Structural characterization and antioxidant activity of polysaccharides extracted from Porphyra haitanensis by different methods.

This study was to investigate the structure and antioxidant activity of Porphyra haitanensis polysaccharides (PHPs) extracted by different methods, including water extraction (PHP), ultra-high pressure (UHP-PHP), ultrasonic (US-PHP) and microwave assisted water extraction (M-PHP). Compared with water extraction, the total sugar, sulfate and uronic acid contents of PHPs was enhanced by ultra-high pressure, ultrasonic and microwave assisted treatments, especially those of UHP-PHP were increased by 24.35 %, 12.84 % and 27.51 %, respectively (p < 0.05). Meanwhile, these assisted treatments affected the monosaccharide ratio of polysaccharides and significantly reduced the protein content, molecular weight as well as particle size of PHPs (p < 0.05), and resulted in a loose microstructure with more porosity and fragments. PHP, UHP-PHP, US-PHP, and M-PHP all possessed in vitro antioxidant capacity. Among them, UHP-PHP had the strongest oxygen radical absorbance capacity, DPPH and ·OH radicals scavenging capacities, which increased by 48.46 %, 116.24 %, and 14.98 % respectively. Moreover, PHPs particularly UHP-PHP effectively increased the cell viability and reduced ROS levels of H2O2 induced RAW264.7 cells (p < 0.05), indicating their good effects against cell oxidative damage. The findings suggested that PHPs with ultra-high pressure assisted treatments has the better potential to develop natural antioxidant.

A study of flavor variations during the flaxseed roasting procedure by developed real-time SPME GC-MS coupled with chemometrics.

Volatile compound variations during the roasting procedure play an essential role in the flaxseed-related product. In this work, we proposed a new strategy to high-throughput characterize the dynamic variations of flavors in flaxseed. Volatile compounds released at various roasting times were comprehensively investigated by a newly developed real-time solid-phase microextraction coupled with gas chromatography-mass spectrometry (GC-MS). Raw data files were analyzed by our advanced GC-MS data analysis software AntDAS-GCMS. Chemometric methods such as principal component analysis and partial least squares-discrimination analysis have realized the differences of samples with various roasting times. Finally, a total of 51 compounds from 11 aromas were accurately identified and confirmed with standards, and their variations as a function of roasting time were studied. In conclusion, we provided a new solution for the online monitoring of volatile compounds during the industrial roasting process.

Using UHPLC-HRMS-based comprehensive strategy to efficiently and accurately screen and identify illegal additives in health-care foods.

Accurately and high-thoroughly screening illegal additives in health-care foods continues to be a challenging task in routine analysis for the ultrahigh performance liquid chromatography-high resolution mass spectrometry based techniques. In this work, we proposed a new strategy to identify additives in complex food matrices, which consists of both experimental design and advanced chemometric data analysis. At first, reliable features in the analyzed samples were screened based on a simple but efficient sample weighting design, and those related to illegal additives were screened with robust statistical analysis. After the MS1 in-source fragment ion identification, both MS1 and MS/MS spectra were constructed for each underlying compound, based on which illegal additives can be precisely identified. The performance of the developed strategy was demonstrated by using mixture and synthetic sample datasets, indicating an improvement of data analysis efficiency up to 70.3 %. Finally, the developed strategy was applied for the screening of unknown additives in 21 batches of commercially available health-care foods. Results indicated that at least 80 % of false-positive results can be reduced and 4 additives were screened and confirmed.

A comparison of feature extraction capabilities of advanced UHPLC-HRMS data analysis tools in plant metabolomics.

Data analysis of ultrahigh performance liquid chromatography-high resolution mass spectrometry (UHPLC-HRMS) is an essential and time-consuming step in plant metabolomics and feature extraction is the fundamental step for current tools. Various methods lead to different feature extraction results in practical applications, which may puzzle users for selecting adequate data analysis tools to deal with collected data. In this work, we provide a comprehensive method evaluation for some advanced UHPLC-HRMS data analysis tools in plant metabolomics, including MS-DIAL, XCMS, MZmine, AntDAS, Progenesis QI, and Compound Discoverer. Both mixtures of standards and various complex plant matrices were specifically designed for evaluating the performances of the involved method in analyzing both targeted and untargeted metabolomics. Results indicated that AntDAS provide the most acceptable feature extraction, compound identification, and quantification results in targeted compound analysis. Concerning the complex plant dataset, both MS-DIAL and AntDAS can provide more reliable results than the others. The method comparison is maybe useful for the selection of suitable data analysis tools for users.

A chemometric strategy to automatically screen selected ion monitoring ions for gas chromatography-mass spectrometry-based pseudotargeted metabolomics.

The pseudotargeted metabolomics based on gas chromatography-mass spectrometry (GC-MS) has the advantage of filtering out artifacts originating from sample treatment and accurately quantifying underlying compounds in the analyzed samples. However, this technique faces the problem of selecting high-quality selective ions for performing selected ion monitoring (SIM) on instruments. In this work, we proposed AntDAS-SIMOpt, an automatic untargeted strategy for SIM ion optimization that was accomplished on the basis of an experimental design combined with advanced chemometric algorithms. First, a group of diluted quality control samples was used to screen underlying compounds in samples automatically. Ions in each of the resolved mass spectrum were then evaluated by using the developed algorithms to identify the SIM ion. A Matlab graphical user interface (GUI) was designed to facilitate routine analysis, which can be obtained from http://www.pmdb.org.cn/antdassimopt. The performance of the developed strategy was comprehensively investigated by using standard and complex plant datasets. Results indicated that AntDAS-SIMOpt may be useful for GC-MS-based metabolomics.

Local delivery of antimicrobial peptides using self-organized TiO2 nanotube arrays for peri-implant infections.

Peri-implant infections have been reported as one of the major complications that lead to the failure of orthopedic implants. An ideal solution to the peri-implant infection is to locally deliver antimicrobial agents through the implant surface. The rising problem of infections caused by multiple antibiotic-resistant bacteria makes traditional antibiotics less desirable for the prevention of peri-implant infections. One of the promising alternatives is the family of antimicrobial peptides (AMPs). In this study, we report the local delivery of AMPs through the nanotubular structure processed on titanium surface. Self-organized and vertically oriented TiO2 nanotubes, about 80 nm in diameter and 7 µm thick, were prepared by the anodization technique. HHC-36 (KRWWKWWRR), one of the most potent broad-spectrum AMPs, was loaded onto the TiO2 nanotubes via a simple vacuum-assisted physical adsorption method. Antimicrobial activity testing against Gram-positive bacterium, Staphylococcus aureus, demonstrated that this AMP-loaded nanotubular surface could effectively kill the bacteria (≈ 99.9% killing) and reduce the total bacterial number adhered to the surface after 4 h of culture. In vitro AMP elution from the nanotubes was investigated using liquid chromatography-mass spectrometry (LC-MS). The release profiles strongly depended on the crystallinity of the TiO2 nanotubes. Anatase TiO2 nanotubes released significantly higher amounts of AMP than amorphous nanotubes during the initial burst release stage. Both followed almost the same slow release profile from 4 h up to 7 days. Despite the differences in release kinetics, no significant difference was observed between these two groups in bactericidal efficiency.