Rapid determination and classification of crude oils by ATR-FTIR spectroscopy and chemometric methods

Enhancing cannabidiol bioaccessibility using ionic liquid as emulsifier to produce nanosystems: Characterization of structures, cytotoxicity assessment, and in vitro digestion

The emulsifying potential of a biocompatible ionic liquid (IL) to produce lipid-based nanosystems developed to enhance the bioaccessibility of cannabidiol (CBD) was investigated. The IL (cholinium oleate) was evaluated at concentrations of 1 % and 2 % to produce nanoemulsions (NE-IL) and nanostructured lipid carriers (NLC-IL) loaded with CBD. The IL concentration of 1 % demonstrated to be sufficient to produce both NE-IL and NLC-IL with excellent stability properties, entrapment efficiency superior to 99 %, and CBD retention rate of 100 % during the storage period evaluated (i.e. 28 days at 25 °C). The in vitro digestion evaluation demonstrated that the NLC-IL provided a higher stability to the CBD, while the NE-IL improved the CBD bioaccessibility, which was mainly related to the composition of the lipid matrices used to obtain each nanosystem. Finally, it was observed that the CBD cytotoxicity was reduced when the compound was entrapped into both nanosystems.

Prediction of some physicochemical properties in Colombian crude oils upgraded by catalytic aquathermolysis using UV-VIS spectroscopy associated with chemometric methods

The simulated distillation curve (ASTM/D-7169) is a quantitative method to determine fractions of crude oils by boiling point temperature ranges (36-720 °C). In this work, 45 samples of typical Colombian crudes were selected, and the samples were produced under conventional process. Also 8 upgraded crude oils under catalytic aquathermolysis conditions at laboratory scale were added. The tests were developed at 270 °C and 800psi (@25 °C) during 66 h of reaction. In addition, 30 samples were selected for density tests, according to the pycnometer method. Subsequently, the crude oil samples under study were diluted in chloroform and analyzed by UV-VIS Spectroscopy. The UV-VIS spectra were correlated with selected properties by using PCA-MLR and PLS models. The distillation curves of the crude oils were modelled using the Riazi probability function. The prediction models of parameters To, A, and B from the Riazi probability function exhibited R2 correlation coefficients, higher than 0.94. The correlation model for the crude oil density showed a much better coefficient, higher than 0.99 and Root-Mean-Squared-Error (RMSE) close to 0.004. Additionally, even more important is the contribution of the use of UV-VIS spectroscopy as a useful tool to quickly evaluate the quality of crude oil.

Rapid discrimination of Lentilactobacillus parabuchneri biofilms via in situ infrared spectroscopy

Microbial contamination in food industry is a source of foodborne illnesses and biofilm-related diseases. In particular, biogenic amines (BAs) accumulated in fermented foods via lactic acid bacterial activity exert toxic effects on human health. Among these, biofilms of histamine-producer Lentilactobacillus parabuchneri strains adherent at food processing equipment surfaces can cause food spoilage and poisoning. Understanding the chain of contamination is closely related to elucidating molecular mechanisms of biofilm formation. In the present study, an innovative approach using integrated chemical sensing technologies is demonstrated to fundamentally understand the temporal behavior of biofilms at the molecular level by combining mid-infrared (MIR) spectroscopy and fluorescence sensing strategies. Using these concepts, the biofilm forming capacity of six cheese-isolated L. parabuchneri strains (IPLA 11151, 11150, 11129, 11125, 11122 and 11117) was examined. The cut-off values for the biofilm production ability of each strain were quantified using crystal violet (CV) assays. Real-time infrared attenuated total reflection spectroscopy (IR-ATR) combined with fluorescence quenching oxygen sensing provides insight into distinct molecular mechanisms for each strain. IR spectra showed significant changes in characteristic bands of amides, lactate, nucleic acids, and extracellular polymeric substances (i.e., lipopolysaccharides, phospholipids, phosphodiester, peptidoglycan, etc.), which are major contributors to biofilm maturation involved in the initial adhesion processes. Chemometric methods including principal component analysis and partial least square-discriminant analysis facilitated the rapid determination and classification of cheese isolated L. parabuchneri strains unambiguously differentiating the IR signatures based on their ability to produce biofilm. All biofilms were morphologically characterized by confocal laser scanning microscopy on relevant industrial equipment surfaces. In summary, this innovative approach combining MIR spectroscopy with luminescence sensing enables real-time insight into the molecular composition and formation of L. parabuchneri biofilms.

