Surface-enhanced Raman spectroscopy (SERS) is considered powerful analytical technique that significantly enhances the Raman scattering which enables the detection of low concentration of biomolecular analytes of blood serum, which can lead to early diagnosis of diseases. The aim of this study is Surface enhanced Raman spectral monitoring of 50 KDa filtrate portions of blood serum samples of patients with iron deficiency anemia. The blood serum components include higher molecular weight fractions (HMWF) and lower molecular weight fractions (LMWF) of biomolecules. The biomarkers of iron deficiency anemia, which are low molecular weight fractions are suppressed by high molecular weight fraction hence hindering them from contributing in the diagnosis of the disease. The ultra-filtration of the blood serum samples is performed using filtration devices of 50 KDa. The specific SERS spectral features related to biochemical markers of iron deficiency anemic subgroups (with different values of hemoglobin) from different blood serum samples are identified including 479, 535, 888, and 1300 cm-1. These SERS features can be associated with different biomarkers of iron deficiency anemia consisting of ferritin, hepcidin, iron bound transferrin, and erythropoietin. Moreover, chemometric statistical techniques including principal component analysis (PCA) and partial least square regression (PLSR) are used for qualitative analysis of variations between spectral data and quantitative prediction of hemoglobin concentration of iron deficiency anemia samples, respectively.

Hyperlipidemia is characterized by elevated levels of lipids including cholesterol and triglycerides in the bloodstream that significantly increases the risk of many cardiovascular diseases. The aim of this study is to detect and screen elevated levels of cholesterol and triglycerides in hyperlipidemia patients by using surface-enhanced Raman spectroscopy (SERS). This technique provides molecular-specific information allowing for the identification of subtle spectral differences that are indicative of pathological conditions. SERS offers a rapid, sensitive, and non-invasive alternative for the detection of lipids by enhancing the Raman scattering signals, enabling the identification and quantification of these molecules and their biochemical components at very low concentrations. The screening and diagnostic ability of SERS was further confirmed by using multivariate statistical tools including partial least square regression analysis (PLSR) and principal component analysis (PCA). PCA is found very helpful for the classification of the SERS spectral groups of hyperlipidemia (hypercholesterolemia and hypertriglyceridemia) and healthy samples. PLSR is used for the quantification of the levels of cholesterol and triglycerides based on differentiating SERS spectral features of the hypertriglyceridemia and hypercholesterolemia patients and healthy serum samples. In this regard, the PLSR model was found very useful as indicated by the root mean squared error of prediction (R2) value of 0.912 for hypertriglyceridemia serum samples and 0.934 for hypercholesterolemia serum samples. This may prove to be quite useful in the future in diagnostic labs for the detection of cholesterol and triglycerides in hyperlipidemia, hypertriglyceridemia, and hypercholesterolemia in this study with an already built PLSR model.

Dengue fever and malaria are two common arthropod-borne diseases prevalent in tropical regions, affecting millions of people every year. Since they share similar symptoms, this makes their accurate and early diagnosis difficult, which is very much essential for identifying appropriate treatment options. Raman spectroscopy is recognized as an affordable, reliable, and quick method for disease screening and diagnosis. This study is focused on using SERS combined with chemometric analysis to compare and analyze the SERS spectral features associated with the specific biomarkers in blood serum samples of dengue and malaria patients, aiming to understand the biochemical differences between these diseases. The 50 kDa centrifugal filtration devices were employed to isolate filtrates of serum samples containing disease biomarkers smaller than this cut-off value, allowing for targeted identification of key biomarkers specific to the disease. The silver nanoparticles (Ag NPs) were used as SERS substrates to enhance the Raman signals of filtrate fractions from blood serum samples. The distinct SERS spectral features were observed in samples of malaria and dengue 370, 392, 415, 562, 678, 799, 810, 835, 837, 944, 1005, 1099, 1131, 1205, 1273, 1322, 1357, 1410, 1453, and 1540 cm−1 clearly highlighting the biochemical differences between the two diseases sharing common symptoms. The diagnostic capability of SERS was further enhanced by applying multivariate analysis such as principal component analysis (PCA), which demonstrated the potential of SERS to distinguish between dengue and malaria blood serum samples. It provided a clear and highly sensitive method for understanding the differences in serum filtrates, making it a valuable tool for studying complex biological systems.

