Near-infrared chemical imaging for quantitative analysis of chlorpheniramine maleate and distribution homogeneity assessment in pharmaceutical formulations

Near infrared chemical imaging(NIR-CI)combines conventional near infrared(NIR)spectros-copy with chemical imaging,thus provides spectral and spatial information simult aneously.It could be utilized to visualize the spatial distribution of the ingredients in a sample.The data acquired using NIR CI instrument are hyperspectral data cube(hypercube)containing thousands of spectra.Chemometric methodologies are necessary to transform spectral information into chemical information.Partial least squares(PLS)method was performed to extract chemical information of chlorpheniramine maleate in pharmaceutical formulations.A series of samples which consisted of different CPM concentrations(w/w)were compressed and hypercube data were measured.The spectra extracted from the hypercube were used to establish the PLS model of CPM.The results of the model were R^(2)_(val)0.981,RMSEC 0.384%,RMSECV 0.483%,RMSEP 0.631%,indicating that this model was reliable.

Multichannel imaging for monitoring chemical composition and germination capacity of cowpea(Vigna unguiculata) seeds during development and maturation

This study aimed to set a computer-integrated multichannel spectral imaging system as a high-throughput phenotyping tool for the analysis of individual cowpea seeds harvested at different developmental stages. The changes in germination capacity and variations in moisture, protein and different sugars during twelve stages of seed development from 10 to 32 days after anthesis were nondestructively monitored. Multispectral data at 20 discrete wavelengths in the ultraviolet, visible and near infrared regions were extracted from individual seeds and then modelled using partial least squares regression and linear discriminant analysis(LDA) models. The developed multivariate models were accurate enough for monitoring all possible changes occurred in moisture, protein and sugar contents with coefficients of determination in prediction R^(2) of 0.93, 0.80 and 0.78 and root mean square errors in prediction(RMSEP) of 6.045%, 2.236% and 0.890%, respectively. The accuracy of PLS models in predicting individual sugars such as verbascose and stachyose was reasonable with R~2 of 0.87 and 0.87 and RMSEP of 0.071%and 0.485%, respectively;but for the prediction of sucrose and raffinose the accuracy was relatively limited with R^(2) of 0.24 and 0.66 and RMSEP of 0.567% and 0.045%, respectively. The developed LDA model was robust in classifying the seeds based on their germination capacity with overall correct classification of96.33% and 95.67% in the training and validation datasets, respectively. With these levels of accuracy,the proposed multichannel spectral imaging system designed for single seeds could be an effective choice as a rapid screening and non-destructive technique for identifying the ideal harvesting time of cowpea seeds based on their chemical composition and germination capacity. Moreover, the development of chemical images of the major constituents along with classification images confirmed the usefulness of the proposed technique as a non-destructive tool for estimating the concentrations and spatial distributions of moisture, protein and sugars during different developmental stages of cowpea seeds.

A Survey on Machine Learning in Chemical Spectral Analysis

Chemical spectral analysis is contemporarily undergoing a revolution and drawing much attention of scientists owing to machine learning algorithms,in particular convolutional networks.Hence,this paper outlines the major machine learning and especially deep learning methods contributed to interpret chemical images,and overviews the current application,development and breakthrough in different spectral characterization.Brief categorization of reviewed literatures is provided for studies per application apparatus:X-Ray spectra,UV-Vis-IR spectra,Micro-scope,Raman spectra,Photoluminescence spectrum.End with the overview of existing circumstances in this research area,we provide unique insight and promising directions for the chemical imaging field to fully couple machine learning subsequently.

Determination of drug,excipients and coating distribution in pharmaceutical tablets using NIR-CI

The growing interest of the pharmaceutical industry in Near Infrared-Chemical Imaging (NIR-CI) is a result of its high usefulness for quality control analyses of drugs throughout their production process (particularly of its non-destructive nature and expeditious data acquisition).In this work,the concentration and distribution of the major and minor components of pharmaceutical tablets are determined and the spatial distribution from the internal and external sides has been obtained.In addition,the same NIR-CI allowed the coating thickness and its surface distribution to be quantified.Images were processed to extract the target data and calibration models constructed using the Partial Least Squares (PLS) algorithms.The concentrations of Active Pharmaceutical Ingredient (API) and excipients obtained for uncoated cores were essentially identical to the nominal values of the pharmaceutical formulation.But the predictive ability of the calibration models applied to the coated tablets decreased as the coating thickness increased.

