Reliable and rapid identification of Listeria monocytogenes and Listeria species by artificial neural network-based Fourier transform infrared spectroscopy

Abstract

Differentiation of the species within the genus Listeria is important for the food industry but only a few reliable methods are available so far. While a number of studies have used Fourier transform infrared (FTIR) spectroscopy to identify bacteria, the extraction of complex pattern information from the infrared spectra remains difficult. Here, we apply artificial neural network technology (ANN), which is an advanced multivariate data-processing method of pattern analysis, to identify Listeria infrared spectra at the species level. A hierarchical classification system based on ANN analysis for Listeria FTIR spectra was created, based on a comprehensive reference spectral database including 243 well-defined reference strains of Listeria monocytogenes, L. innocua, L. ivanovii, L. seeligeri, and L. welshimeri. In parallel, a univariate FTIR identification model was developed. To evaluate the potentials of these models, a set of 277 isolates of diverse geographical origins, but not included in the reference database, were assembled and used as an independent external validation for species discrimination. Univariate FTIR analysis allowed the correct identification of 85.2% of all strains and of 93% of the L. monocytogenes strains. ANN-based analysis enhanced differentiation success to 96% for all Listeria species, including a success rate of 99.2% for correct L. monocytogenes identification. The identity of the 277-strain test set was also determined with the standard phenotypical API Listeria system. This kit was able to identify 88% of the test isolates and 93% of L. monocytogenes strains. These results demonstrate the high reliability and strong potential of ANN-based FTIR spectrum analysis for identification of the five Listeria species under investigation. Starting from a pure culture, this technique allows the cost-efficient and rapid identification of Listeria species within 25 h and is suitable for use in a routine food microbiological laboratory.

FIG. 1.

First derivative of a Listeria FTIR spectrum. The regions of the infrared spectra contributing most significantly to the differentiation of the five Listeria species are highlighted. A.U., arbitrary units.

FIG. 2.

(a) Hierarchical cluster analysis of the first derivative of 243 Listeria spectra included in the reference data set. It was performed by using the regions from 700 to 1,200, 1,500 to 1,800, and 2,800 to 3,100 cm⁻¹, correlation with scaling to first range, and Ward's algorithm. (b) The two major groups resulting from the cluster analysis (a) were used to establish the first level of the architecture of the neural network for the identification of Listeria species. In the first level, the L. innocua-L. ivanovii-L. welshimeri net and the L. monocytogenes-L. seeligeri net were established. In the second level of this classification scheme, the species-specific subnetworks (L. innocua, L. ivanovii, L. monocytogenes, L. seeligeri, and L. welshimeri) were activated.

FIG. 3.

Comparison of the external validation of the ANN model using three different reference data sets including 100, 171, and 243 strains. The number of strains per species included in each data set is indicated in parentheses.