Mass spectrometry in food authentication and origin traceability.

Food authentication and origin traceability are popular research topics, especially as concerns about food quality continue to increase. Mass spectrometry (MS) plays an indispensable role in food authentication and origin traceability. In this review, the applications of MS in food authentication and origin traceability by analyzing the main components and chemical fingerprints or profiles are summarized. In addition, the characteristic markers for food authentication are also reviewed, and the advantages and disadvantages of MS-based techniques for food authentication, as well as the current trends and challenges, are discussed. The fingerprinting and profiling methods, in combination with multivariate statistical analysis, are more suitable for the authentication of high-value foods, while characteristic marker-based methods are more suitable for adulteration detection. Several new techniques have been introduced to the field, such as proton transfer reaction mass spectrometry, ambient ionization mass spectrometry (AIMS), and ion mobility mass spectrometry, for the determination of food adulteration due to their fast and convenient analysis. As an important trend, the miniaturization of MS offers advantages, such as small and portable instrumentation and fast and nondestructive analysis. Moreover, many applications in food authentication are using AIMS, which can help food authentication in food inspection/field analysis. This review provides a reference and guide for food authentication and traceability based on MS.

Raman spectroscopy coupled with chemometrics for identification of adulteration and fraud in muscle foods: a review.

Muscle foods, valued for their significant nutrient content such as high-quality protein, vitamins, and minerals, are vulnerable to adulteration and fraud, stemming from dishonest vendor practices and insufficient market oversight. Traditional analytical methods, often limited to laboratory-scale., may not effectively detect adulteration and fraud in complex applications. Raman spectroscopy (RS), encompassing techniques like Surface-enhanced RS (SERS), Dispersive RS (DRS), Fourier transform RS (FTRS), Resonance Raman spectroscopy (RRS), and Spatially offset RS (SORS) combined with chemometrics, presents a potent approach for both qualitative and quantitative analysis of muscle food adulteration. This technology is characterized by its efficiency, rapidity, and noninvasive nature. This paper systematically summarizes and comparatively analyzes RS technology principles, emphasizing its practicality and efficacy in detecting muscle food adulteration and fraud when combined with chemometrics. The paper also discusses the existing challenges and future prospects in this field, providing essential insights for reviews and scientific research in related fields.

Recent advancements with loop-mediated isothermal amplification (LAMP) in assessment of the species authenticity with meat and seafood products.

Species adulteration or mislabeling with meat and seafood products could negatively affect the fair trade, wildlife conservation, food safety, religion aspect, and even the public health. While PCR-based methods remain the gold standard for assessment of the species authenticity, there is an urgent need for alternative testing platforms that are rapid, accurate, simple, and portable. Owing to its ease of use, low cost, and rapidity, LAMP is becoming increasingly used method in food analysis for detecting species adulteration or mislabeling. In this review, we outline how the features of LAMP have been leveraged for species authentication test with meat and seafood products. Meanwhile, as the trend of LAMP detection is simple, rapid and instrument-free, it is of great necessity to carry out end-point visual detection, and the principles of various end-point colorimetry methods are also reviewed. Moreover, with the aim to enhance the LAMP reaction, different strategies are summarized to either suppress the nonspecific amplification, or to avoid the results of nonspecific amplification. Finally, microfluidic chip is a promising point-of-care method, which has been the subject of a great deal of research directed toward the development of microfluidic platforms-based LAMP systems for the species authenticity with meat and seafood products.

Adulteration Detection and Quantification in Olive Oil Using Excitation-Emission Matrix Fluorescence Spectroscopy and Chemometrics.

This research investigates the use of excitation-emission matrix fluorescence (EEMF) in conjunction with chemometric models to rapidly identify and quantify adulteration in olive oil, a critical concern where sample availability is limited. Adulteration is simulated by blending soybean, peanut, and linseed oils into olive oil, creating diverse adulterated samples. Principal component analysis (PCA) was applied to the EEMF spectral data as an initial exploratory measure to cluster and differentiate adulterated samples. Spatial clustering enabled vivid visualization of the variations and trends in the spectra. The novel application of parallel factor analysis (PARAFAC) for data decomposition in this paper focuses on unraveling correlations between the decomposed components and the actual adulterated components, which offers a novel perspective for accurately quantifying adulteration levels. Additionally, a comparative analysis was conducted between the PCA and PARAFAC methodologies. Our study not only unveils a new avenue for the quantitative analysis of adulterants in olive oil through spectral detection but also highlights the potential for applying these insights in practical, real-world scenarios, thereby enhancing detection capabilities for various edible oil samples. This promises to improve the detection of adulteration across a range of edible oil samples, offering significant contributions to food safety and quality assurance.