Ability of marine-derived fungi isolated from polluted saline environment for enzymatic hydrocarbon remediation

Marine-derived fungi have attracted much attention due to their ability to present a new biosynthetic diversity. About 50 fungal isolates were obtained from Tunisian Mediterranean seawater and then screened for the presence of lignin-peroxidase (LiP), manganese-dependent peroxidase (MnP), and laccase (Lac) activities. The results obtained from both qualitative and quantitative assays showed that four of marine fungi isolates had a high potential to produce lignin-degrading enzymes. They were characterized taxonomically by a molecular method, based on international spacer (ITS) rDNA sequence analysis, as Chaetomium jodhpurense (MH667651.1), Chaetomium maderasense (MH665977.1), Paraconiothyrium variabile (MH667653.1), and Phoma betae (MH667655.1) which have been reported as producers of ligninolytic enzyme in the literature. The enzymatic activities and culture conditions were optimized using a Fractional Factorial design (2 7- 4). Then, fungal strains were incubated with the addition of 1% of crude oil in 50% of seawater for 25 days to evaluate their abilities to simultaneously degrade hydrocarbon compounds and to produce ligninolytic enzymes. The strain P. variabile exhibited the highest crude oil degradation rate (48.3%). Significant production of ligninolytic enzymes was recorded during the degradation process, which reached 2730 U/L for the MnP, 410 U/L for LiP, and 168.5 U/L for Lac. The FTIR and GC-MS analysis confirmed that the isolates rapidly biodegrade crude oil under ecological and economic conditions.

A solar-driven degradation-evaporation strategy for membrane self-cleaning in the efficient separation of viscous crude oil/water emulsions

Membrane separation techniques are promising methods for effectively treating hazardous emulsified oily wastewater, but membrane fouling remains a serious challenge because the high viscosity and complex composition of crude oil make it easy to adhere to membranes and difficult to be removed by conventional physical or chemical cleaning means. Herein, a two-stage solar-driven (photo-Fenton degradation/evaporation) strategy was proposed to realize the self-cleaning of membranes fouled by viscous crude oil (>60,000 mPa s), wherein the photo-Fenton process helped to degrade the heavy components into light components, and all light components removed during the solar-driven evaporation process. A 1D/2D heterostructure membrane with photo-Fenton activity and anti-crude-oil-fouling performance was prepared via a facile self-assembly vacuum-assist method. The addition of rod-like g-C₃N₄ (RCN) increased the interlayer distance of α-FeOOH/porous g-C₃N₄ (FPCN) nanosheets, resulting in a high permeation flux. The FPCN-RCN membrane exhibited both high permeation flux of 779 ± 19 L m^-2h^-1bar^-1 and a separation efficiency of 99.4% for highly viscous crude oil-in-water emulsion. Importantly, the viscous crude oil fouled on the membrane was completely removed by the photo-Fenton degradation/solar-driven evaporation strategy, and the flux recovery rate of the membrane was ∼100%. Therefore, the FPCN-RCN membrane combined with the novel self-cleaning strategy exhibits great potential for practical emulsified oily wastewater treatment.

Multivariate Analysis Applied to Microwave-Driven Cyanide Polymerization: A Statistical View of a Complex System

For the first time, chemometrics was applied to the recently reported microwave-driven cyanide polymerization. Fast, easy, robust, low-cost, and green-solvent processes are characteristic of these types of reactions. These economic and environmental benefits, originally inspired by the constraints imposed by plausible prebiotic synthetic conditions, have taken advantage of the development of a new generation of HCN-derived multifunctional materials. HCN-derived polymers present tunable properties by temperature and reaction time. However, the apparently random behavior observed in the evolution of cyanide polymerizations, assisted by microwave radiation over time at different temperatures, leads us to study this highly complex system using multivariate analytical tools to have a proper view of the system. Two components are sufficient to explain between 84 and 98% of the total variance in the data in all principal component analyses. In addition, two components explain more than 91% of the total variance in the data in the case of principal component analysis for categorical data. These consistent statistical results indicate that microwave-driven polymerization is a more robust process than conventional thermal syntheses but also that plausible prebiotic chemistry in alkaline subaerial environments could be more complex than in the aerial part of these systems, presenting a clear example of the "messy chemistry" approach of interest in the research about the origins of life. In addition, the methodology discussed herein could be useful for the data analysis of extraterrestrial samples and for the design of soft materials, in a feedback view between prebiotic chemistry and materials science.