Drug-resistant pathogenic bacteria are a major cause of infectious diseases in the world and they have become a major threat through the reduced efficacy of developed antibiotics. This issue can be addressed by using bacteriophages, which can kill lethal bacteria and prevent them from causing infections. Surface-enhanced Raman spectroscopy (SERS) is a promising technique for studying the degradation of infectious bacteria by the interaction of bacteriophages to break the vicious cycle of drug-resistant bacteria and help to develop chemotherapy-independent remedial strategies. The phage (viruses)-sensitive Staphylococcus aureus (S. aureus) bacteria are exposed to bacteriophages (Siphoviridae family) in the time frame from 0 min (control) to 50 minutes with intervals of 5 minutes and characterized by SERS using silver nanoparticles as SERS substrate. This allows us to explore the effects of the bacteriophages against lethal bacteria (S. aureus) at different time intervals. The differentiating SERS bands are observed at 575 (C-C skeletal mode), 620 (phenylalanine), 649 (tyrosine, guanine (ring breathing)), 657 (guanine (COO deformation)), 728-735 (adenine, glycosidic ring mode), 796 (tyrosine (C-N stretching)), 957 (C-N stretching (amide lipopolysaccharides)), 1096 (PO2 (nucleic acid)), 1113 (phenylalanine), 1249 (CH2 of amide III, N-H bending and C-O stretching (amide III)), 1273 (CH2, N-H, C-N, amide III), 1331 (C-N stretching mode of adenine), 1373 (in nucleic acids (ring breathing modes of the DNA/RNA bases)) and 1454 cm(-1) (CH2 deformation of saturated lipids), indicating the degradation of bacteria and replication of bacteriophages. Multivariate data analysis was performed by employing principal component analysis (PCA) and partial least squares-discriminant analysis (PLS-DA) to study the biochemical differences in the S. aureus bacteria infected by the bacteriophage. The SERS spectral data sets were successfully differentiated by PLS-DA with 94.47% sensitivity, 98.61% specificity, 94.44% precision, 98.88% accuracy and 81.06% area under the curve (AUC), which shows that at 50 min interval S. aureus bacteria is degraded by the replicating bacteriophages.

Addictive, illicit drugs pose a high threat of relapse. The deaths related to illicit drugs have substantially increased in the last decade. Globally, the illicit drug trade poses a significant challenge to public health and law enforcement. It is essential to utilize advanced analytical methodologies that can characterize illegal substances precisely to monitor these drugs. Surfaced-enhanced Raman spectroscopy (SERS) has become a valuable tool, providing distinct capabilities for analyzing illegal substances. Surface enhancement enables far more sensitive detection and identification of illicit drugs by suppressing the fluorescence and enhancing the Raman signals. In this regard, a simple method with limited sample processing has been established to characterize illicit drugs that include methamphetamine (MAMP) (ice), bhang, marijuana (cannabis), opium, and diacetylmorphine (heroin). Moreover, principal component analysis (PCA) was employed for the identification of the characteristic SERS spectral features of these drugs. This article examines the potential application of SERS in the characterization of illicit substances, focusing on its ability to contribute significantly to law enforcement and forensic investigation.

This study centerd on exploring the adsorptive potential of the raw cotton for methylene blue dye adsorption. Several laboratory experiments conducted to optimize operational factors like pH, concentration of dye, adsorbent dose, contact time, and temperature. Optimized conditions of the study include an adsorbent dose of 0.35g, pH of 7, an initial dye concentration of 20 ppm with contact time of 30minutes at 20 ºC wherein 83% of adsorption take place. The mean values ± standard deviation from triplicate experiments were used to express the results with P < 0.05. The investigational data fit well with the Langmuir isotherm model having R2 of 0.9477 and pseudo second order kinetic model having R2 = 1. The values obtained for adsorption capacities through non-linear models exhibit a significant degree of similarity with those derived from linear models. Thermodynamic study showed a negative ∆G, positive ∆H and increased ∆S values revealing that the reaction was spontaneous and endothermic in nature. The developed method was also tested with tap water which showed 72% of dye removal and subsequent regeneration of the system led to nearly 79% desorption of the dye. Results of the study revealed that additive salts had a minimum effect on the adsorption process. The originality of this study is that no research has been reported till yet, for adsorption of methylene blue by using low-cost raw cotton specifically in its non-modified form.