Fluorescence Spectroscopy Based Characterization of Flaxseed Oil.

Fluorescence spectroscopy has been employed for the compositional analysis of flaxseed oil, detection of its adulteration and investigation of the thermal effects on its molecular composition. Excitation wavelengths from 320 to 420 nm have been used to explore the valued ingredients in flaxseed oil. The emission bands of flaxseed oil centred at 390, 414, 441, 475, 515 and 673/720 nm represent vitamin K, isomers of vitamin E, carotenoids and chlorophylls, which can be used as a marker for quality analysis. Due to its high quality, it is highly prone to adulteration and in this study, detection of its adulteration with canola oil is demonstrated by applying principal component analysis. Moreover, the effects of temperature on the molecular composition of cold pressed flaxseed oil has been explored by heating them at cooking temperatures of 100, 110, 120, 130, 140, 150, 160, 170 and 180 °C, each for 30 min. On heating, the deterioration of vitamin E, carotenoids and chlorophylls occurred with an increase in the oxidation products. However, it was found that up to 140 °C, flaxseed oil retains much of its natural composition whereas up to 180 oC, it loses much of its valuable ingredients along with increase of oxidized products.

A proposed two-level classification approach for forensic detection of diesel adulteration using NMR spectroscopy and machine learning.

Adulteration of diesel fuel poses a major concern in most developing countries including Ghana despite the many regulatory schemes adopted. The solvent tracer analysis approach currently used in Ghana has over the years suffered several limitations which affect the overall implementation of the scheme. There is therefore a need for alternative or supplementary tools to help detect adulteration of automotive fuel. Herein we describe a two-level classification method that combines NMR spectroscopy and machine learning algorithms to detect adulteration in diesel fuel. The training sets used in training the machine learning algorithms contained 20-40% w/w adulterant, a level typically found in Ghana. At the first level, a classification model is built to classify diesel samples as neat or adulterated. Adulterated samples are passed on to the second stage where a second classification model identifies the type of adulterant (kerosene, naphtha, or premix) present. Samples were analyzed by 1H NMR spectroscopy and the data obtained were used to build and validate support vector machine (SVM) classification models at both levels. At level 1, the SVM model classified all 200 samples with only 2.5% classification errors after validation. The level 2 classification model developed had no classification errors for kerosene and premix in diesel. However, 2.5% classification error was recorded for samples adulterated with naphtha. Despite the great performance of the proposed schemes, it showed significantly erratic predictions with adulterant levels below 20% w/w as the training sets for both models contained adulterants above 20% w/w. The proposed method, nevertheless, proved to be a potential tool that could serve as an alternative to the marking system in Ghana for the fast detection of adulterants in diesel.

Existing status and future advancements of adulteration detection techniques in herbal products.

BACKGROUND: Herbal products have been commonly used all over the world for centuries. Its products have gained remarkable acceptance as therapeutic agents for a variety of disorders. However, following recent research disclosing discrepancies between labeling and actual components of herbal products, there is growing concern about the efficacy, quality and safety of the products. The admixture and adulteration of herbal medicinal products pose a risk of serious health compromise and the well-being of the consumers. To prevent adulteration in raw ingredients and final herbal products, it is necessary to use approaches to assess both genomes as well as metabolomics of the products; this offers quality assurance in terms of product identification and purity. The combinations of molecular and analytical methods are inevitable for thorough verification and quality control of herbal medicine. METHODS AND RESULTS: This review discusses the combination of DNA barcoding, DNA metabarcoding, mass spectroscopy as well as HPLC for the authentication of herbal medicine and determination of the level of adulteration. It also discusses the roles of PCR and real-time PCR techniques in validating and ensuring the quality, purity and identity of the herbal products. CONCLUSIONS: In conclusion, each technique has its own pros and cons, but the cumulative of both the chemical and molecular methods is proven to be the best strategy for adulteration detection. Moreover, CRISPR diagnosis tools equipped with multiplexing techniques may be implemented for screening adulteration from herbal drugs, this will play a crucial role in herbal product authentication in the future.