Classification of local diesel fuels and simultaneous prediction of their physicochemical parameters using FTIR-ATR data and chemometrics

Class identification and prediction of physicochemical variables of eight diesel fuel brands collected from several stations within the Atlanta metropolitan area in the State of Georgia were investigated using principal component analysis (PCA), partial least squares discriminant analysis (PLS2-DA), and partial least squares regression (PLSR) as modeling techniques. The fuels were from a common pipeline, therefore, assumed to have very similar characteristics. Ten FTIR-ATR spectra per fuel brand were collected over the 650 - 4000 cm^-1 mid-infrared region, and the 80 x 3351 matrix was submitted to PCA to determine if there were any clusters. Following PCA, the 80 x 3351 matrix was split into a training matrix (56x3351) and a test matrix (24x3351). PLS2-DA models were built and evaluated for class identification using dummy variables (I,0) as input matrix. For physicochemical variable predictions, models were developed via PLSR using the FTIR-ATR spectra training matrix and physicochemical variables obtained from the Georgia Department of Agriculture Labs as input. Correlation coefficients of the eight fuels ranged from 0.9960 to 0.9998. PCA revealed all eight clusters of the diesel fuels, regardless of the tight correlation coefficients range. With a 1.0 ± 0.1 cut-off for fuel identification, the PLS2-DA models showed 100% correct predictions for four or five fuel brands, and 75% correct prediction for all eight fuel brands. PLSR predicted 100% correct physicochemical variables, with a RMSEP range of 0.019 to 1.132 for all 80 variables targeted.

Rapid discrimination and screening of volatile markers for varietal recognition of Curcumae Radix using ATR-FTIR and HS-GC-MS combined with chemometrics

ETHNOPHARMACOLOGICAL RELEVANCE

Curcumae Radix (Yujin) has a long medicinal use history in China, which is used to cure diseases like jaundice, cholelithiasis caused by dampness-heat of gallbladder and liver, and so on. It comes from the dried tuberous roots of C. kwangsiensis (Guiyujin), C. longa (Huangyujin), C. phaeocaulis (Lvyujin) and C. wenyujin (Wenyujin). Though there are differences in chemical compositions and pharmacological activities among the four species of Yujin, they have not been differentiated well in clinical application due to their similar morphological characterizations.

AIM OF THE STUDY

In this study, the four species of Yujin were rapidly and accurately discriminated. The potential volatile markers for varietal recognition were identified.

MATERIALS AND METHODS

Attenuated total reflection fourier transformed infrared (ATR-FTIR) spectroscopy combined with chemometrics was used to rapidly discriminate the four species of Yujin. Headspace-gas chromatography-mass spectrometry (HS-GC-MS) technology coupled with chemometrics was employed to characterize volatile profiling, differentiate species and select potential markers for varietal recognition of Yujin.

RESULTS

By applying PCA (principal components analysis) and HCA (hierarchical cluster analysis), HS-GC-MS realized complete differentiation of the four species of Yujin, while ATR-FTIR only recognized Guiyuijin. Back propagation neural network (BP-NN), KNN (K-nearest neighbor) and LDA (linear discriminant analysis) models based on spectral data achieved 100% discriminant accuracies. Support vector machines (SVM), KNN and PLS-DA (partial least square discriminant analysis) models based on volatile compounds also realized 100% discriminant accuracies. Additionally, the potential volatile markers for varietal recognition of Yujin were screened using PLS-DA, including 2 for Guiyujin, 6 for Lvyujin, 9 for Wenyujin and 13 for Huangyujin.

CONCLUSIONS

The present study developed reliable methods for the varietal discrimination and volatile compounds characterization of Yujin, which will provide references for its quality control and clinical efficacy.

