Chemometric tools for food fraud detection: The role of target class in non-targeted analysis

Miniaturized Near-Infrared spectrophotometers in forensic analytical science - a critical review

The advent of miniaturized NIR instruments, also known as compact, portable, or handheld, is revolutionizing how technology can be employed in forensics. In-field analysis becomes feasible and affordable with these new instruments, and a series of methods has been developed to provide the police and official agents with objective, easy-to-use, tailored, and accurate qualitative and quantitative forensic results. This work discusses the main aspects and presents a comprehensive and critical review of compact NIR spectrophotometers associated with analytical protocols to produce information on forensic matters.

Detection of Adulterated Oregano Samples Using Untargeted Headspace-Gas Chromatography-Ion Mobility Spectrometry Analysis

Oregano is often adulterated for economic reasons. This fraud mainly consists of adding other species with lower commercial value, such as olive leaves. To ensure the authenticity of oregano, an analytical method based on the analysis of the volatile organic compound (VOC) profile obtained by headspace gas chromatography coupled to ion mobility spectrometry (HS-GC-IMS) was developed and validated. Samples of ecological Mediterranean oregano adulterated with different percentages of two types of olive leaves (cornicabra and manzanilla) were studied using a non-targeted analysis. Moreover, a total of 30 VOCs were identified in the analyzed samples, and 24 compounds could be quantified using calibration curves based on Boltzmann's equation. A chemometric model based on orthogonal partial least squares discriminant analysis (OPLS-DA) was used to detect the adulterated oregano samples, obtaining a 100% validation success rate, and partial least squares (PLS) analysis was used to quantify the percentage of adulterant. Finally, the proposed methodology was applied to 15 commercial oregano samples, resulting in two of them being classified as adulterated with 31 and 43% of olive leaves, respectively.

Development of chemometric-assisted supercritical fluid extraction of effective and natural tyrosinase inhibitor from Syzygium aqueum leaves

Tyrosinase is a key enzyme in enzymatic browning, causing quality losses in food through the oxidation process. Thus, the discovery of an effective and natural tyrosinase inhibitor via green technology is of great interest to the global food market due to food security and climate change issues. In this study, Syzygium aqueum (S. aqueum) leaves, which are known to be rich in phenolic compounds (PC), were chosen as a natural source of tyrosinase inhibitor, and the effect of the sustainable, supercritical fluid extraction (SFE) process was evaluated. Response surface methodology-assisted supercritical fluid extraction (RSM-assisted SFE) was utilized to optimize the PCs extracted from S. aqueum. The highest amount of PC was obtained at the optimum conditions (55 °C, 3350 psi, and 70 min). The IC50 (661.815 μg/mL) of the optimized extract was evaluated, and its antioxidant activity (96.8 %) was determined. Gas chromatography-mass spectrometry (GC-MS) results reveal that 2',6'-dihydroxy-4'-methoxychalcone (2,6-D4MC) (82.65 %) was the major PC in S. aqueum. Chemometric analysis indicated that 2,6-D4MC has similar chemical properties to the tyrosinase inhibitor control (kaempferol). The toxicity and physiochemical properties of the novel 2,6-D4MC from S. aqueum revealed that the 2,6-D4MC is safer than kaempferol as predicted via absorption, distribution, metabolism, and excretion (ADME) evaluation. Enzyme kinetic analysis shows that the type of inhibition of the optimized extract is non-competitive inhibition with Km = 1.55 mM and Vmax = 0.017 μM/s. High-performance liquid chromatography (HPLC) analysis shows the effectiveness of S. aqueum as a tyrosinase inhibitor. The mechanistic insight of the tyrosinase inhibition using 2,6-D4MC was successfully calculated using density functional theory (DFT) and molecular docking approaches. The findings could have a significant impact on food security development by devising a sustainable and effective tyrosinase inhibitor from waste by-products that is aligned with the United Nation's SDG 2, zero hunger.