A novel standard-free detection of adulteration method for sildenafil derivatives in dietary supplements.

The rapid and accurate detection of illegal adulteration of chemical drugs into dietary supplements is a big challenge in the food chemistry field. Detection of compounds without a standard reference is even more difficult; however, this is a common situation. Here in this study, a novel "standard-free detection of adulteration" (SFDA) method was proposed and phosphodiesterase-5 inhibitor derivatives were used as an example to figure out the possibility and reliability of this SFDA method. After analysis by quadrupole coupled time of flight-tandem mass spectrometry detection and multivariable statistics, six common fragment ions were chosen to indicate whether adulteration was present or not, while 20 characteristic fragment ions indicated whether adulteration was by nitrogen-containing heterocycles or by anilines. Furthermore, the quantitative methods were conducted by high-performance liquid chromatography-tandem mass spectrometry. In a word, this strategy allows for a quick determination of dietary supplement adulteration without any need for standard materials, improving the efficacy of food safety testing.

A Review of Recent Progresses on Olive Oil Chemical Profiling, Extraction Technology, Shelf-life, and Quality Control.

Olive oil (OO) is widely recognized as a main component in the Mediterranean diet owing to its unique chemical composition and associated health-promoting properties. This review aimed at providing readers with recent results on OO physicochemical profiling, extraction technology, and quality parameters specified by regulations to ensure authentic products for consumers. Recent research progress on OO adulteration were outlined through a bibliometric analysis mapping using Vosviewer software. As revealed by bibliometric analysis, richness in terms of fatty acids, pigments, polar phenolic compounds, tocopherols, squalene, sterols, and triterpenic compounds justify OO health-promoting properties and increasing demand on its global consumption. OO storage is a critical post-processing operation that must be optimized to avoid oxidation. Owing to its great commercial value on markets, OO is a target to adulteration with other vegetable oils. In this context, different chemometric tools were developed to deal with this problem. To conclude, increasing demand and consumption of OO on the global market is justified by its unique composition. Challenges such as oxidation and adulteration stand out as the main issues affecting the OO market.

Exploring Deep Learning to Predict Coconut Milk Adulteration Using FT-NIR and Micro-NIR Spectroscopy.

Accurately identifying adulterants in agriculture and food products is associated with preventing food safety and commercial fraud activities. However, a rapid, accurate, and robust prediction model for adulteration detection is hard to achieve in practice. Therefore, this study aimed to explore deep-learning algorithms as an approach to accurately identify the level of adulterated coconut milk using two types of NIR spectrophotometer, including benchtop FT-NIR and portable Micro-NIR. Coconut milk adulteration samples came from deliberate adulteration with corn flour and tapioca starch in the 1 to 50% range. A total of four types of deep-learning algorithm architecture that were self-modified to a one-dimensional framework were developed and tested to the NIR dataset, including simple CNN, S-AlexNET, ResNET, and GoogleNET. The results confirmed the feasibility of deep-learning algorithms for predicting the degree of coconut milk adulteration by corn flour and tapioca starch using NIR spectra with reliable performance (R2 of 0.886-0.999, RMSE of 0.370-6.108%, and Bias of -0.176-1.481). Furthermore, the ratio of percent deviation (RPD) of all algorithms with all types of NIR spectrophotometers indicates an excellent capability for quantitative predictions for any application (RPD > 8.1) except for case predicting tapioca starch, using FT-NIR by ResNET (RPD < 3.0). This study demonstrated the feasibility of using deep-learning algorithms and NIR spectral data as a rapid, accurate, robust, and non-destructive way to evaluate coconut milk adulterants. Last but not least, Micro-NIR is more promising than FT-NIR in predicting coconut milk adulteration from solid adulterants, and it is portable for in situ measurements in the future.

Novel and Sensitive Touchdown Polymerase Chain Reaction Assays for the Detection of Goat and Sheep Milk Adulteration with Cow Milk.

