Machine Learning Prediction of Listeria monocytogenes Serogroups and Biofilm Formation from Infrared Spectra: A Comparative Study with Genomic Analysis

This study evaluated the performance of Fourier-transform infrared (FTIR) spectroscopy for identifying spectral signatures associated with two key traits of Listeria monocytogenes: serogroup classification and biofilm-forming capacity. A total of 100 strains, previously serogrouped by PCR and categorized as high, intermediate, or low biofilm producers, were analyzed. Whole-genome sequencing was performed, and comparative genomics was conducted at core-genome, pangenome, and whole-genome (k-mer) levels to determine which genomic representation best reflected the phenotypes. Strains were typed using Fourier-Transform Infrared (FTIR Biotyper® system from Bruker Daltonics GmbH and Co., Bremen, Germany) with five technical replicates. Spectral data from the polysaccharide region (1300–800 cm−1) were extracted and used to train twelve statistical models within a machine learning pipeline combined with cross-validation to predict four serogroups and three biofilm clusters from 501 spectral variables. Genomic analyses showed strong concordance between population structure and serogroup, whereas biofilm formation displayed only weak genomic association, explaining less than 0.1% of genomic variance (PERMANOVA R2 ≤ 0.001). Penalized discriminant analysis achieved the highest performance for serogroup prediction (overall accuracy 97.2%), while the k-nearest neighbor model performed best for biofilm prediction (74.8%). Two dedicated R Shiny applications were developed to facilitate model use. Overall, FTIR spectroscopy coupled with machine learning can provide a rapid and cost-effective alternative to PCR, genomic analyses, and in vitro assays for phenotypic trait prediction.