Volatile Compounds for Discrimination between Beef, Pork, and Their Admixture Using Solid-Phase-Microextraction-Gas Chromatography-Mass Spectrometry (SPME-GC-MS) and Chemometrics Analysis.

This study addresses the prevalent issue of meat species authentication and adulteration through a chemometrics-based approach, crucial for upholding public health and ensuring a fair marketplace. Volatile compounds were extracted and analyzed using headspace-solid-phase-microextraction-gas chromatography-mass spectrometry. Adulterated meat samples were effectively identified through principal component analysis (PCA) and partial least square-discriminant analysis (PLS-DA). Through variable importance in projection scores and a Random Forest test, 11 key compounds, including nonanal, octanal, hexadecanal, benzaldehyde, 1-octanol, hexanoic acid, heptanoic acid, octanoic acid, and 2-acetylpyrrole for beef, and hexanal and 1-octen-3-ol for pork, were robustly identified as biomarkers. These compounds exhibited a discernible trend in adulterated samples based on adulteration ratios, evident in a heatmap. Notably, lipid degradation compounds strongly influenced meat discrimination. PCA and PLS-DA yielded significant sample separation, with the first two components capturing 80% and 72.1% of total variance, respectively. This technique could be a reliable method for detecting meat adulteration in cooked meat.

Application of machine learning approach on halal meat authentication principle, challenges, and prospects: A review.

Meat is a source of essential amino acids that are necessary for human growth and development, meat can come from dead, alive, Halal, or non-Halal animal species which are intentionally or economically (adulteration) sold to consumers. Sharia has prohibited the consumption of pork by Muslims. Because of the activities of adulterators in recent times, consumers are aware of what they eat. In the past, several methods were employed for the authentication of Halal meat, but numerous drawbacks are attached to this method such as lack of flexibility, limited application, time,consumption and low level of accuracy and sensitivity. Machine Learning (ML) is the concept of learning through the development and application of algorithms from given data and making predictions or decisions without being explicitly programmed. The techniques compared with traditional methods in Halal meat authentication are fast, flexible, scaled, automated, less expensive, high accuracy and sensitivity. Some of the ML approaches used in Halal meat authentication have proven a high percentage of accuracy in meat authenticity while other approaches show no evidence of Halal meat authentication for now. The paper critically highlighted some of the principles, challenges, successes, and prospects of ML approaches in the authentication of Halal meat.

Rapid detection and quantification of melamine, urea, sucrose, water, and milk powder adulteration in pasteurized milk using Fourier transform infrared (FTIR) spectroscopy coupled with modern statistical machine learning algorithms.

There is an evident requirement for a rapid, efficient, and simple method to screen the authenticity of milk products in the market. Fourier transform infrared (FTIR) spectroscopy stands out as a promising solution. This work employed FTIR spectroscopy and modern statistical machine learning algorithms for the identification and quantification of pasteurized milk adulteration. Comparative results demonstrate modern statistical machine learning algorithms will improve the ability of FTIR spectroscopy to predict milk adulteration compared to partial least square (PLS). To discern the types of substances utilized in milk adulteration, a top-performing multiclassification model was established using multi-layer perceptron (MLP) algorithm, delivering an impressive prediction accuracy of 97.4 %. For quantification purposes, bayesian regularized neural networks (BRNN) provided the best results for the determination of both melamine, urea and milk powder adulteration, while extreme gradient boosting (XGB) and projection pursuit regression (PPR) gave better results in predicting sucrose and water adulteration levels, respectively. The regression models provided suitable predictive accuracy with the ratio of performance to deviation (RPD) values higher than 3. The proposed methodology proved to be a cost-effective and fast tool for screening the authenticity of pasteurized milk in the market.

ATR-FTIR spectroscopy and machine/deep learning models for detecting adulteration in coconut water with sugars, sugar alcohols, and artificial sweeteners.

Packaged coconut water offers various options, from pure to those with added sugars and other additives. While the purity of coconut water is esteemed for its health benefits, its popularity also exposes it to potential adulteration and misrepresentation. To address this concern, our study combines Fourier transform infrared spectroscopy (FTIR) and machine learning techniques to detect potential adulterants in coconut water through classification models. The dataset comprises infrared spectra from coconut water samples spiked with 15 different types of potential sugar substitutes, including: sugars, artificial sweeteners, and sugar alcohols. The interaction of infrared light with molecular bonds generates unique molecular fingerprints, forming the basis of our analysis. Departing from previous research predominantly reliant on linear-based chemometrics for adulterant detection, our study explored linear, non-linear, and combined feature extraction models. By developing an interactive application utilizing principal component analysis (PCA) and t-distributed stochastic neighbor embedding (t-SNE), non-targeted sugar adulterant detection was streamlined through enhanced visualization and pattern recognition. Targeted analysis using ensemble learning random forest (RF) and deep learning 1-dimensional convolutional neural network (1D CNN) achieved higher classification accuracies (95% and 96%, respectively) compared to sparse partial least squares discriminant analysis (sPLS-DA) at 77% and support vector machine (SVM) at 88% on the same dataset. The CNN's demonstrated classification accuracy is complemented by exceptional efficiency through its ability to train and test on raw data.

