Application of microdialysis combined with UHPLC-QTOF/MS to screen for endogenous metabolites in aquatic organisms as biomarkers of exposure to an emerging contaminant, triclosan.

The detection of emerging contaminants (ECs) and understanding their ecotoxicity has brought new challenges to water pollution control. Triclosan (TCS), as an emerging contaminant, is a commonly used antibacterial agent widely present in the environment. Microdialysis (MD), as a sampling technique, can overcome some of the deficiencies of traditional approaches to sampling, using sources such as blood, urine, tissue, and target organs, in terms of invasiveness, time from collection to analysis, and possible changes during sample preparation. In this study, we coupled MD with analysis using UHPLC-QTOF/MS to identify the endogenous metabolites in the liver as biomarkers of the exposure of living crucian carp to TCS. The identified biomarkers were then quantified using UHPLC-MS/MS to continuously monitor the effect of TCS on endogenous metabolites in the liver of living crucian carp, which contributes to a better understanding of the toxicological effect of TCS. The experimental results demonstrated that TCS exposure interfered with the metabolic pathways of amino acids (L-isoleucine and L-histidine), purines (xanthine and hypoxanthine), and small nerve molecules (acetylcholine and choline).

Atmospheric solids analysis probe-mass spectrometry (ASAP-MS) as a rapid fingerprinting technique to differentiate the harvest seasons of Tieguanyin oolong teas.

Atmospheric solids analysis probe-mass spectrometry (ASAP-MS), an ambient mass spectrometry technique, was used to differentiate spring and autumn Tieguanyin teas. Two configurations were used to obtain their chemical fingerprints - ASAP attached to a high-resolution quadrupole time-of-flight mass spectrometer (i.e., ASAP-QTOF) and to a single-quadrupole mass spectrometer (i.e., Radian™ ASAP™ mass spectrometer). Then, orthogonal projections to latent structures-discriminant analysis was conducted to identify features that held promise in differentiating harvest seasons. Four machine learning models - decision tree, linear discriminant analysis, support vector machine, and k-nearest neighbour - were built using these features, and high classification accuracy of up to 100% was achieved. The markers were putatively identified using their accurate masses and MS/MS fragmentation patterns from ASAP-QTOF. This approach was successfully transferred to the Radian ASAP MS, which is more deployable in the field. Overall, this study demonstrated the potential of ASAP-MS as a rapid fingerprinting tool for differentiating spring and autumn Tieguanyin.

New Insights into Cheese Microstructure.

Microscopy is often used to assist the development of cheese products, but manufacturers can benefit from a much broader application of these techniques to assess structure formation during processing and structural changes during storage. Microscopy can be used to benchmark processes, optimize process variables, and identify critical control points for process control. Microscopy can also assist the reverse engineering of desired product properties and help troubleshoot production problems to improve cheese quality. This approach can be extended using quantitative analysis, which enables further comparisons between structural features and functional measures used within industry, such as cheese meltability, shreddability, and stretchability, potentially allowing prediction and control of these properties. This review covers advances in the analysis of cheese microstructure, including new techniques, and outlines how these can be applied to understand and improve cheese manufacture.

The effect of pH on the fat and protein within cream cheese and their influence on textural and rheological properties.

The effect of variation in acid gel pH during cream cheese production was investigated. The gel microstructure was denser and cheese texture firmer, as the pH decreased from pH 5.0 to pH 4.3, despite the viscoelasticity of these gels remaining similar during heating. Protein hydration and secondary structure appeared to be key factors affecting both cheese microstructure and properties. Proteins within the matrix appeared to swell at pH 5.0, leading to a larger corpuscular structure; greater ß-turn structure was also observed by synchrotron-Fourier transform infrared (S-FTIR) microspectroscopy and the cheese was softer. A decrease in pH led to a denser microstructure with increased aggregated ß-sheet structure and a firmer cheese. The higher whey protein loss at low pH likely contributed to increased cheese hardness. In summary, controlling the pH of acid gel is important, as this parameter affects proteins in the cheese, their secondary structure and the resulting cream cheese.