Application of E-nose technology combined with artificial neural network to predict total bacterial count in milk

Abstract

Total bacterial count (TBC) is a widely accepted index for assessing microbial quality of milk, and cultivation-based methods are commonly used as standard methods for its measurement. However, these methods are laborious and time-consuming. This study proposes a method combining E-nose technology and artificial neural network for rapid prediction of TBC in milk. The qualitative model generated an accuracy rate of 100% when identifying milk samples with high, medium, or low levels of TBC, on both the testing and validating subsets. Predicted TBC values generated by the quantitative model demonstrated strong coefficient of multiple determination (R2 > 0.99) with reference values. Mean relative difference between predicted and reference values (mean ± standard deviation) of TBC were 1.1 ± 1.7% and 0.4 ± 0.8% on the testing and validating subsets involving 24 and 28 tested samples, respectively. Paired t-test implied that the difference between predicted and reference values of TBC was insignificant for both the testing and validating subsets. As low as ~1 log cfu/mL of TBC present in tested samples were precisely predicted. Results of this study indicated that combination of E-nose technology and artificial neural network generated reliable predictions of TBC in milk. The method proposed in this study was reliable, rapid, and cost efficient for assessing microbial quality milk, and thus would potentially have realistic application in dairy section.