Cocoa quality: Chemical relationship of cocoa beans and liquors in origin identitation

In this study, HS-SPME-GC-MS was applied in combination with machine learning tools to the identitation of a set of cocoa samples of different origins. Untargeted fingerprinting and profiling approaches were tested for their informative, discriminative and classification ability provided by the volatilome of the raw beans and liquors inbound at the factory in search of robust tools exploitable for long-time studies. The ability to distinguish the country of origin on both beans and liquors is not so obvious due to processing steps accompanying the transformation of the beans, but this capacity is of particular interest to the chocolate industry as both beans and liquors can enter indifferently into the processing of chocolate. Both fingerprinting (untargeted) and profiling (targeted) strategies enable to decipher of the information contained in the complex dataset and the cross-validation of the results, affording to discriminate between the origins with effective classification models.

Authentication and discrimination of new Brazilian Canephora coffees with geographical indication using a miniaturized near-infrared spectrometer

New Brazilian Canephora coffees (Conilon and Robusta) of high added value from specific origins have been protected by geographical indication to guarantee their origin and quality. Recently, benchtop near-infrared (NIR) spectroscopy combined with chemometrics has demonstrated its usefulness to discriminate them. It was the first study, however, and therefore the possibility exists to develop a new portable NIR method for this purpose. This work assessed a miniaturized NIR as a cheaper spectrometer to discriminate and authenticate new Brazilian Canephora coffees with certified geographical origins and to differentiate them from specialty Arabica. Discriminant chemometric and class modeling techniques have been applied and have obtained good predictive ability on external test sets. In addition, models with similar classification purpose were compared with those obtained in previous research carried out with benchtop NIR for the same samples, obtaining comparable results. In this context, the portable method was used as a laboratory technique and has the advantage of being cheaper than benchtop NIR spectrometer. Furthermore, it brings a high possibility to be implemented in small coffee cooperatives, industries or control agencies in the future that do not have high economic resources.

Multi-block DD-SIMCA as a high-level data fusion tool

Multi-block classification method based on the Data Driven Soft Independent Modeling of Class Analogy (DD-SIMCA) is presented. A high-level data fusion approach is used for the joint analysis of data collected with the help of different analytical instruments. The proposed fusion technique is very simple and straightforward. It uses a Cumulative Analytical Signal which is a combination of outcomes of the individual classification models. Any number of blocks can be combined. Although the high-level fusion eventually leads to a rather complex model, the analysis of partial distances makes it possible to establish a meaningful relationship between the classification results and the influence of individual samples and specific tools. Two real world examples are used to demonstrate the applicability of the multi-block algorithm and the consistency of the multi-block method with its predecessor, a conventional DD-SIMCA.

Toward the Non-Targeted Detection of Adulterated Virgin Olive Oil with Edible Oils via FTIR Spectroscopy & Chemometrics: Research Methodology Trends, Gaps and Future Perspectives

Fourier-Transform mid-infrared (FTIR) spectroscopy offers a strong candidate screening tool for rapid, non-destructive and early detection of unauthorized virgin olive oil blends with other edible oils. Potential applications to the official anti-fraud control are supported by dozens of research articles with a "proof-of-concept" study approach through different chemometric workflows for comprehensive spectral analysis. It may also assist non-targeted authenticity testing, an emerging goal for modern food fraud inspection systems. Hence, FTIR-based methods need to be standardized and validated to be accepted by the olive industry and official regulators. Thus far, several literature reviews evaluated the competence of FTIR standalone or compared with other vibrational techniques only in view of the chemometric methodology, regardless of the inherent characteristics of the product spectra or the application scope. Regarding authenticity testing, every step of the methodology workflow, and not only the post-acquisition steps, need thorough validation. In this context, the present review investigates the progress in the research methodology on FTIR-based detection of virgin olive oil adulteration over a period of more than 25 years with the aim to capture the trends, identify gaps or misuses in the existing literature and highlight intriguing topics for future studies. An extensive search in Scopus, Web of Science and Google Scholar, combined with bibliometric analysis, helped to extract qualitative and quantitative information from publication sources. Our findings verified that intercomparison of literature results is often impossible; sampling design, FTIR spectral acquisition and performance evaluation are critical methodological issues that need more specific guidance and criteria for application to product authenticity testing.