Milk is the most consumed liquid food in the world due to its high nutritional value and relatively low cost, characteristics that make it vulnerable to adulteration. One of the most common types of milk adulteration involves the undeclared addition of cow's milk to milk from other mammalian species, such as goats, sheep, buffalo or donkeys. The incidence of such adulteration not only causes a crisis in terms of commercial market and consumer uncertainty but also poses a risk to public health, as allergies can be triggered by proteins in undeclared cow's milk. In this study, a specific qualitative touchdown (TD) PCR method was developed to detect the undeclared addition of cow's milk in goat and sheep milk based on the discrimination of the peak areas of the melting curves after the modification of bovine-specific primers. The developed methodology has high specificity for the DNA templates of other species, such as buffalos and donkeys, and is able to identify the presence of cow's milk down to 1%. Repeatability was tested at low bovine concentrations of 5% and 1% and resulted in %RSD values of 1.53-2.04 for the goat-cow assay and 2.49-7.16 for the sheep-cow assay, respectively. The application of this method to commercial goat milk samples indicated a high percentage of noncompliance in terms of labeling (50%), while a comparison of the results to rapid immunochromatographic and ELISA kits validated the excellent sensitivity and applicability of the proposed PCR methodology that was able to trace more adulterated samples. The developed assays offer the advantage of multiple detection in a single run, resulting in a cost- and time-efficient method. Future studies will focus on the applicability of these assays in dairy products such as cheese and yogurt.

Benchtop NMR Coupled with Chemometrics: A Workflow for Unveiling Hidden Drug Ingredients in Honey-Based Supplements.

Recently, benchtop nuclear magnetic resonance (NMR) spectrometers utilizing permanent magnets have emerged as versatile tools with applications across various fields, including food and pharmaceuticals. Their efficacy is further enhanced when coupled with chemometric methods. This study presents an innovative approach to leveraging a compact benchtop NMR spectrometer coupled with chemometrics for screening honey-based food supplements adulterated with active pharmaceutical ingredients. Initially, fifty samples seized by French customs were analyzed using a 60 MHz benchtop spectrometer. The investigation unveiled the presence of tadalafil in 37 samples, sildenafil in 5 samples, and a combination of flibanserin with tadalafil in 1 sample. After conducting comprehensive qualitative and quantitative characterization of the samples, we propose a chemometric workflow to provide an efficient screening of honey samples using the NMR dataset. This pipeline, utilizing partial least squares discriminant analysis (PLS-DA) models, enables the classification of samples as either adulterated or non-adulterated, as well as the identification of the presence of tadalafil or sildenafil. Additionally, PLS regression models are employed to predict the quantitative content of these adulterants. Through blind analysis, this workflow allows for the detection and quantification of adulterants in these honey supplements.

Fish DNA Sensors for Authenticity Assessment-Application to Sardine Species Identification.

Food and fish adulteration is a major public concern worldwide. Apart from economic fraud, health issues are in the forefront mainly due to severe allergies. Sardines are one of the most vulnerable-to-adulteration fish species due to their high nutritional value. Adulteration comprises the substitution of one fish species with similar species of lower nutritional value and lower cost. The detection of adulteration, especially in processed fish products, is very challenging because the morphological characteristics of the tissues change, making identification by the naked eye very difficult. Therefore, new analytical methods and (bio)sensors that provide fast analysis with high specificity, especially between closely related fish species, are in high demand. DNA-based methods are considered as important analytical tools for food adulteration detection. In this context, we report the first DNA sensors for sardine species identification. The sensing principle involves species recognition, via short hybridization of PCR-amplified sequences with specific probes, capture in the test zone of the sensor, and detection by the naked eye using gold nanoparticles as reporters; thus, avoiding the need for expensive instruments. As low as 5% adulteration of Sardina pilchardus with Sardinella aurita was detected with high reproducibility in the processed mixtures simulating canned fish products.

Rapid detection and quantification of adulteration in saffron by excitation-emission matrix fluorescence combined with multi-way chemometrics.

