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Sandasi M.,Tshwane University of Technology | Kamatou G.P.P.,Tshwane University of Technology | Gavaghan C.,MKS Umetrics | Baranska M.,Jagiellonian University | Viljoen A.M.,Tshwane University of Technology
Vibrational Spectroscopy

Rose-scented geranium, a commercially important cultivar originating from Pelargonium graveolens L'Her. ex Ait., is a high value essential oil extensively used in flavour and fragrance formulations. The oil is variable in composition with 'Bourbon geranium' (from Reunion Island) regarded as the highest quality geranium oil. Quality assessment of geranium oil involves profiling seven major volatile constituents (geraniol, citronellol, geranyl formate, citronellyl formate, linalool, isomenthone and guaia-6,9-diene) using gas chromatography (GC). The aim of this study was to explore the feasibility of vibrational spectroscopy in tandem with chemometric methods as a rapid and low-cost alternative quality control method. Geranium oil samples (n = 70) were obtained from different suppliers representing cultivation sites in South Africa, Egypt, India, Reunion Island, China and Madagascar. Reference analysis was performed using gas chromatography coupled to mass spectrometry (GC-MS). The mid-infrared (MIR) and near-infrared (NIR) spectra of the oils were recorded with a total of 32 scans accumulated for each sample. Partial least squares (PLS) multivariate calibration models were developed. The calibration models obtained for both MIR and NIR data produced good correlation coefficients (R 2 > 0.90) between the predicted and reference values for all seven marker molecules. Generally, the error parameters (RMSEE and RMSEP) after external validation were low (<1.0%) for all compounds guaranteeing reliable predictions. The results show convincingly the potential of both MIRS and NIRS as alternative methods that can be used in quality assessment of geranium oil providing sufficiently accurate results. © 2011 Elsevier B.V. Source

Frost K.,RMIT University | Johansson E.,MKS Umetrics | Shanks R.,RMIT University | Goto S.,Kyoto University | Kirwan G.M.,Kyoto University
Polymer Testing

The combined effect of a plasticiser (glycerol) and a cross-linking agent (borax) on the mechanical properties of commercially extruded thermoplastic hydroxypropylated starch films was examined. The use of Design of Experiment (DOE) was demonstrated and used to predict and optimise formulations for a given set of material properties. As an extension to DOE, Orthogonal 2 Partial Least Squares (O2PLS) provided insight into joint correlations between the machine and transverse direction mechanical properties. Specific information regarding individual measurements or samples was also obtained with this analysis. O2PLS identified unique variables in individual compositions that were potentially incorrect measurements, or processing defects, which in turn can be used to aid quality control or processing optimisation with regards to DOE. Overall, DOE and O2PLS showed that within a starch borax-glycerol blend, borax increased mechanical strength and enhanced creep and recovery, whilst glycerol increased elongation and decreased modulus. There were competing interactions between the two dependent on concentration, and variation between machine and transverse properties was due to the extrusion induced molecular orientation of amylose. The optimum concentrations of borax and glycerol needed to achieve higher elongation, tensile strength, modulus and creep recovery than a control was found to be approximately 0.5% and 10%, respectively. Crown Copyright © 2012 Published by Elsevier Ltd. All rights reserved. Source

Kirwan G.M.,Kyoto University | Johansson E.,MKS Umetrics | Kleemann R.,TNO | Verheij E.R.,TNO | And 5 more authors.
Analytical Chemistry

Systems biology methods using large-scale "omics" data sets face unique challenges: integrating and analyzing near limitless data space, while recognizing and removing systematic variation or noise. Herein we propose a complementary multivariate analysis workflow to both integrate "omics" data from disparate sources and analyze the results for specific and unique sample correlations. This workflow combines principal component analysis (PCA), orthogonal projections to latent structures discriminate analysis (OPLS-DA), orthogonal 2 projections to latent structures (O2PLS), and shared and unique structures (SUS) plots. The workflow is demonstrated using data from a study in which ApoE3Leiden mice were fed an atherogenic diet consisting of increasing cholesterol levels followed by therapeutic intervention (fenofibrate, rosuvastatin, and LXR activator T-0901317). The levels of structural lipids (lipidomics) and free fatty acids in liver were quantified via liquid chromatography-mass spectrometry (LC-MS). The complementary workflow identified diglycerides as key hepatic metabolites affected by dietary cholesterol and drug intervention. Modeling of the three therapeutics for mice fed a high-cholesterol diet further highlighted diglycerides as metabolites of interest in atherogenesis, suggesting a role in eliciting chronic liver inflammation. In particular, O2PLS-based SUS2 plots showed that treatment with T-0901317 or rosuvastatin returned the diglyceride profile in high-cholesterol-fed mice to that of control animals. © 2012 American Chemical Society. Source

Galindo-Prieto B.,Umea University | Eriksson L.,MKS Umetrics | Trygg J.,Umea University
Journal of Chemometrics

A new approach for variable influence on projection (VIP) is described, which takes full advantage of the orthogonal projections to latent structures (OPLS) model formalism for enhanced model interpretability. This means that it will include not only the predictive components in OPLS but also the orthogonal components. Four variants of variable influence on projection (VIP) adapted to OPLS have been developed, tested and compared using three different data sets, one synthetic with known properties and two real-world cases. © 2014 John Wiley & Sons, Ltd. Source

Souihi N.,Umea University | Lindegren A.,MKS Umetrics | Eriksson L.,MKS Umetrics | Trygg J.,Umea University
Analytica Chimica Acta

In batch statistical process control (BSPC), data from a number of "good" batches are used to model the evolution (trajectory) of the process and they also define model control limits, against which new batches may be compared. The benchmark methods used in BSPC include partial least squares (PLS) and principal component analysis (PCA).In this paper, we have used orthogonal projections to latent structures (OPLS) in BSPC and compared the results with PLS and PCA. The experimental study used was a batch hydrogenation reaction of nitrobenzene to aniline characterized by both UV spectroscopy and process data.The key idea is that OPLS is able to separate the variation in data that is correlated to the process evolution (also known as 'batch maturity index') from the variation that is uncorrelated to process evolution. This separation of different types of variations can generate different batch trajectories and hence lead to different established model control limits to detect process deviations.The results demonstrate that OPLS was able to detect all process deviations and provided a good process understanding of the root causes for these deviations. PCA and PLS on the other hand were shown to provide different interpretations for several of these process deviations, or in some cases they were unable to detect actual process deviations. Hence, the use of OPLS in BSPC can lead to better fault detection and root cause analysis as compared to existing benchmark methods and may therefore be used to complement the existing toolbox. © 2014 Elsevier B.V. Source

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