Study On Growth Model And Rapid Detection Technology Of Salmonella In Fresh-cut Mushroom

Salmonella was a widely occurring foodborne pathogen in nature.It could cause food poisoning by contaminating various types of food,which might seriously endanger life safety.Mushroom was a large fungus used for both food and medicine.Its meat was thick,delicious,and rich in various nutrients.The surface protection layer of mushroom was relatively thin,especially for fresh-cut mushroom.Fresh-cut mushroom,whose fruiting body was cut,caused nutrients to seep out and were highly susceptible to contamination by foodborne pathogens.Fresh-cut mushroom contaminated by humans could affected people’s health.However,the research on the contamination of mushroom by pathogenic bacteria was still in its infancy.Rapid detection methods for pathogenic bacteria mostly relied on large-scale instruments and equipment,which couldnot meet the requirements of rapid detection.Therefore,establishing the growth curve of Salmonella in fresh-cut mushroom and developing fast and sensitive visual detection methods for Salmonella had became the main research directions.The main research content and results of this article were as follows:(1)This chapter established a growth model of Salmonella at different temperatures in fresh-cut mushroom.Fresh-cut mushroom inoculated with Salmonella after UV treatment.Salmonella in fresh-cut mushroom were cultured at constant temperatures of 10℃,25℃,and 35℃to explored the growth laws of Salmonella at different temperatures.The Gompertz model,Logistic model in Curve Expert Professional software,and Baranyi model in Microrisk Lab v1.2 online software were selected to fit the growth laws of Salmonella in fresh-cut mushroom under different temperatures.The growth kinetics curve of Salmonella in fresh-cut mushroom were established.By compared the correlation coefficient R analysis,it was found that the Baranyi model had a good fitting degree.It was the most suitable first level model for Salmonella in fresh-cut mushroom.Based on this,a secondary model between temperature and maximum specific growth rate was constructed using the square root equation of Roatkowsky.Finally,accuracy factor(A_f),bias factor(B_f),and root mean square error(RMSE)were selected to verify the effectiveness of the secondary model.A_fwas 1.1657,and B_fwas 0.8910.The results indicated that the fitting degree of the equation was high.RMSE of 0.0232,indicated a small error in the model.This indicated that the model could well predict the growth pattern of Salmonella on the surface of fresh-cut mushroom under different temperatures.(2)According to the above research,Salmonella was very easy to grow and reproduce on the surface of fresh-cut mushroom.Therefore,an etching method was developed for the preparation of paper-based microfluidic chips for the detection of Salmonella.Firstly,the parameters for preparing paper-based microfluidic chips were optimized.Secondly,various methods were used to characterize the performance of filter paper before and after modification,and their changes were analyzed.The results showed that the hydrophobic substrate was Whatman No.1 filter paper.The concentration of trimethoxysilane solution was 8%.Soaking time was 3 min.The surfactant was 1%Sodium Dodecyl Sulfate.The etching time under the optimal process parameters was 4 min.Scanning Electron Microscopy and Energy Dispersive Spectroscopy analysis showed that the surface of the filter paper after hydrophobic modification would be covered with a layer of hydrophobic barrier,the gap between the fibers became smaller,and 0.87%Si element appeared on the surface of the filter paper.After etching,the membrane structure was destroyed,the surface of the filter paper became porous,and the Si element decreased to 0.25%.Research has shown that the etching method can quickly etch hydrophilic pathways within 4 min.This method has the advantages of simple operation,low cost,and no need for special equipment.(3)Paper-based microfluidic chips sensor model was established to achieve rapid visual detection of Salmonella.A method for rapid visual detection of salmonella using labeled colorimetric aptasensor was constructed.Gr/Ni@Pd bimetallic nanoenzymes was synthesized.The characterization of Gr/Ni@Pd nanoenzymes was evaluated by various methods.Gr/Ni@Pd nanoenzymes had good peroxidase activity and could catalyze the blueing of chromogenic substrates in the presence of hydrogen peroxide.The aptamer bound to Graphene with noncovalent bonds,and occupied the catalytic active site of nanoenzymes.Nanoenzymes activity was inhibited.The aptamer specifically bound to Salmonella.The active site of the nanoenzymes was exposed,and the catalytic activity of the nanoenzymes was restored.Color change was represented by absorbance.Under optimal conditions,there was a good linear relationship between absorbance and the logarithmic value of Salmonella concentration.Within the range of 3.2×10~1～3.2×10~6cfu/m L(R~2=0.9961),the detection limit was 32 cfu/m L.This method had been applied to the detection of Salmonella in fresh-cut mushroom with high specificity.The aptasensor in this study had high specificity.