Detection of seed purity of hybrid wheat using reflectance and transmittance hyperspectral imaging technology

Chemical hybridization and genic male sterility systems are two main methods of hybrid wheat production; however, complete sterility of female wheat plants cannot be guaranteed owing to the influence of the growth stage and weather. Consequently, hybrid wheat seeds are inevitably mixed with few parent seeds, especially female seeds. Therefore, seed purity is a key factor in the popularization of hybrid wheat. However, traditional seed purity detection and variety identification methods are time-consuming, laborious, and destructive. Therefore, to establish a non-destructive classification method for hybrid and female parent seeds, three hybrid wheat varieties (Jingmai 9, Jingmai 11, and Jingmai 183) and their parent seeds were sampled. The transmittance and reflectance spectra of all seeds were collected via hyperspectral imaging technology, and a classification model was established using partial least squares-discriminant analysis (PLS-DA) combined with various preprocessing methods. The transmittance spectrum significantly improved the classification of hybrids and female parents compared to that obtained using reflectance spectrum. Specifically, using transmittance spectrum combined with a characteristic wavelength-screening algorithm, the Detrend-CARS-PLS-DA model was established, and the accuracy rates in the testing sets of Jingmai 9, Jingmai 11, and Jingmai 183 were 95.69%, 98.25%, and 97.25%, respectively. In conclusion, transmittance hyperspectral imaging combined with a machine learning algorithm can effectively distinguish female parent seeds from hybrid seeds. These results provide a reference for rapid seed purity detection in the hybrid production process. Owing to the non-destructive and rapid nature of hyperspectral imaging, the detection of hybrid wheat seed purity can be improved by online sorting in the future.

Assessment of rapid low-cost isotope (δ15 N, δ18 O) analyses of nitrate in fruit extracts by Ti(III) reduction to differentiate organic from conventional production

RATIONALE

The isotopic composition (δ¹⁵ N, δ¹⁸ O) of nitrate in fruits and vegetables differentiates organic from conventional food production practices. Organic systems do not use synthetic nitrate fertilizers high in ¹⁸ O and low in ¹⁵ N and thereby help reveal producers' fertilization claims. Isotope analyses of nitrate extracted from fruits and vegetables are done by bacterial reduction which is costly and by specialized laboratories. Rapid, low-cost methods are needed to promulgate nitrate isotope analyses of food products to support organic food product certification and to verify the authenticity of production claims.

METHODS

Fresh strawberry samples were obtained from certified organic and conventional growers in Andalucía, Spain. We applied a new, rapid, one-step Ti(III) reduction method to convert the nitrate from strawberry extracts to N₂ O gas for headspace isotope analyses using isotope-ratio mass spectrometry. Using the Ti(III) reduction method, 70 samples, controls and references were prepared and analyzed for NO₃ ^- , δ¹⁵ N and δ¹⁸ O per 48 h. We also analyzed extracts and solids for anions and cations and for bulk δ¹⁵ N for multivariate chemometric evaluation.

RESULTS

The Ti(III)-based isotope analyses of nitrate in strawberry extracts revealed clear differentiation between organic and conventional production with mean δ¹⁸ O and δ¹⁵ N values of +18.3 ± 1.2 ‰ and +17.6 ± 1.2 ‰ versus +28.2 ± 4.5 ‰ and +14.9 ± 3.0 ‰, respectively. The δ¹⁵ N of strawberry dry mass differed slightly (+3.0 ± 1.4 ‰ versus +4.0 ± 1.4 ‰) between organic and conventional samples, respectively. Chemometric analyses of nitrate isotopes and extract chemistry revealed that the δ¹⁸ O of nitrate along with δ¹⁵ N and Ca²⁺ fully differentiated organic from conventional strawberry production.

CONCLUSIONS

Our results show the Ti(III) reduction method provides a new low-cost and rapid analytical method to facilitate compound-specific δ¹⁵ N and δ¹⁸ O isotope analyses of nitrate in selected fruit types, and likely other food products, for the purposes of assessing nitrate fertilization practices of organic versus conventional production claims and to support authenticity investigations.

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%.

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.

