Kinetics of “Laba” garlic greening and its physiochemical properties treated by Dense Phase Carbon Dioxide

Quark Cheese Processed by Dense-Phase Carbon Dioxide: Shelf-Life Evaluation and Physiochemical, Rheological, Microstructural and Volatile Properties Assessment

Dense-phase carbon dioxide (DPCD), a novel non-thermal processing technology, has attracted extensive attention due to its excellent performance in food sterilization and enzyme inactivation without quality deterioration. In this work, we aimed to extend the shelf life of quark cheese with DPCD and explore the effect of DPCD treatment as well as storage time on the quality of quark cheese. The sterilization parameters were optimized by means of orthogonal experiments, and the physiochemical, rheological, microstructural and volatile properties of cheese were investigated. The optimal DPCD treatment (20 MPa, 45 min, 55 °C) successfully extended the shelf life of quark cheese due to its inhibition effect on yeast and was able to slow down the proteolysis and alterations in pH and color of cheese. Cheese processed using DPCD after 14-day storage even displayed similar rheological properties to the control at day 0, from which bound water significantly migrated during storage. Moreover, DPCD contributed to the retention of the volatile profile of cheese during storage. This study demonstrated that DPCD is a promising pasteurization technology for quark cheese to improve its quality stability during storage.

A new methodology for sensory quality assessment of garlic based on metabolomics and an artificial neural network

This study has established a new method for the sensory quality determination of garlic and garlic products on the basis of metabolomics and an artificial neural network. A total of 89 quality indicators were obtained, mainly through the metabolomics analysis using gas chromatography/mass spectrometry (GC/MS) and high performance liquid chromatography coupled with tandem mass spectrometry (HPLC-MS/MS). The quality indicator data were standardized and fused at a low level, and then seven representative indicators including the a* (redness) value, and the contents of S-methyl-l-cysteine, 3-vinyl-1,2-dithiacyclohex-5-ene, glutamic acid, l-tyrosine, d-fructose and propene were screened by partial least squares discriminant analysis (PLS-DA), analysis of variance (ANOVA) and correlation analysis (CA). Subsequently, the seven representative indicators were employed as the input data, while the sensory scores for the garlic obtained by a traditional sensory evaluation were regarded as the output data. A back propagation artificial neural network (BPANN) model was constructed for predicting the sensory quality of garlic from four different areas in China. The R² value of the linear regression equation between the predicted scores and the traditional sensory scores for the garlic was 0.9866, with a mean square error of 0.0034, indicating that the fitting degree was high and that the BPANN model built in this study could predict the sensory quality of garlic accurately. In general, the method developed in this study for the sensory quality determination of garlic and garlic products is rapid, simple and efficient, and can be considered as a potential method for application in quality control in the food industry.

This study has established a new method for the sensory quality determination of garlic and garlic products on the basis of metabolomics and an artificial neural network.

Investigation of the dynamic changes in the chemical constituents of Chinese “Laba” garlic during traditional processing

In this study, the whole chemical constituents influencing colour, flavor and physiological function of “Laba” garlic during traditional processing have been investigated.