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Surface-enhanced Raman spectroscopy for the characterization of xylanases enzyme

化学 拉曼光谱 表征(材料科学) 光谱学 曲面(拓扑) 表面增强拉曼光谱 化学工程 纳米技术 分析化学(期刊) 生物化学 色谱法 拉曼散射 光学 材料科学 物理 几何学 数学 工程类 量子力学
作者
Naeema Kanwal,Nosheen Rashid,Muhammad Irfan Majeed,Haq Nawaz,Arooj Amber,Muhammad Zohaib,Aqsa Bano,Norah A. Albekairi,Abdulrahman Alshammari,Anum Shahzadi,S. A. Yaseen,Arslan Bashir
出处
期刊:Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy [Elsevier BV]
卷期号:325: 125065-125065
标识
DOI:10.1016/j.saa.2024.125065
摘要

Xylanases are essential hydrolytic enzymes which break down the plant cell wall polysaccharide, xylan composed of D-xylose monomers. Surface-enhanced Raman Spectroscopy (SERS) was utilized for the characterization of interaction of xylanases with xylan at varying concentrations. The study focuses on the application of SERS for the characterization of enzymatic activity of xylanases causing hydrolysis of Xylan substrate with increase in its concentration which is substrate for this enzyme in the range of 0.2% to 1.0%. SERS differentiating features are identified which can be associated with xylanases treated with different concentrations of xylan. SERS measurements were performed using silver nanoparticles as SERS substrate to amplify Raman signal intensity for the characterization of xylan treated with xylanases. Principal Component Analysis (PCA) and Partial Least Square Discriminant Analysis (PLS-DA) were applied to analyze the spectral data to analyze differentiation between the SERS spectra of different samples. Mean SERS spectra revealed significant differences in spectral features particularly related to carbohydrate skeletal mode and O-C-O and C-C-C ring deformations. PCA scatter plot effectively differentiates data sets, demonstrating SERS ability to distinguish treated xylanases samples and the PC-loadings plot highlights the variables responsible for differentiation. PLS-DA was employed as a quantitative classification model for treated xylanase enzymes with increasing concentrations of xylan. The values of sensitivity, specificity, and accuracy were found to be 0.98%, 0.99%, and 100% respectively. Moreover, the AUC value was found to be 0.9947 which signifies the excellent performance of PLS-DA model. SERS combined with multivariate techniques, effectively characterized and differentiated xylanase samples as a result of interaction with different concentrations of the Xylan substrate. The identified SERS features can help to characterize xylanases treated with various concentrations of xylan with promising applications in the bio-processing and biotechnology industries.

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