Food fingerprints - A valuable tool to monitor food authenticity and safety

Unveiling metabolome heterogeneity in seed and husk from three cardamom species for quality control and valorization purposes of its waste products via NMR-based metabolomics in relation to in vitro biological effects

NMR-based Metabolomics approach assessed phytochemical profile in seed and husk of three cardamom species: Elettaria cardamomum (green), Amomum subulatum (black), and Aframomum corrorima (white). NMR Spectroscopy identified 20 metabolites belonging to sugars, amino-, organic-, fatty acids, terpenes, and phenolics. Multivariate data analyses revealed distinct metabolic profiles among the 3 species, and further in seed versus husk. A. subulatum seed showed the highest sugar and amino acid levels, while E. cardamomum seed was richer in ω-3 fatty acids. Husk, especially from A. subulatum and E. cardamomum, contained high levels of phenolic acids. Compared to other cardamom taxa, A. corrorima exhibited lower levels of most chemicals. This study highlights the potential value of cardamom husk, particularly from A. subulatum and E. cardamomum species enriched in phenolic acids and terpenes known for their antioxidant and antimicrobial properties, for use as a food preservative. The antimicrobial and antioxidant activities were assessed through in vitro assays, revealing their potential for value-added applications in food preservation and therapeutic uses.

Rapid headspace analysis of commercial spearmint and peppermint teas using volatile 'fingerprints' and an electronic nose

BACKGROUND

Spearmint and peppermint teas are widely consumed around the world for their flavor and therapeutic properties. Dynamic headspace sampling (HS) coupled to gas chromatography/mass spectrometry (GC-MS) with principal component analysis (PCA) of 'fingerprint' volatile profiles were used to investigate 27 spearmint and peppermint teas. Additionally, comparisons between mint teas were undertaken with an electronic nose (enose).

RESULTS

Twenty compounds, all previously known in the literature, were identified using HS-GC-MS. PCA found distinct differences between the fingerprint volatile profiles of spearmint, peppermint and spearmint/peppermint combination teas. HS-GC-MS analysis performed with an achiral column allowed faster processing time and yielded tighter clustering of PCA tea groups than the analysis which used a chiral column. Two spearmint outliers were detected. One showed a high degree of variation in volatile composition and a second wholly overlapped with the peppermint PCA grouping. Enose analysis separated all treatments with no overlaps.

CONCLUSION

Characterizing the volatile fingerprints of mint teas is critical to quality control for this valuable agricultural product. The results of this study show that fingerprint volatile profiles and enose analysis of mint teas are distinctive and could be used to rapidly identify unknown samples. With specific volatile profiles identified for each tea, samples could be tested in the laboratory, or potentially on a farm or along the supply chain, to confirm the provenance and authenticity of mint food or beverage commodities. © 2024 Society of Chemical Industry. This article has been contributed to by U.S. Government employees and their work is in the public domain in the USA.

The high-resolution molecular portrait of coffee: A gateway to insights into its roasting chemistry and comprehensive authenticity profiles

The direct-infusion of 130 coffee samples into a Fourier-transform ion cyclotron mass spectrometer (FT-ICR-MS) provided an ultra-high resolution perspective on the molecular complexity of coffee: The exceptional resolving power and mass accuracy (± 0.2 ppm) facilitated the annotation of unambiguous molecular formulas to 11,500 mass signals. Utilizing this molecular diversity, we extracted hundreds of compound signals linked to the roasting process through guided Orthogonal Partial Least Squares (OPLS) analysis. Visualizations such as van Krevelen diagrams and Kendrick mass defect analysis provided deeper insights into the intrinsic compositional nature of these compounds and the complex chemistry underlying coffee roasting. Predictive OPLS-DA models established universal molecular profiles for rapid authentication of Coffea arabica versus Coffea canephora (Robusta) coffees. Compositional analysis revealed Robusta specific signals, indicative of tryptophan-conjugates of hydroxycinnamic acids. Complementary LC-ToF-MS2 confirmed their compound class, building blocks and structures. Their water-soluble nature allows for application across raw and roasted beans, as well as in ready-made coffee products.

Lipidomics in forensic science: a comprehensive review of applications in drugs, alcohol, latent fingermarks, fire debris, and seafood authentication

Forensic science, an interdisciplinary field encompassing the collection, examination, and presentation of evidence in legal proceedings, has recently embraced lipidomics as a valuable tool. Lipidomics, a subfield of metabolomics, specializes in the analysis of lipid structures and functions, offering insights into biological processes that can aid forensic investigations. While not a substitute for DNA analysis in personal identification, lipidomics complements this technique by focusing on small biological molecules, with distinct sample requirements. This review comprehensively explores the current applications of lipidomics in forensic science. The review commences with an introduction to the concept and historical background of lipidomics, subsequently delving into its utilization in diverse areas such as drug analysis, ethyl alcohol and substitute assessment, latent fingermark detection, fire debris analysis, and seafood authentication. By showcasing the various biological materials and methods employed, this review underscores the potential of lipidomics as a powerful adjunct in forensic investigations.

