In this work attenuated total reflection Fourier transform infrared (ATR-FTIR) spectroscopy was used to probe the thermal gelation behavior of aqueous solutions of hydroxypropyl methylcellulose (HPMC), specifically thermal gelation and accompanying precipitation. Cloud point measurements are usually evaluated through turbidity in dilute solutions but the method cannot readily be applied to more concentrated or highly viscous solutions. From the ATR-FTIR data, intensity changes of the nu(CO) band marked the onset of gelation and information about the temperature of gelation and the effect of the gel structure on the water hydrogen bonding network was elucidated. Changes in the relative intensities of bands associated with the methoxyl groups and hydrogen-bond-forming secondary alcohol groups indicated that hydrophobic polymer chain interactions were involved in the gelation process. The dominance of inter-molecular H bonding over intra-molecular H bonding within the cellulose ether in solution was also observed. The ATR-FTIR data was in good agreement with measurements of turbidity conducted on the same systems. The work indicates significant potential for the use of ATR-FTIR for the investigation of gelation and cloud point measurements in viscous cellulosic formulations.
Exposure to respirable crystalline silica (RCS) is potentially hazardous to the health of thousands of workers in Great Britain. Both X-ray diffraction (XRD) and Fourier transform infrared (FTIR) spectroscopy can be used to measure RCS to assess exposures. The current method outlined in the Health and Safety Executive’s (HSE) Methods for the Determination of Hazardous Substances (MDHS) guidance series is ‘MDHS 101 Crystalline silica in respirable airborne dust - Direct-on-filter analyses by infrared spectroscopy or x-ray’. This describes a procedure for the determination of time-weighted average concentrations of RCS either as quartz or cristobalite in airborne dust. FTIR is more commonly employed because it is less expensive, potentially portable and relatively easy to use. However, the FTIR analysis of RCS is affected by spectral interference from silicates. Chemometric techniques, known as Partial Least Squares Regression (PLSR) and Principal Component Regression (PCR), are two computational processes that have the capability to remove spectral interference from FTIR spectra and correlate spectral features with constituent concentrations. These two common chemometric processes were tested on artificial mixtures of quartz and kaolinite in coal dust using the same commercially available software package. Calibration, validation and prediction samples were prepared by collecting aerosols of these dusts onto polyvinylchloride (PVC) filters using a Safety in Mines Personal Dust Sampler (SIMPEDS) respirable cyclone. PCR and PLSR analyses were compared when processing the same spectra. Good correlations between the target values, measured using XRD, were obtained for both the PCR and PLSR models e.g. 0.98–0.99 (quartz), 0.98–0.98 (kaolinite) and 0.96–0.97 (coal). The level of agreement between PCR and PLSR was within the 95% confidence value for each analyte. Slight differences observed between predicted PCR and PLSR values were due to the number of optimal principal components applied to each chemometric process. The presence of kaolinite in these samples caused an 18% overestimation of quartz, for the FTIR, when following MDHS 101 without a chemometric method. Chemometric methods are a useful approach to obtain interference-free results for the measurement of RCS from some workplace environments and to provide a multicomponent analysis to better characterise exposures of workers.
Parvez Haris opened the discussion of the introductory lecture by Max Diem: Varying degrees of accuracy have been obtained for discrimination between cancerous and non-cancerous tissues using vibrational spectroscopic methods. What are the explanations for these variations in accuracy between cancerous and non-cancerous tissues and how do they correlate with accuracy from other techniques including histopathology? It was reported that changes in a specic protein (PDL-1) in tissue sections were detected by FTIR spectroscopic imaging, to compare cancerous with non-cancerous tissues. This is very interesting but needs to be proven with additional data as peaks in the spectra of complex tissue samples could arise from not only chemical differences but physical differences associated with changes in tissue stiffness, dynamics etc. Therefore, combining histopathology, proteomics, metabolomics and ultrasound elastography with vibrational spectroscopic imaging could pave the way for a better understanding of the changes associated with cancer and could also improve our understanding of the vibrational spectra of cells and tissues. Basic studies on understanding the factors inuencing spectral changes in tissues need to be better understood for the technique to be widely accepted as a tool for cancer diagnosis.Rohit Bhargava replied: Variations in accuracy arise from at least the following factors:1. Measurement noise, unless it is reduced to a level where it does not affect classication (see ref. 1).2. Population variance, person to person variance, tumor heterogeneity and clinic-to-clinic variations that likely arise from sample processing. For a complete discussion of all factors see ref. 2.3. Shortcomings in the design of experiments, especially in assessing condence intervals as related to sample sizes (see the article by Pounder, Reddy and This journal is
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.