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

Modern machine-learning applications in ambient ionization mass spectrometry

This article provides a comprehensive overview of the applications of methods of machine learning (ML) and artificial intelligence (AI) in ambient ionization mass spectrometry (AIMS). AIMS has emerged as a powerful analytical tool in recent years, allowing for rapid and sensitive analysis of various samples without the need for extensive sample preparation. The integration of ML/AI algorithms with AIMS has further expanded its capabilities, enabling enhanced data analysis. This review discusses ML/AI algorithms applicable to the AIMS data and highlights the key advancements and potential benefits of utilizing ML/AI in the field of mass spectrometry, with a focus on the AIMS community.

Demonstration of an End-To-End Workflow Using Atmospheric Solids Analysis Probe-Mass Spectrometry (ASAP-MS) With Real-Time Sample Recognition Software for the Identification of Falsified and Substandard Pharmaceutical Tablets

Counterfeit pharmaceuticals are a subclass of falsified and substandard medicines. They are illicit products, purporting to be genuine medicines, that are made and sold by criminal organisations. They represent a significant risk to patient safety, as well as a financial and reputational threat to the companies who make the genuine medicines. It is essential to have analytical methods to determine if suspect samples seized by law enforcement agencies are counterfeit, with mass spectrometry (MS) being a commonly used technique in forensic cases. Speed-to-answer is vital to enable law enforcement agencies to progress investigations, as well as for pharmaceutical companies so that they can notify health authorities of the circulation of counterfeit medicines. In this work, an atmospheric solids analysis probe (ASAP)-MS was assessed as a fast and simple-to-use approach to analyse tablets on a commercially available instrument. Complementing the analytics with real-time sample recognition software demonstrated that the classification of tablets as authentic or counterfeit could be achieved quickly (< 2 min) and without the need for MS interpretation skills. Authentication of five tablets (two authentic pharmaceuticals, one placebo and two counterfeits containing the correct active pharmaceutical ingredient [API] but at lower quantities than in the genuine medicine and with different excipient contents) of unknown origin was achieved with 100% success. This creates the opportunity to deploy the end-to-end workflow as a tool for non-scientists, such as law enforcement officers and border control staff, for use in-territory to obtain fast answers and make data-led decisions to control the illegal trading of medicines.

A review of the emerging technologies and systems to mitigate food fraud in supply chains

Food fraud has serious consequences including reputational damage to businesses, health and safety risks and lack of consumer confidence. New technologies targeted at ensuring food authenticity has emerged and however, the penetration and diffusion of sophisticated analytical technologies are faced with challenges in the industry. This review is focused on investigating the emerging technologies and strategies for mitigating food fraud and exploring the key barriers to their application. The review discusses three key areas of focus for food fraud mitigation that include systematic approaches, analytical techniques and package-level anti-counterfeiting technologies. A notable gap exists in converting laboratory based sophisticated technologies and tools in high-paced, live industrial applications. New frontiers such as handheld laser-induced breakdown spectroscopy (LIBS) and smart-phone spectroscopy have emerged for rapid food authentication. Multifunctional devices with hyphenating sensing mechanisms together with deep learning strategies to compare food fingerprints can be a great leap forward in the industry. Combination of different technologies such as spectroscopy and separation techniques will also be superior where quantification of adulterants are preferred. With the advancement of automation these technologies will be able to be deployed as in-line scanning devices in industrial settings to detect food fraud across multiple points in food supply chains.

Recent advances in the application of direct analysis in real time-mass spectrometry (DART-MS) in food analysis

Direct analysis in real time-mass spectrometry (DART-MS) has evolved as an effective analytical technique for the rapid and accurate analysis of food samples. The current advancements of DART-MS in food analysis are described in this paper. We discussed the DART principles, which include devices, ionization mechanisms, and parameter settings. Numerous applications of DART-MS in the fields of food and food products analysis published during 2018-2023 were reviewed, including contamination detection, food authentication and traceability, and specific analyte analysis in the food matrix. Furthermore, the challenges and limitations of DART-MS, such as matrix effect, isobaric component analysis, cost considerations and accessibility, and compound selectivity and identification, were discussed as well.

ASAP-MS combined with mass spectrum similarity and binary code for rapid and intelligent authentication of 78 edible flowers

This is the first report to use Atmospheric Pressure Solids Analysis Probe (ASAP) for rapid and intelligent authentication of 78 edible flowers. Mass spectra of 451 batches were collected, with each run for 1-2 min. Experimental raw data was automatically extracted and aligned to create a MS database, based on which flowers were identified by MS similarity scores and rankings. To avoid background interference, top 25 ions of each flower were screened and gathered into an m/z pool containing 292 ions (+) and 399 ions (-). Binary sequence IDs were then generated by automatically assigning "1″ for presence and "0″ for absence, resulting in 78 binary codes. Binary code similarity with 78 IDs was used for authentication. Above two approaches were automatically performed by MATLAB, and compared to k-nearest neighbor model, and samples were all successfully identified (100 %). The proposed method provides a high-throughput authentication approach for large-scale food samples.