Changes in the small-molecule fingerprints of rice planted near an industrial explosion site in Taiwan

A fire and explosion accident at a petrochemical complex sparked concerns over the rice health and production in nearby paddy fields. To unveil the potential effects, this study investigated small molecule changes in rice harvested in nearby counties using non-target analysis. Rice grains were harvested three, eight, 15, and 20 months after the accident from a total of ten townships. Small-molecule (m/z 70-1100) data in brown rice (n = 27) were acquired using high-resolution mass spectrometry (HRMS). Partial least squares discriminant analysis (PLS-DA) models were constructed to illustrate the temporal and spatial trends of rice's small-molecule fingerprints, and markers of production locations were identified. The small-molecule fingerprint in the rice directly exposed to the accident and harvested three months after the explosion differed significantly from those planted after the accident (PLS-DA model Q2 = 0.943, Q2/R2Y = 0.962), probably indicating the exclusion of long-term effects. Besides, in the rice directly exposed to the accident, the rice collected from near the explosion site (< 15 km) exhibited reduced jasmonic acid and increased imidacloprid levels (log2 fold change: -1.53 and 5.46, respectively), compared to that from farther locations. The result would suggest compromised disease defence in rice grown under the stress of explosion. In addition, lipid and amino acid metabolism perturbations are deemed relevant to plant development.

NMR-based approach to detect white wine vinegar fraud

Low-Field Nuclear Magnetic Resonance (LF-NMR) can be a valid tool in food fingerprint analyses to detect commercial frauds. Thus, the work aims at exploring the potential of LF-NMR, coupled with chemometrics, in discriminating authentic white wine vinegars from products adulterated with alcohol vinegars (i.e., 5-25% v/v adulteration levels). The monodimensional spectra and transverse relaxation times (T2) of 88 samples, including 32 authentic vinegars and 56 adulterated samples, were collected. Three different spectral regions were investigated (i.e., 3.75-0.90, 3.75-2.00, and 1.50-0.90 ppm) and, for each, fifteen variables were selected from the pretreated monodimensional spectra. Linear Discriminant Analysis (LDA) on monodimensional spectra in the range 3.75-0.90 ppm gave 100% correct classification of authentic and adulterated vinegars in prediction, whereas LDA models developed with acetic acid or water T2 failed. In conclusion, LF-NMR spectra can be effectively used to detect, in a rapid and non-destructive way, white wine vinegar adulteration with alcohol vinegar.

Differentiating Pond-Intensive, Paddy-Ecologically, and Free-Range Cultured Crayfish (Procambarus clarkii) Using Stable Isotope and Multi-Element Analysis Coupled with Chemometrics

The farming pattern of crayfish significantly impacts their quality, safety, and nutrition. Typically, green and ecologically friendly products command higher economic value and market competitiveness. Consequently, intensive farming methods are frequently employed in an attempt to replace these environmentally friendly products, leading to potential instances of commercial fraud. In this study, stable isotope and multi-element analysis were utilized in conjunction with multivariate modeling to differentiate between pond-intensive, paddy-ecologically, and free-range cultured crayfish. The four stable isotope ratios of carbon, nitrogen, hydrogen, and oxygen (δ13C, δ15N, δ2H, δ18O) and 20 elements from 88 crayfish samples and their feeds were determined for variance analysis and correlation analysis. To identify and differentiate three different farming pattern crayfish, unsupervised methods such as hierarchical cluster analysis (HCA) and principal component analysis (PCA) were used, as well as supervised multivariate modeling, specifically partial least squares discriminant analysis (PLS-DA). The HCA and PCA exhibited limited effectiveness in classifying the farming pattern of crayfish, whereas the PLS-DA demonstrated a more robust performance with a predictive accuracy of 90.8%. Additionally, variables such as δ13C, δ15N, δ2H, Mn, and Co exhibited relatively higher contributions in the PLS-DA model, with a variable influence on projection (VIP) greater than 1. This study is the first attempt to use stable isotope and multi-element analysis to distinguish crayfish under three farming patterns. It holds promising potential as an effective strategy for crayfish authentication.

Food authenticity and the interactions with human health and climate change

Food authenticity and fraud, as well as the interest in food traceability have become a topic of increasing interest not only for consumers but also for the research community and the food manufacturing industry. Food authenticity and fraud are becoming prevalent in both the food supply and value chains since ancient times where different issues (e.g., food spoilage during shipment and storage, mixing decay foods with fresh products) has resulted in foods that influence consumers health. The effect of climate change on the quality of food ingredients and products could also have the potential to influence food authenticity. However, this issue has not been considered. This article focused on the interactions between consumer health and the potential effects of climate change on food authenticity and fraud. The role of technology and development of risk management tools to mitigate these issues are also discussed. Where applicable papers that underline the links between the interactions of climate change, human health and food fraud were referenced.

Analytical insights for ensuring authenticity of Greek agriculture products: Unveiling chemical marker applications

Food authentication, including the differentiation of geographical or botanical origin, the method of production i.e. organic vs. conventional farming as well as the detection of food fraud/adulteration, has been a rapidly growing field over the past two decades due to increasing public awareness regarding food quality and safety, nutrition, and health. Concerned parties include consumers, producers, and legislators. Thus, the development of rapid, accurate, sensitive, and reproducible analytical methods to guarantee the authenticity of foods is of primary interest to scientists and technologists. The aim of the present article is to summarize research work carried out on the authentication of Greek agricultural products using spectroscopic (NIR, FTIR, UV-Vis, Raman and fluorescence spectroscopy, NMR, IRMS, ICP-OES, ICP-MS) and chromatographic (GC, GC/MS, HPLC, HPLC/MS, etc.) methods of analysis in combination with chemometrics highlighting the chemical markers that enable product authentication. The review identified a large number of chemical markers including volatiles, phenolic substances, natural pigments, elements, isotopes, etc. which can be used for (i) the differentiation of botanical/geographical origin; conventional from organic farming; production procedure and vintage year, etc. and (ii) detection of adulteration of high quality plant and animal origin foods with lower value substitutes. Finally, the constant development of reliable analytical techniques in combination with law enforcement authorities will ensure authentic foods in terms of quality and safety for consumers.

