Chemometric Models Research Articles

Detection of Adulterated Oregano Samples Using Untargeted Headspace-Gas Chromatography-Ion Mobility Spectrometry Analysis.

Oregano is often adulterated for economic reasons. This fraud mainly consists of adding other species with lower commercial value, such as olive leaves. To ensure the authenticity of oregano, an analytical method based on the analysis of the volatile organic compound (VOC) profile obtained by headspace gas chromatography coupled to ion mobility spectrometry (HS-GC-IMS) was developed and validated. Samples of ecological Mediterranean oregano adulterated with different percentages of two types of olive leaves (cornicabra and manzanilla) were studied using a non-targeted analysis. Moreover, a total of 30 VOCs were identified in the analyzed samples, and 24 compounds could be quantified using calibration curves based on Boltzmann's equation. A chemometric model based on orthogonal partial least squares discriminant analysis (OPLS-DA) was used to detect the adulterated oregano samples, obtaining a 100% validation success rate, and partial least squares (PLS) analysis was used to quantify the percentage of adulterant. Finally, the proposed methodology was applied to 15 commercial oregano samples, resulting in two of them being classified as adulterated with 31 and 43% of olive leaves, respectively.

Pioneering Just-in-Time (JIT) Strategy for Accelerating Raman Method Development and Implementation for Biologic Continuous Manufacturing.

Raman spectroscopy is a popular process analytical technology (PAT) tool that has been increasingly used to monitor and control the monoclonal antibody (mAb) manufacturing process. Although it allows the characterization of a variety of quality attributes by developing chemometric models, a large quantity of representative data is required, and hence, the model development process can be time-consuming. In recent years, the pharmaceutical industry has been expediting new drug development in order to achieve faster delivery of life-changing drugs to patients. The shortened development timelines have impacted the Raman application, as less time is allowed for data collection. To address this problem, an innovative Just-in-Time (JIT) strategy is proposed with the goal of reducing the time needed for Raman model development and ensuring its implementation. To demonstrate its capabilities, a proof-of-concept study was performed by applying the JIT strategy to a biologic continuous process for producing monoclonal antibody products. Raman spectroscopy and online two-dimensional liquid chromatography (2D-LC) were integrated as a PAT analyzer system. Raman models of antibody titer and aggregate percentage were calibrated by chemometric modeling in real-time. The models were also updated in real-time using new data collected during process monitoring. Initial Raman models with adequate performance were established using data collected from two lab-scale cell culture batches and subsequently updated using one scale-up batch. The JIT strategy is capable of accelerating Raman method development to monitor and guide the expedited biologics process development.

Generative data augmentation and automated optimization of convolutional neural networks for process monitoring.

Chemometric modeling for spectral data is considered a key technology in biopharmaceutical processing to realize real-time process control and release testing. Machine learning (ML) models have been shown to increase the accuracy of various spectral regression and classification tasks, remove challenging preprocessing steps for spectral data, and promise to improve the transferability of models when compared to commonly applied, linear methods. The training and optimization of ML models require large data sets which are not available in the context of biopharmaceutical processing. Generative methods to extend data sets with realistic in silico samples, so-called data augmentation, may provide the means to alleviate this challenge. In this study, we develop and implement a novel data augmentation method for generating in silico spectral data based on local estimation of pure component profiles for training convolutional neural network (CNN) models using four data sets. We simultaneously tune hyperparameters associated with data augmentation and the neural network architecture using Bayesian optimization. Finally, we compare the optimized CNN models with partial least-squares regression models (PLS) in terms of accuracy, robustness, and interpretability. The proposed data augmentation method is shown to produce highly realistic spectral data by adapting the estimates of the pure component profiles to the sampled concentration regimes. Augmenting CNNs with the in silico spectral data is shown to improve the prediction accuracy for the quantification of monoclonal antibody (mAb) size variants by up to 50% in comparison to single-response PLS models. Bayesian structure optimization suggests that multiple convolutional blocks are beneficial for model accuracy and enable transfer across different data sets. Model-agnostic feature importance methods and synthetic noise perturbation are used to directly compare the optimized CNNs with PLS models. This enables the identification of wavelength regions critical for model performance and suggests increased robustness against Gaussian white noise and wavelength shifts of the CNNs compared to the PLS models.

A Slug Flow Platform with Multiple Process Analytics Facilitates Flexible Reaction Optimization.

