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In order to investigate the effect of inoculated fermentation with different Aspergillus strains on the umami taste of Liuyang Douchi, the differences in the umami taste and protein degradation of Liuyang Douchi fermented naturally (NF) or by four dominant Aspergillus flavus strains isolated from Douchi (A. flavus 7214, A. flavus 7622, A. flavus 6112, and A. flavus 5322) alone and their mixture in an equal proportion (M4) were compared. The results showed that the major protease produced by each of the four strains was serine proteases, and A. flavus 5322 had the strongest protease activity, with significantly higher activity of acidic protease than the other three strains. The contents of amino acid nitrogen and water-soluble protein in Aspergillus fermented Douchi were higher than those in NF Douchi after the end of pile-fermentation stage. Sodium dodecyl sulfate-polyacrylamide gel electrophoresis (SDS-PAGE) analysis showed that the degradation trends of six proteins in Douchi were consistent, all of which exhibited rapid degradation during the pile-fermentation process. Additionally, the results of sensory evaluation and electronic tongue analysis showed that Douchi fermented by A. flavus 5322 showed the strongest umami taste, which significantly increased when compared with NF. Using ultra-high performance liquid chromatography-electrospray/quadrupole time-of-flight tandem mass spectrometry (UPLC-ESI-Q-TOF-MS/MS) combined with correlation and peptidomics analysis, a total of 43 potential umami peptides were identified. The total peak area of potential umami peptides in Douchi fermented by A. flavus 6112 was the highest, followed by A. flavus 5322 with little difference, significantly greater than that in NF Douchi. In summary, inoculated fermentation, especially with A. flavus 5322, could promote protein degradation and the release of potential umami peptides, thereby enhancing the umami taste of Liuyang Douchi. This study will provide theoretical guidance for improving the umami taste of Liuyang Douchi and development of industrial production strains.

Food safety incidents caused by pesticide residues are threatening the life and health of people. At present, pesticide residues in food samples are miscellaneous, low and unknown to testing personnel and thus difficult to detect. Therefore, it is of great significance to develop high-throughput and non-targeted for the detection of pesticide residues in food samples. Chromatography-mass spectrometry has been widely used in the field of pesticide residue detection because of its advantages of high separation and identification efficiency as well as high-throughput and non-targeted nature. This paper summarizes the literature on high-throughput and non-targeted detection of pesticide residues in food samples by chromatography-mass spectrometry in the past 10 years. The current status of multi-pesticide residue detection in food samples is briefly described. Besides, this paper focuses on the application of chromatography-mass spectrometry in the detection of pesticide residues in food samples from the perspectives of the development of multidimensional chromatography and high-resolution mass spectrometry, the improvement of sorbent materials and sample pretreatment techniques, and the optimization of chemometric algorithms. Some suggestions for the problems existing in this field of research are put forward aiming to provide a new reference for the application of chromatography-mass spectrometry in the detection of multi-pesticide residues in food samples.

The aroma components of two grades of Huangjincha tea 2 were investigated by using headspace solid-phase microextraction (HS-SPME) coupled with gas chromatography-mass spectrometry (GC-MS) to establish their aroma fingerprints. The results were analyzed using chemometrics. Totally 81 aroma components were identified, 41 of which were common to both. Based on these common aroma components, the two grades could be discriminated by orthogonal partial least squares-discrimination analysis (OPLS-DA), and nine differential aroma components between them were identified including heptanal, limonene, trans-β-ocimene, 1-octanol, (E)-4,8-dimethylnona-1,3,7-triene, trans-nerolidol, 1,10-diepicubenol, epicubenol, and methyl hexadecanoate. A linear discriminant model to distinguish the two grades was established using the differential aroma components as independent variables. The self-validation and cross-validation accuracy of this model were 100%. These results may provide an objective and quantitative reference for the classification of Huangjincha 2.

Spectral data of the outer wall and inner capsule of Pericarpium Citri Reticulatae Viride from different regions (Anhui, Guangdong, and Sichuan) were collected with portable near infrared spectrometer. Multiple interferences in the spectra were eliminated using single and combined pretreatment methods. Pattern recognition methods such as principal component analysis (PCA), cluster independent soft pattern classification (SIMCA) and Fisher linear discriminant analysis (FLDA) were used to establish traceability models of Pericarpium Citri Reticulatae Viride origin. The results showed that spectral pretreatment could eliminate the interferences of baseline drift, background, and spectral peak overlap to a certain extent. However, the traceability analysis of the origin couldn't be achieved. Among the three pattern recognition methods, accurate traceability analysis of Pericarpium Citri Reticulatae Virid couldn't be achieved with PCA method. The whole identification rate of SIMCA model with the outer wall and inner capsule original spectra were 99.14% and 98.28%, respectively. The whole identification rates of FLDA models were both 99.57%, better than that of SIMCA models. The identification rates of SIMCA and FLDA models with appropriate spectral pretreatment were 100%. Therefore, portable near infrared spectroscopy technology combined with the supervised pattern recognition methods can achieve non-destructive traceability analysis of Pericarpium Citri Reticulatae Viride origin, expanding a new way for the traceability analysis of food and drug homologous substances origin. [ABSTRACT FROM AUTHOR]

To the increasing difficulty of the detection of multi-pesticide residues in cowpea, optimized moving window target conversion factor analysis (OMWTTFA) algorithm was proposed, in which an adaptive iterative re-weighted penalized least squares calculation had been added to the moving window target transfer factor analysis (MWTTFA) algorithm. And a new method for non-targeted detection of multi-pesticide residues in cowpea was established by combining with gas chromatography-mass spectrometry (GC-MS). A mass spectrometry database of 500 common pesticides was established and high throughput analysis of cowpea GC-MS data was realized by using OMWTTFA algorithm. The method was verified with actual cowpea and spiked samples. The results showed that the method could realize non-targeted detection of all pesticide residues in the two groups of spiked cowpea samples within 30 min retention time, while no pesticide residues were detected in the actual cowpea samples. The matching values of all components were larger than 800‰, and this method was more accurate than MWTTFA algorithm. The above results showed that this method promised to become a new way for non-targeted and high-throughput detection of multi-pesticide residues in fruits and vegetables. [ABSTRACT FROM AUTHOR]