Multivariate class modeling for the verification of food-authenticity claims

The impact of different Saccharomyces cerevisiae strains on microbial composition and quality of Chinese rice wine fermentations

Chinese rice wine (CRW) is a popular fermented product in China, with complicated microbial composition and flavour compounds. To reveal the effects of different strains of Saccharomyces cerevisiae (N85 and XZ11) on the microbial composition in the process of brewing, metagenomic sequencing approaches were carried out to explore the dynamic changes of bacteria and fungi. Furthermore, the volatile compounds and organic acids in CRW were identified by headspace solid phase microextraction (HS-SPME)/gas chromatography-mass spectrometry (GC-MS) and liquid chromatography-mass spectrometry (LC-MS) at the end of the brewing. Our results indicated that different S. cerevisiae strains could influence microbial compositions and especially affected the growth of Lactobacillus brevis and Pantoea ananatis. The changes in the microbial community structure contributed to the remarkable difference in the content of lactic acid, esters, alcohols, and aldehydes. Moreover, functional network analysis provided insights into the biological correlations between microbial species and metabolic pathways, that is, Lactobacillus genus had negative effects on metabolic activities. This study expands the idea of improving the quality of CRW by controlling the microbiome.

Rapid Authentication of 100% Italian Durum Wheat Pasta by FT-NIR Spectroscopy Combined with Chemometric Tools

Italy is the country with the largest durum wheat pasta production and consumption. The mandatory labelling for pasta indicating the country of origin of wheat has made consumers more aware about the consumed pasta products and is influencing their choice towards 100% Italian wheat pasta. This aspect highlights the need to promote the use of domestic wheat as well as to develop rapid methodologies for the authentication of pasta. A rapid, inexpensive, and easy-to-use method based on infrared spectroscopy was developed and validated for authenticating pasta made with 100% Italian durum wheat. The study was conducted on pasta marketed in Italy and made with durum wheat cultivated in Italy (n = 176 samples) and on pasta made with mixtures of wheat cultivated in Italy and/or abroad (n = 185 samples). Pasta samples were analyzed by Fourier transform-near infrared (FT-NIR) spectroscopy coupled with supervised classification models. The good performance results of the validation set (sensitivity of 95%, specificity and accuracy of 94%) obtained using principal component-linear discriminant analysis (PC-LDA) clearly demonstrated the high prediction capability of this method and its suitability for authenticating 100% Italian durum wheat pasta. This output is of great interest for both producers of Italian pasta pointing toward authentication purposes of their products and consumer associations aimed to preserve and promote the typicity of Italian products.

Insights into chemical and sensorial aspects to understand and manage beer aging using chemometrics

Beer chemical instability remains, at present, the main challenge in maintaining beer quality. Although not fully understood, after decades of research, significant progress has been made in identifying "aging compounds," their origin, and formation pathways. However, as the nature of aging relies on beer manufacturing aspects such as raw materials, process variables, and storage conditions, the chemical profile differs among beers. Current research points to the impact of nonoxidative reactions on beer quality. The effect of Maillard and Maillard intermediates on the final beer quality has become the focus of beer aging research, as prevention of oxidation can only sustain beer quality to some extent. On the other hand, few studies have focused on tracing a profile of whose compound is sensory relevant to specific types of beer. In this matter, the incorporation of "chemometrics," a class of multivariate statistic procedures, has helped brewing scientists achieve specific correlations between the sensory profile and chemical data. The use of chemometrics as exploratory data analysis, discrimination techniques, and multivariate calibration techniques has made the qualitatively and quantitatively translation of sensory perception of aging into manageable chemical and analytical parameters. However, despite their vast potential, these techniques are rarely employed in beer aging studies. This review discusses the chemical and sensorial bases of beer aging. It focuses on how chemometrics can be used to their full potential, with future perspectives and research to be incorporated in the field, enabling a deeper and more specific understanding of the beer aging picture.

