RESUMEN
Biofilms are structured microbial communities of single or multiple populations in which microbial cells adhere to a surface and get embedded in extracellular polymeric substances (EPS). This review attempts to explain biofilm architecture, development phases, and forces that drive bacteria to promote biofilm mode of growth. Bacterial chemical communication, also known as Quorum sensing (QS), which involves the production, detection, and response to small molecules called autoinducers, is highlighted. The review also provides a brief outline of interspecies and intraspecies cell-cell communication. Additionally, we have performed docking studies using Discovery Studio 4.0, which has enabled our understanding of the prominent interactions between autoinducers and their receptors in different bacterial species while also scoring their interaction energies. Receptors, such as LuxN (Phosphoreceiver domain and RecA domain), LuxP, and LuxR, interacted with their ligands (AI-1, AI-2, and AHL) with a CDocker interaction energy of - 31.6083 kcal/mole; - 34.5821 kcal/mole, - 48.2226 kcal/mole and - 41.5885 kcal/mole, respectively. Since biofilms are ideal for the remediation of contaminants due to their high microbial biomass and their potential to immobilize pollutants, this article also provides an overview of biofilm-mediated bioremediation.
Asunto(s)
Bacterias/metabolismo , Proteínas Bacterianas/metabolismo , Biodegradación Ambiental , Biopelículas , Ligandos , Percepción de Quorum/fisiología , Simulación por ComputadorRESUMEN
Adulteration of milk to gain economic benefit has become a common practice in recent years. Sucrose is illegally added in milk to reconstitute its compositional requirement by improving the total solid contents. The present study is aimed to use FTIR spectroscopy in combination with multivariate chemometric modelling for the differentiation and quantification of sucrose in cow milk. Pure milk and adulterated milk spectra (0.5-7.5% w/v) were observed in the spectral region 4000-400 cm-1. Principal component analysis (PCA) was used for the discrimination of pure milk and adulterated milk. Soft independent modelling of class analogy (SIMCA) was able to classify test samples with a classification efficiency of 100%. Partial least square regression (PLS-R) and principle component regression (PCR) models were established for normal spectra, 1st derivative and 2nd derivative for the quantification of sucrose in milk. PLS-R model (normal spectra) in the combined wavenumber range of 1070-980 cm-1 showed the best prediction based on parameters like coefficient of determination (R2) (Cal: 0.996; Val: 0.993), RMSE (Cal: 0.15% w/v; Val: 0.20% w/v), RE% (Cal: 4.9% w/v; Val: 5.1% w/v) and RPD (13.40). This method has a detection level of 0.5% w/v sucrose adulteration.
Asunto(s)
Contaminación de Alimentos , Leche , Animales , Bovinos , Femenino , Contaminación de Alimentos/análisis , Análisis de los Mínimos Cuadrados , Análisis Multivariante , Espectroscopía Infrarroja por Transformada de Fourier , SacarosaRESUMEN
A Fourier Transform Infrared Spectroscopy based chemometric model was evaluated for the rapid identification and estimation of cane sugar as an added sugar adulterant in apple fruit juices. For all the ninety samples, spectra were acquired in the mid-infrared range (4000 cm-1-400 cm-1). The spectral analysis provided information regarding the distinctive variable region, which lies in the range of 1200cm-1 to 900cm-1, designated as fingerprint region for the carbohydrates. A specific peak in the fingerprint region was observed at 997cm-1 in all the adulterated samples and was undetectable in pure samples. Based on different levels of cane sugar adulteration (5, 10, 15, and 20%), principal component analysis showed the clustering of samples and further helped us in compression of data by selecting wavenumbers with maximum variability based on the loading line plot. Supervised classification methods (SIMCA and LDA) were evaluated based on their classification efficiencies for a test set. Though SIMCA showed 100% classification efficiency (Raw data set), LDA was able to classify the test set with an accuracy of only 96.67% (Raw as well as Transformed data set) between pure and 5% adulterated samples. For the quantitative estimation, calibration models were developed using partial least square regression (PLS-R) and principal component regression method (PCR) methods. PLS-1st derivative showed a maximum coefficient of determination (R2) with a value of 0.991 for calibration and 0.992 for prediction. The RMSECV, RMSEP, LOD and LOQ observed for PLS-1st derivative model were 0.75% w/v, 0.61% w/v, 1.28%w/v and 3.88%w/v, respectively. The coefficient of variation as a measure of precision (repeatability) was also determined for all models, and it ranged from 0.23% to 1.83% (interday), and 0.25% to 1.43% (intraday).
Asunto(s)
Aditivos Alimentarios/análisis , Análisis de los Alimentos , Contaminación de Alimentos/análisis , Jugos de Frutas y Vegetales/análisis , Modelos Químicos , Malus/química , Saccharum/química , Espectroscopía Infrarroja por Transformada de FourierRESUMEN
Microorganisms have evolved themselves to thrive under various extreme environmental conditions such as extremely high or low temperature, alkalinity, and salinity. These microorganisms adapted several metabolic processes to survive and reproduce efficiently under such extreme environments. As the major proportion of earth is covered with the cold environment and is exploited by human beings, these sites are not pristine anymore. Human interventions are a great reason for disturbing the natural biogeochemical cycles in these regions. The survival strategies of these organisms have shown great potential for helping us to restore these pristine sites and the use of isolated cold-adapted enzymes from these organisms has also revolutionized various industrial products. This review gives you the insight of psychrophilic enzyme adaptations and their industrial applications.