Selected-ion flow-tube mass spectrometry for the identification of volatile spoilage markers for fresh pork packaged under modified atmospheres

Contribution of mono- and co-culture of Pseudomonas paralactis, Acinetobacter MN21 and Stenotrophomonas maltophilia to the spoilage of chill-stored lamb

Exploring the contribution of common microorganisms to spoilage is of great significance in inhibiting spoilage in lamb. This work investigated the extent of protein degradation and profile changes of free amino acids (FAAs), free fatty acids (FFAs) and volatile organic compounds (VOCs) in lamb caused by single- and co-culture of the common aerobic spoilage bacteria, P. paralactis, Ac. MN21 and S. maltophilia. Meanwhile, some key VOCs produced by the three bacteria during lamb spoilage were also screened by orthogonal partial least square discriminant analysis and difference value in VOCs content between inoculated groups and sterile group. Lamb inoculated with P. paralactis had the higher total viable counts, pH, total volatile base nitrogen and TCA-soluble peptides than those with the other two bacteria. Some FAAs and FFAs could be uniquely degraded by P. paralactis but not Ac. MN21 and S. maltophilia, such as Arg, Glu, C15:0, C18:0 and C18:1n9t. Co-culture of the three bacteria significantly promoted the overall spoilage, including bacterial growth, proteolysis and lipolysis. Key VOCs produced by P. paralactis were 2, 3-octanedione, those by Ac. MN21 were 1-octanol, octanal, hexanoic acid, 1-pentanol and hexanoic acid methyl ester, and that by S. maltophilia were hexanoic acid. The production of extensive key-VOCs was significantly and negatively correlated with C20:0, C23:0 and C18:ln9t degradation. This study can provide a basis for inhibiting common spoilage bacteria and promoting high-quality processing of fresh lamb.

The exploration of dominant spoilage bacteria in blue mussels (Mytilus edulis) stored under different modified atmospheres by MALDI-TOF MS in combination with 16S rRNA sequencing

Few studies have addressed species-level identification of spoilage bacteria in blue mussels packed under modified atmospheres (MAs). We investigated the effect of MAs and seasons on the tentative species-level of dominant spoilage bacteria in blue mussels. Summer (s) and winter (w) blue mussels were stored at 4 °C in the atmospheres (%CO2/O2/N2): A40s (30/40/30), B60s (40/60/0), C60s (0/60/40), A40w (30/40/30), and D75w (25/75/0). In total, 122 culturable isolates were obtained at the final stage of shelf life, when mortality was high (56-100%) and total psychrotrophic bacteria counted >7 log CFU g-1. Biochemical properties were analyzed using gram reactions, catalase and oxidase activities, and salt tolerance tests. Culturable isolates were identified through matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) and 16 S rRNA gene sequence analysis. Spoilage potential tests were investigated by evaluating protease, lipase, and fermentation activities as well as gas and H2S production. The culturable isolates showed tolerance to varied salt concentrations. Psychromonas arctica, Pseudoalteromonas elyakovii, and Shewanella frigidimarina were dominating in specific MAs. Winter blue mussels resulted in a higher variation of spoilage bacteria, including S. frigidimarina, S. vesiculosa, S. polaris, Micrococcus luteus, Paeniglutamicibacter terrestris sp. nov., and Alteromonas sp.

Rapid and non-destructive microbial quality prediction of fresh pork stored under modified atmospheres by using selected-ion flow-tube mass spectrometry and machine learning

Volatile organic compounds (VOCs) indicative of pork microbial spoilage can be quantified rapidly at trace levels using selected-ion flow-tube mass spectrometry (SIFT-MS). Packaging atmosphere is one of the factors influencing VOC production patterns during storage. On this basis, machine learning would help to process complex volatolomic data and predict pork microbial quality efficiently. This study focused on (1) investigating model generalizability based on different nested cross-validation settings, and (2) comparing the predictive power and feature importance of nine algorithms, including Artificial Neural Network (ANN), k-Nearest Neighbors, Support Vector Regression, Decision Tree, Partial Least Squares Regression, and four ensemble learning models. The datasets used contain 37 VOCs' concentrations (input) and total plate counts (TPC, output) of 350 pork samples with different storage times, including 225 pork loin samples stored under three high-O2 and three low-O2 conditions, and 125 commercially packaged products. An appropriate choice of cross-validation strategies resulted in trustworthy and relevant predictions. When trained on all possible selections of two high-O2 and two low-O2 conditions, ANNs produced satisfactory TPC predictions of unseen test scenarios (one high-O2 condition, one low-O2 condition, and the commercial products). ANN-based bagging outperformed other employed models, when TPC exceeded ca. 6 log CFU/g. VOCs including benzaldehyde, 3-methyl-1-butanol, ethanol and methyl mercaptan were identified with high feature importance. This elaborated case study illustrates great prospects of real-time detection techniques and machine learning in meat quality prediction. Further investigations on handling low VOC levels would enhance the model performance and decision making in commercial meat quality control.

Analysis of Volatile Markers and Their Biotransformation in Raw Chicken during Staphylococcus aureus Early Contamination

To address the potential risks to food safety, headspace solid-phase microextraction coupled with gas chromatography-mass spectrometry (HS-SPME-GC-MS) and headspace gas chromatography-ion mobility spectrometry (HS-GC-IMS) were used to analyze the volatile organic compounds (VOCs) generated from chilled chicken contaminated with Staphylococcus aureus during early storage. Together with the KEGG database, we analyzed differential metabolites and their possible biotransformation pathways. Orthogonal partial least squares discriminant analysis (OPLS-DA) was applied to characterize VOCs and identify biomarkers associated with the early stage of chicken meat contamination with S. aureus. The results showed 2,6,10,15-tetramethylheptadecane, ethyl acetate, hexanal, 2-methylbutanal, butan-2-one, 3-hydroxy-2-butanone, 3-methylbutanal, and cyclohexanone as characteristic biomarkers, and 1-octen-3-ol, tetradecane, 2-hexanol, 3-methyl-1-butanol, and ethyl 2-methylpropanoate as potential characteristic biomarkers. This provides a theoretical basis for the study of biomarkers of Staphylococcus aureus in poultry meat.