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

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

Rapid analysis technologies with chemometrics for food authenticity field: A review

In recent years, the problem of food adulteration has become increasingly rampant, seriously hindering the development of food production, consumption, and management. The common analytical methods used to determine food authenticity present challenges, such as complicated analysis processes and time-consuming procedures, necessitating the development of rapid, efficient analysis technology for food authentication. Spectroscopic techniques, ambient ionization mass spectrometry (AIMS), electronic sensors, and DNA-based technology have gradually been applied for food authentication due to advantages such as rapid analysis and simple operation. This paper summarizes the current research on rapid food authenticity analysis technology from three perspectives, including breeds or species determination, quality fraud detection, and geographical origin identification, and introduces chemometrics method adapted to rapid analysis techniques. It aims to promote the development of rapid analysis technology in the food authenticity field.

Thermal desorption direct analysis in real-time high-resolution mass spectrometry and machine learning allow the rapid authentication of ground black pepper and dried oregano: A proof-of-concept study

Thermal desorption direct analysis in real-time high-resolution mass spectrometry (TD-DART-HRMS) approaches have gained popularity for fast screening of a variety of samples. With rapid volatilization of the sample at increasing temperatures outside the mass spectrometer, this technique can provide a direct readout of the sample content with no sample preparation. In this study, TD-DART-HRMS's utility for establishing spice authenticity was examined. To this aim, we directly analyzed authentic (typical) and adulterated (atypical) samples of ground black pepper and dried oregano in positive and negative ion modes. We analyzed a set of authentic ground black pepper samples (n = 14) originating from Brazil, Sri Lanka, Madagascar, Ecuador, Vietnam, Costa Rica, Indonesia, Cambodia, and adulterated samples (n = 25) consisting of mixtures of ground black pepper with this spice's nonfunctional by-products (pinheads or spent) or with different exogenous materials (olive kernel, green lentils, black mustard seeds, red beans, gypsum plaster, garlic, papaya seeds, chili, green aniseed, or coriander seeds). TD-DART-HRMS facilitated the capture of informative fingerprinting of authentic dried oregano (n = 12) originating from Albania, Turkey, and Italy and those spiked (n = 12) with increasing percentages of olive leaves, sumac, strawberry tree leaves, myrtle, and rock rose. A predictive LASSO classifier was built, after merging by low-level data fusion, the positive and negative datasets for ground black pepper. Fusing multimodal data allowed retrieval of more comprehensive information from both datasets. The resultant classifier achieved on the withheld test set accuracy, sensitivity, and specificity of 100%, 75%, and 90%, respectively. On the contrary, the sole TD-(+)DART-HRMS spectra of the oregano samples allowed construction of a LASSO classifier that predicted the adulteration of the oregano with excellent statistical indicators. This classifier achieved, on the withheld test set, 100% each for accuracy, sensitivity, and specificity.

Food Fingerprinting: LC-ESI-IM-QTOF-Based Identification of Blumeatin as a New Marker Metabolite for the Detection of Origanum majorana Admixtures to O. onites/vulgare

Oregano (Origanum vulgare and O. onites) is one of the most frequently counterfeited herbs in the world and is diluted with the leaves of a wide variety of plants. In addition to olive leaves, marjoram (O. majorana) is often used for this purpose in order to achieve a higher profit. However, apart from arbutin, no marker metabolites are known to reliably detect marjoram admixtures in oregano batches at low concentrations. In addition, arbutin is relatively widespread in the plant kingdom, which is why it is of great relevance to look for further marker metabolites in order to secure the analysis accordingly. Therefore, the aim of the present study was to use a metabolomics-based approach to identify additional marker metabolites with the aid of an ion mobility mass spectrometry instrument. The focus of the analysis was on the detection of non-polar metabolites, as this study was preceded by nuclear magnetic resonance spectroscopic investigations of the same samples based mainly on the detection of polar analytes. Using the MS-based approach, numerous marjoram specific features could be detected in admixtures of marjoram >10% in oregano. However, only one feature was detectable in admixtures of >5% marjoram. This feature was identified as blumeatin, which belongs to the class of flavonoid compounds. Initially, blumeatin was identified based on MS/MS spectra and collision cross section values using a database search. In addition, the identification of blumeatin was confirmed by a reference standard. Moreover, dried leaves of olive, myrtle, thyme, sage and peppermint, which are also known to be used to adulterate oregano, were measured. Blumeatin could not be detected in these plants, so this substance can be considered as an excellent marker compound for the detection of marjoram admixtures.