Evaluation of Antimicrobial and Preservative Effects of Cinnamaldehyde and Clove Oil in Catfish (Ictalurus punctatus) Fillets Stored at 4 °C

This study aimed to evaluate cinnamaldehyde (CN) and clove oil (CO) effectiveness in inhibiting growth and killing spoilage and total aerobic bacteria when overlaid with catfish fillet stored at 4 °C. A 1.00 mL concentration of CO inhibited growth by 2.90, 1.96, and 1.96 cm, respectively, for S. baltica, A. hydrophilia, and total bacteria. Similarly, treatment with 1.00 mL of CN resulted in ZIB of 2.17, 2.10, and 1.10 cm, respectively, for S. baltica, A. hydrophilia, and total bacteria from catfish exudates. Total bacteria from catfish exudates treated with 0.50 mL CN for 40 min, resulted in a 6.84 log decrease, and treatment with 1.00 mL resulted in a 5.66 log decrease at 40 min. Total bacteria exudates treated with 0.50 mL CO resulted in a 9.69 log reduction at 40 min. Total bacteria treated with 1.00 mL CO resulted in a 7.69 log decrease at 7 days, while untreated pads overlaid with catfish resulted in ≥9.00 CFU/mL. However, treated absorbent pads with catfish at 7 days, using 0.50 mL and 1.00 mL CN, had a bacterial recovery of 5.53 and 1.88 log CFU/mL, respectively. Furthermore, CO at 0.50 mL and 1.00 mL reduced the bacteria count to 5.21 and 1.53 log CFU/mL, respectively, at day 7.

A diet enriched with Pistacia atlantica fruits improves the female rats' reproductive system

Background and aim

Baneh (Pistacia atlantica) is a plant species that is commonly consumed as food and has a long-standing traditional use as a sexual enhancer. Despite its widespread use, a limited amount of academic and scientific literature is available regarding its potential impact on the reproductive system. The present research aimed to study the effect of a diet enriched with Baneh on the female rats' reproductive system.

Experimental procedure

Three groups of rats (n = 8) were subjected to the intended diet for six weeks. Subsequently, their histomorphometric parameters, sex hormone levels, as well as the expression of oxytocin (OXT) and oxytocin receptor (OXTR) genes were measured. The rats' serum vitamin D, zinc, and lipid profiles were also evaluated.

Results and conclusion

Results revealed that compared to the normal food, the diet containing 20 % Baneh significantly increased the progesterone and estradiol levels three and two times, respectively. It decreased the total body weight while increasing the ratio of ovary weight to the body weight. Furthermore, the Baneh-enriched diet raised HDL, zinc, and vitamin D levels, though it reduced the LDL and TG levels by 15 μg/dl and 24 μg/dl, respectively, and the concentration of ovary malondialdehyde decreased by 50 % in the treated group. Also, the diet increased the follicle graph, corpus luteum, the thickness of the epithelium, the number of endometrial glands, and the expression of both OXT and OXTR genes. Our findings suggested that P. atlantica could considerably improve the female sex hormone levels and their reproductive system.

Authentication of Laying Hen Housing Systems Based on Egg Yolk Using 1H NMR Spectroscopy and Machine Learning

(1) Background: The authenticity of eggs in relation to the housing system of laying hens is susceptible to food fraud due to the potential for egg mislabeling. (2) Methods: A total of 4188 egg yolks, obtained from four different breeds of laying hens housed in colony cage, barn, free-range, and organic systems, were analyzed using 1H NMR spectroscopy. The data of the resulting 1H NMR spectra were used for different machine learning methods to build classification models for the four housing systems. (3) Results: The comparison of the seven computed models showed that the support vector machine (SVM) model gave the best results with a cross-validation accuracy of 98.5%. The test of classification models with eggs from supermarkets showed that only a maximum of 62.8% of samples were classified according to the housing system labeled on the eggs. (4) Conclusion: The classification models developed in this study included the largest sample size compared to the literature. The SVM model is most suitable for evaluating 1H NMR data in terms of the hen housing system. The test with supermarket samples showed that more authentic samples to analyze influencing factors such as breed, feeding, and housing changes are required.

Rapid and Precise Differentiation and Authentication of Agricultural Products via Deep Learning-Assisted Multiplex SERS Fingerprinting

Accurate and rapid differentiation and authentication of agricultural products based on their origin and quality are crucial to ensuring food safety and quality control. However, similar chemical compositions and complex matrices often hinder precise identification, particularly for adulterated samples. Herein, we propose a novel method combining multiplex surface-enhanced Raman scattering (SERS) fingerprinting with a one-dimensional convolutional neural network (1D-CNN), which enables the effective differentiation of the category, origin, and grade of agricultural products. This strategy leverages three different SERS-active nanoparticles as multiplex sensors, each tailored to selectively amplify the signals of preferentially adsorbed chemicals within the sample. By strategically combining SERS spectra from different NPs, a 'SERS super-fingerprint' is constructed, offering a more comprehensive representation of the characteristic information on agricultural products. Subsequently, utilizing a custom-designed 1D-CNN model for feature extraction from the 'super-fingerprint' significantly enhances the predictive accuracy for agricultural products. This strategy successfully identified various agricultural products and simulated adulterated samples with exceptional accuracy, reaching 97.7% and 94.8%, respectively. Notably, the entire identification process, encompassing sample preparation, SERS measurement, and deep learning analysis, takes only 35 min. This development of deep learning-assisted multiplex SERS fingerprinting establishes a rapid and reliable method for the identification and authentication of agricultural products.