Critical evaluation of ambient mass spectrometry coupled with chemometrics for the early detection of adulteration scenarios in Origanum vulgare L

Nowadays, most of the screening methods in food manufacturing are based on spectroscopic techniques. Ambient Mass Spectrometry is a relatively new field of analytical chemistry which has proven to offer similar speed and ease-of-use when compared to other fingerprinting techniques, alongside the advantages of good selectivity, sensitivity and chemical information. Numerous applications have been explored in food authenticity, based either on the target detection of adulteration markers or, less frequently, on the development of multivariate classification models. The aim of the present work was to evaluate and compare the capabilities of Direct Analysis in Real Time (DART) and Atmospheric Solid Analysis Probe (ASAP) Mass Spectrometry (MS) for the high-throughput authenticity screening of commercial herbs and spices products. The gross addition of bulking material to dried Mediterranean oregano was taken as case study. First, a pilot sample set, constituted by authentic dried oregano, olive leaves (a frequently reported adulterant) and mixtures thereof at different levels (i.e. 10, 20, 30 and 50% w/w) was used. Each sample was fingerprinted by both ambient-MS techniques. After appropriate pre-processing, the whole mass spectra were used for the subsequent multivariate data analysis. Soft Independent Modelling of Class Analogy was adopted as classification algorithm and the model was challenged with both new authentic oregano and in-house prepared blends. To the best of our knowledge, this is the first report of DART-MS and ASAP-MS used in full scan mode and coupled to chemometric modelling as rapid fingerprinting approach for food authentication. Although both the techniques provided satisfactory results, ASAP-MS clearly showed greater potential, leading to reproducible, diagnostic feature-rich mass spectra. For this reason, ASAP-MS was further tested under a more convoluted scenario, where the training and validation sets were enlarged with additional authentic oregano samples and a wider range of adulterant species, respectively. Overall good results were achieved, with 93% model predictive accuracy, and screening detection capability estimated between 5-20% (w/w) addition, depending on the adulterant considered with the only exception of majorana. Investigation of Q residuals could highlight the statistically-relevant chemical markers which could be tentatively annotated by coupling the ASAP probe with a high resolution mass analyser. The results from the validation study confirmed the great potential of ASAP-MS in combination with chemometrics as fast MS-based screening solution and demonstrated its feasibility for classification model building.

Procrustes Cross-Validation of short datasets in PCA context

We suggest using a new tool, Procrustes cross-validation, as an alternative to a regular cross-validation for short datasets where each sample is important and, therefore, cannot be removed in line with the conventional leave-one-out cross-validation procedure. The advantages of the new approach are demonstrated using two real-world examples: the first one contains discrete variables (chemical profiles). The second one is based on continuous data (spectra). The method is implemented in R and Matlab as a small procedure that any analyst can easily use.

MALDI-TOF Mass Spectrometry Applications for Food Fraud Detection

Chemical analysis of food products relating to the detection of the most common frauds is a complex task due to the complexity of the matrices and the unknown nature of most processes. Moreover, frauds are becoming more and more sophisticated, making the development of reliable, rapid, cost-effective new analytical methods for food control even more pressing. Over the years, MALDI-TOF MS has demonstrated the potential to meet this need, also due to a series of undeniable intrinsic advantages including ease of use, fast data collection, and capability to obtain valuable information even from complex samples subjected to simple pre-treatment procedures. These features have been conveniently exploited in the field of food frauds in several matrices, including milk and dairy products, oils, fish and seafood, meat, fruit, vegetables, and a few other categories. The present review provides a comprehensive overview of the existing MALDI-based applications for food quality assessment and detection of adulterations.

Detecting Food Fraud in Extra Virgin Olive Oil Using a Prototype Portable Hyphenated Photonics Sensor

Background

Current developments in portable photonic devices for fast authentication of extra virgin olive oil (EVOO) or EVOO with non-EVOO additions steer towards hyphenation of different optic technologies. The multiple spectra or so-called “fingerprints” of samples are then analyzed with multivariate statistics. For EVOO authentication, one-class classification (OCC) to identify “out-of-class” EVOO samples in combination with data-fusion is applicable.

Objective

Prospecting the application of a prototype photonic device (“PhasmaFood”) which hyphenates visible, fluorescence, and near-infrared spectroscopy in combination with OCC modelling to classify EVOOs and discriminate them from other edible oils and adulterated EVOOs.

Method

EVOOs were adulterated by mixing in 10–50% (v/v) of refined and virgin olive oils, olive-pomace olive oils, and other common edible oils. Samples were analyzed by the hyphenated sensor. OCC, data-fusion, and decision thresholds were applied and optimized for two different scenarios.