Rapid analysis of spatial distribution of PVPP and hardness of Yinhuang dispersible tablets by NIR-CI

The present study aimed at investigating the relationship between tablet hardness and homogeneity of different Yinhuang dispersible tablets by near-infrared chemical imaging(NIR-CI)technology.The regularity of best hardness was founded between tablet hardness and the spatial distribution uniformity of Yinhuang dispersible tablets.The ingredients homogeneity of Yinhuang dispersible tablets could be spatially determined using basic analysis of correlation between analysis(BACRA)method and binary image.Then different hardnesses of Yinhuang dispersible tablets were measured.Finally,the regularity between tablet hardness and the spatial distribution uniformity of Yinhuang dispersible tablets was illuminated by quantifying the agglomerate of polyvinyl poly pyrrolidone(PVPP).The result demonstrated that the distribution of PVPP was unstable when the hardness was too large or too small,while the agglomerate of PVPP was smaller and more stable when the best tablet hardness was 75 N.This paper provided a novel methodology for selecting the best hardness in the tabletting process of Chinese Medicine Tablet.

An easy way to controllably synthesize one-dimensional Sm B_6 topological insulator nanostructures and exploration of their field emission applications

A convenient fabrication technique for samarium hexaboride（SmB6） nanostructures（nanowires and nanopencils） is developed, combining magnetron-sputtering and chemical vapor deposition. Both nanostructures are proven to be single crystals with cubic structure, and they both grow along the [001] direction. Formation of both nanostructures is attributed to the vapor-liquid-solid（VLS） mechanism, and the content of boron vapor is proposed to be the reason for their different morphologies at various evaporation distances. Field emission（FE） measurements show that the maximum current density of both the as-grown nanowires and nanopencils can be several hundred μA/cm^2, and their FN plots deviate only slightly from a straight line. Moreover, we prefer the generalized Schottky-Nordheim（SN） model to comprehend the difference in FE properties between the nanowires and nanopencils. The results reveal that the nonlinearity of FN plots is attributable to the effect of image potential on the FE process, which is almost independent of the morphology of the nanostructures.All the research results suggest that the SmB6 nanostructures would have a more promising future in the FE area if their surface oxide layer was eliminated in advance.

The Value of In-Phase and Opposed-Phase T1-Weighted Breath-Hold FLASH Sequences for Hepatic Imaging

The value of the combined in-phase (IP) and opposed-phase (OP) T1-weighted (T1-W) breath-hold FLASH sequences for hepatic imaging, especially for fat content, was evaluated. Non- contrast-enhanced IP and OP T1-W GRE breath-hold images were obtained in 76 patients refereed for abdominal MRI at 1. 5T. 76 patients were divided into three groups for analysis: (1) liver with- out mass (n=8); (2) liver with hepatoma (n=34); (3) liver with haemangioma or cyst (n=34). Liver/spleen and liver/lesion signal-to-noise (SNR) and contrast-to-noise ratio (CNR) were assessed for lesion detection. Images between IP and OP sequences were compared quantitatively. The results showed that there was not statistically significant difference in liver/spleen and liver/lesion SNR be- tween IP and OP sequences. In the patients with fatty infiltration, the OP sequences yielded substan- tially lower values for liver/spleen and liver/lesion SNR than those of the IP sequences. Further- more, OP imaging showed fatty infiltration in 14 cases and demonstrated hyperintense peritumor rim in 4 cases. In 14 cases of fatty infiltration, many lesions were identified using IP images. The use of IP and OP GRE sequences provides complementary diagnostic information for hepatic lesions and fat content. Focal hepatic lesions may be obscured in the setting of fatty infiltration if only OP sequences are employed. A complete assessment of the liver with MR should include both IP and OP imaging.

Determination of serum biomarkers in osteoarthritis patients：a previous interventional imaging study revisited