miRNAs as Biomolecular Markers for Food Safety, Quality, and Traceability in Poultry Meat-A Preliminary Study

In this study, the expression and abundance of two candidate chicken (Gallus gallus; gga) microRNAs (miRNAs, miR), gga-miR-21-5p (miR-21) and gga-miR-126-5p (miR-126), have been analyzed in order to identify biomarkers for the traceability and quality of poultry meat. Two breeds of broiler chickens were tested: the most common Ross308 (fast-growing) and the high-quality Ranger Gold (slow-growing). A preliminary analysis of the two miRNAs expressions was conducted across various tissues (liver, lung, spleen, skeletal muscle, and kidney), and the three tissues (lung, spleen, and muscle) with a higher expression were chosen for further analysis. Using quantitative reverse transcription polymerase chain reaction (RT-qPCR), the expression of miRNAs in the three tissues of a total of thirteen animals was determined. The results indicate that miR-126 could be a promising biomarker for the lung tissue in the Ranger Gold (RG) breed (p < 0.01), thus suggesting a potential applicability for tracing hybrids. RG exhibits a significantly higher miR-126 expression in the lung tissue compared to the Ross308 broilers (R308), an indication of greater respiratory capacity and, consequently, a higher oxidative metabolism of the fast-growing hybrid. During sampling, two R308 broilers presented some anomalies, including airsacculitis, hepatic steatosis, and enlarged spleen. The expression of miR-126 and miR-21 was compared in healthy animals and in those presenting anomalies. Chickens with airsacculitis and hepatic steatosis showed an up-regulation of miR-21 and miR-126 in the most commercially valuable tissue, the skeletal muscle or breast (p < 0.05).

Application of Sorbent-Based Extraction Techniques in Food Analysis

This review presents an outline of the application of the most popular sorbent-based methods in food analysis. Solid-phase extraction (SPE) is discussed based on the analyses of lipids, mycotoxins, pesticide residues, processing contaminants and flavor compounds, whereas solid-phase microextraction (SPME) is discussed having volatile and flavor compounds but also processing contaminants in mind. Apart from these two most popular methods, other techniques, such as stir bar sorptive extraction (SBSE), molecularly imprinted polymers (MIPs), high-capacity sorbent extraction (HCSE), and needle-trap devices (NTD), are outlined. Additionally, novel forms of sorbent-based extraction methods such as thin-film solid-phase microextraction (TF-SPME) are presented. The utility and challenges related to these techniques are discussed in this review. Finally, the directions and need for future studies are addressed.

Towards Verifying the Imported Soybeans of China Using Stable Isotope and Elemental Analysis Coupled with Chemometrics

Verifying the geographical origin of soybeans (Glycine max [Linn.] Merr.) is a major challenge as there is little available information regarding non-parametric statistical origin approaches for Chinese domestic and imported soybeans. Commercially procured soybean samples from China (n = 33) and soybeans imported from Brazil (n = 90), the United States of America (n = 6), and Argentina (n = 27) were collected to characterize different producing origins using stable isotopes (δ2H, δ18O, δ15N, δ13C, and δ34S), non-metallic element content (% N, % C, and % S), and 23 mineral elements. Chemometric techniques such as principal component analysis (PCA), linear discriminant analysis (LDA), and BP-artificial neural network (BP-ANN) were applied to classify each origin profile. The feasibility of stable isotopes and elemental analysis combined with chemometrics as a discrimination tool to determine the geographical origin of soybeans was evaluated, and origin traceability models were developed. A PCA model indicated that origin discriminant separation was possible between the four soybean origins. Soybean mineral element content was found to be more indicative of origin than stable isotopes or non-metallic element contents. A comparison of two chemometric discriminant models, LDA and BP-ANN, showed both achieved an overall accuracy of 100% for testing and training sets when using a combined isotope and elemental approach. Our findings elucidate the importance of a combined approach in developing a reliable origin labeling method for domestic and imported soybeans in China.

Detection of adulteration in Eragrostis tef (Zucc.) Trotter flour using EDXRF and ComDim-MLR data fusion

The teff cereal gained worldwide attention because it is gluten-free and rich in iron; thus, its flour is subject to fraud. This study evaluated the ability of Energy Dispersive X-Ray Fluorescence Spectroscopy (EDXRF) to identify teff flours adulterated with rice, whole wheat, oat, and rye flours. The adulteration followed a {5,4} simplex-lattice design. After smoothing and pretreatments, 15 kV and 50 kV spectra were fused by Common Dimension Analysis (ComDim). Multiple Linear Regression (MLR) models using EDXRF-ComDim scores and percentage of teff were adjusted. The best model presented four common dimensions (CD), r2prediction = 0.8534, low RMSEP (0.0564), and absence of overfitting. The obtained model was robust to quantify adulteration in teff flour even with the differences in the intensity of EDXRF spectra of different crops. Therefore, EDXRF, in tandem with ComDim data fusion, was an efficient tool for the adulteration control of teff flours.

