Rapid Pseudomonas species identification from chicken by integrating colorimetric sensors with near-infrared spectroscopy.

Abstract

Pseudomonas spp. are the dominant spoilage bacteria which can cause chicken spoilage. Some traditional detection methods are often unsuitable for their rapid real-time detection. Thus, in this paper, a fusion strategy based on colorimetric sensors and near-infrared spectroscopy was applied to rapidly identify Pseudomonas spp. in chicken. First, four different species of Pseudomonas—Pseudomonas gessardii, Pseudomonas psychrophila, Pseudomonas fragi, and Pseudomonas fluorescens—were isolated from putrid chicken, and then, the odor and spectral information of the Pseudomonas species and their mixture were obtained by colorimetric sensors and near-infrared spectroscopy, respectively. Thirty-six odor characteristic variables and 33 spectral characteristic variables were extracted from each technique and used for data fusion based on principal component analysis (PCA). Back-propagation artificial neural network (BP-ANN) was used to build identification model for the discrimination of the different Pseudomonas species. The results showed that the discrimination capability of the model based on data fusion was superior to that based on the two techniques independently, and eventually BP-ANN achieved 100% classification rate by cross-validation and 98.75% classification rate in predication set. This work indicates that the combination of colorimetric sensors and near-infrared spectroscopy is promising for the rapid identification of Pseudomonas species in chicken extract, and hence may be applied towards quality monitoring.