Rapid in situ identification of honey authenticity based on RP-Nano-ESI-MS using online desalting.

High-content sugar in honey frequently results in severe matrix effects and requires complex pretreatment prior to analysis, posing significant challenges for the rapid analysis of honey. In this study, the reversal polarity nano-electrospray ionization mass spectrometry (RP-Nano-ESI-MS) analysis was developed for the direct evaluation of honey samples. The results indicated that RP-Nano-ESI-MS significantly mitigated the matrix effects induced by high-content sugar through the implementation of online desalting. Furthermore, RP-Nano-ESI-MS has been proven capable of not only differentiating acacia honey adulterated with 10% rape honey, but also effectively distinguishing six types of honey and exhibiting remarkable proficiency in detecting honey adulteration and botanical traceability. Additionally, RP-Nano-ESI-MS exhibited strong quantitative abilities, effectively characterizing variations in amino acid composition among six types of honey with high stability and reproducibility. Our studies underscore the significant potential of RP-Nano-ESI-MS for its rapid in situ analysis of sugar-rich foods like honey, especially in their authenticity verification.

Analytical strategies for sensitive and precise determination of 87Sr/86Sr in olive oil through ion extraction, chromatographic separation, and multicollector inductively-coupled plasma mass-spectrometry.

Several food regulatory bodies regard olive oil as highly susceptible to food fraud, largely due to its substantial economic worth. Precise analytical tools are being developed to uncover these types of fraud. This study examines an innovative approach to extract strontium (Sr) from the olive oil matrix (via EDTA complexation and ion-exchange chromatography) and to determine its isotope composition by MC-ICP-MS. This technique was compared to a commonly used technique (i.e. acid extraction and extraction chromatography), and then validated. Three olive oils that are sold in France were prepared and analyzed by two methods: 1) acid extraction prior to Sr purification by Sr-spec resin and 2) complexation by EDTA prior to Sr purification by AG50W-X8. These methods were applied for the determination of the 87Sr/86Sr isotope ratio of 23 olive oils from various countries. We also demonstrated the feasibility of the method for the detection of olive oil mixtures.

Myoglobin as a molecular biomarker for meat authentication and traceability.

The global incidence of economically motivated meat adulteration represents a crucial issue for the food industry. Undeclared addition of cheaper or low-quality species to meat products of high commercial value has become a common practice that needs to be countered with specific measures. In this framework, myoglobin (Mb) is a sarcoplasmic haemoprotein, primarily responsible for meat colour and has been successfully used in meat fraud authentication. Mb is highly soluble in water, easily monitored at 409 nm and species-specific. Knowing that various analytical DNA-based and protein-based methods, as well as spectroscopic techniques have been developed over the years for the detection of meat fraud, the aim of the present review is to take stock of the situation regarding the possible use of Mb as a molecular biomarker for the easy and rapid detection of undeclared species in meat products, avoiding the need of sophisticated or expensive equipment and specialised operators.

Hybridization of bimetallic cobalt-molybdenum oxide multihole nanosheets with selenium adulteration as advanced bifunctional electrocatalysts for boosting overall water splitting.

Electrocatalytic water splitting produces green and pollution-free hydrogen as a clean energy carrier, which can effectively alleviate energy crisis. In this paper, bimetallic and selenium doped cobalt molybdate (Se-CoMoO4) nanosheets with rough surface are resoundingly prepared. The multihole Se-CoMoO4 nanosheets display ultrathin and rectangular architecture with the dimensions of âˆ¼ 3.5 µm and 700 nm for length and width, respectively. The Se-CoMoO4 electrocatalyst shows remarkable water electrolysis activity and stability. The overpotentials of oxygen evolution reaction (OER) and hydrogen evolution reaction (HER) are 270 and 63.3 mV at 10 mA cm-2, along with low Tafel slopes of 51.6 and 62.0 mV dec-1. Furthermore, the Se-CoMoO4 couple electrolyzer merely requires a cell voltage of 1.48 V to achieve 10 mA cm-2 current density and presents no apparent attenuation for 30 h. This investigation declares that the hybridization of transition bimetallic oxide with nonmetallic adulteration can afford a tactic for the preparation of bifunctional non-precious metal-based electrocatalysts.