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.

The Importance of Testing the Quality and Authenticity of Food Products: The Example of Honey

The aim of this study was to review methods of honey testing in the assessment of its quality and authenticity. The quality of honey, like other food products, is multidimensional. This quality can be assessed not only on the basis of the characteristics evaluated by the consumer during purchase and consumption, but also on the basis of various physicochemical parameters. A number of research methods are used to verify the quality of honeys and to confirm their authenticity. Obligatory methods of assessing the quality of honey are usually described in legal acts. On the other hand, other, non-normative methods of honey quality assessment are used worldwide; they can be used to determine not only the elementary chemical composition of individual types of honey, but also the biological activity of honey and its components. However, so far, there has been no systematization of these methods together with a discussion of problems encountered when determining the authenticity of honeys. Therefore, the aim of our study was to collect information on the methods of assessing the quality and authenticity of honeys, and to identify the problems that occur during this assessment. As a result, a tabular summary of various research methods was created.

Quantitation of L-cystine in Food Supplements and Additives Using 1H qNMR: Method Development and Application

Cystine-enriched food supplements are increasingly popular due to their beneficial health effects. However, the lack of industry standards and market regulations resulted in quality issues with cystine food products, including cases of food adulteration and fraud. This study established a reliable and practical method for determining cystine in food supplements and additives using quantitative NMR (qNMR). With the optimized testing solvent, acquisition time, and relaxation delay, the method exhibited higher sensitivity, precision, and reproducibility than the conventional titrimetric method. Additionally, it was more straightforward and more economical than HPLC and LC-MS. Furthermore, the current qNMR method was applied to investigate different food supplements and additives regarding cystine quantity. As a result, four of eight food supplement samples were found to be inaccurately labeled or even with fake labeling, with the relative actual amount of cystine ranging from 0.3% to 107.2%. In comparison, all three food additive samples exhibited satisfactory quality (the relative actual amount of cystine: 97.0-99.9%). Notably, there was no obvious correlation between the quantifiable properties (price and labeled cystine amount) of the tested food supplement samples and their relative actual amount of cystine. The newly developed qNMR-based approach and the subsequent findings might help standardization and regulation of the cystine supplement market.

Biogenic Solution Map Based on the Definition of the Metabolic Correlation Distance between 4-Dimensional Fingerprints

Accurate discrimination and classification of unknown species are the basis to predict its characteristics or applications to make correct decisions. However, for biogenic solutions that are ubiquitous in nature and our daily lives, direct determination of their similarities and disparities by their molecular compositions remains a scientific challenge. Here, we explore a standard and visualizable ontology, termed "biogenic solution map", that organizes multifarious classes of biogenic solutions into a map of hierarchical structures. To build the map, a novel 4-dimensional (4D) fingerprinting method based on data-independent acquisition data sets of untargeted metabolomics is developed, enabling accurate characterization of complex biogenic solutions. A generic parameter of metabolic correlation distance, calculated based on averaged similarities between 4D fingerprints of sample groups, is able to define "species", "genus", and "family" of each solution in the map. With the help of the "biogenic solution map", species of unknown biogenic solutions can be explicitly defined. Simultaneously, intrinsic correlations and subtle variations among biogenic solutions in the map are accurately illustrated. Moreover, it is worth mentioning that samples of the same analyte but prepared by alternative protocols may have significantly different metabolic compositions and could be classified into different "genera".

Using HPLC with In-Column Derivatization to Authenticate Coffee Samples

Coffee is one of the world's most popular beverages, with the global coffee capsule market worth over USD 4 billion and growing. The incidence of coffee fraud is estimated to be up to one in five coffees being contaminated with cheaper blends of coffee. Given the worsening extent of climate change, coffee crop yields are harder to maintain, while demand is increasing. The 2021 Brazil frost delaying or destroying many coffee crops is an example. Hence, the incidence of coffee fraud is expected to increase, and as the market becomes more complex, there needs to be faster, easier, and more robust means of real-time coffee authentication. In this study, we propose the use of novel approaches to postcolumn derivatization (termed herein as in-column derivatization) to visualize the antioxidant profiles of coffee samples, to be later used as indicators for authentication purposes. We propose three simple mathematical similarity metrics for the real-time identification of unknown coffee samples from a sample library. Using the CUPRAC assay, and these metrics, we demonstrate the capabilities of the technique to identify unknown coffee samples from within our library of thirty.

Agronomic, Physicochemical, Aromatic and Sensory Characterization of Four Sweet Cherry Accessions of the Campania Region

Sweet cherries (Prunus avium L.) are greatly appreciated fruits worldwide due to their taste, color, nutritional value, and beneficial health effects. The characterization of autochthonous germplasm allows to identify genotypes that possess superior characteristics compared to standard cultivars. In this work, four accessions of sweet cherry from the Campania region (Limoncella, Mulegnana Riccia, Mulegnana Nera and Montenero) were investigated for their morpho-physiological, qualitative, aromatic, and sensorial traits in comparison with two standard cultivars (Ferrovia and Lapins). A high variability in the pomological traits resulted among the samples. Montenero showed comparable fruit weight and titratable acidity to Ferrovia and Lapins, respectively. The highest total soluble solid content was detected in Mulegnana Riccia. A considerable variability in the skin and pulp color of the cherries was observed, varying from yellow-red in Limoncella to a dark red color in Montenero. Mulegnana Nera showed the highest content of polyphenols, flavonoids, anthocyanins, and ascorbic acid compared to the standard cultivars. Volatile organic compounds profile analysis identified 34 volatile compounds, 12 of which were observed at different concentrations in all the sweet cherry genotypes while the others were genotype-dependent. Conservation and cultivation of autochthonous accessions with suitable nutritional and morpho-physiologic characteristics promotes our agrobiodiversity knowledge and allows to better plan future breeding programs.

