulent labelling practices and replacement with non-game meat. We have developed and validated a new method for authentication of wild rabbit meat using elemental metabolomics approach. Elemental analysis was performed using rapid ultra-trace multi-element measurement by inductively coupled plasma mass spectrometry (ICP-MS). Elemental signatures showed excellent ability to discriminate the wild rabbit from non-wild rabbit meat. Our results demonstrate the usefulness of metabolic markers -rare earth signatures, as well as other trace element signatures for game meat authentication.

The Use of Partial Least Square Regression and Spectral Data in UV-Visible Region for Quantification of Adulteration in Indonesian Palm Civet Coffee

Asian palm civet coffee or kopi luwak (Indonesian words for coffee and palm civet) is well known as the world's priciest and rarest coffee. To protect the authenticity of luwak coffee and protect consumer from luwak coffee adulteration, it is very important to develop a robust and simple method for determining the adulteration of luwak coffee. In this research, the use of UV-Visible spectra combined with PLSR was evaluated to establish rapid and simple methods for quantification of adulteration in luwak-arabica coffee blend. Several preprocessing methods were tested and the results show that most of the preprocessing spectra were effective in improving the quality of calibration models with the best PLS calibration model selected for Savitzky-Golay smoothing spectra which had the lowest RMSECV (0.039) and highest RPD_cal value (4.64). Using this PLS model, a prediction for quantification of luwak content was calculated and resulted in satisfactory prediction performance with high both RPD _p and RER values.

Class-modelling in food analytical chemistry: Development, sampling, optimisation and validation issues - A tutorial

Qualitative data modelling is a fundamental branch of pattern recognition, with many applications in analytical chemistry, and embraces two main families: discriminant and class-modelling methods. The first strategy is appropriate when at least two classes are meaningfully defined in the problem under study, while the second strategy is the right choice when the focus is on a single class. For this reason, class-modelling methods are also referred to as one-class classifiers. Although, in the food analytical field, most of the issues would be properly addressed by class-modelling strategies, the use of such techniques is rather limited and, in many cases, discriminant methods are forcedly used for one-class problems, introducing a bias in the outcomes. Key aspects related to the development, optimisation and validation of suitable class models for the characterisation of food products are critically analysed and discussed.

What is in your cup of tea? DNA Verity Test to characterize black and green commercial teas

In this study, we used several molecular techniques to develop a fast and reliable protocol (DNA Verity Test, DVT) for the characterization and confirmation of the species or taxa present in herbal infusions. As a model plant for this protocol, Camellia sinensis, a traditional tea plant, was selected due to the following reasons: its historical popularity as a (healthy) beverage, its high selling value, the importation of barely recognizable raw product (i.e., crushed), and the scarcity of studies concerning adulterants or contamination. The DNA Verity Test includes both the sequencing of DNA barcoding markers and genotyping of labeled-PCR DNA barcoding fragments for each sample analyzed. This protocol (DVT) was successively applied to verify the authenticity of 32 commercial teas (simple or admixture), and the main results can be summarized as follows: (1) the DVT protocol is suitable to detect adulteration in tea matrices (contaminations or absence of certified ingredients), and the method can be exported for the study of other similar systems; (2) based on the BLAST analysis of the sequences of rbcL+matK±rps7-trnV^(GAC) chloroplast markers, C. sinensis can be taxonomically characterized; (3) rps7-trnV^(GAC) can be employed to discriminate C. sinensis from C. pubicosta; (4) ITS2 is not an ideal DNA barcode for tea samples, reflecting potential incomplete lineage sorting and hybridization/introgression phenomena in C. sinensis taxa; (5) the genotyping approach is an easy, inexpensive and rapid pre-screening method to detect anomalies in the tea templates using the trnH^(GUG)-psbA barcoding marker; (6) two herbal companies provided no authentic products with a contaminant or without some of the listed ingredients; and (7) the leaf matrices present in some teabags could be constituted using an admixture of different C. sinensis haplotypes and/or allied species (C. pubicosta).