Chemometric Strategies for Spectroscopy-Based Food Authentication

In the last decades, spectroscopic techniques have played an increasingly crucial role in analytical chemistry, due to the numerous advantages they offer. Several of these techniques (e.g., Near-InfraRed—NIR—or Fourier Transform InfraRed—FT-IR—spectroscopy) are considered particularly valuable because, by means of suitable equipment, they enable a fast and non-destructive sample characterization. This aspect, together with the possibility of easily developing devices for on- and in-line applications, has recently favored the diffusion of such approaches especially in the context of foodstuff quality control. Nevertheless, the complex nature of the signal yielded by spectroscopy instrumentation (regardless of the spectral range investigated) inevitably calls for the use of multivariate chemometric strategies for its accurate assessment and interpretation. This review aims at providing a comprehensive overview of some of the chemometric tools most commonly exploited for spectroscopy-based foodstuff analysis and authentication. More in detail, three different scenarios will be surveyed here: data exploration, calibration and classification. The main methodologies suited to addressing each one of these different tasks will be outlined and examples illustrating their use will be provided alongside their description.

Geographical Origin Discrimination of Monofloral Honeys by Direct Analysis in Real Time Ionization-High Resolution Mass Spectrometry (DART-HRMS)

An untargeted method using direct analysis in real time and high resolution mass spectrometry (DART-HRMS) combined to multivariate statistical analysis was developed for the discrimination of two monofloral (chestnut and acacia) honeys for their geographical origins-i.e., Italy and Portugal for chestnut honey and Italy and China for acacia honey. Principal Component Analysis, used as an unsupervised approach, showed samples of clusterization for chestnut honey samples, while overlapping regions were observed for acacia honeys. Three supervised statistical approaches, such as Principal Components-Linear Discriminant Analysis, Partial Least Squares-Discriminant Analysis and k-nearest neighbors, were tested on the dataset gathered and relevant performances were compared. All tested statistical approaches provided comparable prediction abilities in cross-validation and external validation with mean values falling between 89.2-98.4% for chestnut and between 85.8-95.0% for acacia honey. The results obtained herein indicate the feasibility of the DART-HRMS approach in combination with chemometrics for the rapid authentication of honey's geographical origin.

Monitoring the composition, authenticity and quality dynamics of commercially available Nigerian fat-filled milk powders under inclement conditions using NIRS, chemometrics, packaging and microbiological parameters

Fat-filled milk powders (FMP) are inexpensive milk alternatives predominantly exported to developing countries to satisfy growing demands for dairy proteins. Harsh climatic and sanitary conditions, poor border controls and relatively long periods for distribution and storage enhance the inherent vulnerability of FMP to fraud and stability. Rapid, low-cost methods are needed for extensive routine authentication of FMP products. This study investigated, for the first time, the sample integrity and the quality dynamics of 7 Nigerian FMP brands stored for 7 weeks at 40 °C. The prominent melamine and urea absorption peaks were absent, but protein contents were below the permitted limit. The peak absorbance of the OH functional group increased while the tryptophan contents decreased with storage time. Multiclass analyses differentiated the fresh FMP brands from one another, and from those that were aged. Robust interval-PLS predictions obtained for storage time may be excellent indicators of FMP freshness and stability.

Indonesian wild honey authenticity analysis using attenuated total reflectance-fourier transform infrared (ATR-FTIR) spectroscopy combined with multivariate statistical techniques

Wild honeys in Indonesia are still widely believed to be good for health with high economic value. This honey is naturally produced by Apisdorsata bee. In this study, authentication analysis by classification and discrimination of attenuated total reflectance-fourier infrared spectroscopy (ATR-FTIR) spectra was conducted on several wild honeys from various places in Indonesia (n = 186) which then compared to adulterated honey contained commercial sugars of aren (Arenga pinnata), coconut, and cane sugar at 10-50% concentration (n = 57). Combination of spectra measurement at 4,000-650 cm-1 with Chemometric technique by several multivariate analyses resulted in visualization of honey grouping, classification, and regression model that differentiate these honeys, both partial and overall. Principle component analysis multivariate analysis was able to visualize the differentiation of adulterated honey from the authentic ones. Discriminant analysis, a supervised classification technique, was used to differentiate the fake from the authentic honey among those from various origins at wave number range of 4000-800 cm-1 with performance index of 91,8, 90.32-100% sensitivity, and 95. 70-100% specificity. Partial least-squares analysis was used to build a model provided quantitative results of commercial sugars content in honey allegedly added during adulteration. Authentic honeys had commercial sugars content less than 10% with R2 of aren, coconut, and cane sugar of 0.9995, 0.9980 and 0.9998, respectively, with their predictive R2 values of 0.9977, 0.9983 and 0.9946, respectively.