Metabolite Profiling of Premium Civet Luwak Bio-Transformed Coffee Compared with Conventional Coffee Types, as Analyzed Using Chemometric Tools

Luwak (civet) coffee is one of the most precious and exotic coffee commodities in the world. It has garnered an increasing reputation as the rarest and most expensive coffee, with an annual production. Many targeted analytical techniques have been reported for the discrimination of specialty coffee commodities, such as Luwak coffee, from other ordinary coffee. This study presents the first comparative metabolomics approach for Luwak coffee analysis compared to other coffee products, targeting secondary and aroma metabolites using nuclear magnetic resonance (NMR), gas chromatography (GC), or liquid chromatography (LC) coupled with mass spectrometry (MS). Chemometric modeling of these datasets showed significant classification among all samples and aided in identifying potential novel markers for Luwak coffee from other coffee samples. Markers have indicated that C. arabica was the source of Luwak coffee, with several new markers being identified, including kahweol, chlorogenic acid lactones, and elaidic acid. Aroma profiling using solid-phase micro-extraction (SPME) coupled with GC/MS revealed higher levels of guaiacol derivatives, pyrazines, and furans in roasted Luwak coffee compared with roasted C. arabica. Quantification of the major metabolites was attempted using NMR for Luwak coffee to enable future standardization. Lower levels of alkaloids (caffeine 2.85 µg/mg, trigonelline 0.14 µg/mg, and xanthine 0.03 µg/mg) were detected, compared with C. arabica. Other metabolites that were quantified in civet coffee included kahweol and difurfuryl ether at 1.37 and 0.15 µg/mg, respectively.

Development of Non-Targeted Mass Spectrometry Method for Distinguishing Spelt and Wheat

Food fraud, even when not in the news, is ubiquitous and demands the development of innovative strategies to combat it. A new non-targeted method (NTM) for distinguishing spelt and wheat is described, which aids in food fraud detection and authenticity testing. A highly resolved fingerprint in the form of spectra is obtained for several cultivars of spelt and wheat using liquid chromatography coupled high-resolution mass spectrometry (LC-HRMS). Convolutional neural network (CNN) models are built using a nested cross validation (NCV) approach by appropriately training them using a calibration set comprising duplicate measurements of eleven cultivars of wheat and spelt, each. The results reveal that the CNNs automatically learn patterns and representations to best discriminate tested samples into spelt or wheat. This is further investigated using an external validation set comprising artificially mixed spectra, samples for processed goods (spelt bread and flour), eleven untypical spelt, and six old wheat cultivars. These cultivars were not part of model building. We introduce a metric called the D score to quantitatively evaluate and compare the classification decisions. Our results demonstrate that NTMs based on NCV and CNNs trained using appropriately chosen spectral data can be reliable enough to be used on a wider range of cultivars and their mixes.

Essential terminology and considerations for validation of non-targeted methods

Through their suggestive name, non-targeted methods (NTMs) do not aim at a predefined "needle in the haystack." Instead, they exploit all the constituents of the haystack. This new type of analytical method is increasingly finding applications in food and feed testing. However, the concepts, terms, and considerations related to this burgeoning field of analytical testing need to be propagated for the benefit of those associated with academic research, commercial development, or official control. This paper addresses frequently asked questions regarding terminology in connection with NTMs. The widespread development and adoption of these methods also necessitate the need to develop innovative approaches for NTM validation, i.e., evaluating the performance characteristics of a method to determine if it is fit-for-purpose. This work aims to provide a roadmap for approaching NTM validation. In doing so, the paper deliberates on the different considerations that influence the approach to validation and provides suggestions therefor.

Analysis and Comparison of Aroma Compounds of Brown Sugar in Guangdong, Guangxi and Yunnan Using GC-O-MS

Guangdong, Guangxi and Yunnan are the three provinces in China that yield the most brown sugar, a brown-red colored solid or powdered sugar product made from sugar cane. In the present study, the differences between odor compounds of brown sugar from Guangdong, Guangxi, and Yunnan provinces in China were compared and analyzed by gas chromatography-olfactometry-mass spectrometry (GC-O-MS). A total of 80 odor compounds, including 5 alcohols, 9 aldehydes, 8 phenols, 21 acids, 14 ketones, 5 esters, 12 pyrazines, and 6 other compounds, were detected. The fingerprint analysis of the brown sugar odor compounds showed 90% similarity, indicating a close relationship among the odor properties of brown sugar in each province. Moreover, the orthogonal partial least squares discriminant analysis (OPLS-DA) was performed to identify the compounds contributing to the volatile classification of the brown sugar from three provinces, which confirmed that OPLS-DA could be a potential tool to distinguish the brown sugar of three origins.

