Discrimination of Chinese traditional soy sauces based on their physico-chemical properties

Safety evaluation of Staphylococcus and its application in soy sauce brewing

The strain Staphylococcus PT-1 was isolated from soy sauce mash and whole genome sequencing revealed it didn't contain drug resistance genes and virulence genes. Salt tolerance test showed that PT-1 could withstand 20% NaCl. It was inoculated into a pork broth medium. Through volatile component detection, the content of pyrazine in the fermentation broth was 0.83% pyrazine and 1.36% 2, 5-dimethylpyrazine. When the strain was applied to the brewing process of soy sauce, the contents of total nitrogen and amino acid nitrogen in the resulting product were higher than those in the control group; the contents of ammonium salt and NaCl were lower than those in the control group; the total acid content was similar. Solid phase microextraction-gas chromatography-mass spectrometry was used to detect the volatile components of two finished soy sauce products. Compared to soy sauce fermented by typical means, that added with PT-1 contained unique components, such as 2,5-dimethylpyrazine (1.8027%), 2-methylpyrazine (0.0158%), 3-ethyl-2,5-dimethylpyrazine (0.5809%), and 4-ethyl guaiacol (8.7477%). Sensory evaluation showed that the total score of PT-1-fermented soy sauce was higher than that of the control group and was characterized by full and rich flavor. These results provide new insights for improving the quality of soy sauce by using novel strains for fermentation.

Classification of foodborne pathogens using near infrared (NIR) laser scatter imaging system with multivariate calibration

An optical sensor system, namely NIR laser scatter imaging system, was developed for rapid and noninvasive classification of foodborne pathogens. This developed system was used for images acquisition. The current study is focused on exploring the potential of this system combined with multivariate calibrations in classifying three categories of popular bacteria. Initially, normalization and Zernike moments extraction were performed, and the resultant translation, scale and rotation invariances were applied as the characteristic variables for subsequent discriminant analysis. Both linear (LDA, KNN and PLSDA) and nonlinear (BPANN, SVM and OSELM) pattern recognition methods were employed comparatively for modeling, and optimized by cross validation. Experimental results showed that the performances of nonlinear tools were superior to those of linear tools, especially for OSELM model with 95% discrimination rate in the prediction set. The overall results showed that it is extremely feasible for rapid and noninvasive classifying foodborne pathogens using this developed system combined with appropriate multivariate calibration.