Evaluation of the analytical performances of two Raman handheld spectrophotometers for pharmaceutical solid dosage form quantitation

This paper addresses the issue of pharmaceutical solid dosage form quantitation using handheld Raman spectrophotometers. The two spectrophotometers used are designed with different technologies: one allows getting a more representative sampling with the Orbital Raster Scanning technology and the other one allows setting acquisition parameters. The goal was to evaluate which technology could provide the best analytical results. Several parameters were optimized to get the lowest prediction error in the end. The main objective of this study was to evaluate if this kind of instrument would be able to identify substandard medicines. For that purpose, two case-study were explored. At first, a full ICH Q2 (R1) compliant validation was performed for moderate Raman scatterer active pharmaceutical ingredient (API) in a specific formulation. It was successfully validated in the ±15% relative total error acceptance limits, with a RMSEP of 0.85% (w/w). Subsequently, it was interesting to evaluate the influence of excipients when the API is a high Raman scatterer. For that purpose, a multi-formulation model was developed and successfully validated with a RMSEP of 2.98% (w/w) in the best case. These two studies showed that thanks to the optimization of acquisition parameters, Raman handheld spectrophotometers methods were validated for two different case-study and could be applied to identify substandard medicines.

Chemometric tools for food fraud detection: The role of target class in non-targeted analysis

The chemometric issues related to the application of non-targeted analysis for the detection of food frauds were analyzed employing discriminant analysis and a one-class classifier. The similarities and differences between the two methods were investigated. The results of classification are characterized by a set of indices called figures of merit. They comprehensively characterized the quality and reliability of classification. The principle is illustrated using an actual example of Oregano herbs adulteration. The informative region 9000-4000 cm^-1 of near-Infrared spectroscopy is used as analytical means. The results of the application of each method for Oregano data collection are presented. It is shown that the discriminant method is only partially appropriate for solving the authentication problem. One class classifier is a powerful and devoted for non-targeted analysis. The step by step analysis introduced in the paper can also be successfully utilized in apply for revealing of forgeries of various food products.

Origin identification of Panax notoginseng by multi-sensor information fusion strategy of infrared spectra combined with random forest

Traditional Chinese medicine Panax notoginseng is a valuable geo-authentic herbal material. The difference of growth environment in different producing areas has significant influence on the quality of traditional Chinese medicine, and origin identification is an important part of the quality assessment of P. notoginseng. In this study, Fourier transform mid-infrared (FT-MIR) and near infrared (NIR) sensor technologies combined with single spectra analysis and multi-sensor information fusion strategy (low-, mid- and high-level) for the origin identification of 210 P. notoginseng samples from five cities in Yunnan Province, China. FT-MIR spectra were considered to play a greater role in data analysis than NIR spectra. Random forest (RF) was used to establish classification models. The result of the random forest Boruta (RF-Bo) model and the random forest variable selection (RF-Vs) model based on high-level multi-sensor information fusion strategy was satisfactory. In addition, the RF-Bo model based on high-level multi-sensor information fusion strategy was faster and simpler in data analysis and the accuracy was 95.6%.

Differential diagnosis of hereditary hemolytic anemias in a single multiscreening test by TGA/chemometrics

A multi-screening test based on the coupling of thermogravimetry and chemometrics was optimized for the differential diagnosis of hereditary hemolytic anemias.

Capturing the Geoherbalism Differentiation in Wild Paris polyphylla var. yunnanensis Raw Materials through the Application of Multispectral Information Fusion Combined with Chemometrics

Paris polyphylla var. yunnanensis is a famous medicinal plant distributed in some Asian countries. This species has attracted a great deal of attention and is often used as raw materials in traditional medicine practices. With the purpose of gaining insight into the geoherbalism of wild P. polyphylla var. yunnanensis, a total of 183 dried rhizome samples from eight different regions including 16 typical or nontypical natural habitats have been analyzed by multispectral information fusion based on ultraviolet and Fourier transform infrared spectroscopies combined with partial least squares discriminant analysis (PLS-DA) and hierarchical cluster analysis. From the results, the use of multispectral information fusion strategy could improve the correct classification of samples, and good classification performances have been shown according to PLS-DA models. The discrimination of samples was obtained successfully with respect to the typical and nontypical natural habitats, different collection areas of typical natural habitats, and various sampling sites in nontypical natural habitats. Additionally, the similarities among samples were presented as well. Overall, the rhizome of wild P. polyphylla var. yunnanensis exhibited various regional dependence and individual differences according to the geographical origins, and the relatively appropriate growth region with better quality consistency of samples was preliminarily selected. This study also revealed that the developed multispectral information fusion method has the potential to be a reliable analytical methodology for capturing the geoherbalism differentiation in wild P. polyphylla var. yunnanensis. Furthermore, it could provide more chemical evidence for the critical supplement of quality evaluation on P. polyphylla var. yunnanensis.

