Rapid evaporative ionization mass spectrometry coupled with an electrosurgical knife for the rapid identification of Mediterranean Sea species

Machine learning approach in canine mammary tumour classification using rapid evaporative ionization mass spectrometry

Rapid evaporative ionization mass spectrometry (REIMS) coupled with a monopolar handpiece used for surgical resection and combined with chemometrics has been previously explored by our research group (Mangraviti et al. in Int J Mol Sci 23(18):10562, 2022) to identify several mammary gland pathologies. Here, the increased sample size allowed the construction of three statistical models to distinguish between benign and malignant canine mammary tumours (CMTs), facilitating a more in-depth investigation of changes in cellular metabolic phenotype during neoplastic transformation and biological behaviour. The results demonstrate that REIMS is effective in identifying neoplastic tissues with an accuracy of 97%, with differences in MS spectra characterized by the relative abundance of phospholipids compared to triglycerides more commonly identified in normal mammary glands. The increased rate of phospholipid synthesis represents an informative feature for tumour recognition, with phosphatidylcholine and phosphatidylethanolamine, the two major phospholipid species identified here together with sphingolipids, playing a crucial role in carcinogenesis. REIMS technology allowed the classification of different histotypes of benign CMTs with an accuracy score of 95%, distinguishing them from normal glands based on the increase in sphingolipids, glycolipids, phospholipids, and arachidonic acid, demonstrating the close association between cancer and inflammation. Finally, dysregulation of fatty acid metabolism with increased signalling for saturated, mono- and polyunsaturated fatty acids characterized the metabolic phenotype of neoplastic cells and their malignant transformation, supporting the increased formation of new organelles for cell division. Further investigations on a more significant number of tumour histotypes will allow for the creation of a more extensive database and lay the basis for how understanding metabolic alterations in the tumour microenvironment can improve surgical precision.

An in situ and real-time analytical method for detection of freeze-thew cycles in tuna via IKnife rapid evaporative ionization mass spectrometry

Freezing is one of the most commonly used preservation methods for Bluefin tuna (Thunnus orientalis). However, repeated freezing and thawing would inevitably occur due to the temperature fluctuation, leading to the microstructure damage, lipid oxidation and protein integrity decline of tuna muscle without notable visual appearance change. In this study, we used a rapid evaporative ionization mass spectrometry (REIMS) technique for the real-time determination of the extent of repeated freezing and thawing cycles in tuna fillets. We found significant variance in the relative abundance of fatty acids between bluefin tuna and its fresh counterpart following freeze-thaw cycles. Meanwhile, the difference is statistically significant (p < 0.05). The quality of tuna remains largely unaffected by a single freeze-thaw cycle but significantly deteriorates after freeze-thaw cycles (freeze-thaw count ≥2), and the relative fatty acid content of the ionized aerosol analysis in the REIMS system positively correlated with the number of freeze-thaw cycles. Notably, palmitic acid (C 16:0, m/z 255.23), oleic acid (C 18:1, m/z 281.24), and docosahexaenoic acid (C 22:6, m/z 327.23) displayed the most pronounced changes within the spectrum of fatty acid groups.

Deep machine learning identified fish flesh using multispectral imaging

Food fraud is widespread in the aquatic food market, hence fast and non-destructive methods of identification of fish flesh are needed. In this study, multispectral imaging (MSI) was used to screen flesh slices from 20 edible fish species commonly found in the sea around Yantai, China, by combining identification based on the mitochondrial COI gene. We found that nCDA images transformed from MSI data showed significant differences in flesh splices of the 20 fish species. We then employed eight models to compare their prediction performances based on the hold-out method with 70% training and 30% test sets. Convolutional neural network (CNN), quadratic discriminant analysis (QDA), support vector machine (SVM), and linear discriminant analysis (LDA) models perform well on cross-validation and test data. CNN and QDA achieved more than 99% accuracy on the test set. By extracting the CNN features for optimization, a very high degree of separation was obtained for all species. Furthermore, based on the Gini index in RF, 11 bands were selected as key classification features for CNN, and an accuracy of 98% was achieved. Our study developed a successful pipeline for employing machine learning models (especially CNN) on MSI identification of fish flesh, and provided a convenient and non-destructive method to determine the marketing of fish flesh in the future.

Rapid evaporative ionization mass spectrometry: A survey through 15 years of applications

Rapid evaporative ionization mass spectrometry (REIMS) is a relatively recent MS technique explored in many application fields, demonstrating high versatility in the detection of a wide range of chemicals, from small molecules (phenols, amino acids, di- and tripeptides, organic acids, and sugars) to larger biomolecules, that is, phospholipids and triacylglycerols. Different sampling devices were used depending on the analyzed matrix (liquid or solid), resulting in distinct performances in terms of automation, reproducibility, and sensitivity. The absence of laborious and time-consuming sample preparation procedures and chromatographic separations was highlighted as a major advantage compared to chromatographic methods. REIMS was successfully used to achieve a comprehensive sample profiling according to a metabolomics untargeted analysis. Moreover, when a multitude of samples were available, the combination with chemometrics allowed rapid sample differentiation and the identification of discriminant features. The present review aims to provide a survey of literature reports based on the use of such analytical technology, highlighting its mode of operation in different application areas, ranging from clinical research, mostly focused on cancer diagnosis for the accurate identification of tumor margins, to the agri-food sector aiming at the safeguard of food quality and security.

