Metabolomic profiling of food matrices: Preliminary identification of potential markers of microbial contamination

Surveillance of pathogenic bacteria on a food matrix using machine-learning-enabled paper chromogenic arrays

Global food systems can benefit significantly from continuous monitoring of microbial food safety, a task for which tedious operations, destructive sampling, and the inability to monitor multiple pathogens remain challenging. This study reports significant improvements to a paper chromogenic array sensor - machine learning (PCA-ML) methodology sensing concentrations of volatile organic compounds (VOCs) emitted on a species-specific basis by pathogens by streamlining dye selection, sensor fabrication, database construction, and machine learning and validation. This approach enables noncontact, time-dependent, simultaneous monitoring of multiple pathogens (Listeria monocytogenes, Salmonella, and E. coli O157:H7) at levels as low as 1 log CFU/g with over 90% accuracy. The report provides theoretical and practical frameworks demonstrating that chromogenic response, including limits of detection, depends on time integrals of VOC concentrations. The paper also discusses the potential for implementing PCA-ML in the food supply chain for different food matrices and pathogens, with species- and strain-specific identification.

Identification and biotransformation analysis of volatile markers during the early stage of Salmonella contamination in chicken

Salmonella is one of the most prevalent foodborne pathogens in poultry and its products. Its rapid detection based on volatile organic compounds (VOC) has been widely accepted. However, the variation in the VOCs of Salmonella-contaminated chicken during the early stage (48 h) remains uncertain. Headspace-SPME-gas chromatography-mass spectrometry (HS-SPME-GC-MS) and headspace-gas chromatography-ion migration spectroscopy (HS-GC-IMS) were used to identify VOCs and their variations after the chicken meat was contaminated with Salmonella. Chemometric and KEGG enrichment analyses were performed to identify VOC markers and their potential metabolic pathways. A total of 64 volatile compounds were detected using HS-GC-IMS, which showed a better differentiation than HS-SPME-GC-MS (45 volatile compounds) based on principal component analysis (PCA) and orthogonal partial least squares-discriminant analysis (OPLS-DA). Fatty acid degradation was the main cause of VOC variation. 2-Propanol, hexadecane, 3-methylbutanol, acetic acid, propyl acetate, acetic acid methyl ester, and 3-butenenitrile were identified as VOC markers in the middle stage of decomposition, and 1-octen-3-ol was recognized as a VOC marker of Salmonella-contaminated chicken during the first 48 h of contamination. This provides a theoretical basis for the study of Salmonella contamination VOC markers in poultry meat.

Identification of Volatile Markers during Early Zygosaccharomyces rouxii Contamination in Mature and Immature Jujube Honey

Osmotolerant yeasts are considered one of the major contaminants responsible for spoilage in honey. To address the signature volatile components of jujube honey contaminated by Zygosaccharomyces rouxii, headspace solid-phase microextraction-gas chromatography-mass spectrometry (HS-SPME-GC-MS) and chemometrics analyses were used to analyze the variation of volatile substances during early contamination of mature and immature jujube honey. Undecanal, methyl butyrate, methyl 2-nonenoate, methyl hexanoate, and 2-methyl-3-pentanone were identified as signature volatiles of jujube honey contaminated with Z. rouxii. In addition, methyl heptanoate, 2,6,10-trimethyltetradecane, and heptanal were identified as potential volatile signatures for immature jujube honey. The R² and Q² of OPLS-DA analyses ranged from 0.736 to 0.955, and 0.991 to 0.997, which indicates that the constructed model was stable and predictive. This study has demonstrated that HS-SPME-GC-MS could be used to distinguish Z. rouxii-contaminated jujube honey from uncontaminated honey based on variation in VOCs, and could provide theoretical support for the use of HS-SPME-GC-MS for the rapid detection of honey decomposition caused by Z. rouxii, which could improve nutritional quality and reduce economic losses.

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.

The Potential Effect of Microbiota in Predicting The Freshness of Chilled Chicken

1. The goals of this study were to analyse the changes in microbiota composition of chilled chicken during storage and identify microbial biomarkers related to meat freshness.2. The study used 16S rDNA sequencing to track the microbiota shift in chilled chicken during storage. Associations between microbiota composition and storage time were analysed and microbial biomarkers were identified.3. The results showed that microbial diversity of chilled chicken decreased with the storage time. A total of 27 and 24 microbial biomarkers were identified by using orthogonal partial least squares (OPLS) and the random forest regression approach, respectively. The receiver operating characteristic (ROC) curve analysis indicated that the OPLS regression approach had better performance in identifying freshness-related biomarkers. The multiple stepwise regression analysis identified four key microbial biomarkers, including Streptococcus, Carnobacterium, Serratia and Photobacterium genera and constructed a predictive model.4. The study provided microbial biomarkers and a model related to the freshness of chilled chicken. These findings provide a basis for developing detection methods of the freshness of chilled chicken.

Detection of Volatiles from Raw Beef Meat from Different Packaging Systems Using Solid-Phase Microextraction GC-Accurate Mass Spectrometry

The volatile profile of raw beef contains vital information related to meat quality and freshness. This qualitative study examines the effect of packaging system on the formation and release of volatile organic compounds (VOCs) from raw beef over time, relative to the packaging best before date (BBD). The three packaging systems investigated were modified atmospheric packaging, vacuum packaging, and cling-wrapped packaging. Porterhouse steak samples with the same BBD were analysed from 3 days before to 3 days after the BBD. VOCs were detected via preconcentration using solid-phase microextraction combined with gas chromatography–accurate mass quadrupole time-of-flight mass spectrometry. In total, 35 different VOCs were tentatively identified. Interestingly, there was no clear relationship of the VOCs detected between the three packaging systems, with only carbon disulphide and acetoin, both known volatiles of beef, detected in all three. This is the first study to investigate the effects of commercial packaging systems on VOC formation; it provides an understanding of the relationship of VOCs to the BBD that is essential for the development of on-pack freshness and quality sensors.

Lanthanide ion (Ln3+ )-based upconversion sensor for quantification of food contaminants: A review

The food safety issue has gradually become the focus of attention in modern society. The presence of food contaminants poses a threat to human health and there are a number of interesting researches on the detection of food contaminants. Upconversion nanoparticles (UCNPs) are superior to other fluorescence materials, considering the benefits of large anti-Stokes shifts, high chemical stability, non-autofluorescence, good light penetration ability, and low toxicity. These properties render UCNPs promising candidates as luminescent labels in biodetection, which provides opportunities as a sensitive, accurate, and rapid detection method. This paper intended to review the research progress of food contaminants detection by UCNPs-based sensors. We have proposed the key criteria for UCNPs in the detection of food contaminants. Additionally, it highlighted the construction process of the UCNPs-based sensors, which includes the synthesis and modification of UCNPs, selection of the recognition elements, and consideration of the detection principle. Moreover, six kinds of food contaminants detected by UCNPs technology in the past 5 years have been summarized and discussed fairly. Last but not least, it is outlined that UCNPs have great potential to be applied in food safety detection and threw new insight into the challenges ahead.