Time-of-Flight Secondary Ion Mass Spectrometry Coupled with Unsupervised Methods for Advanced Saffron Authenticity Screening.

Saffron, renowned for its aroma and flavor, is susceptible to adulteration due to its high value and demand. Current detection methods, including ISO standards, often fail to identify specific adulterants such as safflower or turmeric up to 20% (w/w). Therefore, the quest continues for robust screening methods using advanced techniques to tackle this persistent challenge of safeguarding saffron quality and authenticity. Advanced techniques such as time-of-flight secondary ion mass spectrometry (TOF-SIMS), with its molecular specificity and high sensitivity, offer promising solutions. Samples of pure saffron and saffron adulterated with safflower and turmeric at three inclusion levels (5%, 10%, and 20%) were analyzed without prior treatment. Spectral analysis revealed distinct signatures for pure saffron, safflower, and turmeric. Through principal component analysis (PCA), TOF-SIMS effectively discriminated between pure saffron and saffron adulterated with turmeric and safflower at different inclusion levels. The variation between the groups is attributed to the characteristic peaks of safflower and the amino group peaks and mineral peaks of saffron. Additionally, a study was conducted to demonstrate that semi-quantification of the level of safflower inclusion can be achieved from the normalized values of its characteristic peaks in the saffron matrix.

Organic-Acid-Sensitive Visual Sensor Array Based on Fenton Reagent-Phenol/Aniline for the Rapid Species and Adulteration Assessment of Baijiu.

Baijiu is an ancient, distilled spirit with a complicated brewing process, unique taste, and rich trace components. These trace components play a decisive role in the aroma, taste, and especially the quality of baijiu. In this paper, the redox reaction between the Fenton reagent and four reducing agents, including o-phenylenediamine (OPD), p-phenylenediamine (PPD), 4-aminophenol (PAP), and 2-aminophenol (OAP), was adopted to construct a four-channel visual sensor array for the rapid detection of nine kinds of common organic acids in baijiu and the identification of baijiu and its adulteration. By exploiting the color-changing fingerprint response brought by organic acids, each organic acid could be analyzed accurately when combined with an optimized variable-weighted least-squares support vector machine based on a particle swarm optimization (PSO-VWLS-SVM) model. What is more, this novel sensor also could achieve accurate semi-quantitative analysis of the mixed organic acid samples via partial least squares discriminant analysis (PLSDA). Most importantly, the sensor array could be further used for the identification of baijiu with different species through the PLSDA model and the adulteration assessment with the one-class partial least squares (OCPLS) model simultaneously.

Validation and measurement uncertainty of analytical methods for various azo dye adulterants in Curcuma longa L.

Food adulteration involving the illegal addition of dyes to foodstuffs has become an alarming issue in recent years. This study developed and validated a high-performance liquid chromatography (HPLC)-DAD (diode array detector) method for the simultaneous determination of nine azo dyes (Butter Yellow, Sudan Orange G, Para Red, Sudan I, Sudan II, Sudan III, Sudan IV, Sudan Red 7B, and Scarlet 808). Moreover, a qualitative analysis method using liquid chromatography-tandem mass spectrometry was developed to more accurately identify peaks detected in HPLC-DAD. The calibration curve represented good linearity (r2 ≥ 0.9998) over the measured concentration range of 0.5-25 mg/kg. limit of detection and limit of quantification were 0.01-0.04 and 0.04-0.12 mg/kg, respectively. Accuracy and precision were 96.0-102.6 and 0.16-2.01 (relative standard deviation%), respectively. Additionally, the measurement uncertainty and HorRat value were estimated. Several Curcuma longa L. distributed in Korea were collected and monitored for azo dye contaminants. PRACTICAL APPLICATION: The proposed HPLC-DAD method represents a significant advancement in the field, offering a reliable means of quantifying azo dyes and identifying their presence even at trace levels in adulterated turmeric. This not only contributes to ensuring the safety and integrity of turmeric products but also establishes precedent for robust analytical techniques in addressing food safety challenges.

Quality Control and Safety Assessment of Online-Purchased Food Supplements Containing Red Yeast Rice (RYR).