Flow processing offers many opportunities to optimize reactions in a rapid and automated manner, yet often requires relatively large quantities of input materials. To combat this, the use of a flexible slug flow reactor, equipped with two analytical instruments, for low-volume optimization experiments are reported. A Buchwald-Hartwig amination toward the drug olanzapine, with 6 independent optimizable variables, is optimized using three different automated approaches: self-optimization, design of experiments, and kinetic modeling. These approaches are complementary and provide differing information on the reaction: pareto optimal operating points, response surface models, and mechanistic models, respectively. The results are achieved using <10% of the material that would be required for standard flow operation. Finally, a chemometric model is built utilizing automated data handling and three subsequent validation experiments demonstrate good agreement between the slug flow reactor and a standard (larger scale) flow reactor.

Advanced chemometric methods for simultaneous quantitation of caffeine, codeine, paracetamol, and p-aminophenol in their quaternary mixture

Two different multivariate techniques have been applied for the quantitative analysis of caffeine, codeine, paracetamol and p-aminophenol (PAP) in quaternary mixture, namely, Partial Least Squares (PLS-1) and Artificial Neural Networks (ANN). For suitable analysis, a calibration set of 25 mixtures with various ratios of the drugs and PAP impurity were established using a 4-factor 5-level experimental design. The most meaningful wavelengths for the chemometric models were chosen using Genetic Algorithm (GA) as a variable selection technique. By using an independent validation set, the validity of the proposed methods was evaluated. A comparative study was established between the three multivariate models (PLS-1, GA–PLS and GA–ANN). The comparison between the various models revealed that the GA–ANN model was superior at resolving the highly overlapped spectra of this quaternary combination. The drugs were successfully quantified in their pharmaceutical dosage form utilizing the GA–ANN models.

Improving feedstock quality control in formaldehyde-based resin and wood-based panel production through near infrared spectroscopy

To assure the quality control of industrial processes, it is important to adopt reproducible and efficient methodologies. Spectroscopic methods, such as near infrared (NIR), are a good option as they are fast and may be used to indirectly estimate multiple physicochemical properties. In this study, NIR spectra of key feedstock samples used in the production of formaldehyde-based resin and wood-based panels, namely urea, ammonium sulfate, ammonium nitrate, sodium hydroxide, and acetic acid, were acquired. Multivariate data analysis was applied to establish the correlation between the spectra and the properties being measured. Quantitative models were then created using partial least squares regression to predict the concentrations of feedstock samples. This study presents quantitative models that were created by combining spectra measured on two probes, which achieved similar prediction results as single-probe based models. The performances of the best models were compared with the reference methods for each of the evaluated samples. For the samples under study, the proposed approach is suitable for routine analysis across multiple equipment configurations using the same quantitative model. NIR spectroscopy combined with chemometric models could be a valuable complement to support in-line raw material monitoring and plant digitalization in the wood panels industry.

Use of near-infrared spectroscopy for the online monitoring of natural gas composition (hydrocarbons, water and CO2 content) at high pressure

The natural gas production from Brazilian pre-salt fields imposed new challenges for petrochemical industry. Actual treatment facilities are not adequate for this new scenario and studies have been conducted to apply new adsorbents materials and membranes for water and CO2 removal from natural gas at high pressures. To better develop such investigations, sensors for on-line monitoring of natural gas properties like CO2 and water content are important, since their presence affects the quality of the final product. Near infrared (NIR) spectroscopy associated to chemometric models (partial least squares) were employed for on-line monitoring of representative natural gas systems (methane/CO2, methane/water and methane/CO2/water) and a real natural gas at temperature and pressure ranges from 20 to 60 °C and 10 to 200 bar, respectively, water content up to gas saturation and CO2 content up to 50 wt%. Water solubility values used as reference for NIR Spectrometer calibration in model systems were taken from the literature and for real natural gas calculated with Cubic Plus Association (CPA) Equation of State (EoS), while CO2 content was experimentally controlled aiming to calibrate the chemometric models in the full range of pressure and temperature. Several strategies were adopted for the chemometric model’s development to obtain the best correlation between NIR spectra and experimental data. Results indicate good correlation in both calibration and validation steps attaining linear correlation coefficients (R2) higher than 0.96 for all systems investigated. The proposed methodology is a potential tool for on-line monitoring of natural gas composition, including CO2 and water content, at high-pressures and can be applied at petrochemical industries or in laboratories, dispensing sampling or any sample preparation.

