Surface-Enhanced Raman Spectral Profiling of Gamma Radiation-Induced Mutations in Aspergillus niger Supernatant Samples

The present study involves the mutation of Aspergillus niger fungus induced by gamma radiation for enhancement of the production of cellulase enzyme, which randomly leads to generating one strain producing less and the other producing more enzyme. Moreover, the mutated fungus is characterized by using surface-enhanced Raman scattering (SERS) spectroscopy, where silver nanoparticles are employed as SERS substrate. This can help to characterize changes in the biological composition of the supernatant samples of the mutated and non-mutated fungus and also in the identification of the differentiating SERS spectral features. Multivariate techniques for data analysis, such as principal component analysis (PCA) and partial least squares discriminant analysis (PLS-DA), are used to analyze the SERS spectral features. The analysis of SERS spectral features observed at 422 cm-1 ((COC) exocyclic deformations of saccharides), 533 (phosphatidylinositol), 547 (polysaccharides), 594 (C-C mode of twisting in phenylalanine), 651 (C-H bending), 855 (C-C str, C-O-C stretching and deformation), 930 (upsilon(C-C), stretching in amino acid), 1044 (C-O stretching in proteins), 1140 ((C - C) stretching in proteins), 1239 (amide III), 1333 (amide III), 1394 (C-N stretching, in quinoid ring-benzoid in proteins), and 1428 cm-1 (CH2, CH3 deformation in lipids) are differentiating features indicating changes in the concentrations of biomolecules produced extracellularly by the fungus as a result of mutation Aspergillus niger fungus. PCA provides differentiation of the SERS spectral data sets of mutated A. niger fungus from that of non-mutated. The PLS-DA model is employed for the semi-quantitative classification of the SERS spectra, obtaining 95% sensitivity, 92% specificity, and 96% accuracy.