Dietary supplements containing red yeast rice (RYR), a fermentation product of the fungus Monascus purpureus grown on white rice, remain popular in Europe as proclaimed cholesterol-lowering aids. The cholesterol-lowering effects are due to the occurrence of monacolin K, which is often present as a mixture of monacolin K lactone (MK) and as monacolin K hydroxy acid (MKA). MK is structurally similar to the cholesterol-lowering medicine lovastatin. Recently, due to safety concerns linked to the use of statins, the European Commission prohibited RYR supplements with a maximum serving exceeding 3 mg of total monacolins per day. Moreover, the amount of the mycotoxin citrinin, potentially produced by M. purpureus, was also reduced to 100 µg/kg. Evidently, manufacturers that offer their products on the European market, including the online market, must also be compliant with these limits in order to guarantee the safety of their products. Therefore, thirty-five different RYR supplements, purchased from an EU-bound e-commerce platform or from registered online pharmacies, were screened for their compliance to the European legislation for citrinin content and the amount of total monacolin K. This was conducted by means of a newly developed LC-MS/MS methodology that was validated according to ISO 17025. Moreover, these supplements were also screened for possible adulteration and any contamination by micro-organisms and/or mycotoxins. It was found that at least four of the thirty-five RYR supplements (≈11%) might have reason for concern for the safety of the consumer either due to high total monacolin K concentrations exceeding the European predefined limits for total monacolins or severe bacterial contamination. Moreover, three samples (≈9%) were likely adulterated, and the labeling of six of the seventeen samples (≈35%) originating from an EU-based e-commerce platform was not compliant, as either the mandatory warning was missing or incomplete or the total amount of monacolins was not mentioned.

Rapid detection and quantification of milk adulterants using a nanoclusters-based fluorescent optical tongue.

A paper-based sensor array consisting of eight nanoclusters (NCs) combined with multivariate analysis was used as a rapid method for the determination of animal sources of milk; goat, camel, sheep and cow. It was also used to detect and quantify three adulterants including sodium hypochlorite, hydrogen peroxide and formaldehyde in milk. The changes in fluorescence intensity of the NCs were quantified using a smartphone when the sensor array was immersed in the milk samples. The device generated a specific colorimetric signature for milk samples from different animals and for different adulterants. This allowed simultaneous identification of animal and adulterant sources with 100% accuracy. The device was found to be capable of accurately measuring the level of contaminants with a detection limit as low as 0.01% using partial least squares regression. In conclusion, a paper-based optical tongue device has been developed for the detection of adulterants in milk with point-of-need capability.

An integrative review of analytical techniques used in food authentication: A detailed description for milk and dairy products.

The use of suitable analytical techniques for the detection of adulteration, falsification, deliberate substitution, and mislabeling of foods has great importance in the industrial, scientific, legislative, and public health contexts. This way, this work reports an integrative review with a current analytical approach for food authentication, indicating the main analytical techniques to identify adulteration and perform the traceability of chemical components in processed and non-processed foods, evaluating the authenticity and geographic origin. This work presents results from a systematic search in Science Direct® and Scopus® databases using the keywords "authentication" AND "food", "authentication," AND "beverage", from published papers from 2013 to, 2024. All research and reviews published were employed in the bibliometric analysis, evaluating the advantages and disadvantages of analytical techniques, indicating the perspectives for direct, quick, and simple analysis, guaranteeing the application of quality standards, and ensuring food safety for consumers. Furthermore, this work reports the analysis of natural foods to evaluate the origin (traceability), and industrialized foods to detect adulterations and fraud. A focus on research to detect adulteration in milk and dairy products is presented due to the importance of these products in the nutrition of the world population. All analytical tools discussed have advantages and drawbacks, including sample preparation steps, the need for reference materials, and mathematical treatments. So, the main advances in modern analytical techniques for the identification and quantification of food adulterations, mainly milk and dairy products, were discussed, indicating trends and perspectives on food authentication.

Rapid quantification of single component oil in perilla oil blends by ultraviolet-visible spectroscopy combined with chemometrics.

As a unconventional oil, perilla oil is much more expensive than conventional oils since it has the highest content of α-linolenic acid among vegetable oils. Thus the adulteration of perilla oil is serious, which needs to be solved. In this study, the single component oil in perilla oil blends were first quantitatively analyzed by ultraviolet-visible (UV-vis) spectroscopy combined with chemometric methods. Soybean oil and palm oil were added into perilla oil to form binary and ternary perilla oil blends. Partial least squares (PLS), back propagation-artificial neural network (BP-ANN), support vector regression (SVR) and extreme learning machine (ELM) were compared and the best model was selected for calibration. In order to improve the prediction performance of the calibration model, ten preprocessing methods and five variable selection methods were investigated. Results show that PLS was the best calibration method for binary and ternary perilla oil blends. For binary perilla oil blends, the correlation coefficients of prediction (Rp) obtained by PLS were both above 0.99, which does not need preprocessing and variable selection. For ternary perilla oil blends, after the best continuous wavelet transform (CWT) preprocessing and discretized whale optimization algorithm (WOA) variable selection, the Rp values obtained by the best model CWT-WOA-PLS were all above 0.97. This research provides a common framework for calibration of perilla oil blends, which maybe a promising method for quality control of perilla oil in industry.

