Comprehensive Gas Chromatography: Food and Metabolomocs Applications

Rapid detection of Escherichia coli in dairy milk using static headspace-comprehensive two-dimensional gas chromatography

A new approach is introduced for rapid and reliable bacteria detection in food. Namely, static headspace-comprehensive two-dimensional gas chromatography (HS-GC × GC) with backflushing. The introduced approach provides fast detection of Escherichia coli (E. coli) in enriched ultra-high-temperature processed (UHT) dairy milk. The presence of E. coli may be indicated by detecting microbial volatile organic compounds emanating from test solutions inoculated with E. coli. In the present investigation, HS-GC × GC analysis is preceded by conventional enrichment in nutrient broth and inoculated samples are clearly discernable from controls following as little as 15 h sample enrichment. Headspace equilibration for 28 min followed by an 8 min GC × GC analysis of enriched test solutions reduces time-to-response by approximately one full day compared to conventional culture-based methods. The presence of ethanol, 1-propanol, and acetaldehyde may be used as a putative marker of E. coli contamination in milk and the introduced approach is able to detect single-cell initial bacterial load. Faster, reliable detection of pathogens and/or spoilage microbes in food products is desirable for the food industry. The described approach has great potential to complement the conventional workflow and be utilised for rapid microbial screening of foodstuff.

NMR-based metabolomics of dried berries in comparison with dietary supplements

Consumption of dried berries is increasing worldwide due to their health benefits. This popularity has introduced berry-based supplements as an easier way to take in berry nutrients. The chemical composition of six dried berries (blueberry, cranberry, goji berry, golden berry, maqui berry, and raspberry) were compared to their berry-based supplements by metabolomics using nuclear magnetic resonance spectroscopy (NMR). Thirty-three metabolites were identified and 23 were quantified. Chemometric analysis of berries revealed that goji berry showed the highest content of amino and organic acids, while cranberry and golden berry showed a high carbohydrate content. Fatty acids were predominant in blueberry, golden berry, maqui berry, and raspberry. Additionally, an exploratory analysis of phenolic compounds in berry extracts were conducted. phenolic compounds in berry extracts could be correlated with their antioxidant activity. Additionally, derived supplements did not show similarities with their respective berry, suggesting the minimal addition of dried berry in their formulation. Thus, non-declared additives have highlighted the importance of food safety investigation.

Volatile compounds associated with growth of Asaia bogorensis and Asaia lannensis-unusual spoilage bacteria of functional beverages

Acetic acid bacteria of the genus Asaia are recognized as common bacterial spoilage in the beverage industry. Their growth in contaminated soft drinks can be visible in the form of flocs, turbidity and flavor changes. Volatile profiles associated with the growth and metabolic activities of Asaia lannensis and As. bogorensis strains were evaluated using comprehensive gas chromatography-time of flight mass spectrometry (GC × GC-ToF MS). Based on obtained results, 33 main compounds were identified. The greatest variety of volatile metabolites was noted for As. lannensis strain W4. 2-Phenylethanol, 3-pentanone, 2-nonanol, 2-hydroxy-3-pentanone, and 2-nitro-1-butanol were detected as dominant volatile compounds. Additionally, As. lannensis strains formed 2-propenoic acid ethyl ester. As. bogorensis ISD1 was distinguished by the higher concentration of 2-hydroxy-3-pentanone and 3-methyl-1-butene but the lowest concentration of 2-phenylethanol. Based on these results, it was found that volatile profiles of Asaia spp. are unique among acetic acid bacteria. Moreover, obtained profiles depended not only on bacterial species and strains but also on the composition of culture media.

Nontargeted Detection Methods for Food Safety and Integrity

Nontargeted workflows for chemical hazard analyses are highly desirable in the food safety and integrity fields to ensure human health. Two different analytical strategies, nontargeted metabolomics and chemical database filtering, can be used to screen unknown contaminants in food matrices. Sufficient mass and chromatographic resolutions are necessary for the detection of compounds and subsequent componentization and interpretation of candidate ions. Analytical chemistry–based technologies, including gas chromatography–mass spectrometry (GC-MS), liquid chromatography–mass spectrometry (LC-MS), nuclear magnetic resonance (NMR), and capillary electrophoresis–mass spectrometry (CE-MS), combined with chemometrics analysis are being used to generate molecular formulas of compounds of interest. The construction of a chemical database plays a crucial role in nontargeted detection. This review provides an overview of the current sample preparation, analytical chemistry–based techniques, and data analysis as well as the limitations and challenges of nontargeted detection methods for analyzing complex food matrices. Improvements in sample preparation and analytical platforms may enhance the relevance of food authenticity, quality, and safety.

Miniaturized array gas membrane separation strategy for rapid analysis of complex samples by surface-enhanced Raman scattering

It remains a significant challenge for fast and high-throughput detection of trace analytes in complex samples with surface-enhanced Raman scattering (SERS) strategy due to the severe interference from matrices. In this work, a miniaturized array gas membrane separation (AGMS) device coupled with SERS was designed and drew up to eliminate matrix influence and improve the reproducibility of SERS signal during real sample analysis. The design of miniaturized AGMS tube was optimized based on quantitative calculation of its air permeability by computational fluid dynamics simulation. A 10 mm height tube was selected as an optimized design with a recovery of 98.3% for acetaldehyde. The practical feasibility of miniaturized AGMS was validated based on the applications in biochemical analysis and food analysis, such as albuminuria and acetaldehyde in urine sample and metaldehyde and thiram in food samples. The results showed that SERS responses of all analytes dramatically increased by eliminating sample matrices after miniaturized AGMS process. Acetaldehyde, albuminuria, metaldehyde and thiram in real samples could be accurately quantified with recoveries of 82.0-123.3%, and the analytical results were validated by corresponding standard methods with relative error ranging from -4.8% to 5.3%. Time consumption of miniaturized AGMS-SERS for one real sample analysis including sample preparation and determination was less than 20 min and could treat 96 samples with 45 min in one run. It is potential that the miniaturized AGMS technique automated by implementation with a robotic arm could greatly expand the range and accelerate the speed of SERS analysis.