Differentiation of foodborne bacteria using NIR hyperspectral imaging and multivariate data analysis

Area of Science:

• Microbiology
• Analytical Chemistry
• Spectroscopy

Background:

• Bacterial identification is crucial in various fields.
• Current methods can be time-consuming and destructive.
• Need for rapid, non-destructive bacterial detection techniques.

Purpose of the Study:

• To investigate near-infrared (NIR) hyperspectral imaging and multivariate data analysis for rapid, non-destructive bacterial detection and differentiation.
• To assess the capability of NIR hyperspectral imaging to distinguish between different bacterial species and genera.
• To evaluate the accuracy of the developed method for bacterial identification.

Main Methods:

• Collected NIR hyperspectral images of five bacterial species (Bacillus cereus, Escherichia coli, Salmonella enteritidis, Staphylococcus aureus, Staphylococcus epidermidis).
• Applied data preprocessing techniques including Standard Normal Variate (SNV) correction and Savitzky-Golay smoothing.
• Utilized Principal Component Analysis (PCA) and Partial Least Squares Discriminant Analysis (PLS-DA) for data analysis and bacterial differentiation.

Main Results:

• PCA successfully differentiated between bacterial species with similar colony colors, separating B. cereus from E. coli and S. enteritidis along PC1.
• Distinguished E. coli from S. enteritidis using PC2, and S. epidermidis from B. cereus and S. aureus along PC1.
• Achieved high prediction accuracy (82.0% to 99.96%) for B. cereus and Staphylococcus species using PLS-DA models.

Conclusions:

• NIR hyperspectral imaging is a promising rapid, non-destructive tool for bacterial detection and differentiation.
• The method can distinguish between bacterial genera, Gram-positive/negative status, and pathogenic/non-pathogenic species.
• This technique offers a valuable alternative for bacterial identification in various applications.