Aoife Gowen

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.

Preventing over-fitting in PLS calibration models of near-infrared (NIR) spectroscopy data using regression coefficients

Selection of the number of latent variables (LVs) to include in a partial least squares (PLS) model is an important step in the data analysis. Inclusion of too few or too many LVs may lead to, respectively, under or over‐fitting of the data and subsequently result in poor future model performance. One well‐known sign of over‐fitting is the appearance of noise in regression coefficients; this often takes the form of a reduction in apparent structure and the presence of sharp peaks with a high degree of directional oscillation, features which are usually estimated subjectively. In this work, a simple method for quantifying the shape and size of a regression coefficient is presented. This measure can be combined with an indicator of model bias (e.g. root mean square error) to aid in estimation of the appropriate number of LVs to include in a PLS model. The performance of the proposed method is evaluated on simulated and and real NIR spectroscopy datasets sets and compared with several existing methods. Copyright

Recent applications of hyperspectral imaging in microbiology

Hyperspectral chemical imaging (HSI) is a broad term encompassing spatially resolved spectral data obtained through a variety of modalities (e.g. Raman scattering, Fourier transform infrared microscopy, fluorescence and near-infrared chemical imaging). It goes beyond the capabilities of conventional imaging and spectroscopy by obtaining spatially resolved spectra from objects at spatial resolutions varying from the level of single cells up to macroscopic objects (e.g. foods). In tandem with recent developments in instrumentation and sampling protocols, applications of HSI in microbiology have increased rapidly. This article gives a brief overview of the fundamentals of HSI and a comprehensive review of applications of HSI in microbiology over the past 10 years. Technical challenges and future perspectives for these techniques are also discussed.

Hyperspectral Imaging and Chemometrics: A Perfect Combination for the Analysis of Food Structure, Composition and Quality

Abstract Computer vision systems have become typical tools of increasing importance to control manufacturing processes and product quality in a non-destructive manner in food industrial processing. During the past several years, we have heard about how hyperspectral imaging, joined with chemometrics, could offer a set of possibilities that may help to increase the control of the final quality assessment in production lines. This chapter will not review the main applications of HSI and chemometrics for food quality assessment, since this has already been extensively covered in several reviews. Instead, we will discuss the application and feasibility of the main chemometric techniques applied to different foodstuffs. The reader will be provided with a detailed overview of how to use chemometrics in hyperspectral data, along with a critical discussion on their respective advantages and potential pitfalls. The examples that we will use for this purpose are the detection of water in cheese, classification of bitterness in almonds in a set of samples, detection and classification of contaminants in cheese, and hydration of chickpeas during soaking.

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.

Initial studies on the quantitation of bruise damage and freshness in mushrooms using visible-near-infrared spectroscopy.

Identification of mushrooms that have been physically damaged and the measurement of time elapsed from harvest are very important quality issues in industry. The purpose of this study was to assess whether the chemical changes induced by physical damage and the aging of mushrooms can: (a) be detected in the visible and near infrared absorption spectrum and (b) be modeled using multivariate data analysis. The effect of pre-treatment and the use of different spectral ranges to build PLS models were studied. A model that can identify damaged mushrooms with high sensitivity (0.98) and specificity (1.00), and models that allow estimation of the age (1.0-1.4 days root mean square error of cross-validation) were developed. Changes in water matrix and alterations caused by enzymatic browning were the factors that most influenced the models. The results reveal the possibility of developing an automated system for grading mushrooms based on reflectance in the visible and near infrared wavelength ranges.

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.

Prediction of Polyphenol Oxidase Activity Using Visible Near-Infrared Hyperspectral Imaging on Mushroom (Agaricus bisporus) Caps.

Physical stress (i.e., bruising) during harvesting, handling, and transportation triggers enzymatic discoloration of mushrooms, a common and detrimental phenomenon largely mediated by polyphenol oxidase (PPO) enzymes. Hyperspectral imaging (HSI) is a nondestructive technique that combines imaging and spectroscopy to obtain information from a sample. The objective of this study was to assess the ability of HSI to predict the activity of PPO on mushroom caps. Hyperspectral images of mushrooms subjected to various damage treatments were taken, followed by enzyme extraction and PPO activity measurement. Principal component regression (PCR) models (each with three PCs) built on raw reflectance and multiple scatter-corrected (MSC) reflectance data were found to be the best modeling approach. Prediction maps showed that the MSC model allowed for compensation of spectral differences due to sample curvature and surface irregularities. Results reveal the possibility of developing a sensor that could rapidly identify mushrooms with a higher likelihood to develop enzymatic browning, hence aiding produce management decision makers in the industry.

A review of recent trends in polymer characterization using non-destructive vibrational spectroscopic modalities and chemical imaging

This review focuses on the recent developments in vibrational spectroscopy and chemical imaging (i.e. Raman, Near Infrared, Mid Infrared) to characterize polymers in diverse forms, their behaviour and transient phenomenon. First, important polymeric properties and traditional methods of their characterization are outlined. Then relative advantages & disadvantages have been presented of different characterization methods are presented. This is followed by a detailed review of applications of chemical imaging and spectroscopic techniques in polymer characterization, including the limitations encountered. The article ends with a discussion on the future of chemical imaging with regards to polymer characterization.

Use of near infrared hyperspectral imaging to identify water matrix co-ordinates in mushrooms (Agaricus bisporus) subjected to mechanical vibration

In this study, white mushrooms (Agaricus bisporus) were subjected to physical perturbation by mechanical vibration. Hyperspectral images were obtained after perturbation using a pushbroom line-scanning instrument operating in the wavelength range of 1000–1700 nm (7nm spectral resolution). Changes in sample spectra arising from perturbation were examined by observation of difference spectra and partial least squares regression (PLSR) coefficients. Different spectral pre-treatments [multiplicative scatter correction (MSC), extended multiplicative scatter correction (EMSC) and standard normal variate (SNV)] were employed in order to decrease spectral variability caused by scattering and differences in the optical path length due to physical changes in the mushrooms induced by the perturbation. Candidate water matrix co-ordinates were proposed at 950 nm, 1174 nm, 1398 nm, 1433 nm, 1454 nm, 1496 nm and 1510 nm. Mechanical vibration increased the concentration of weakly hydrogen-bonded water and decreased that of strongly hydrogen-bonded water in the mushrooms without causing changes in the bulk moisture content.