Results

By high-level data-fusion of the classification results from the three spectral databases and several multivariate model vectors, a 100% correct classification of all pure edible oils using OCC in the first scenario was found. Reducing samples being falsely classified as EVOOs in a second scenario, 97% of EVOOs adulterated with non-EVOO olive oils were correctly identified and ones with other edible oils correctly classified at score of 91%.

Conclusions

Photonic sensor hyphenation in combination with high-level data fusion, OCC, and tuned decision thresholds delivers significantly better screening results for EVOO compared to individual sensor results.

Highlights

Hyphenated photonics and its data handling solutions applied to extra virgin olive oil authenticity testing was found to be promising.

Lipid Compositions and Geographical Discrimination of 94 Geographically Authentic Wheat Samples Based on UPLC-MS with Non-Targeted Lipidomic Approach

Wheat is the staple food for the world’s major populations. However, chemical characters of geographically authentic wheat samples, especially for the lipids, have not been deeply studied. The present research aimed to investigate lipid compositions of Chinese wheat samples and clarify the major markers that contribute to the geographical differences. A total of 94 wheat samples from eight main wheat-producing provinces in China were evaluated to differentiate their lipid compositions. Based on the data collected from ultra-high-performance-liquid-chromatography tandem time-of-flight mass spectrometry (UPLC-Q/TOF MS), an optimized non-targeted lipidomic method was utilized for analyses. As the results, 62 lipid compounds, including fatty acids, phospholipids, galactolipids, triglycerides, diglycerides, alkylresorcinol, and ceramide were tentatively identified. Partial least squares discriminant analysis (PLS-DA) demonstrated a more satisfying performance in distinguishing wheat samples from different origins compared with principal component analysis (PCA). Further, the abundances of triglycerides and glycerophospholipids with more unsaturated fatty acids were found greater in wheat samples from northern origins of China, while more glycolipids and unsaturated fatty acids arose in southern original wheat samples. These findings describe the lipid profiles of wheat samples in China and could contribute to the quality and safety control for the wheat flour products.

The potential of handheld near infrared spectroscopy to detect food adulteration: Results of a global, multi-instrument inter-laboratory study

Fraud in the food supply system will be exacerbated by shortages caused by climate change and COVID-19's impact. The dried herbs market exemplifies complex supply chains attractive to criminals seeking financial gain. Real-time remote testing is achievable through development of globally accessible chemometric models for portable near infrared devices, deployed throughout supply chains. This study describes building of models for detection of oregano adulteration, on portable near infrared devices, and comparison to a laboratory-based Fourier-Transform Infrared spectroscopy method. 33/34 portable devices were able to correctly classify 5 out of 6 samples successfully with all adulterated samples being correctly classified following the use of appropriate transferability pre-processing routines. The devices native setup shows limited ability to perform a true screening of oregano using the setup offered. However modifications to the setup could in the future offer a solution that facilitates fit-for-purpose real time detection of adulterated samples within the supply chain.

Comparison of Targeted (HPLC) and Nontargeted (GC-MS and NMR) Approaches for the Detection of Undeclared Addition of Protein Hydrolysates in Turkey Breast Muscle

The adulteration of fresh turkey meat by the undeclared addition of protein hydrolysates is of interest for fraudsters due to the increase of the economic gain by substituting meat with low cost ingredients. The aim of this study was to compare the suitability of three different analytical techniques such as GC-MS and ¹H-NMR with HPLC-UV/VIS as a targeted method, for the detection of with protein hydrolysates adulterated turkey meat. For this, turkey breast muscles were treated with different plant- (e.g., wheat) and animal-based (e.g., gelatin, casein) protein hydrolysates with different hydrolyzation degrees (15–53%: partial; 100%: total), which were produced by enzymatic and acidic hydrolysis. A water- and a nontreated sample (REF) served as controls. The data analyses revealed that the hydrolysate-treated samples had significantly higher levels of amino acids (e.g., leucine, phenylalanine, lysine) compared with REF observed with all three techniques concordantly. Furthermore, the nontargeted metabolic profiling (GC-MS and NMR) showed that sugars (glucose, maltose) and/or by-products (build and released during acidic hydrolyses, e.g., levulinic acid) could be used for the differentiation between control and hydrolysates (type, degrees). The combination of amino acid profiling and additional compounds gives stronger evidence for the detection and classification of adulteration in turkey breast meat.