| Title: | Predicting the growth situation of Pseudomonas aeruginosa on agar plates and meat stuffs using gas sensors |
| Author(s): | Gu X; Sun Y; Tu K; Dong Q; Pan L; |
| Address: | "College of Food Science and Technology, Nanjing Agricultural University, No.1, Weigang Road, Nanjing, Jiangsu 210095, PR China. School of Medical Instrument and Food Engineering, University of Shanghai for Science and Technology, 516 Jun Gong Rd., Shanghai 200093, PR China" |
| ISSN/ISBN: | 2045-2322 (Electronic) 2045-2322 (Linking) |
| Abstract: | "A rapid method of predicting the growing situation of Pseudomonas aeruginosa is presented. Gas sensors were used to acquire volatile compounds generated by P. aeruginosa on agar plates and meat stuffs. Then, optimal sensors were selected to simulate P. aeruginosa growth using modified Logistic and Gompertz equations by odor changes. The results showed that the responses of S(8) or S(10) yielded high coefficients of determination (R(2)) of 0.89-0.99 and low root mean square errors (RMSE) of 0.06-0.17 for P. aeruginosa growth, fitting the models on the agar plate. The responses of S(9), S(4) and the first principal component of 10 sensors fit well with the growth of P. aeruginosa inoculated in meat stored at 4 degrees C and 20 degrees C, with R(2) of 0.73-0.96 and RMSE of 0.25-1.38. The correlation coefficients between the fitting models, as measured by electronic nose responses, and the colony counts of P. aeruginosa were high, ranging from 0.882 to 0.996 for both plate and meat samples. Also, gas chromatography-mass spectrometry results indicated the presence of specific volatiles of P. aeruginosa on agar plates. This work demonstrated an acceptable feasibility of using gas sensors-a rapid, easy and nondestructive method for predicting P. aeruginosa growth" |
| Keywords: | Food Microbiology/*methods *Gas Chromatography-Mass Spectrometry Meat/*microbiology Odorants/*analysis Pseudomonas aeruginosa/*growth & development Volatile Organic Compounds/*analysis/metabolism; |
| Notes: | "MedlineGu, Xinzhe Sun, Ye Tu, Kang Dong, Qingli Pan, Leiqing eng Research Support, Non-U.S. Gov't England 2016/12/13 Sci Rep. 2016 Dec 12; 6:38721. doi: 10.1038/srep38721" |