To evaluate in an interventional trial on knee osteoarthritis（OA） the level and change of two serum biomarkers and their correlation with imaging parameters.The previously reported interventional OA study（ClinicalTrials.gov：NCT00536302） identified a positive effect of collagen hydrolysate（CH） on cartilage morphology in patients with knee OA using delayed gadolinium enhanced magnetic resonance imaging（dGEMRIC）.It was the objective in this research project to evaluate in an interventional clinical trial on knee OA the level and change of two serum biomarkers and their correlation with imaging parameters.In blood samples of study participants,we determined the concentration of procollagen type II N-terminal propeptide（PIIANP） and aggrecan chondroitin sulfate 846 epitope（CS846） at baseline（BL） and at the follow-up（FU） visits at 24 and 48 weeks.We measured the level and change of biomarker concentrations in both study groups,and the correlation of those changes with changes in dGEMRIC.For the biomarker PIIANP,we observed a significantly greater increase in the CH group（29.9%vs.1.2%at week 24,P =0.001）.For CS846,the mean concentration was lower among the CH treated participants at 24 weeks（78%vs.96%,P= 0.045）.Consistent correlations of changes in biomarkers PIIANP and CS846 with changes of the dGEMRIC score could not be observed.In this study,different changes per treatment group,CH and placebo were seen for dGEMRIC and PIIANP BL to 24 weeks FU,but only weak correlations between changes in dGEMRIC and biochemical markers.

Inspection of maleic anhydride in starch powder using line-scan hyperspectral Raman chemical imaging technique

Excessive use of maleic anhydride(MAN)in starch production is potentially harmful for consumers’health.This study presents a macro-scale Raman chemical imaging method for detection and quantification of MAN particles mixed in starch powder.MAN was mixed into corn starch at eight concentration levels from 50 ppm to 6400 ppm(w/w).Each mixture was put in a sample holder with a 150 mm×100 mm area and a 2 mm depth to create a large surface and a thin layer of the powdery sample for inspection.A 30 W 785 nm line laser was projected on the sample surface,from which hyperspectral images were obtained by a line-scan Raman imaging system with a spatial resolution of 0.2 mm.Fluorescence signals generated by laser-sample interactions were eliminated by a mathematical baseline correction method.A unique Raman peak was selected at 1839 cm-1 for the MAN detection,at which single-band fluorescence-corrected images were extracted from the mixture of each concentration and used to generate chemical images for MAN detection and mapping.The MAN detection limit was estimated at 100 ppm based on the Raman imaging measurement results.Pixel concentrations of the MAN in the chemical images were found linearly correlated with mass concentrations of the MAN particles in the starch powder,suggesting the Raman chemical imaging method has the potential for quantitative detection of the MAN in the starch-MAN mixtures.

Subnanometer-resolved chemical imaging via multivariate analysis of tip-enhanced Raman maps

Tip-enhanced Raman spectroscopy(TERS)is a powerful surface analysis technique that can provide subnanometer-resolved images of nanostructures with site-specific chemical fingerprints.However,due to the limitation of weak Raman signals and the resultant difficulty in achieving TERS imaging with good signal-to-noise ratios(SNRs),the conventional single-peak analysis is unsuitable for distinguishing complex molecular architectures at the subnanometer scale.Here we demonstrate that the combination of subnanometer-resolved TERS imaging and advanced multivariate analysis can provide an unbiased panoramic view of the chemical identity and spatial distribution of different molecules on surfaces,yielding high-quality chemical images despite limited SNRs in individual pixel-level spectra.This methodology allows us to exploit the full power of TERS imaging and unambiguously distinguish between adjacent molecules with a resolution of~0.4 nm,as well as to resolve submolecular features and the differences in molecular adsorption configurations.Our results provide a promising methodology that promotes TERS imaging as a routine analytical technique for the analysis of complex nanostructures on surfaces.

Probing On-Surface Chemistry at the Nanoscale Using Tip-Enhanced Raman Spectroscopy

Chemistry on solid surfaces is central to many research areas of practical interest,such as synthesis,catalysis,electrochemistry,photochemistry,and materials science.A comprehensive understanding of the nanoscale on-surface chemistry involved in these areas is important for establishing composition-structure-performance relationships.With the rapid development of tip-enhanced Raman spectroscopy(TERS),it has become possible to investigate physical and chemical processes on suitable surfaces at the nanoscale level and in real space.In this review,after a brief introduction of the background of onsurface chemistry and TERS,we systematically discuss the progress in the application of TERS in this field.Our focus is the applications of TERS to nanoscale coordination processes,decomposition reactions,polymerization processes,electrochemical reactions,catalytic chemistry,and functionalization chemistry on solid surfaces.We conclude by discussing the future challenges and development of TERS techniques and related applications in on-surface chemistry.

New sensor technologies in quality evaluation of Chinese materia medica:2010–2015

New sensor technologies play an important role in quality evaluation of Chinese materia medica(CMM) and include near-infrared spectroscopy,chemical imaging,electronic nose and electronic tongue.This review on quality evaluation of CMM and the application of the new sensors in this assessment is based on studies from 2010 to 2015,with prospects and opportunities for future research.