fast discrimination of milk contaminated with salmonella sp. via near-infrared spectroscopy

Conventional methods to detect salmonella in milk generally take a long time and are significantly laborious besides requiring the use of different media and reagents. thus, this research aimed to offer an alternative methodology based on near-infrared spectroscopy (nirs) to discriminate the presence of salmonella in milk. for this purpose, different milk samples, contaminated and free of salmonella, were prepared to be measured by nirs and evaluated by chemometric method of partial least squares with discriminant analysis. the model to discriminate skimmed milk samples used four latent variables presenting root mean square error of calibration (rmsec) 0.1639 and prediction (rmsep) of 0.0971, while the model to the whole milk discrimination six latent variables were needed, presenting rmsec 0.1351 and rmsep 0.0928. the validation parameters, sensitivity, and specificity shows that all samples were correctly classified. the results suggest that the methodology could be used to discriminate the presence/absence of salmonella in milk samples using nir as a screening method.