Fast discrimination and quantification analysis of Curcumae Radix from four botanical origins using NIR spectroscopy coupled with chemometrics tools

Curcumae Radix (Yujin) is a multi-origin herbal medicine with excellent clinical efficacy. For fast discrimination and quantification analysis of Yujin from four botanical origins (Guiyujin, Huangyujin, Lvyujin and Wenyujin), near infrared (NIR) spectroscopy combined with chemometrics tools was employed in this study. Based on NIR data, principal component analysis (PCA) could only realize the separation between Guiyujin and Wenyujin samples, and the partial least squares-discrimination analysis (PLS-DA), support vector machine (SVM) and k-nearest neighbors (KNN) models achieved the complete discrimination of the four species of Yujin with 100% accuracy. Moreover, the method for the simultaneous determination of six bioactive compounds in Yujin was developed by HPLC. Germacrone, curdione and curcumenol could be found in all samples, and curcumin, demethoxycurcumin and bisdemethoxycurcumin were only observed in Huangyujin samples. Then, the support vector machine regression (SVMR) model for the prediction of germacrone content was successfully constructed. And the coefficients of determination were 0.88 and 0.89 for calibration and validation sets, respectively. The present work proposes a quick, economic and reliable method for the discrimination of Yujin from four botanical origins and the prediction of germacrone content, which will contribute to its quality control researches.

Aesculus hippocastanum L. as a Stabilizer in Hemp Seed Oil Nanoemulsions for Potential Biomedical and Food Applications

Nanoemulsion systems receive a significant amount of interest nowadays due to their promising potential in biomedicine and food technology. Using a two-step process, we produced a series of nanoemulsion systems with different concentrations of hemp seed oil (HSO) stabilized with Aesculus hippocastanum L. extract (AHE). Water and commercially-available low-concentrated hyaluronic acid (HA) were used as the liquid phase. Stability tests, including an emulsifying index (EI), and droplet size distribution tests performed by dynamic light scattering (DLS) proved the beneficial impact of AHE on the emulsion’s stability. After 7 days of storage, the EI for the water-based system was found to be around 100%, unlike the HA systems. The highest stability was achieved by an emulsion containing 5% HSO and 2 g/L AHE in water, as well as the HA solution. In order to obtain the detailed characteristics of the emulsions, UV-Vis and FTIR spectra were recorded, and the viscosity of the samples was determined. Finally, a visible microscopic analysis was used for the homogeneity evaluation of the samples, and was compared with the DLS results of the water system emulsion, which showed a desirable stability. The presented results demonstrate the possible use of oil emulsions based on a plant extract rich in saponins, such as AHE. Furthermore, it was found that the anti-inflammatory properties of AHE provide opportunities for the development of new emulsion formulations with health benefits.

Genetic algorithm based support vector machine regression for prediction of SARA analysis in crude oil samples using ATR-FTIR spectroscopy

In the current research, an analytical method was proposed for rapid quantitative determination of saturates, aromatics, resins and asphaltenes (SARA) fractions of crude oil samples. Rapid assessments of SARA analysis of crude oil samples are of substantial value in the oil industry. The conventional SARA analysis procedures were determined with the standards established by the American Society for Testing and Materials (ASTM). However, the standard test methods are time consuming, environmental nonfriendly, expensive, and require large amounts of the crude oil samples to be analyzed. Thus, it be would useful to approve some supportive approaches for rapid evaluation of the crude oils. The attenuated total reflection Fourier-transform infrared spectroscopy ATR-FTIR coupled with chemometric methods could be used as analytical method for crude oil analysis. A hybrid of genetic algorithm (GA) and support vector machine regression (SVM-R) model was applied to predict SARA analysis of crude oil samples from different Iranian oil field using ATR-FTIR spectroscopy. The result of GA-SVM-R model were compared with genetic algorithm-partial least square regression (GA-PLS-R) model. Correlation coefficient (R²) and root mean square error (RMSE) for calibration and prediction of samples were also calculated, in order to evaluate the calibration models for each component of SARA analysis in crude oil samples. The performance of GA-SVM-R is found to be reliably superior, so that it can be successfully applied as an alternative approach for the quantitative determination of the SARA analysis of crude oil samples.