Attenuated Total Reflection-Fourier Transform Infrared Spectroscopy (ATR-FTIR) Combined with Chemometrics Methods for the Classification of Lingzhi Species

Due to the existence of Lingzhi adulteration, there is a growing demand for species classification of medicinal mushrooms by various techniques. The objective of this study was to explore a rapid and reliable way to distinguish between different Lingzhi species and compare the influence of data pretreatment methods on the recognition results. To this end, 120 fresh fruiting bodies of Lingzhi were collected, and all of them were analyzed by attenuated total reflection-Fourier transform infrared spectroscopy (ATR-FTIR). Random forest (RF), support vector machine (SVM) and partial least squares discriminant analysis (PLS-DA) classification models were established for raw and pretreated second derivative (SD) spectral matrices to authenticate different Lingzhi species. The results of multivariate statistical analysis indicated that the SD preprocessing method displayed a higher classification ability, which may be attributed to the analysis of powder samples that requires removal of overlapping peaks and baseline shifts. Compared with RF, the results of the SVM and PLS-DA methods were more satisfying, and their accuracies for the test set were both 100%. Among SVM and PLS-DA, the training set and test set accuracy of PLS-DA were both 100%. In conclusion, ATR-FTIR spectroscopy data pretreated by SD combined with PLS-DA is a simple, rapid, non-destructive and relatively inexpensive method to discriminate between mushroom species and provide a good reference to quality assessment.

Original plant traceability of Dendrobium species using multi-spectroscopy fusion and mathematical models

Dendrobium is the largest genus of orchids most of which have excellent medicinal properties. Fresh stems of some species have been consumed in daily life by Asians for thousands of years. However, there are differences in flavour and clinical efficacy among different species. Therefore, it is necessary for a detector to establish an effective and rapid method controlling botanical origins of these crude materials. In our study, three spectroscopies including mid-infrared (MIR) (transmission and reflection mode) and near-infrared (NIR) spectra were investigated for authentication of 12 Dendrobium species. Generally, two fusion strategies, reflection MIR and NIR spectra, were combined with three mathematical models (random forest, support vector machine with grid search (SVM-GS) and partial least-squares discrimination analysis (PLS-DA)) for discrimination analysis. In conclusion, a low-level fusion strategy comprising two spectra after pretreated by the second derivative and multiplicative scatter correction was recommended for discrimination analysis because of its excellent performance in three models. Compared with MIR spectra, NIR spectra were more responsible for the discrimination according to a bi-plot analysis of PLS-DA. Moreover, SVM-GS and PLS-DA were suitable for accurate discrimination (100% accuracy rates) of calibration and validation sets. The protocol combined with low-level fusion strategy and chemometrics provides a rapid and effective reference for control of botanical origins in crude Dendrobium materials.

Comparing the qualitative performances of handheld NIR and Raman spectrophotometers for the detection of falsified pharmaceutical products

Over the last decade, the growth of the global pharmaceutical market has led to an overall increase of substandard and falsified drugs especially on the African market (or emerging countries). Recently, several methods using handheld/portable vibrational spectroscopy have been developed for rapid and on-field drug analysis. The objective of this work was to evaluate the performances of various NIR and Raman handheld spectrophotometers in specific brand identification of medicines through their primary packaging. Three groups of drug samples (artemether-lumefantrine, paracetamol and ibuprofen) were used in tablet or capsule forms. In order to perform a critical comparison, the analytical performances of the two analytical systems were compared statistically using three methods: hierarchical clustering algorithm (HCA), data-driven soft independent modelling of class analogy (DD-SIMCA) and hit quality index (HQI). The overall results show good detection abilities for NIR systems compared to Raman systems based on Matthews's correlation coefficients, generally close to one. Raman systems are less sensitive to the physical state of the samples than the NIR systems, it also suffers of the auto-fluorescence phenomenon and the signal of highly dosed active pharmaceutical ingredient (e.g. paracetamol or lumefantrine) may mask the signal of low-dosed and weaker Raman active compounds (e.g. artemether). Hence, Raman systems are less effective for specific product identification purposes but are interesting in the context of falsification because they allow a visual interpretation of the spectral signature (presence or absence of API).

