Volatile Organic Compound Profile Fingerprints Using DART-MS Shows Species-Specific Patterns in Fusarium Mycotoxin Producing Fungi

Volatile fingerprints of beef cooking methods using sol-gel-based solid-phase microextraction (SPME) and direct analysis in real-time mass spectrometry (DART-MS)

RATIONALE

The aroma profile of food is a complex mixture of volatile compounds that constitutes a major component of the overall eating experience. The food service industry and chefs therefore constantly seek ways to investigate and thereby enhance the aroma profile. Oven cooking, sous vide and pan fry are three cooking methods of beef commonly practised by chefs. Near real-time analysis of volatile compounds from these three cooking methods will provide insight into respective volatile fingerprints and help improve cooking techniques.

METHODS

Volatile compounds from three beef cooking methods were captured using an in-house sol-gel based solid phase microextraction (SPME) method and analysed using direct analysis in real-time mass spectrometry (DART-MS). A volatile organic compound (VOC) standard was used to demonstrate successful implementation of the sol-gel coating technique. Volatile features discriminating the three cooking methods were shortlisted and statistically assessed by univariate and multivariate analyses.

RESULTS

The VOC standard was successfully adsorbed by the sol-gel method and detected by DART-MS. Hierarchical cluster analysis clearly demarcated three beef cooking methods based on their volatile fingerprints. Out of 65 significant features differentiating the cooking methods, 50 were at highest concentrations from pan-fry cooking only, followed by 14 with highest concentrations from oven cooking followed by pan frying. Sous vide followed by pan frying showed lowest concentrations of almost all volatile features.

CONCLUSIONS

The sol-gel-based solid-phase microextraction technique combined with DART-MS was successful in differentiating beef cooking methods based on their volatile fingerprints. A workflow for rapid assessment of the volatile profile from beef cooking methods was established, providing a baseline to further explore volatile profiles from other key ingredients.

An easy adjustment of instrument settings ('Peak MALDI') improves identification of organisms by MALDI-ToF mass spectrometry

Matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-ToF MS) is a mature technolaogy with 'auto-execute' instrument settings and peak processing parameters tailored for rapid bacterial identification. Adoption for other organisms has been problematic, with optimisation efforts focusing on sample preparation. Using the Bruker MALDI Biotyper, we demonstrate 'Peak MALDI': easily-applied settings that immediately enhance sensitivity, improve spectrum quality, and increase identification confidence for any target, establishing its potential value for all MALDI-ToF MS systems.

E-Nose Technology for Mycotoxin Detection in Feed: Ready for a Real Context in Field Application or Still an Emerging Technology?

Mycotoxin risk in the feed supply chain poses a concern to animal and human health, economy, and international trade of agri-food commodities. Mycotoxin contamination in feed and food is unavoidable and unpredictable. Therefore, monitoring and control are the critical points. Effective and rapid methods for mycotoxin detection, at the levels set by the regulations, are needed for an efficient mycotoxin management. This review provides an overview of the use of the electronic nose (e-nose) as an effective tool for rapid mycotoxin detection and management of the mycotoxin risk at feed business level. E-nose has a high discrimination accuracy between non-contaminated and single-mycotoxin-contaminated grain. However, the predictive accuracy of e-nose is still limited and unsuitable for in-field application, where mycotoxin co-contamination occurs. Further research needs to be focused on the sensor materials, data analysis, pattern recognition systems, and a better understanding of the needs of the feed industry for a safety and quality management of the feed supply chain. A universal e-nose for mycotoxin detection is not realistic; a unique e-nose must be designed for each specific application. Robust and suitable e-nose method and advancements in signal processing algorithms must be validated for specific needs.

Volatile Organic Compound (VOC) Profiles of Different Trichoderma Species and Their Potential Application

Fungi emit a broad spectrum of volatile organic compounds (VOCs), sometimes producing species-specific volatile profiles. Volatilomes have received over the last decade increasing attention in ecological, environmental and agricultural studies due to their potential to be used in the biocontrol of plant pathogens and pests and as plant growth-promoting factors. In the present study, we characterised and compared the volatilomes from four different Trichoderma species: T. asperellum B6; T. atroviride P1; T. afroharzianum T22; and T. longibrachiatum MK1. VOCs were collected from each strain grown both on PDA and in soil and analysed using proton transfer reaction quadrupole interface time-of-flight mass spectrometry (PTR-Qi-TOF-MS). Analysis of the detected volatiles highlighted a clear separation of the volatilomes of all the four species grown on PDA whereas the volatilomes of the soil-grown fungi could be only partially separated. Moreover, a limited number of species-specific peaks were found and putatively identified. In particular, each of the four Trichoderma species over-emitted somevolatiles involved in resistance induction, promotion of plant seed germination and seedling development and antimicrobial activity, as 2-pentyl-furan, 6PP, acetophenone and p-cymene by T. asperellum B6, T. atroviride P1, T. afroharzianum T22 and T. longibrachiatum MK1, respectively. Their potential role in interspecific interactions from the perspective of biological control is briefly discussed.

Machine Learning Approaches to Identify Discriminative Signatures of Volatile Organic Compounds (VOCs) from Bacteria and Fungi Using SPME-DART-MS

Point-of-care screening tools are essential to expedite patient care and decrease reliance on slow diagnostic tools (e.g., microbial cultures) to identify pathogens and their associated antibiotic resistance. Analysis of volatile organic compounds (VOC) emitted from biological media has seen increased attention in recent years as a potential non-invasive diagnostic procedure. This work explores the use of solid phase micro-extraction (SPME) and ambient plasma ionization mass spectrometry (MS) to rapidly acquire VOC signatures of bacteria and fungi. The MS spectrum of each pathogen goes through a preprocessing and feature extraction pipeline. Various supervised and unsupervised machine learning (ML) classification algorithms are trained and evaluated on the extracted feature set. These are able to classify the type of pathogen as bacteria or fungi with high accuracy, while marked progress is also made in identifying specific strains of bacteria. This study presents a new approach for the identification of pathogens from VOC signatures collected using SPME and ambient ionization MS by training classifiers on just a few samples of data. This ambient plasma ionization and ML approach is robust, rapid, precise, and can potentially be used as a non-invasive clinical diagnostic tool for point-of-care applications.