Multi-model fusion stacking ensemble learning method for the prediction of berberine by FT-NIR spectroscopy

Rhizoma Coptidis is a Chinese herbal medicine with antibacterial and anti-inflammatory properties. It has extensive applications in modern medicine. The content of berberine in Rhizoma Coptidis directly determines its quality. Fourier transforms near-infrared (FT-NIR) spectroscopy is a commonly used non-destructive method for rapidly detecting berberine content. In contrast to single-supervised learning algorithms in machine learning, ensemble learning combines individual learning algorithms to create a stable and better-performing strong-supervised model. This study collected spectral data of Rhizoma Coptidis using FT-NIR spectroscopy technology and established a chemometric model using a stacking ensemble approach with multiple models. Partial Least Squares (PLS), Adaptive Boosting (AdaBoost), Gradient boosting decision trees (GBDT), random forest (RF), and extreme gradient boosting (XGBoost) regression models were chosen as alternative base models, different Stacking models were established by random combinations. To fully leverage the strengths of each model and enhance predictive capability, an adaptive inertia weight particle swarm optimization algorithm (AWPSO) was used to search for the optimal parameters. The correlation coefficient of the test (RT) and the root mean square error of the test (RMSET) systematically evaluated the model performance. Finally, AWPSO-RF, AWPSO-XGBoost, and AWPSO-AdaBoost were selected as the base models. The RMSET and RT for RF, XGBoost, and AdaBoost were 0.226, 0.250, 0.195, and 0.871, 0.830, 0.927. After optimizing with the AWPSO algorithm, the RMSET and RT for AWPSO-RF, AWPSO-XGBoost, and AWPSO-AdaBoost were 0.226, 0.245, 0.194, and 0.871, 0.843, 0.922, respectively. The RMSET and RT values for the stacking ensemble were 0.174 and 0.932. The prediction accuracy and generalization ability of multi-model fusion stacking ensemble learning are superior to the single-model regression methods. Therefore, the stacking ensemble learning method that combines AdaBoost, RF, and XGBoost regression models is effective and feasible for assisting in the detection of berberine content in Rhizoma Coptidis.

Comparing Sensor Fusion and Multimodal Chemometric Models for Monitoring U(VI) in Complex Environments Representative of Irradiated Nuclear Fuel.

Optical sensors and chemometric models were leveraged for the quantification of uranium(VI) (0-100 μg mL-1), europium (0-150 μg mL-1), samarium (0-250 μg mL-1), praseodymium (0-350 μg mL-1), neodymium (0-1000 μg mL-1), and HNO3 (2-4 M) with varying corrosion product (iron, nickel, and chromium) levels using laser fluorescence, Raman scattering, and ultraviolet-visible-near-infrared absorption spectra. In this paper, an efficient approach to developing and evaluating tens of thousands of partial least-squares regression (PLSR) models, built from fused optical spectra or multimodal acquisitions, is discussed. Each PLSR model was optimized with unique preprocessing combinations, and features were selected using genetic algorithm filters. The 7-factor D-optimal design training set contained just 55 samples to minimize the number of samples. The performance of PLSR models was evaluated by using an automated latent variable selection script. PLS1 regression models tailored to each species outperformed a global PLS2 model. PLS1 models built using fused spectra data and a multimodal (i.e., analyzed separately) approach yielded similar information, resulting in percent root-mean-square error of prediction values of 0.9-5.7% for the seven factors. The optical techniques and data processing strategies established in this study allow for the direct analysis of numerous species without measuring luminescence lifetimes or relying on a standard addition approach, making it optimal for near-real-time, in situ measurements. Nuclear reactor modeling helped bound training set conditions and identified elemental ratios of lanthanide fission products to characterize the burnup of irradiated nuclear fuel. Leveraging fluorescence, spectrophotometry, experimental design, and chemometrics can enable the remote quantification and characterization of complex systems with numerous species, monitor system performance, help identify the source of materials, and enable rapid high-throughput experiments in a variety of industrial processes and fundamental studies.

Assessment of Beeswax Adulteration by Paraffin and Stearic Acid Using ATR-IR Spectroscopy and Multivariate Statistics-An Analytical Method to Detect Fraud.