Prospect on Ionomic Signatures for the Classification of Grapevine Berries According to Their Geographical Origin

The determination of food geographical origin has been an important subject of study over the past decade, with an increasing number of analytical techniques being developed to determine the provenance of agricultural products. Agricultural soils can differ for the composition and the relative quantities of mineral nutrients and trace elements whose bioavailability depends on soil properties. Therefore, the ionome of fruits, vegetables and derived products can reflect the mineral composition of the growth substrate. Multi-elemental analysis has been successfully applied to trace the provenance of wines from different countries or different wine-producing regions. However, winemaking process and environmental and cultural conditions may affect a geographical fingerprint. In this article, we discuss the possibility of applying ionomics in wines classification on a local scale and also by exploiting grape berry analyses. In this regard, we present the ionomic profile of grapevine berries grown within an area of approximately 300 km2 and the subsequent application of chemometric methods for the assignment of their geographical origin. The best discrimination was obtained by using a dataset composed only of rare earth elements. Considering the experiences reported in the literature and our results, we concluded that sample representativeness and the application of a preliminary Principal Component Analysis, as pattern recognition techniques, might represent two necessary starting points for the geographical determination of the geographical origin of grape berries; therefore, on the basis of these observations we also include some recommendations to be considered for future application of these techniques for grape and wines classification.

Electrochemical detection of magnetically-entrapped DNA sequences from complex samples by multiplexed enzymatic labelling: Application to a transgenic food/feed quantitative survey

Monitoring of genetically modified organisms in food and feed demands molecular techniques that deliver accurate quantitative results. Electrochemical DNA detection has been widely described in this field, yet most reports convey qualitative data and application in processed food and feed samples is limited. Herein, the applicability of an electrochemical multiplex assay for DNA quantification in complex samples is assessed. The method consists of the simultaneous magnetic entrapment via sandwich hybridisation of two DNA sequences (event-specific and taxon-specific) onto the surface of magnetic microparticles, followed by bienzymatic labelling. As proof-of-concept, we report its application in a transgenic food/feed survey where relative quantification (two-target approach) of Roundup Ready Soybean® (RRS) was performed in food and feed. Quantitative coupling to end-point PCR was performed and calibration was achieved from 22 and 243 DNA copies spanning two orders of magnitude for the event and taxon-specific sequences, respectively. We collected a total of 33 soybean-containing samples acquired in local supermarkets, four out of which were found to contain undeclared presence of genetically modified soybean. A real-time PCR method was used to verify these findings. High correlation was found between results, indicating the suitability of the proposed multiplex method for food and feed monitoring.

Stable Isotope Ratio Analysis for Assessing the Authenticity of Food of Animal Origin

The main elemental constituents (H, C, N, O, and S) of bio-organic material have different stable isotopes (² H, ¹ H; ¹³ C,¹² C; ¹⁵ N,¹⁴ N; ¹⁸ O,¹⁷ O,¹⁶ O; ³⁶ S, ³⁴ S, ³³ S, and ³² S). Isotopic ratios can be measured precisely and accurately using dedicated analytical techniques such as isotope ratio mass spectrometry (IRMS). Analysis of these ratios shows potential for assessing the authenticity of food of animal origin. In this review, IRMS analysis of food of animal origin and variability factors related to stable isotope ratios in animals are described. The study also lists examples of application of stable isotope ratio analysis to meat, dairy products, fish, and shellfish and emphasizes the strengths and weaknesses of the technique. Geographical, climatic, pedological, geological, botanical, and agricultural factors affect the stable isotope ratios (SIR) of bio-elements, and SIR variations are ultimately incorporated into animal tissue through eating, drinking, breathing, and exchange with the environment, being recorded in the resulting foods. SIR analysis was capable of determining geographical origin, animal diet, and the production system (such as organic/conventional or wild/farmed) for pork, beef, lamb, poultry, milk, butter, cheese, fish, and shellfish. In the case of the hard PDO (protected designations of origin) cheeses Grana Padano and Parmigiano Reggiano it is also used in real-life situations to assess the authenticity of grated and shredded cheese on the market.