Raman model development for the protein conformational state classification in different freeze-dried formulations

The aim of this work is to build a multivariate calibration (MVC) model from Raman spectra for the prediction of the protein conformational state class (i.e. native-like or non-native) in different freeze-dried pharmaceutical formulations of a model protein lactate dehydrogenase (LDH). As this model would be intended to facilitate and better understand formulation and process development, it should allow acceptable classification performance despite variations in formulation type and batch. Therefore, it was attempted to (1) find which factors interfere the Raman spectra, (2) understand them, and (3) make the MVC model robust for them. A variance analysis within the Raman spectral data space identified significant spectral background variations among certain formulation types and batches in the studied samples. Raw material (i.e. LDH) batch variability and the presence of a Maillard reaction in formulations were the main reasons for this. We demonstrate the successful use of both exhaustive calibration and external parameter orthogonalization (EPO) pre-processing for making the Raman classification model more robust for the expected spectral interferences.     

Improvement of GC‐MS Analysis of Shahrbabak Ziziphora tenuior Essential Oil by Using Multivariate Curve Resolution Approaches

Essential oil of aerial parts of Ziziphora tenuior growing in Shahrbabak in central Iran are isolated by hydrodistillation. Due to complexity of essential oils, there are fundamental problems such as co‐elution in their direct gas chromatography‐mass spectrometry analysis. These problems can result in low similarity matches in MS library search, so that true identification and determination of individual components may fail. In the present work, each component was identified and determined using GC‐MS coupled with multivariate curve resolution (MCR) techniques. In this way, more information along with higher accuracy and precision can be extracted from pure experimental GC‐MS data. The number of identified components found increased from 37 in direct similarity search to 80 in GC‐MS/MCR method. To identify each individual component, similarity search and Kovat's retention index comparison were implemented. The results found showed that pulegone (38.3%), 3′,5′‐dihydroxyacetophenone (22.83%), isomenthone (7.06%), 2‐methyl‐5‐(1‐methylethyl)‐phenol (3.41%), limonene (2.59%) and 2‐acetyl‐4,4‐dimethyl‐cyclopent‐2‐enone (2.49%) were the most abundant components. The reported compounds accounted for 94.39% of total content of the essential oil. A characteristic feature of the Iranian Ziziphora tenuior is the absence of piperitenone in its constituents compared with the oil of other Ziziphora species from Turkey.     

Quantitative analysis of biodiesel in blends of biodiesel and conventional diesel by comprehensive two-dimensional gas chromatography and multivariate curve resolution

In this paper, a method to determine the composition of blends of biodiesel with mineral diesel (BXX) by multivariate curve resolution with Alternating Least Squares (MRC-ALS) combined to comprehensive two-dimensional gas chromatography with Flame Ionization Detection (GC × GC-FID) is presented. Chromatographic profiles of BXX blends produced with biodiesels from different sources were used as input data. An initial evaluation carried out after multiway principal component analysis (MPCA) was used to reveal regions of the chromatograms were the signal was likely to be dependent on the concentration of biodiesel, regardless its vegetable source. After this preliminary step MCR-ALS modeling was carried out only using relevant parts of the chromatograms. The resulting procedure was able to predict accurately the concentration of biodiesel in the BXX samples regardless of its origin.     

Quantitative analysis of the effect of zidovudine,efavirenz, and ritonavir on insulin aggregation by multivariate curve resolution alternating least squares of infrared spectra

Quantification of the effect of antiretroviral drugs on the insulin aggregation process is an important area of research due to the serious metabolic diseases observed in AIDS patients after prolonged treatment with these drugs. In this work, multivariate curve resolution alternating least squares (MCR-ALS) was applied to infrared monitoring of the insulin aggregation process in the presence of three antiretroviral drugs to quantify their effect. To evidence concentration dependence in this process, mixtures at two different insulin:drug molar ratios were used. The interaction between insulin and each drug was analysed by ¹H NMR spectroscopy. In all cases, the aggregation process was monitored during 45 min by infrared spectroscopy. The aggregates were further characterised by scanning electron microscopy (SEM). MCR-ALS provided the spectral and concentration profiles of the different insulin–drug conformations that are involved in the process. Their feasible band boundaries were calculated using the MCR-BANDS methodology. The kinetic profiles describe the aggregation pathway and the spectral profiles characterise the conformations involved. The retrieved results show that each of the three drugs modifies insulin conformation in a different way, promoting the formation of aggregates. Ritonavir shows the strongest promotion of aggregation, followed by efavirenz and zidovudine. In the studied concentration range, concentration dependence was only observed for zidovudine, with shorter aggregation time obtained as the amount of zidovudine increased. This factor also affected the aggregation pathway.     

Potential of visible-near infrared spectroscopy combined with chemometrics for analysis of some constituents of coffee and banana residues

Banana (stalk, leaf, rhizome, rachis and stem) and coffee (leaf and husks) residues are promising feedstock for fuel and chemical production. In this work we show the potential of near-infrared spectroscopy (NIR) and multivariate analysis to replace reference methods in the characterization of some constituents of coffee and banana residues. The evaluated parameters were Klason lignin (KL), acid soluble lignin (ASL), total lignin (TL), extractives, moisture, ash and acid insoluble residue (AIR) contents of 104 banana residues (B) and102 coffee (C) residues from Brazil. PLS models were built for banana (B), coffee (C) and pooled samples (B + C). The precision of NIR methodology was better (p < 0.05) than the reference method for almost all the parameters, being worse for moisture. With the exception of ash (B and C) and ASL (C) content, which was predicted poorly (R² < 0.80), the models for all the analytes exhibited R² > 0.80. The range error ratios varied from 4.5 to 16.0. Based on the results of external validation, the statistical tests and figures of merit, NIR spectroscopy proved to be useful for chemical prediction of banana and coffee residues and can be used as a faster and more economical alternative to the standard methodologies.     