Hyperspectral identification of oil adulteration using machine learning techniques.

Food adulteration is a global concern, drawing attention from safety authorities due to its potential health risks. Detecting and categorizing oil adulteration is crucial for consumer safety and food industry integrity. This research explores hyperspectral imaging (HSI) analysis to identify substandard oil adulteration at different stages. Using the non-destructive HSI Specim Fx 10 system, a method for precise and easy imaging-based fraud detection and classification was proposed. The 670 oil samples, including pure (Almond, Mustard, Coconut, Olive) and adulterated (Sunflower, Castor, Liquid Paraffin), were analyzed. The Savitzky-Golay filter preprocessed the images to remove noise and smooth spectral signatures. The oils were identified using various machine learning approaches, including Support Vector Machines, Logistic Regression, Linear Discriminant Analysis, Random Forests, Decision Trees, K-Nearest Neighbors, and Naïve Bayes with Linear Discriminant Analysis excelling in identification. Performance parameters, including precision, recall, F1-score, and overall accuracy, were calculated. The proposed method achieved a validation accuracy of 100%, outperforming numerous state-of-the-art approaches. This study introduces a robust pipeline for effective oil adulteration detection, offering a significant advancement in food safety and quality control.

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.

Antibody Production and Immunoassay Development for Authenticating Chlorpheniramine Maleate Adulteration in Herbal Tea.

Chlorphenamine maleate is a prohibited additive found in herbal teas and health foods. Excessive intake of this substance can result in adverse health effects. In this study, two novel haptens, PEM and bepotastine (PB1), mimicking chlorphenamine maleate structure were designed and synthesized based on molecular simulation for developing two corresponding polyclonal antibodies (PEM-Ab and PB1-Ab), respectively. Afterward, an indirect competitive enzyme-linked immunosorbent assay (ic-ELISA) was developed to quickly and accurately detect chlorphenamine maleate in herbal teas using PB1-Ab, which has a high sensitivity and specificity. For chlorphenamine maleate, the half-maximal inhibitory concentration (IC50) and limit of detection (LOD) of PB1-Ab under ideal circumstances were found to be 1.18 µg/L and 0.07 µg/L, respectively. Besides, an environmentally friendly sample pre-treatment strategy was employed that allowed easy and effective elimination of complex matrices. The ic-ELISA method observed the average recovery rate from 87.7% to 94.0% with the variance coefficient (CV) ranging from 2.2% to 9.4%. Additionally, the identification of 25 commercially available herbal teas using liquid chromatography-tandem mass spectrometry (LC-MS/MS) further confirmed the validity of our detection. The results of the two methods are consistent. Overall, the proposed ic-ELISA could be an ultrasensitive and reliable method for chlorphenamine maleate adulterated in foods or exposure to the environment.

A Dual and Rapid RPA-CRISPR/Cas12a Method for Simultaneous Detection of Cattle and Soybean-Derived Adulteration in Goat Milk Powder.

The adulteration of goat milk powder occurs frequently; cattle-derived and soybean-derived ingredients are common adulterants in goat milk powder. However, simultaneously and rapidly detecting cattle-derived and soybean-derived components is still a challenge. An efficient, high-throughput screening method for adulteration detection is needed. In this study, a rapid method was developed to detect the adulteration of common cattle-derived and soybean-derived components simultaneously in goat milk powder by combining the CRISPR/Cas12a system with recombinant polymerase amplification (RPA). A dual DNA extraction method was employed. Primers and crRNA for dual detection were designed and screened, and a series of condition optimizations were carried out in this experiment. The optimized assay rapidly detected cattle-derived and soybean-derived components in 40 min. The detection limits of both cattle-derived and soybean-derived components were 1% (w/w) for the mixed adulteration models. The established method was applied to a blind survey of 55 commercially available goat milk powder products. The results revealed that 36.36% of the samples contained cattle-derived or soybean-derived ingredients, which revealed the noticeable adulteration situation in the goat milk powder market. This study realized a fast flow of dual extraction, dual amplification, and dual detection of cattle-derived and soybean-derived components in goat milk powder for the first time. The method developed can be used for high-throughput and high-efficiency on-site primary screening of goat milk powder adulterants, and provides a technical reference for combating food adulteration.

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.