Hyperspectral imaging (HSI) provides both spatial and spectral information of a sample by combining imaging with spectroscopy. The objective of this study was to generate hyperspectral graphs of common foodborne pathogens and to develop and validate prediction models for the classification of these pathogens. Four strains of
Cronobacter sakazakii
, five strains of
Salmonella
spp., eight strains of
Escherichia coli
, and one strain each of
Listeria monocytogenes
and
Staphylococcus aureus
were used in the study. Principal component analysis and
k
NN (
k
‐nearest neighbor) classifier model were used for the classification of hyperspectra of various bacterial cells, which were then validated using the cross‐validation technique. Classification accuracy of various strains within genera including
C. sakazakii
,
Salmonella
spp., and
E. coli
, respectively, was 100%; except within
C. sakazakii
, strain BAA‐894, and
E. coli
, strains O26, O45, and O121 had 66.67% accuracy. When all strains were studied together (irrespective of their genus) for the classification, only
C. sakazakii
P1,
E. coli
O104, O111, and O145,
S
. Montevideo, and
L. monocytogenes
had 100% classification accuracy, whereas
E. coli
O45 and
S
. Tennessee were not classified (classification accuracy of 0%). Lauric arginate treatment of
C. sakazakii
BAA‐894,
E. coli
O157,
S
. Senftenberg,
L. monocytogenes
, and
S. aureus
significantly affected their hyperspectral signatures, and treated cells could be differentiated from the healthy, nontreated cells.