A spectroscopic investigation of beeswax adulteration by paraffin and/or stearic acid was undertaken via Attenuated Total Reflectance Infra-Red spectroscopy (ATR-IR) combined with multivariate statistical analyses. Principal Component Analysis (PCA) was successfully applied for the first time as an exploratory tool for the differentiation among pure beeswax and adulterated beeswax by paraffin and stearic acid with detection limits (LOD) of ~5% and 1%, respectively. Partial Least Square (PLS) modelling was used to build chemometric models based on beeswax/paraffin and beeswax/stearic acid calibration mixtures and subsequently used to predict concentrations of paraffin and stearic acid on a set of unknown test samples. PLS predictions demonstrated that beeswax adulteration by paraffin is much more prominent (74%) than the one by stearic acid (26%) and that commercial beeswax products (candles, pearls, blocks, etc.) are more prone to adulteration (27%) than honeycomb-type samples (12.5%).

Rapid monitoring of heavy metal-ammonia complexes in solutions by UV–vis/ATR-FTIR spectroscopy and chemometric models

Ammonia can form stable chemical complexes with heavy metal ions, which significantly affect the treatment efficiency of these wastewaters. Thus, accurately determining the concentration of heavy metal ammonia complex ions is crucial to understanding the complex decomposition mechanism. The current study predicted the composition of copper ammonia solution through thermodynamic calculations. The mid-infrared first derivative spectra obtained from density functional theory (DFT) calculation and measurements of the simulated solutions were compared, which indicated that [Cu(NH3)4]2+ was the primary form of complexes in solution. The acquired FTIR spectra and UV–vis spectra demonstrated a strong correlation with the [Cu(NH3)4]2+ concentrations; whereas a principal component regression (PCR) model was implemented for concentration computation. The UV–vis-PCR model was found to be a fast and accurate method for quantification of copper-ammonia complex concentration, and its detection limit and quantification limit could reach 5 mg·L−1 and 10 mg·L−1, respectively, in the 5–200 mg·L−1 range. This study established qualitative and quantitative methods for analyzing copper ammonia complex in water, which provided the feasibility for rapid real-time monitoring of copper ammonia complex.

A Raman spectroscopy based chemometric approach to predict the derived cetane number of hydrocarbon jet fuels and their mixtures

Fuel ignition quality, measured in the form of Derived Cetane Number (DCN), is an important part of integrating fuels, including sustainable aviation fuels, in compression ignition engines. DCN has been correlated with simulated and/or real spectroscopic measurements as well as other physical and chemical properties, but rarely have these correlations developed into a pathway to application. One application of the correlations is the use of miniaturized onboard fuel sensors that could assist, by using predicted DCN, in real-time feedforward engine control. To aid in the application of developing such DCN fuel sensors, Raman spectra coupled with chemometrics and a selection of influential spectral features were investigated. In this study, the Raman spectra were obtained from a database that included jet fuels, jet fuel mixtures, pure hydrocarbon components, and their weighted mixtures. The resulting Raman spectral database from the experimental measurements included spectra of components that span a wide range of DCNs and covered all the expected chemical functional groups present in a standard jet fuel. Chemometric models were developed to associate Raman spectra with DCN in subsets of the spectral range to aid in sensor miniaturization. The models were tested on jet fuels such as National Jet Fuel Combustion Program fuels designated A-1, A-2, and A-3 along with mixtures of jet fuels that spanned a wide range of DCN, simulating fuels that could represent real-world scenarios. An Artificial Neural Network (ANN) model trained on the fingerprint region (500 cm−1 – 1800 cm−1) of the Raman spectra was able to capture the non-linearity of the association between the Raman spectra and DCN with a test R2 score of 0.926, a test MSE of 3.61, and a test MPE of 3.41. Around 97 % of the unseen test samples were predicted within 10 % of the DCN measured with an Ignition Quality Tester. One hundred features of the fingerprint region influencing DCN predictions in the optimal ANN model were extracted using a Global Surrogate (GS) model. A reduced ANN model trained on only these one hundred features performed slightly better with a test R2 score of 0.935, test MSE of 3.19, test MPE of 3.20 and with the entire set of unseen test samples predicted within 10 % of the measured DCN. For assessing applicability of real-time and online DCN sensing, the Raman spectrometer was integrated with a flow cell capable of allowing measurements of DCN in flowing fuel samples and included the optimal ANN model of the fingerprint region and the 100-feature GS-ANN model on a Raspberry Pi computer. A number of unseen F-24/alcohol-to-jet fuel mixtures composed of unknown volumes were tested using the flow cell for DCN, and all of these samples were predicted within 10 % of the measured DCN.