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.

Machine learning-guided REIMS pattern recognition of non-dairy cream, milk fat cream and whipping cream for fraudulence identification

The illegal adulteration of non-dairy cream in milk fat cream during the manufacturing process of baked goods has significantly hindered the robust growth of the dairy industry. In this study, a method based on rapid evaporative ionization mass spectrometry (REIMS) lipidomics pattern recognition integrated with machine learning algorithms was established. A total of 26 ions with importance were picked using multivariate statistical analysis as salient contributing features to distinguish between milk fat cream and non-dairy cream. Furthermore, employing discriminant analysis, decision trees, support vector machines, and neural network classifiers, machine learning models were utilized to classify non-dairy cream, milk fat cream, and minute quantities of non-dairy cream adulterated in milk fat cream. These approaches were enhanced through hyperparameter optimization and feature engineering, yielding accuracy rates at 98.4-99.6%. This artificial intelligent method of machine learning-guided REIMS pattern recognition can accurately identify adulteration of whipped cream and might help combat food fraud.

Metabolomic profiling and antianginal activity of the bark of Sterculia setigera from Mali

The present work focuses on the phytochemical characterization and evaluation of antianginal activity of the bark of Sterculia setigera. It was collected and authenticated in the African region of Mali, where the local population largely employs this plant for the treatment of several diseases. In the context of traditional or folk medicine and recent progresses in alternative medicine practices, it is essential to expand the knowledge about the chemical composition of such medicinal plants. In this research, a direct-Mass Spectrometry (MS) technique, known as Rapid Evaporative Ionization Mass Spectrometry (REIMS) was used for the identification of the main constituents of the Sterculia setigera bark. The REIMS source is here coupled with an electroknife as sampling device, so that the dried and pulverized bark was directly cut through the electroknife to generate a vapor, which was online transferred to the source via a Venture tube. In this way, an ambient MS approach was realized, which avoids any sample preparation procedure or pretreatment; the sample was analyzed in its native state according to a time-saving analytical process. A quadrupole-time of flight MS/MS analyzer was exploited for the identification process, based on mass accuracy data and MS/MS experiments for structure elucidation purposes. Lipids, including triterpenes, fatty acids, γ-sitosterol and α-tocopherol, and phenolic compounds were identified, some of them reported for the first time in a plant of the Sterculia genus and further confirmed through a gas chromatography-mass spectrometry analysis. The obtained metabolomic profile was successfully correlated to the antianginal activity of this plant.

Harmonization of Rapid Evaporative Ionization Mass Spectrometry Workflows across Four Sites and Testing Using Reference Material and Local Food-Grade Meats

Rapid evaporative ionization mass spectrometry (REIMS) is a direct tissue metabolic profiling technique used to accurately classify tissues using pre-built mass spectral databases. The reproducibility of the analytical equipment, methodology and tissue classification algorithms has yet to be evaluated over multiple sites, which is an essential step for developing this technique for future clinical applications. In this study, we harmonized REIMS methodology using single-source reference material across four sites with identical equipment: Imperial College London (UK); Waters Research Centre (Hungary); Maastricht University (The Netherlands); and Queen's University (Canada). We observed that method harmonization resulted in reduced spectral variability across sites. Each site then analyzed four different types of locally-sourced food-grade animal tissue. Tissue recognition models were created at each site using multivariate statistical analysis based on the different metabolic profiles observed in the m/z range of 600-1000, and these models were tested against data obtained at the other sites. Cross-validation by site resulted in 100% correct classification of two reference tissues and 69-100% correct classification for food-grade meat samples. While we were able to successfully minimize between-site variability in REIMS signals, differences in animal tissue from local sources led to significant variability in the accuracy of an individual site's model. Our results inform future multi-site REIMS studies applied to clinical samples and emphasize the importance of carefully-annotated samples that encompass sufficient population diversity.

Real-Time Profiling and Distinction of Lipids from Different Mammalian Milks Using Rapid Evaporative Ionization Mass Spectrometry Combined with Chemometric Analysis

The price of mammalian milk from different animal species varies greatly due to differences in their yield and nutritional value. Therefore, the authenticity of dairy products has become a hotspot issue in the market due to the replacement or partial admixture of high-cost milk with its low-cost analog. Herein, four common commercial varieties of milk, including goat milk, buffalo milk, Holstein cow milk, and Jersey cow milk, were successfully profiled and differentiated from each other by rapid evaporative ionization mass spectrometry (REIMS) combined with chemometric analysis. This method was developed as a real-time lipid fingerprinting technique. Moreover, the established chemometric algorithms based on multivariate statistical methods mainly involved principal component analysis, orthogonal partial least squares-discriminant analysis, and linear discriminant analysis as the screening and verifying tools to provide insights into the distinctive molecules constituting the four varieties of milk. The ions with m/z 229.1800, 243.1976, 257.2112, 285.2443, 299.2596, 313.2746, 341.3057, 355.2863, 383.3174, 411.3488, 439.3822, 551.5051, 577.5200, 628.5547, 656.5884, 661.5455, 682.6015, and 684.6146 were selected as potential classified markers. The results of the present work suggest that the proposed method could serve as a reference for recognizing dairy fraudulence related to animal species and expand the application field of REIMS technology.