MALDI mass spectrometry imaging: From constituents in fresh food to ingredients, contaminants and additives in processed food

Mass Spectrometry imaging (MS imaging) provides spatial information for a wide range of compound classes in different sample matrices. We used MS imaging to investigate the distribution of components in fresh and processed food, including meat, dairy and bakery products. The MS imaging workflow was optimized to cater to the specific properties and challenges of the individual samples. We successfully detected highly nonpolar and polar constituents such as beta-carotene and anthocyanins, respectively. For the first time, the distributions of a contaminant and a food additive were visualized in processed food. We detected acrylamide in German gingerbread and investigated the penetration of the preservative natamycin into cheese. For this purpose, a new data analysis tool was developed to study the penetration of analytes from uneven surfaces. Our results show that MS imaging has great potential in food analysis to provide relevant information about components' distributions, particularly those underlying official regulations.

LC/MS analysis of storage-induced plasmalogen loss in ready-to-eat fish

Plasmalogens are functional and oxidation-sensitive phospholipids abundant in fish. Chilling and freezing are common storage methods for maintaining the quality of fish, but their effect on plasmalogen preservation has not been studied. Therefore, plasmalogen loss in ready-to-eat tuna meat during storage under different conditions was investigated. LC/MS was used to analyze the time- and temperature-dependent changes of plasmalogens, which was the most evident for the species with an ethanolamine headgroup and polyunsaturated fatty acyl chains. Moreover, a series of oxidized plasmalogen molecules were identified, and their storage-induced accumulation was observed. Plasmalogen loss was strongly correlated with total lipid oxidation and phospholipid degradation. Repeated freeze-thaw cycles were found to accelerate the loss of plasmalogens, whereas the different thawing methods did not. The present study provides a deeper understanding of changes in lipid nutrients from fish meat during storage and demonstrates the importance of using advanced strategies to maintain food quality.

Comprehensive Lipidomic Analysis of Three Edible Brown Seaweeds Based on Reversed-Phase Liquid Chromatography Coupled with Quadrupole Time-of-Flight Mass Spectrometry

A comprehensive lipidomic analysis was performed onto three edible brown seaweeds, namely Laminaria japonica, Undaria pinnatifida, and Scagassum natans, using reversed-phase liquid chromatography coupled with quadrupole time-of-flight mass spectrometry (RPLC-Q-TOF-MS/MS). In total, 675 lipid molecules, including glycolipids (GLs), phospholipids, sphingolipids (SLs), betaine lipids, and glycerolipids, were identified and semiquantified. With the exception of the high content of diacylglycerols found in L. japonica (54.6% of total lipids), GLs were the dominant component in the three brown seaweeds (27.7-56.7% of total lipids), containing a high proportion of eicosapentaenoic acid. Interestingly, SLs represented by ceramide and hexosylceramide containing phytosphingosine and α-hydroxy fatty acid structures were detected in the three brown seaweeds. A large number of acylated GLs were identified and reported for the first time in these seaweeds, including acylated monogalactosyldiacylglycerol and acylated digalactosyldiacylglycerol containing nonoxidized fatty acids. The bioactive lipids identified herein could be considered potential biomarkers for identifying these seaweeds, evaluating their nutritional value and further promoting their utilization.

Improved approach based on MALDI-TOF MS for establishment of the fish mucus protein pattern for geographic discrimination of Sparus aurata

Food fraud is still a recurrent practice throughout food supply chains. In the case of seafood, misidentification of species and products repackaging constitute the most common frauds. Therefore, the development of appropriate analytical approaches to be used against food fraud is necessary. The present study goal is to explore for the first time, the possibility to differentiate between Sparus aurata from two different mariculture farms located in Madeira Island (Caniçal and Ribeira Brava), using the mass fingerprint of fish mucus obtained from MALDI-TOF MS and analyzed using Mass-UP software for multivariate statistical analysis and biomarker identification. It was possible to establish, from the mucus protein fraction, a set of potential biomarkers for each location in a total of 35 peaks, being 17 peaks specific to Caniçal located farm and 18 to Ribeira Brava. The proposed analytical approach revealed a useful strategy providing accurate and fast results for fish geographical origin discrimination.

Biochemical Characterization of Some Varieties of Apricot Present in the Vesuvius Area, Southern Italy

The witnesses of the millenary history of Campania felix in southern Italy highlighted that several fruit and vegetables cultivated in such territory could potentially be a treasure trove of important health elements. Our work evaluated the content of β-carotene, ascorbic acid, and total phenolics and the antioxidant activity of ten typical varieties of apricots cultivated in the Vesuvius area in the Campania region. The total polyphenols varied between 10.24 and 34.04 mg/100 g of a fresh sample. The amount of ascorbic acid also varied greatly, ranging from 2.65 to 10.65 mg/100 g of a fresh product. B-Carotene reached values up to 0.522 mg/100 g of the fresh sample. The correlation analysis performed, accounting for these parameters, showed that the antioxidant activity, calculated by 2,2-diphenyl-1-picrylhydrazyl (DPPH assay) and azino-bis (3-ethylbenzothiazoline-6-sulfonic acid) tests, was influenced mainly by the content of total polyphenols, with ρ = −0.762 and ρ = 0.875 when we considered DPPH and ABTS tests, respectively, slightly less by the content of ascorbic acid, and not by β-carotene. The dendrogram clustered eight varieties into two main groups; on the other hand, two varieties (“Vitillo” and “Preveta bella”) seemed hierarchically distant. The gas chromatography–mass spectrometry (GC–MS) analysis of volatile organic compounds (VOCs), herein performed for the first time, demonstrated the influence of the varieties on the VOC profiles, both from a qualitative and semiquantitative perspective, discriminating the varieties in different clusters, each of which was characterized by specific notes. α-Terpinolene was the only terpene identified by GC–MS that appeared to affect the antioxidant activity.