SVR Chemometrics to Quantify β-Lactoglobulin and α-Lactalbumin in Milk Using MIR.

Protein content variation in milk can impact the quality and consistency of dairy products, necessitating access to in-line real time monitoring. Here, we present a chemometric approach for the qualitative and quantitative monitoring of β-lactoglobulin and α-lactalbumin, using mid-infrared spectroscopy (MIR). In this study, we employed Hotelling T2 and Q-residual for outlier detection, automated preprocessing using nippy, conducted wavenumber selection with genetic algorithms, and evaluated four chemometric models, including partial least squares, support vector regression (SVR), ridge, and logistic regression to accurately predict the concentrations of β-lactoglobulin and α-lactalbumin in milk. For the quantitative analysis of these two whey proteins, SVR performed the best to interpret protein concentration from 197 MIR spectra originating from 42 Cornell University samples of preserved pasteurized modified milk. The R2 values obtained for β-lactoglobulin and α-lactalbumin using leave one out cross-validation (LOOCV) are 92.8% and 92.7%, respectively, which is the highest correlation reported to date. Our approach introduced a combination of preprocessing automation, genetic algorithm-based wavenumber selection, and used Optuna to optimize the framework for tuning hyperparameters of the chemometric models, resulting in the best chemometric analysis of MIR data to quantitate β-lactoglobulin and α-lactalbumin to date.

Mitigating the impact of gelatin capsule variability on detection of substandard and falsified pharmaceuticals with near-IR spectroscopy.

Portable NIR spectrometers are effective in detecting authentic pharmaceutical products in intact capsule formulations, which can be used to screen for substandard or falsified versions of those authentic products. However, the chemometric models are trained on libraries of authentic products, and are generally unreliable for detection of quality problems in products from outside their training set, even for products that are nominally the same active pharmaceutical ingredient and same dosage as products in the training set. As part of our research directed at developing better non-brand-specific strategies for pharmaceutical screening, we investigated the impact of capsule composition on NIR modeling. We found that capsule features like gelatin type, color, or thickness, give rise to a similar amount of variance in the NIR spectra as the type of API stored within the capsules. Our results highlight the efficacy of orthogonal projection to latent structures in mitigating the impacts of different types of capsules on the accuracy of NIR chemometric models for classification and regression analysis of lab-made samples. The models showed good performance for classification of field-collected doxycycline capsules as good or bad quality when an NIR-based % w/w metric was used, identifying five samples that were adulterated with talc. However, the % w/w was systematically underestimated, so when evaluating the capsules based on their absolute API content according to the monograph standard, the classification accuracy decreased from 100% to 70%. The underestimation was attributed to an unforeseen variability in the quantities and types of excipients present in the capsules.

Non-destructive quantification of key quality characteristics in individual grapevine berries using near-infrared spectroscopy.

It is crucial for winegrowers to make informed decisions about the optimum time to harvest the grapes to ensure the production of premium wines. Global warming contributes to decreasing acidity and increasing sugar levels in grapes, resulting in bland wines with high contents of alcohol. Predicting quality in viticulture is thus pivotal. To assess the average ripeness, typically a sample of one hundred berries representative for the entire vineyard is collected. However, this process, along with the subsequent detailed must analysis, is time consuming and expensive. This study focusses on predicting essential quality parameters like sugar and acid content in Vitis vinifera (L.) varieties 'Chardonnay', 'Riesling', 'Dornfelder', and 'Pinot Noir'. A small near-infrared spectrometer was used measuring non-destructively in the wavelength range from 1 100 nm to 1 350 nm while the reference contents were measured using high-performance liquid chromatography. Chemometric models were developed employing partial least squares regression and using spectra of all four grapevine varieties, spectra gained from berries of the same colour, or from the individual varieties. The models exhibited high accuracy in predicting main quality-determining parameters in independent test sets. On average, the model regression coefficients exceeded 93% for the sugars fructose and glucose, 86% for malic acid, and 73% for tartaric acid. Using these models, prediction accuracies revealed the ability to forecast individual sugar contents within an range of ± 6.97 g/L to ± 10.08 g/L, and malic acid within ± 2.01 g/L to ± 3.69 g/L. This approach indicates the potential to develop robust models by incorporating spectra from diverse grape varieties and berries of different colours. Such insight is crucial for the potential widespread adoption of a handheld near-infrared sensor, possibly integrated into devices used in everyday life, like smartphones. A server-side and cloud-based solution for pre-processing and modelling could thus avoid pitfalls of using near-infrared sensors on unknown varieties and in diverse wine-producing regions.