Atmospheric solids analysis probe-mass spectrometry (ASAP-MS) as a rapid fingerprinting technique to differentiate the harvest seasons of Tieguanyin oolong teas

Atmospheric solids analysis probe-mass spectrometry (ASAP-MS), an ambient mass spectrometry technique, was used to differentiate spring and autumn Tieguanyin teas. Two configurations were used to obtain their chemical fingerprints - ASAP attached to a high-resolution quadrupole time-of-flight mass spectrometer (i.e., ASAP-QTOF) and to a single-quadrupole mass spectrometer (i.e., Radian™ ASAP™ mass spectrometer). Then, orthogonal projections to latent structures-discriminant analysis was conducted to identify features that held promise in differentiating harvest seasons. Four machine learning models - decision tree, linear discriminant analysis, support vector machine, and k-nearest neighbour - were built using these features, and high classification accuracy of up to 100% was achieved. The markers were putatively identified using their accurate masses and MS/MS fragmentation patterns from ASAP-QTOF. This approach was successfully transferred to the Radian ASAP MS, which is more deployable in the field. Overall, this study demonstrated the potential of ASAP-MS as a rapid fingerprinting tool for differentiating spring and autumn Tieguanyin.

Impact of Ambient Vapors on Spectra of 4-Nitroaniline Recorded under Atmospheric Solids Analysis Probe (ASAP) Mass Spectrometric Conditions

Thermally desorbed 4-nitroaniline (4-NA), upon atmospheric pressure chemical ionization (APCI), generates gaseous ions for its protonated species. The APCI mass spectrum recorded under mild in-source ion-activating conditions from 4-NA showed a peak at m/z 139, whereas that acquired under high ion-activating conditions showed two additional peaks at m/z 122 (^•OH loss) and 92 (^•NO loss). The spectrum changed instantaneously when acetonitrile vapor was introduced to the source. In the new spectrum, both m/z 122 and 92 peaks were absent, while a new peak appeared at m/z 93. Ion-mobility separation carried out with the m/z 139 ion revealed that the initial ion represented the thermodynamically favored nitro-protonated tautomer. The ion population changed to an ensemble dominated by the less-favored amino-protomer when acetonitrile vapor was introduced to the ion source. The amino-protomer, upon collisional activation, loses ^•NO₂ to generate an m/z 93 ion, which was confirmed to be the 4-dehydroanilinium ion. Ion mobility provided a practical way to monitor the changes secured by acetonitrile vapor because the two protomers showed different arrival times. Under spray-ionization conditions, the formation of the thermodynamically less favored protomer has been attributed to kinetic trapping. Our study demonstrated that the less favored amino-protomer could be generated by introducing acetonitrile vapor under nonspray conditions. Apparently, under APCI conditions, protonated water vapor attaches to the nitro group to generate a proton-bound heterodimer, which upon activation dissociates to yield the nitro-protomer. In contrast, protonated acetonitrile makes a tighter complex preferentially with the amino group, which upon activation breaks to the amino-protomer.

Fast and eco-friendly method using atmospheric solids analysis probe mass spectrometry to characterize orange varieties

Orange fruit is one of the most popular types of fruit in the world, and its juice is the main product of its processing. This study aimed to evaluate a simple, fast, and eco-friendly methodology, atmospheric solids analysis probe mass spectrometry (ASAP-MS), to assess the chemical profile of four oranges varieties (Valencia, Folha Murcha, Pera, and Iapar). The oranges' varieties were evaluated for the physicochemical composition (extraction yield, pH, total titratable acidity, total soluble solids [TSS], °Brix), ratio (TSS/TA), and bioactive compounds (ASAP-MS analysis). The characterization of oranges resulted in great values of oranges yield according to the varieties (44.00-48.10% [w/w], adequate and characteristic acidity [0.73-1.35%], soluble solids content (10.24-13.80°Brix), pH (3.30-3.96), and ratio (7.59-19.90) level for this fruit. This powerful method showed that all analysis procedures were simple, fast, and easy because there is no need to prepare the sample and the analysis time lasted 2 min. Besides, results obtained exhibited a vast array of chemical groups. Principal component analysis (PCA) defined and distinguished the varieties of the orange. Therefore, ASAP-MS and PCA showed that they are very attractive candidates for routine analysis to monitor the varieties of the orange with its pronounced advantages, besides being contributing to the environment because it does not use any quantities of organic solvents. This methodology was applied for the first time to this type of sample.

Simplified screening approach of anabolic steroid esters using a compact atmospheric solid analysis probe mass spectrometric system

Due to the absence of chromatographic separation, ambient ionization mass spectrometry had the potential to improve the throughput of control laboratories in the last decades and will soon be an excellent approach for on-site use as well. In this study, an atmospheric solids analysis probe (ASAP) with a single quadrupole mass analyzer has been evaluated to identify anabolic steroid esters rapidly. Sample introduction, applied scan time, and probe temperature were optimized for sensitivity. The in-source fragmentations of seventeen selected steroid esters, commonly found in illicit samples, were determined by applying different cone voltages (12, 20, 30, and 40 V). A spectral library was created for these steroid esters based on the four stages of in-source fragmentation spectra. The applicability of this method was demonstrated for the rapid identification of steroid esters in oily injection solutions, providing test results in less than 2 min.