Correction: Feasibility of attenuated total reflection-fourier transform infrared (ATR-FTIR) chemical imaging and partial least squares regression (PLSR) to predict protein adhesion on polymeric surfaces.

Correction for 'Feasibility of attenuated total reflection-fourier transform infrared (ATR-FTIR) chemical imaging and partial least squares regression (PLSR) to predict protein adhesion on polymeric surfaces' by S. Mukherjee et al., Analyst, 2019, 144, 1535-1545. DOI.

Feasibility of attenuated total reflection-fourier transform infrared (ATR-FTIR) chemical imaging and partial least squares regression (PLSR) to predict protein adhesion on polymeric surfaces.

Predicting the degree to which proteins adhere to a polymeric surface is an ongoing challenge in the scientific community to prevent non-specific protein adhesion and drive favourable protein - surface interactions. This work explores the potential of multivariate PLSR modelling in conjunction with Attenuated Total Reflection - Fourier Transform Infrared (ATR-FTIR) chemical imaging to investigate whether experimentally characterised surface chemistry can be used to predict surface protein adhesion. ATR-FTIR spectra were collected on dry and wetted polymeric surfaces, followed by evaluation of adhered fibrinogen on surfaces using the micro bicinchoninic (BCA) protein assay as a reference method. Partial Least Squares Regression (PLSR) models were built using IR spectra as the predictor variable. Overall the models built with 'wetted polymer' IR spectra performed better as compared to the models built using 'dry polymer' IR spectra (average coefficient of determination, R2P 0.998, 0.996 respectively), with the lowest error in prediction (4 ± 0.6 µg) for ultra-high molecular weight polyethylene (UHMPE) as a test surface. This indicates the potential of this method to predict the degree to which protein adhesion occurs on polymeric surfaces using experimentally determined surface chemistry.

Predictive modelling of the water contact angle of surfaces using attenuated total reflection - Fourier transform infrared (ATR-FTIR) chemical imaging and partial least squares regression (PLSR).

The static water contact angle (CA) quantifies the degree of wetting that occurs when a surface encounters a liquid, e.g. water. This property is a result of factors such as surface chemistry and local roughness and is an important analytical parameter linked to the suitability of a surface for a given bioanalytical process. Monitoring the spatial variation in wettability over surfaces is increasingly critical to analysts and manufacturers for improved quality control. However, CA acquisition is often time-consuming because it involves measurements over multiple spatial locations, independent sampling and the need for a single instrument operator. Furthermore, surfaces exposed to local environments specific to an intended application may affect the surface chemistry thereby modifying the surface properties. In this study, Attenuated Total Reflection-Fourier Transform Infrared (ATR-FTIR) chemical imaging data acquired from wet and dry polymer surfaces were used to develop multivariate predictive models for CA prediction. Partial Least Squares Regression (PLSR) models were built using IR spectra from surfaces presenting differences in the experimentally measured CA in the range 16°-141°. The best performing PLSR models were locally developed and combined to make a global model utilising wet IR spectra which performed well (R2p = 0.98, RMSECV ∼ 5°) when tested on an independent experimental set. This model was subsequently applied to IR spectra acquired from a surface exhibiting spatial differences in surface chemistry and the CA with a reasonable confidence and precision (prediction error within 10°), demonstrating the potential of this method for prediction of the spatially varying CA as a non-destructive in-line process monitoring technique.

Evaluation of ensemble Monte Carlo variable selection for identification of metabolite markers on NMR data.

The aim of this study was to investigate the potential of the recently developed ensemble Monte Carlo Variable Selection (EMCVS) method to identify the relevant portions of high resolution 1H NMR spectra as a metabolite fingerprinting tool and compare to a widely used method (Variable importance on projection (VIP)) and recently proposed variable selected methods i.e. selectivity ratio (SR) and significance multivariate correlation (sMC). As case studies two quantitative publicly available datasets: wine samples, urine samples of rats, and an experiment on mushroom (Agaricus bisporus) were examined. EMCVS outperformed the three other variable selection methods in most cases, selecting fewer chemical shifts and leading to improved classification of mushrooms and prediction of onion by-products intake and wine components. These fewer chemical shift regions facilitate the interpretation of the NMR spectra, fingerprinting and identification of metabolite markers.

On the feasibility of near infrared spectroscopy to detect contaminants in water using single salt solutions as model systems.