A novel algorithm for linear multivariate calibration based on the mixed model of samples

We present a novel algorithm for linear multivariate calibration that can generate good prediction results. This is accomplished by the idea of that testing samples are mixed by the calibration samples in proper proportion. The algorithm is based on the mixed model of samples and is therefore called MMS algorithm. With both theoretical support and analysis of two data sets, it is demonstrated that MMS algorithm produces lower prediction errors than partial least squares (PLS2) model, has similar prediction performance to PLS1. In the anti-interference test of background, MMS algorithm performs better than PLS2. At the condition of the lack of some component information, MMS algorithm shows better robustness than PLS2.     

Application of Multivariate Curve Resolution Alternating Least Squares (MCR-ALS) to remote sensing hyperspectral imaging

The application of the MCR-ALS method is demonstrated on two simulated remote sensing spectroscopic images and on one experimental reference remote sensing spectroscopic image obtained by the Airborn Visible/Infrared Imaging Spectrometer (AVIRIS). By application of MCR-ALS, the spectra signatures of the pure constituents present in the image and their concentration distribution at a pixel level are estimated. Results obtained by MCR-ALS are compared to those obtained by other methods frequently used in the remote sensing spectroscopic imaging field like VCA and MVSA. In the case of the analysis of the experimental data set, the resolved pure spectra signatures were compared to reference spectra from USGS library for their identification. In all cases, results were also evaluated for the presence of rotational ambiguities using the MCR-BANDS method. The obtained results confirmed that the MCR-ALS method can be successfully used for remote sensing hyperspectral image resolution purposes. However, the amount of rotation ambiguity still present in the solutions obtained by this and other resolution methods (like VCA or MVSA) can still be large and it should be evaluated with care, trying to reduce its effects by selecting the more appropriate constraints. Only in this way it is possible to increase the reliability of the solutions provided by these methods and decrease the uncertainties associated to their use.     

A quick and effective multivariate statistical strategy for imaging mass spectrometry

A new multivariate statistical strategy for analyzing large datasets that are produced by imaging mass spectrometry（IMS） techniques is reported.The strategy divides the whole datacube of the sample into several subsets and analyses them one by one to obtain the results.Instead of analyzing the whole datacube at one time,the strategy makes the analysis easier and decreases the computation time greatly.In this report,the IMS data are produced by the air flow-assisted ionization IMS（AFAI-IMS）.The strategy can be used in combination with most multivariate statistical analysis methods.In this paper,the strategy was combined with the principal component analysis（PCA） and partial least square analysis（PLS）.It was proven to be effective by analyzing the handwriting sample.By using the strategy,the m/z corresponding to the specific lipids in rat brain tissue were distinguished successfully.Moreover the analysis time grew linearly instead of exponentially as the size of sample increased.The strategy developed in this study has enormous potential for searching for the mjz of potential biomarkers quickly and effectively.     

Multivariate optimization of headspace‐GC for the determination of monoaromatic compounds (benzene,toluene, ethylbenzene,and xylenes) in waters and wastewaters

The objective of this study was to optimize, by employing a central composite rotatable design, and validate an analytical method to detect and quantify monoaromatic compounds (benzene, toluene, ethylbenzene, and xylenes) in waters and wastewaters by using headspace extraction followed by GC coupled with photoionization detection. The extraction parameters optimized were: salinity, sample volume, incubation time, and extraction temperature. The results revealed that the sample volume was the most significant parameter in the extraction process, whereas the salinity effect was negligible, which extends the applicability of the analytical method to waters with different salinities. Finally, the studied method was very selective and, at the optimal extraction conditions (15 mL sample volume, 15 min incubation time, and temperature of 70°C), presented excellent repeatability (<4%), linearity (R > 0.999 for each compound), and sensitivity, since very low LODs (0.13–0.48 μg/L) and LOQs (0.43–1.61 μg/L) were achieved.     

A metabolomic strategy to screen the prototype components and metabolites of Qingkailing injection in rat urine by high‐performance liquid chromatography with tandem mass spectrometry

Xenobiotic metabolome identification of Chinese herbal formula in biological systems is a very challenging task. Qingkailing injection is a typical Chinese herbal injection, which is wildly used clinically in China. However, the holistic metabolic fate of the ingredient from Qingkailing injection remains unclear. In this work, a metabolomic strategy for comprehensively elucidating Qingkailing injection derived prototype components and metabolites in rat urine conducted by hybrid linear ion trap high‐resolution mass spectrometry was developed. High‐performance liquid chromatography coupled with hybrid linear ion trap high‐resolution mass spectrometry was developed to obtain the urine profiling between the control group and Qingkailing injection treated group. Orthogonal partial least squares discriminate analysis was applied to distinguish the exogenous and the endogenous. In the S‐plot, 37 xenobiotics derived from Qingkailing injection were found in urine, including 18 prototype compounds and 19 metabolites. The characterization of the prototype components and metabolites in rat's urine provided essential data for further pharmacological studies of Qingkailing injection. Our results indicated that the metabolomic approach was an effective tool to discover, screen, and analyze the multiple prototype components and their metabolites from complicated traditional Chinese preparations in vivo.