Factor selection strategies for orthogonal signal correction applied to calibration of near-infrared spectra

Yee, Nigel; Coghill, G.G.

doi:10.1016/s0169-7439(03)00066-2

Cited by 16 publications

(4 citation statements)

References 21 publications

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“…Feng et al (2011Feng et al ( , 2014 proposed background interference elimination in e-nose for wound infection. Yee and Coghill (2003) used orthogonal signal correct techniques for calibration of near-infrared spectra. Zhang et al (2013a), Tian et al (2016) and Zhang and Deng (2017) proposed pattern mismatched and neural network techniques for online counteraction of unwanted odour.…”

Section: Unwanted Odourmentioning

confidence: 99%

Handling non-stationarity in E-nose design: a review

Pareek

Chaudhury

Singh

2021

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Purpose The electronic nose is an array of chemical or gas sensors and associated with a pattern-recognition framework competent in identifying and classifying odorant or non-odorant and simple or complex gases. Despite more than 30 years of research, the robust e-nose device is still limited. Most of the challenges towards reliable e-nose devices are associated with the non-stationary environment and non-stationary sensor behaviour. Data distribution of sensor array response evolves with time, referred to as non-stationarity. The purpose of this paper is to provide a comprehensive introduction to challenges related to non-stationarity in e-nose design and to review the existing literature from an application, system and algorithm perspective to provide an integrated and practical view. Design/methodology/approach The authors discuss the non-stationary data in general and the challenges related to the non-stationarity environment in e-nose design or non-stationary sensor behaviour. The challenges are categorised and discussed with the perspective of learning with data obtained from the sensor systems. Later, the e-nose technology is reviewed with the system, application and algorithmic point of view to discuss the current status. Findings The discussed challenges in e-nose design will be beneficial for researchers, as well as practitioners as it presents a comprehensive view on multiple aspects of non-stationary learning, system, algorithms and applications for e-nose. The paper presents a review of the pattern-recognition techniques, public data sets that are commonly referred to as olfactory research. Generic techniques for learning in the non-stationary environment are also presented. The authors discuss the future direction of research and major open problems related to handling non-stationarity in e-nose design. Originality/value The authors first time review the existing literature related to learning with e-nose in a non-stationary environment and existing generic pattern-recognition algorithms for learning in the non-stationary environment to bridge the gap between these two. The authors also present details of publicly available sensor array data sets, which will benefit the upcoming researchers in this field. The authors further emphasise several open problems and future directions, which should be considered to provide efficient solutions that can handle non-stationarity to make e-nose the next everyday device.

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Section: Unwanted Odourmentioning

confidence: 99%

Handling non-stationarity in E-nose design: a review

Pareek

Chaudhury

Singh

2021

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“…Trygg and Wold [23] and Gottfries et al [9] highlight the importance of analyzing the orthogonal information to identify and understand it, aiming at future process improvements. The orthogonal framework has also been evaluated by different authors [43][44][45][46][47] and used in different contexts [48][49][50][51][52][53][54][55][56][57].…”

Section: Other Examplesmentioning

confidence: 99%

Advantages of orthogonal inspection in chemometrics

Pinto

Trygg

Gottfries

2012

Journal of Chemometrics

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The demand for chemometrics tools and concepts to study complex problems in modern biology and medicine has prompted chemometricians to shift their focus away from a traditional emphasis on model predictive capacity toward optimizing information exchange via model interpretation for biological validation. The interpretation of projection‐based latent variable models is not straightforward because of its confounding of different systematic variations in the model components. Over the last 15 years, this has spurred the development of orthogonal‐based methods that are capable of separating the correlated variation (to Y) from the noncorrelated (orthogonal to Y) variations in a single model. Here, we aim to provide a conceptual explanation of the advantages of orthogonal variation inspection in the context of Partial Least Squares (PLS) in multivariate classification and calibration. We propose that by inspecting the orthogonal variation, both model interpretation and information quality are improved by enhancement of the resulting level of knowledge. Although the predictive capacity of PLS using orthogonal methods may be identical to that of PLS alone, the combined result can be superior when it comes to the model interpretation. By discussing theory and examples, several new advantages revealed by inspection of orthogonal variation are highlighted. Copyright © 2012 John Wiley & Sons, Ltd.

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“…OSC methods help to find the same subspace found by PLS, but using less LVs. Yee et al 73 compared various strategies to select the OSC components. Svensson et al 71 compared the characteristics of these algorithms and also their ability to improve predictions.…”

Section: 04422(i)(a) Indirect Approachmentioning

confidence: 99%

Preprocessing Methods

Zeaiter

2009

Comprehensive Chemometrics

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Factor selection strategies for orthogonal signal correction applied to calibration of near-infrared spectra

Cited by 16 publications

References 21 publications

Handling non-stationarity in E-nose design: a review

Handling non-stationarity in E-nose design: a review

Advantages of orthogonal inspection in chemometrics

Preprocessing Methods

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