Random forest as one-class classifier and infrared spectroscopy for food adulteration detection

This paper proposes the use of random forest for adulteration detection purposes, combining the random forest algorithm with the artificial generation of outliers from the authentic samples. This proposal was applied in two food adulteration studies: evening primrose oils using ATR-FTIR spectroscopy and ground nutmeg using NIR diffuse reflectance spectroscopy. The primrose oil was adulterated with soybean, corn and sunflower oils, and the model was validated using these adulterated oils and other different oils, such as rosehip and andiroba, in pure and adulterated forms. The ground nutmeg was adulterated with cumin, commercial monosodium glutamate, soil, roasted coffee husks and wood sawdust. For the primrose oil, the proposed method presented superior performance than PLS-DA and similar performance to SIMCA and for the ground nutmeg, the random forest was superior to PLS-DA and SIMCA. Also, in both applications using the random forest, no sample was excluded from the external validation set.

Geographical Authentication of Macrohyporia cocos by a Data Fusion Method Combining Ultra-Fast Liquid Chromatography and Fourier Transform Infrared Spectroscopy

Macrohyporia cocos is a medicinal and edible fungi, which is consumed widely. The epidermis and inner part of its sclerotium are used separately. M. cocos quality is influenced by geographical origins, so an effective and accurate geographical authentication method is required. Liquid chromatograms at 242 nm and 210 nm (LC₂₄₂ and LC₂₁₀) and Fourier transform infrared (FTIR) spectra of two parts were applied to authenticate the geographical origin of cultivated M. cocos combined with low and mid-level data fusion strategies, and partial least squares discriminant analysis. Data pretreatment involved correlation optimized warping and second derivative. The results showed that the potential of the chromatographic fingerprint was greater than that of five triterpene acids contents. LC₂₄₂-FTIR low-level fusion took full advantage of information synergy and showed good performance. Further, the predictive ability of the FTIR low-level fusion model of two parts was satisfactory. The performance of the low-level fusion strategy preceded those of the single technique and mid-level fusion strategy. The inner parts were more suitable for origin identification than the epidermis. This study proved the feasibility of the data fusion of chromatograms and spectra, and the data fusion of different parts for the accurate authentication of geographical origin. This method is meaningful for the quality control of food and the protection of geographical indication products.

Meningococcal polysaccharides identification by NIR spectroscopy and chemometrics

Near-infrared (NIR) spectroscopy is an attractive tool for pharmaceutical analyses. The main purpose of this study was to assess the potential of NIR spectroscopy coupled with different multivariate classification tools for the identification of meningococcal polysaccharide serogroups A and C. Moreover, it sought to determine, if the models established on production batches, could be used to correctly identify National Institute for Biological Standards and Control standards. Two different classification tools were investigated: soft independent modeling of class analogy (SIMCA) and partial least squares discriminant analysis (PLS-DA). Models' performance was evaluated by external validation. Although both models were able to correctly classify 100% of meningococcal polysaccharides from serogroups A and C, they performed differently in the presence of similar non-target serogroups W135 and Y. These results demonstrate that NIR spectroscopy, coupled with either SIMCA or PLS-DA, provides a method suitable for the identification of meningococcal polysaccharides A and C.

Evaluation of a low-cost portable near-infrared spectrophotometer for in situ cocaine profiling

The potential of a low-cost (∼US$ 1000) portable near-infrared (NIR) spectrophotometer for in situ characterization of seized cocaine samples was evaluated. A set of 240 samples of cocaine seized in several regions and cities across Brazil by its federal police was employed in this study. These samples were previously analyzed by chromatography at the Forensic Chemistry Laboratory of the National Institute of Criminalistics in Brasília-DF for the contents of several constituents to chemically characterize the samples. A low-cost NanoNIR spectrophotometer (Texas instruments) was used to acquire the NIR spectra of the samples in the range 900-1700 nm. The spectra set was treated by the second derivative to construct and validate multivariate regression (Partial Least Square - PLS) and classification (software independent modeling of class analogy - SIMCA) models aiming to characterize the samples. Consequently, an informative toll for objective decision making could be used by the police agents to produce immediate answers to forensic questions raised at the point of seizing. Among those questions the most relevant are: does the seized sample contain cocaine? what is the cocaine form? what is its content? is the sample adulterated and/or diluted? what is the content of adulterant? is the sample significantly adulterated and/or diluted? what is the degree of oxidation of the cocaine? The results of this work allow to propose a NIR/chemometrics based analytical protocol providing fast answers to these questions with satisfactory confidence level for the purpose of reliably screen the seized samples.