This research work evaluates the feasibility of NIRS to detect contaminants in water using single salt solutions as model systems. Previous research has indicated the potential of near infrared spectroscopy (NIRS) for detecting solutes in water; however, a comprehensive investigation of the limit of detection of this technique has not been carried out. Near infrared transmittance spectra of aqueous salt solutions in the concentration range 0.002-0.1 mol L(-1) (equivalent to 117-13,334 ppm or 0.0001-0.01% mass/mass) were investigated. The first overtone region of the near infrared spectrum (1300-1600 nm) was found to be the most effective wavelength range for prediction of salt concentration in aqueous solutions. Calibration models built using this wavelength range and employing the extended multiplicative scatter spectral pre-treatment resulted in root mean squared error of prediction values ranging from 0.004 to 0.01 mol L(-1). The limit of detection (LOD) was estimated to be of the order of 0.1% (mass/mass) or 1000 ppm. Within the framework of Aquaphotomics, it was possible to examine the effect of different salts on the NIR spectra of water in the first overtone range. Our results were confirmed through test experiments at various geographical locations employing dispersive and Fourier transform type NIRS instruments.

Characterisation of hydrogen bond perturbations in aqueous systems using aquaphotomics and multivariate curve resolution-alternating least squares.

Aquaphotomics is a new discipline that provides a framework for understanding changes in the structure of water caused by various perturbations, such as variations in temperature or the addition of solutes, using near infrared spectroscopy (NIRS). One of the main purposes of aquaphotomics is to identify water bands as main coordinates of future absorbance patterns to be used as biomarkers. These bands appear as consequence of perturbations in the NIR spectra. Curve resolution techniques may help to resolve and find new water bands or confirm already known bands. The aim of this study is to investigate the application of multivariate curve resolution-alternating least squares (MCR-ALS) to characterise the effects of various perturbations on the NIR spectra of water in terms of hydrogen bonding. For this purpose, the perturbations created by temperature change and the addition of four solutions of different ionic strength and Lewis acidity were studied (NaCl, KCl, MgCl(2) and AlCl(3), with concentrations ranging from 0.2 to 1 mol L(-1) in steps of 0.2 mol L(-1)). Transmission spectra of all salt solutions and pure water were obtained at temperatures ranging from 28 to 45°C. We have found that three distinct components with varying temperature dependence are present in water perturbed by temperature. The salt solutions studied exhibited similar trends with respect to the temperature perturbation, while the peak locations of their MCR-ALS pure components varied according to the ionic strength of the salt used.

Time series hyperspectral chemical imaging data: challenges, solutions and applications.

Hyperspectral chemical imaging (HCI) integrates imaging and spectroscopy resulting in three-dimensional data structures, hypercubes, with two spatial and one wavelength dimension. Each spatial image pixel in a hypercube contains a spectrum with >100 datapoints. While HCI facilitates enhanced monitoring of multi-component systems; time series HCI offers the possibility of a more comprehensive understanding of the dynamics of such systems and processes. This implies a need for modeling strategies that can cope with the large multivariate data structures generated in time series HCI experiments. The challenges posed by such data include dimensionality reduction, temporal morphological variation of samples and instrumental drift. This article presents potential solutions to these challenges, including multiway analysis, object tracking, multivariate curve resolution and non-linear regression. Several real world examples of time series HCI data are presented to illustrate the proposed solutions.

Influence of polymer packaging films on hyperspectral imaging data in the visible-near-infrared (450-950 nm) wavelength range.

Hyperspectral imaging (HSI) has recently emerged as a useful tool for quality analysis of consumer goods (e.g., food and pharmaceutical products). These products are typically packaged in polymeric film prior to distribution; however, HSI experiments are typically carried out on such samples ex-packaging (either prior to or after removal from packaging). This research examines the effects of polymer packaging films (polyvinyl chloride (PVC) and polyethylene terephthalate (PET)) on spectral and spatial features of HSI data in order to investigate the potential of HSI for quality evaluation of packaged goods. The effects of packaging film were studied for hyperspectral images of samples obtained in the visible-near-infrared (Vis-NIR, i.e., 450-950 nm) wavelength range, which is relevant to many food, agricultural, and pharmaceutical products. The dominant influence of the films tested in this wavelength range could be attributed to light scattering. Relative position of the light source, film, and detector were shown to be highly influential on the scattering effects observed. Detection of features on samples imaged through film was shown to be possible after some data preprocessing. This suggests that quality analysis of products packaged in polymer film is feasible using HSI. These findings would be useful in the development of quality monitoring tools for consumer products post-packaging using HSI.

Recent applications of Chemical Imaging to pharmaceutical process monitoring and quality control.

Chemical Imaging (CI) is an emerging platform technology that integrates conventional imaging and spectroscopy to attain both spatial and spectral information from an object. Vibrational spectroscopic methods, such as Near Infrared (NIR) and Raman spectroscopy, combined with imaging are particularly useful for analysis of biological/pharmaceutical forms. The rapid, non-destructive and non-invasive features of CI mark its potential suitability as a process analytical tool for the pharmaceutical industry, for both process monitoring and quality control in the many stages of drug production. This paper provides an overview of CI principles, instrumentation and analysis. Recent applications of Raman and NIR-CI to pharmaceutical quality and process control are presented; challenges facing CI implementation and likely future developments in the technology are also discussed.