Breaking Through the Barrier: Regulatory Considerations Relevant to Ambient Mass Spectrometry at the Bedside

Rapid characterization of tissue disorder using ambient mass spectrometry (MS) techniques, requiring little to no preanalytical preparations of sampled tissues, has been shown using a variety of ion sources and with many disease classes. A brief overview of ambient MS in clinical applications, the state of the art in regulatory affairs, and recommendations to facilitate adoption for use at the bedside are presented. Unique challenges in the validation of untargeted MS methods and additional safety and compliance requirements for deployment within a clinical setting are further discussed. Development of a harmonized validation strategy for ambient MS methods is emphasized.

Rapid Analysis and Authentication of Meat Using the MasSpec Pen Technology

Food authenticity and safety are major public concerns due to the increasing number of food fraud cases. Meat fraud is an economically motivated practice of covertly replacing one type of meat with a cheaper alternative raising health, safety, and ethical concerns for consumers. In this study, we implement the MasSpec Pen technology for rapid and direct meat analysis and authentication. The MasSpec Pen is an easy-to-use handheld device connected to a mass spectrometer that employs a solvent droplet for gentle chemical analysis of samples. Here, MasSpec Pen analysis was performed directly on several meat and fish types including grain-fed beef, grass-fed beef, venison, cod, halibut, Atlantic salmon, sockeye salmon, and steelhead trout, with a total analysis time of 15 s per sample. Statistical models developed with the Lasso method using a training set of samples yielded per-sample accuracies of 95% for the beef model, 100% for the beef versus venison model, and 84% for the multiclass fish model. Predictors of meat type selected included several molecules previously reported in the skeletal muscles of animals, including carnosine, anserine, succinic acid, xanthine, and taurine. When testing the models on independent test sets of samples, per-sample accuracies of 100% were achieved for all models, demonstrating the robustness of our method for unadulterated meat authentication. MasSpec Pen feasibility testing for classifying venison and grass-fed beef samples adulterated with grain-fed beef achieved per-sample prediction accuracies of 100% for both classifiers using test sets of samples. Altogether, the results obtained in this study provide compelling evidence that the MasSpec Pen technology is a powerful alternative analytical method for meat analysis and investigation of meat fraud.

Rapid Evaporative Ionization Mass Spectrometry: A Review on Its Application to the Red Meat Industry with an Australian Context

The red meat supply chain is a complex network transferring product from producers to consumers in a safe and secure way. There can be times when fragmentation can arise within the supply chain, which could be exploited. This risk needs reduction so that meat products enter the market with the desired attributes. Rapid Evaporative Ionisation Mass Spectrometry (REIMS) is a novel ambient mass spectrometry technique originally developed for rapid and accurate classification of biological tissue which is now being considered for use in a range of additional applications. It has subsequently shown promise for a range of food provenance, quality and safety applications with its ability to conduct ex vivo and in situ analysis. These are regarded as critical characteristics for technologies which can enable real-time decision making in meat processing plants and more broadly throughout the sector. This review presents an overview of the REIMS technology, and its application to the areas of provenance, quality and safety to the red meat industry, particularly in an Australian context.

Exploration of Rapid Evaporative-Ionization Mass Spectrometry as a Shotgun Approach for the Comprehensive Characterization of Kigelia Africana (Lam) Benth. Fruit

Rapid evaporative-ionization mass spectrometry (REIMS) coupled with an electroknife as a sampling device was recently employed in many application fields to obtain a rapid characterization of different samples without any need for extraction or cleanup procedures. In the present research, REIMS was used to obtain a metabolic profiling of the Kigelia africana fruit, thus extending the applicability of such a technique to the investigation of phytochemical constituents. In particular, the advantages of REIMS linked to a typical electrosurgical handpiece were applied for a comprehensive screening of this botanical species, by exploiting the mass accuracy and tandem MS capabilities of a quadrupole-time of flight analyzer. Then, 78 biomolecules were positively identified, including phenols, fatty acids and phospholipids. In the last decade, Kigelia africana (Lam.) Benth. fruit has attracted special interest for its drug-like properties, e.g., its use for infertility treatments and as anti-tumor agent, as well as against fungal and bacterial infections, diabetes, and inflammatory processes. Many of these properties are currently correlated to the presence of phenolic compounds, also detected in the present study, while the native lipid composition is here reported for the first time and could open new directions in the evaluation of therapeutic activity.