Alternative data mining/machine learning methods for the analytical evaluation of food quality and authenticity - A review

In recent years, the variety and volume of data acquired by modern analytical instruments in order to conduct a better authentication of food has dramatically increased. Several pattern recognition tools have been developed to deal with the large volume and complexity of available trial data. The most widely used methods are principal component analysis (PCA), partial least squares-discriminant analysis (PLS-DA), soft independent modelling by class analogy (SIMCA), k-nearest neighbours (kNN), parallel factor analysis (PARAFAC), and multivariate curve resolution-alternating least squares (MCR-ALS). Nevertheless, there are alternative data treatment methods, such as support vector machine (SVM), classification and regression tree (CART) and random forest (RF), that show a great potential and more advantages compared to conventional ones. In this paper, we explain the background of these methods and review and discuss the reported studies in which these three methods have been applied in the area of food quality and authenticity. In addition, we clarify the technical terminology used in this particular area of research.

Non-destructive identification of native egg by near-infrared spectroscopy and data driven-based class-modeling

Eggs are very important parts of human diets worldwide. It is very common to pass feed eggs off as native ones of high commercial values in Chinese markets. One urgent and challenging work is to develop a non-destructive method for verifying the authenticity of native eggs. The present work focuses on exploring the feasibility of combining near-infrared (NIR) spectroscopy with data driven-based class-modeling (DDCM) and model-independent variable selection, i.e., joint mutual information (JMI). A total of 122 eggs of three types were collected. Principal component analysis (PCA) was utilized for exploratory analysis. The JMI algorithm selected only 20 informative variables out of 1557 original variables for class-modeling. DDCM constructed a class-model for each kind of eggs by optimizing parameters such as degrees of freedom (DoF) and the number of principal components (NPC). All class-models and the decision rules were validated on the corresponding test sets. In short, these models achieved an acceptable performance and are also more consistent with actual needs than classification models. The results show that NIR spectroscopy combined with class-modeling is a potential tool for detecting the authenticity of a specific kind of native eggs.

Comprehensive quality assessment of Dendrubium officinale using ATR-FTIR spectroscopy combined with random forest and support vector machine regression

Dendrobium officinale, as a tonic herb, has attracted more and more consumers to consume in daily life. In order to protect the wild resource, the herb has made great progress though cultivation in vitro. However, the quality is fluctuated in Chinese herbal medicine market due to influence such as cultivated areas and harvesting period. Therefore, the herbal samples from different cultivated locations were evaluated with high-performance liquid chromatography with diode array detector (HPLC-DAD) in terms of two chemical components, quercetin and erianin. In addition, two markers in leaf and stem also were used for support vector machine regression (SVMR) prediction. Samples from different harvesting periods were also classified using attenuated total reflectance mid-infrared spectroscopy coupled with random forest model. The results indicated that Pu'er and Menghai in Yunnan Province were suitable places for the herb cultivation and the leaf of the herb was also an exploitable resource just in light of the content of two components. What's more, combination of suitable spectra pretreatment and grid search method efficiently improved the prediction performance of the regression model. The results of random forest model indicated that important variables combination between stem and leaf was an effective tool to predict the harvesting time of the herb with 94.44% accuracy in calibration set and 97.92% classification correct rate in validation set. The results of combination were better than the models using individual stem and leaf spectra. In addition, the suitable harvesting time (December) could be classified efficiently. Our study provides a reference for quality control of raw materials from D. officinale herb.

Utility of neurological smears for intrasurgical brain cancer diagnostics and tumour cell percentage by DESI-MS

Analysis of neurological smears by desorption electrospray ionization mass spectrometry (DESI-MS) is an emerging diagnostic strategy for intraoperative consultation in brain tumor resection. DESI-MS allows rapid sampling while providing accurate diagnostic information. We assess the chemical homogeneity of neurological smears using DESI-MS imaging and the quality of rapid DESI-MS diagnosis.