Surface-enhanced Raman spectroscopy for identification of food processing bacteria

Area of Science:

• Food microbiology
• Analytical chemistry
• Spectroscopy

Background:

• Bacteria are crucial in food processing for flavor and ingredients, but some cause spoilage.
• Accurate identification of food processing bacteria is essential for developing improved food methodologies and ensuring safety.

Purpose of the Study:

• To develop and evaluate an in-house Ag NPs-based surface-enhanced Raman spectroscopy (SERS) method for differentiating food processing bacterial species.
• To compare the efficacy of SERS with traditional Raman spectroscopy (RS) for bacterial identification.

Main Methods:

• Analysis of bacterial species including Lactobacillus fermentum, Fructobacillus fructosus, Pediococcus pentosaceus, and Halalkalicoccus jeotgali.
• Utilized Ag NPs-based SERS for spectral data acquisition.
• Applied multivariate data analysis techniques: Principal Component Analysis (PCA) and Partial Least Square Discriminant Analysis (PLS-DA).

Main Results:

• SERS, combined with PCA and PLS-DA, proved effective for identifying and differentiating bacterial species.
• The PLS-DA model achieved a high differentiation accuracy of 99.5% and sensitivity of 99.7% using leave-one-out cross-validation.
• SERS demonstrated significant potential compared to conventional Raman spectroscopy for this application.

Conclusions:

• Ag NPs-based SERS is an efficient and highly accurate technique for the rapid identification and differentiation of food processing bacteria.
• This method offers a valuable tool for quality control in the food industry and the advancement of food processing techniques.