BACKGROUND: Saffron has gained people's attention and love for its unique flavor and valuable edible value, but the problem of saffron adulteration in the market is serious. It is urgent for us to find a simple and rapid identification and quantitative estimation of adulteration in saffron. Therefore, excitation-emission matrix (EEM) fluorescence combined with multi-way chemometrics was proposed for the detection and quantification of adulteration in saffron. RESULTS: The fluorescence composition analysis of saffron and saffron adulterants (safflower, marigold and madder) were accomplished by alternating trilinear decomposition (ATLD) algorithm. ATLD and two-dimensional principal component analysis combined with k-nearest neighbor (ATLD-kNN and 2DPCA-kNN) and ATLD combined with data-driven soft independent modeling of class analogies (ATLD-DD-SIMCA) were applied to rapid detection of adulteration in saffron. 2DPCA-kNN and ATLD-DD-SIMCA methods were adopted for the classification of chemical EEM data, first with 100% correct classification rate. The content of adulteration of adulterated saffron was predicted by the N-way partial least squares regression (N-PLS) algorithm. In addition, new samples were correctly classified and the adulteration level in adulterated saffron was estimated semi-quantitatively, which verifies the reliability of these models. CONCLUSION: ATLD-DD-SIMCA and 2DPCA-kNN are recommended methods for the classification of pure saffron and adulterated saffron. The N-PLS algorithm shows potential in prediction of adulteration levels. These methods are expected to solve more complex problems in food authenticity. © 2023 Society of Chemical Industry.

Graphene based T-shaped monopole antenna sensor for food quality evaluation.

BACKGROUND: Food adulteration has long been considered a major problem. It compromises the quality, safety, and nutritional value of food, posing significant risks to public health. Novel techniques are required to control it. RESULTS: A graphene-based T-shaped monopole antenna sensor was tested for its ability to detect adulteration in liquid foods. Mustard oil was the pure reference sample used for product quality analysis. Olive oil and rice bran oil were adulterants added to the pure sample. It was found that the sensor could be immersed easily in the liquid sample and provided precise results. CONCLUSION: The graphene-based T-shaped monopole antenna sensor can be used for the quality assessment of liquid food products and is suitable for real-time monitoring. © 2024 Society of Chemical Industry.

Volatilomics as a tool to ascertain food adulteration, authenticity, and origin.

Over recent years, there has been an increase in the number of reported cases of food fraud incidents, whereas at the same time, consumers demand authentic products of high quality. The emerging volatilomics technology could be the key to the analysis and characterization of the quality of different foodstuffs. This field of omics has aroused the interest of scientists due to its noninvasive, rapid, and cost-profitable nature. This review aims to monitor the available scientific information on the use of volatilomics technology, correlate it to the relevant food categories, and demonstrate its importance in the food adulteration, authenticity, and origin areas. A comprehensive literature search was performed using various scientific search engines and "volatilomics," "volatiles," "food authenticity," "adulteration," "origin," "fingerprint," "chemometrics," and variations thereof as keywords, without chronological restriction. One hundred thirty-seven relevant publications were retrieved, covering 11 different food categories (meat and meat products, fruits and fruit products, honey, coffee, tea, herbal products, olive oil, dairy products, spices, cereals, and others), the majority of which focused on the food geographical origin. The findings show that volatilomics typically involves various methods responsible for the extraction and consequential identification of volatile compounds, whereas, with the aid of data analysis, it can handle large amounts of data, enabling the origin classification of samples or even the detection of adulteration practices. Nonetheless, a greater number of specific research studies are needed to unlock the full potential of volatilomics.

[Authenticity and adulteration identification of Bubali Cornu based on DNA fingerprints].

Whether adulteration exists is a difficult problem in the identification of traditional Chinese medicine(TCM). Bubali Cornu is mainly available in the medicinal material market in the form of buffalo horn silk or buffalo horn powder but lacks obvious identification characteristics, so there is a risk of adulteration. However, the method of identification of adulteration in Bubali Cornu is lacking at present. In order to ensure authenticity and identify adulteration of TCM Bubali Cornu, control the quality of TCM Bubali Cornu, and ensure the authenticity of clinical use, the DNA fingerprints of 43 batches of samples from pharmaceutical companies and medicinal material markets were identified, and the amplification primers of fluorescent DNA fingerprints of Bubali Cornu and Bovis Grunniens Cornu were screened. The DNA fingerprints of Bubali Cornu were obtained by fluorescent capillary typing. The identification effect of fluorescent capillary typing on different adulteration ratios was also tested. Two pairs of fluorescent STR typing primers, namely 16Sa and CRc, which can distinguish Bubali Cornu and Bovis Grunniens Cornu, were screened out, and a DNA fingerprint identification method was established. The 16Sa migration peaks of Bovis Grunniens Cornu and Bubali Cornu were 223.4-223.9 bp and 225.5-226.1 bp. The CRc migration peaks of Bovis Grunniens Cornu and Bubali Cornu were 518.8-524.8 bp and 535.9-542.5 bp. The peak height of the migration peak could be used for preliminary quantification of the adulterants with an adulteration ratio below 50%, and the quantitative results were similar to the adulteration ratio. In this study, a simple and quick universal DNA fingerprint method was established for the identification of Bubali Cornu and its adulterants, which could realize the identification of TCM Bubali Cornu and the semi-quantitative identification of the adulterants.

