Nondestructive Metabolomic Fingerprinting: FTIR, NIR and Raman Spectroscopy in Food Screening

Abstract

In recent years, there has been renewed interest in the maintenance of food quality and food safety on the basis of metabolomic fingerprinting using vibrational spectroscopy combined with multivariate chemometrics. Nontargeted spectroscopy techniques such as FTIR, NIR and Raman can provide fingerprint information for metabolomic constituents in agricultural products, natural products and foods in a high-throughput, cost-effective and rapid way. In the current review, we tried to explain the capabilities of FTIR, NIR and Raman spectroscopy techniques combined with multivariate analysis for metabolic fingerprinting and profiling. Previous contributions highlighted the considerable potential of these analytical techniques for the detection and quantification of key constituents, such as aromatic amino acids, peptides, aromatic acids, carotenoids, alcohols, terpenoids and flavonoids in the food matrices. Additionally, promising results were obtained for the identification and characterization of different microorganism species such as fungus, bacterial strains and yeasts using these techniques combined with supervised and unsupervised pattern recognition techniques. In conclusion, this review summarized the cutting-edge applications of FTIR, NIR and Raman spectroscopy techniques equipped with multivariate statistics for food analysis and foodomics in the context of metabolomic fingerprinting and profiling.

Keywords: FTIR; NIR; Raman; food quality; metabolomics; pattern recognition.

Figure 1

A general overview of multivariate data analysis techniques frequently employed with vibrational spectroscopy techniques.

Figure 2

Principal component analysis results of bacteria species: E. coli (green square), B. cereus (blue inverse triangle), S. aureus (dark green diamond), Salmonella typhimurium (red inverse triangle). Liquid media (A) and solid culture (B). Reproduced with permission [40], Copyright 2019, Elsevier.

Figure 3

Classification pattern of citrus species: principal component analysis (A); hierarchical cluster analysis (B). Reproduced with permission [54]. Copyright 2019, Elsevier.