Quantification of natural rubber blends by reflection/reflectance infrared and confocal Raman spectroscopy: a comparison of statistical methods.

The blend of butadiene and acrylonitrile copolymer (NBR) with natural poly-cis-isoprene (NR) shows increased resistance to swelling in solvents in comparison to the individual components. In aerospace, NBR rubber is used as thermal protection for rockets and shall not contain other polymers, even in low contents, otherwise, it can affect the protection performance and rocket safety by causing detachment of the elastomer/propellant interface; therefore, this investigation presents methodologies to determine the NR/NBR contents. This study explores different analytical techniques, such as Raman spectroscopy and Fourier transform infrared (FTIR) spectroscopy, in the mid-infrared (MIR) by reflection and in the near-infrared by reflectance (NIRA) modes, Furthermore, quantification strategies by univariate, bivariate and multivariate (chemometric) models are evaluated and compared. A proposed methodology, based on multivariate Raman microscopy with partial least squares regression (PLS), showed high linearity (R2 > 0.99) and low error (< 0.82 %). The validation of FT-MIR data for the CH3, which presented lower error (1.3%) than vinylidene band (6%), showed that both methodologies (reflection and NIRA reflectance) can be used for the quantification of NR in NR/NBR. These results constitute a contribution to the state of the art in researching industrial and aerospace elastomeric applications.

Suitability study of Ag nanosheet SERS substrates as a screening method for imidacloprid after QuEChERS extraction

SERS substrates with silver nanosheets (AgNS) on a copper surface were synthesized. A quantitative analysis of the pesticide imidacloprid was then performed by applying a PLSR chemometric model.

Determination of Alzheimer's Drugs in a Human Urine Sample by Different Chemometric Methods: Chemometric Determination of Alzheimer's Drugs.

In this study, spectrophotometric determination of donepezil and rivastigmine in healthy human urine samples was carried out by the statistical method. Partial least squares (PLS) and principal component regression (PCR) from multivariate calibration methods were used to evaluate the data obtained from the UV-Vis spectroscopy analysis of the urine sample. Mixtures of each early substance were prepared prior to urine sample analysis, and simultaneous determination of donepezil and rivastigmine was performed on the established chemometric model without any prior separation. The calibration curves of each drug were analyzed, and linearity values were also analyzed. For donepezil and rivastigmine, they were 0.9989 and 0.9997, respectively, and were linear over the concentration range of the synthetic mixture. When both chemometric methods (PLS and PCR) were evaluated in terms of accuracy and reproducibility, very high recoveries and small standard deviations were determined. In the PLS method, the standard error of prediction (SEC), the sum of the prediction residual errors (PRESS), the limit of quantitation (LOQ), and the limit of detection (LOD) values were 0.015, 0.0030, 0.067, 0.24, 0.018, 0.0042, 0.089, and 0.301 for donepezil and rivastigmine, respectively. In the PCR method, SEC, PRESS, LOD, and LOQ values are 0.016, 0.0054, 0.066, and 0.23 for donepezil and 0.022, 0.0062, 0.091, and 0.300 for rivastigmine. Chemometrics is used for speed, simplicity, and reliability. The proposed methods have been successfully applied to a sample of urine.

Chemometric modeling of bioconcentration factor of 6-chloro-1,3,5-triazine derivatives based on MLR-QSPR approach

Chemometric modeling of bioconcentration factor of 6-chloro-1,3,5-triazine derivatives based on MLR-QSPR approach

Intelligent Chemometric Modelling of Al2O3 Supported Mixed Metal Oxide Catalysts for Oxidative Dehydrogenation of n-Butane Using Simple Features

The development of efficient and selective catalysts for the oxidative dehydrogenation (ODH) of n-butane to produce butenes and butadiene with high performance has been subject of intense research in recent...