In this study, surface-enhanced Raman spectroscopy (SERS) technique, along with principal component analysis (PCA) and partial least-squares discriminant analysis (PLS-DA), is used as a simple, quick, and cost-effective analysis method for identifying biochemical changes occurring due to induced mutations in the Aspergillus niger fungus strain. The goal of this study is to identify the biochemical changes in the mutated fungal cells (cell mass) as compared to the control/nonmutated cells. Furthermore, multivariate data analysis tools, including PCA and PLS-DA, are used to further confirm the differentiating SERS spectral features among fungal samples. The mutations are caused in A. niger by the clustered regularly interspaced palindromic repeat CRISPR-Cas9 genomic editing method to improve their biotechnological potential for the production of cellulase enzyme. SERS was employed to detect the changes in the cells of mutated A. niger fungal strains, including one mutant producing low levels of an enzyme and another mutant producing high levels of the enzyme as a result of mutation as compared with an unmutated fungal strain as a control sample. The distinctive features of SERS corresponding to nucleic acids and proteins appear at 546, 622, 655, 738, 802, 835, 959, 1025, 1157, 1245, 1331, 1398, and 1469 cm-1. Furthermore, PLS-DA is used to confirm the 89% accuracy, 87.7% precision, 87% sensitivity, and 88.9% specificity of this method, and the value of the area under the curve (AUROC) is 0.67. It has been shown that surface-enhanced Raman spectroscopy is an effective method for identifying and differentiating biochemical changes in genome-modified fungal samples.

Identification of adulterants in commercial samples of methyl eugenol is necessary because it is a botanical insecticide, a tephritid male attractant lure that is used to attract and kill invasive pests such as oriental fruit flies and melon flies on crops. In this study, Raman spectroscopy was used to qualitatively and quantitatively assess commercial methyl eugenol along with adulterants. For this purpose, commercial methyl eugenol was adulterated with different concentrations of xylene. The Raman spectral features of methyl eugenol and xylene in liquid formulations were examined, and Raman peaks were identified as associated with the methyl eugenol and adulterant. Principal component analysis (PCA) and partial least-squares regression analysis (PLSR) have been used to qualitatively and quantitatively analyze the Raman spectral features. PCA was applied to differentiate Raman spectral data for various concentrations of methyl eugenol and xylene. Additionally, PLSR has been used to develop a predictive model to observe a quantitative relationship between various concentrations of adulterated methyl eugenol and their Raman spectral data sets. The root-mean-square errors of calibration and prediction were calculated using this model, and the results were found to be 1.90 and 3.86, respectively. The goodness of fit of the PLSR model is found to be 0.99. The proposed approach showed excellent potential for the rapid, quantitative detection of adulterants in methyl eugenol, and it may be applied to the analysis of a range of pesticide products.

A low-cost adsorbent developed from unmodified Azadirachta indica leaves in the form of powder was used for adsorptive removal of the Congo Red dye from an aqueous medium. The adsorbent was characterized by the Fourier transform infrared spectroscopy (FTIR), Brunauer- Emmett-Teller (BET), and scanning electron microscopy (SEM) techniques. For optimization of operational parameters such as dye concentration, solution pH, adsorbent dose, contact time, and temperature, batch adsorption experiments were performed. It was found that for neem leaves powder (NLP), the optimum conditions were as follows: adsorbent dose of 0.8 g, contact time of 100 min having a solution with pH value of 5, adsorbate initial concentration of 40 ppm at temperature 60 degrees C where maximum amount of dye, i.e., 84%, removal was observed. The process followed pseudo-first-order kinetics, which reveals physical adsorption. According to isothermal investigations, sorption data were best fit with the Freundlich isotherm model (also confirmed from correlation with linear and nonlinear plots and lowest value of five error analysis models for each isotherm and kinetic model). Thermodynamically, the adsorption of the Congo Red dye by the neem leaf powder was exothermic. Furthermore, the mechanistic removal of the Congo Red dye by the NLP has been explored with the help of the surface complex formation (PHREEQC) mechanism. Overall, the results of the study explore the promising nature of NLP for Congo Red dye removal.

In the present research work, a selenium N-heterocyclic carbene (Se-NHC) complex/adduct was synthesized and characterized by using different analytical methods including FT-IR, 1HNMR, and 13CNMR. The antifungal activity of the Se-NHC complex against Aspergillus flavus (A. flavus) fungus was investigated with disc diffusion assay. Moreover, the biochemical changes occurring in this fungus due to exposure of different concentrations of the in-house synthesized compound are characterized by surface-enhanced Raman spectroscopy (SERS) and are illustrated in the form of SERS spectral peaks. SERS analysis yields valuable information about the probable mechanisms responsible for the antifungal effects of the Se-NHC complex. As demonstrated by the SERS spectra, this Se-NHC complex caused denaturation and conformational changes in the proteins as well as decomposition of the fungal cell membrane. The SERS spectra were analyzed using two chemometric tools such as principal component analysis (PCA) and partial least-squares discriminant analysis (PLS-DA). The fungal samples' SERS spectra were differentiated using PCA, while various groups of spectra were discriminated with ultrahigh sensitivity (98%), high specificity (99.7%), accuracy (100%), and area under the receiver operating characteristic curve (87%) using PLS-DA.