Non-Targeted NMR Method to Assess the Authenticity of Saffron and Trace the Agronomic Practices Applied for Its Production

The development of analytical methods aimed at tracing agri-food products and assessing their authenticity is essential to protect food commercial value and human health. An NMR-based non-targeted method is applied here to establish the authenticity of saffron samples. Specifically, 40 authentic saffron samples were compared with 18 samples intentionally adulterated by using turmeric and safflower at three different concentration levels, i.e., 5, 10, and 20 wt%. Statistical processing of NMR data furnished useful information about the main biomarkers contained in aqueous and dimethyl sulfoxide extracts, which are indicative of the presence of adulterants within the analyzed matrix. Furthermore, a discrimination model was developed capable of revealing the type of agronomic practice adopted during the production of this precious spice, distinguishing between organic and conventional cultivation. The main objective of this work was to provide the scientific community involved in the quality control of agri-food products with an analytical methodology able to extract useful information quickly and reliably for traceability and authenticity purposes. The proposed methodology turned out to be sensitive to minor variations in the metabolic composition of saffron that occur in the presence of the two adulterants studied. Both adulterants can be detected in aqueous extracts at a concentration of 5 wt%. A lower limit of detection was observed for safflower contained in organic extracts in which case the lowest detectable concentration was 20%.

Comprehensive Evaluation of the Volatomic Fingerprint of Saffron from Campania towards Its Authenticity and Quality

The volatile profiles of eight saffron samples (seven cultivated and one spontaneous) grown in different geographical districts within the Campania region (southern Italy) were compared. Using headspace solid-phase microextraction coupled to gas chromatography-mass spectrometry (HS-SPME/GC-MS), overall, 80 volatiles were identified in the eight landraces. Among them, safranal and its isomers and other related compounds such as isophorones, which are not only key odorants but also pharmacologically active metabolites, have been detected in all the investigated samples. Principal Component Analysis performed on the volatiles' compounds revealed that the spontaneous sample turned out to be an outlier. In particular, the volatile organic compounds (VOCs) profile of the spontaneous saffron presented four lilac aldehydes and four lilac alcohol isomers, which, to the authors' knowledge, have never been identified in the volatile signature of this spice. The multivariate statistical analysis allowed the discrimination of the seven cultivate saffron ecotypes in four well-separated clusters according to variety. Moreover, 20 VOCs, able to differentiate the clusters in terms of single volatile metabolite, were discovered. Altogether, these results could contribute to identifying possible volatile signature metabolites (biomarkers) or patterns that discriminate saffron samples grown in Campania region on a molecular basis, encouraging future biodiversity programs to preserve saffron landraces revealing valuable genetic resources.

Cocoa bean fingerprinting via correlation networks

Cocoa products have a remarkable chemical and sensory complexity. However, in contrast to other fermentation processes in the food industry, cocoa bean fermentation is left essentially uncontrolled and is devoid of standardization. Questions of food authenticity and food quality are hence particularly challenging for cocoa. Here we provide an illustration how network science can support food fingerprinting and food authenticity research. Using a large dataset of 140 cocoa samples comprising three cocoa fermentation/processing stages and eight countries, we obtain correlation networks between the cocoa samples by computing measures of pairwise correlation from their liquid chromatography-mass spectrometry (LC-MS) profiles. We find that the topology of correlation networks derived from untargeted LC-MS profiles is indicative of the fermentation and processing stage as well as the origin country of cocoa samples. Progressively increasing the correlation threshold firstly reveals network clusters based on processing stage and later country-based clusters. We present both, qualitative and quantitative evidence through network visualization, network statistics and concepts from machine learning. In our view, this network-based approach for classifying mass spectrometry data has broad applicability beyond cocoa.

Handheld Spectral Sensing Devices Should Not Mislead Consumers as Far as Non-Authentic Food Is Concerned: A Case Study with Adulteration of Milk Powder

With the rising trend of consumers being offered by start-up companies portable devices and applications for checking quality of purchased products, it appears of paramount importance to assess the reliability of miniaturized sensors embedded in such devices. Here, eight sensors were assessed for food fraud applications in skimmed milk powder. The performance was evaluated with dry- and wet-blended powders mimicking adulterated materials by addition of either ammonium sulfate, semicarbazide, or cornstarch in the range 0.5-10% of profit. The quality of the spectra was assessed for an adequate identification of the outliers prior to a deep assessment of performance for both non-targeted (soft independent modelling of class analogy, SIMCA) and targeted analyses (partial least square regression with orthogonal signal correction, OPLS). Here, we show that the sensors have generally difficulties in detecting adulterants at ca. 5% supplementation, and often fail in achieving adequate specificity and detection capability. This is a concern as they may mislead future users, particularly consumers, if they are intended to be developed for handheld devices available publicly in smartphone-based applications.

Chromatographic Fingerprinting and Food Identity/Quality: Potentials and Challenges

Chromatograms are a valuable source of information about the chemical composition of the food being analyzed. Sometimes, this information is not explicit and appears in a hidden or not obvious way. Thus, the use of chemometric tools and data-mining methods to extract it is required. The fingerprint provided by a chromatogram offers the possibility to perform both identity and quality testing of foodstuffs. This perspective is aimed at providing an updated opinion of chromatographic fingerprinting methodology in the field of food authentication. Furthermore, the limitations, its absence in official analytical methods, and the future directions of this methodology are discussed.