Authentication of traditional meat products (Haleem) sold in India: a first report confirming species mislabelling.

Traditional meat products like Haleem play a pivotal role in the culinary landscapes of Indian consumers, along with high economic value and business potential. Due to anticipated gains associated with adulterating 'Haleem' and constant evasion from regulatory oversight, the susceptibility to adulteration has substantially increased. Furthermore, no reports/surveillance regarding their labelling compliance has been reported. Hence, we conducted a 2-year surveillance using 100 samples collected from Hyderabad, India, using the Chipron™ DNA macroarray analysis technique. The method was validated for sensitivity (1%), specificity, and with proficiency test samples. Following this, the surveillance samples (beef, chicken, and mutton Haleem) were tested. The surveillance revealed an alarming adulteration of 46% of the samples, with different proportions of adulterant species. Adulteration of unconventional meat like camel meat was also found. These concerning results necessitate the requirement of stricter and constant regulatory surveillance to safeguard consumer trust and preserve the authenticity of traditional meat products. Supplementary Information: The online version contains supplementary material available at 10.1007/s13197-024-05947-9.

Hepatotoxicity due to dietary supplements: state-of-the-art, gaps and perspectives.

Food supplements are products intended to complement the normal diet and consist of concentrated sources of nutrients or other substances with a nutritional or physiological effect. Although they are generally considered safe if the manufacturer's recommendations are followed, many of them have shown hepatotoxic properties. This can cause many diseases (e.g. steatohepatitis and cirrhosis) characterized by progressive damage and malfunction of the liver that in the long term can lead to death. A review of the literature was carried out to elucidate which dietary supplements have been associated with cases of hepatotoxicity in recent years, with emphasis on those relevant to the consumer and the new trends (e.g. cannabidiol). It has been reported that the supplements described as hepatotoxic are mainly of botanical origin (e.g. green tea or turmeric) and those used in sports (mainly anabolic androgenic steroids). There is a great variability of compounds described as causing liver damage, although sometimes it is not possible to identify them, because they are contaminants or adulterants of the products. In addition, the prevalence of toxic effects after the administration of supplements is difficult to define due to underreporting and the lack of specific studies. Globally regarding hepatotoxicity of dietary supplements, there is a paucity of well-conducted clinical trials on the efficacy of these compounds and the frequency of related liver damage, as the use of these products is largely uncontrolled.

Wheat authentication：An overview on different techniques and chemometric methods.

Wheat (Triticum aestivum L.) is one of the most important cereal crops and is consumed as a staple food around the globe. Wheat authentication has become a crucial issue over the last decades. Recently, many techniques have been applied in wheat authentication including the authentication of wheat geographical origin, wheat variety, organic wheat, and wheat flour from other cereals. This paper collected related literature in the last ten years, and attempted to highlight the recent studies on the discrimination and authentication of wheat using different determination techniques and chemometric methods. The stable isotope analysis and elemental profile of wheat are promising tools to obtain information regarding the origin, and variety, and to differentiate organic from conventional farming of wheat. Image analysis, genetic parameters, and omics analysis can provide solutions for wheat variety, organic wheat, and wheat adulteration. Vibrational spectroscopy analyses, such as NIR, FTIR, and HIS, in combination with multivariate data analysis methods, such as PCA, LDA, and PLS-DA, show great potential in wheat authenticity and offer many advantages such as user-friendly, cost-effective, time-saving, and environment friendly. In conclusion, analytical techniques combining with appropriate multivariate analysis are very effective to discriminate geographical origin, cultivar classification, and adulterant detection of wheat.