Anwer Ayesha, Shahzadi Aqsa, Nawaz Haq, Majeed Muhammad Irfan, Alshammari Abdulrahman, Albekairi Norah A, Hussain Muhammad Umar, Amin Itfa, Bano Aqsa, Ashraf Ayesha, Rehman Nimra, Pallares Roger M, Akhtar Nasrin
Department of Chemistry, University of Agriculture Faisalabad Faisalabad 38000 Pakistan
Department of Pharmacology and Toxicology, College of Pharmacy, King Saud University Post Box 2455 Riyadh 11451 Saudi Arabia.
RSC Adv. 2024 Jun 26;14(28):20290-20299. doi: 10.1039/d4ra01735h. eCollection 2024 Jun 18.
Fossil fuels are considered vital natural energy resources on the Earth, and sulfur is a natural component present in them. The combustion of fossil fuels releases a large amount of sulfur in the form of SO in the atmosphere. SO is the major cause of environmental problems, mainly air pollution. The demand for fuels with ultra-low sulfur is growing rapidly. In this aspect, microorganisms are proven extremely effective in removing sulfur through a process known as biodesulfurization. A major part of sulfur in fossil fuels (coal and oil) is present in thiophenic structures such as dibenzothiophene (DBT) and substituted DBTs. In this study, the identification and characterization of DBT desulfurizing bacteria ( sp. IS, sp. 4N, sp. J2, and sp. J16) based on their specific biochemical constituents were conducted using surface-enhanced Raman spectroscopy (SERS). By differentiating DBT desulfurizing bacteria, researchers can gain insights into their unique characteristics, thus leading to improved biodesulfurization strategies. SERS was used to differentiate all these species based on their biochemical differences and different SERS vibrational bands, thus emerging as a potential technique. Moreover, multivariate data analysis techniques such as principal component analysis (PCA) and partial least squares discriminant analysis (PLS-DA) were employed to differentiate these DBT desulfurizing bacteria on the basis of their characteristic SERS spectral signals. For all these isolates, the accuracy, sensitivity, and specificity are above 90%, and an AUC (area under the curve) value of close to 1 was achieved for all PLS-DA models.
化石燃料被认为是地球上至关重要的天然能源资源，而硫是其中存在的一种天然成分。化石燃料的燃烧会以二氧化硫的形式在大气中释放大量硫。二氧化硫是环境问题的主要成因，主要造成空气污染。对超低硫燃料的需求正在迅速增长。在这方面，微生物已被证明在通过一种称为生物脱硫的过程去除硫方面极为有效。化石燃料（煤和石油）中的大部分硫以噻吩结构存在，如二苯并噻吩（DBT）和取代的DBT。在本研究中，基于其特定生化成分，使用表面增强拉曼光谱（SERS）对DBT脱硫细菌（菌株IS、菌株4N、菌株J2和菌株J16）进行了鉴定和表征。通过区分DBT脱硫细菌，研究人员可以深入了解它们的独特特性，从而改进生物脱硫策略。SERS被用于根据它们的生化差异和不同的SERS振动带区分所有这些物种，因此成为一种潜在技术。此外，还采用了主成分分析（PCA）和偏最小二乘判别分析（PLS-DA）等多元数据分析技术，根据其特征SERS光谱信号区分这些DBT脱硫细菌。对于所有这些分离株，准确率、灵敏度和特异性均高于90%，并且所有PLS-DA模型的曲线下面积（AUC）值接近1。
