Multivariate Analysis for the Processing of Signals

Beattie, J. Renwick

doi:10.2516/ogst/2013185

Cited by 6 publications

(4 citation statements)

References 54 publications

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“…The Raman data collected from the nails were processed using models created in a previous study. 13 Briefly, this was a singular-value decomposition-based background removal [32][33][34] modelled on an archive of 1500 nails (excluding all nails used in test set for this study), with linear interpolation of the loadings using 12 points. The data were then normalised to the first PC score, using MATLAB 2013a (Natick, MA, USA).…”

Section: Nail Spectral Measurement and Processingmentioning

confidence: 99%

A Preliminary Evaluation of the Ability of Keratotic Tissue to Act as a Prognostic Indicator of Hip Fracture Risk

Beattie¹,

Feskanich

Caraher³

et al. 2018

Clin Med�Insights�Arthritis�Musculoskelet Disord

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Studies have shown that Raman spectroscopic analysis of fingernail clippings can help differentiate between post-menopausal women who have and who have not suffered a fracture. However, all studies to date have been retrospective in nature, comparing the proteins in nails sourced from women, post-fracture. The objective of this study was to investigate the potential of a prospective test for hip fracture based on spectroscopic analysis of nail tissue. Archived toenail samples from post-menopausal women aged 50 to 63 years in the Nurses’ Health Study were obtained and analysed by Raman spectroscopy. Nails were matched case-controls sourced from 161 women; 82 who underwent a hip fracture up to 20 years after nail collection and 81 age-matched controls. A number of clinical risk factors (CRFs) from the Fracture Risk Assessment (FRAX) tool had been assessed at toenail collection. Using 80% of the spectra, models were developed for increasing time periods between nail collection and fracture. Scores were calculated from these models for the other 20% of the sample and the ability of the score to predict hip fracture was tested in model with and without the CRFs by comparing the odds ratios (ORs) per 1 SD increase in standardised predictive values. The Raman score successfully distinguished between hip fracture cases and controls. With only the score as a predictor, a statistically significant OR of 2.2 (95% confidence interval [CI]: 1.5-3.1) was found for hip fracture for up to 20 years after collection. The OR increased to 3.8 (2.6-5.4) when the CRFs were added to the model. For fractures limited to 13 years after collection, the OR was 6.3 (3.0-13.1) for the score alone. The test based on Raman spectroscopy has potential for identifying individuals who may suffer hip fractures several years in advance. Higher powered studies are required to evaluate the predictive capability of this test.

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Section: Nail Spectral Measurement and Processingmentioning

confidence: 99%

A Preliminary Evaluation of the Ability of Keratotic Tissue to Act as a Prognostic Indicator of Hip Fracture Risk

Beattie¹,

Feskanich

Caraher³

et al. 2018

Clin Med�Insights�Arthritis�Musculoskelet Disord

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“…Currently, there is no standardization of Raman spectral preprocessing, 13,31,106 and a number of sequences have been used or proposed. 19,38,105,119 Note that some sequences do not work because the data are being processed in the wrong order. For example, BLF is required before NRM; reversing the order produces improperly scaled results.…”

Section: Methodsmentioning

confidence: 99%

“…Equation 5 consists of univariate or multivariate analyses; Raman band-based, component analysis score-based, or cluster-based component mapping; clustering and classification procedures; mixture deconvolution; or other ways in which to selectively emphasize or quantify the information of interest. [35][36][37][38] We next define preprocessing and data analysis as stages while, within each stage, steps denote those particular processes required to achieve specific objectives (more details below). Eqs.…”

Section: Theorymentioning

confidence: 99%

Development and Integration of Block Operations for Data Invariant Automation of Digital Preprocessing and Analysis of Biological and Biomedical Raman Spectra

Schulze

Turner

2015

Appl Spectrosc

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High-throughput information extraction from large numbers of Raman spectra is becoming an increasingly taxing problem due to the proliferation of new applications enabled using advances in instrumentation. Fortunately, in many of these applications, the entire process can be automated, yielding reproducibly good results with significant time and cost savings. Information extraction consists of two stages, preprocessing and analysis. We focus here on the preprocessing stage, which typically involves several steps, such as calibration, background subtraction, baseline flattening, artifact removal, smoothing, and so on, before the resulting spectra can be further analyzed. Because the results of some of these steps can affect the performance of subsequent ones, attention must be given to the sequencing of steps, the compatibility of these sequences, and the propensity of each step to generate spectral distortions. We outline here important considerations to effect full automation of Raman spectral preprocessing: what is considered full automation; putative general principles to effect full automation; the proper sequencing of processing and analysis steps; conflicts and circularities arising from sequencing; and the need for, and approaches to, preprocessing quality control. These considerations are discussed and illustrated with biological and biomedical examples reflecting both successful and faulty preprocessing.

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“…The Raman data collected from the nails were processed in Matlab 2013a (Natick, MA, USA) using models created in a previous study. 28 A principal component analysis (PCA)-based background removal [31][32][33][34] model derived from an archive of 1500 nails was applied to the data which was then normalised to the first PC score. 35,36 Spectra were acquired from 400 to 1800 cm À1 .…”

Section: Sample Analysis Using Raman Spectroscopymentioning

confidence: 99%

Raman spectroscopy as a tool for monitoring osteoporosis therapy in postmenopausal osteoporosis

Beattie,

Towler

2023

J Raman Spectroscopy

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Bisphosphonate‐based pharmacological therapy of osteoporosis reduces risk of fracture, but modulation of bone mineral density does not solely explain this. Additional bone quality aspects affecting fragility need to be better understood, alongside methods to monitor them. Systemic factors that influence bone collagen remodelling also remodel keratin in parallel processes. In this study, human fingernail clippings from subjects with and without bone active pharmaceutical intervention are compared. A discriminant model was able to distinguish between the nails from patients that were treatment‐naive and treated with an area under the curve of 71% in the test set. A time series of changes relative to baseline revealed that after 1 year, the scores of the treated group (95 confidence interval 37% to 377% change) differed from both the 12 week measurements (−198% to 34%) and scores from untreated subjects (−92% to −674%). Analysis of the spectral differences and model coefficients revealed features that were inverse to those observed in three previous osteoporosis models, indicating that treatment was reverting damaged protein structure. This study provides preliminary evidence that bone active medication systemically influences keratin structure in humans and provides some discussion on the underlying mechanism. The study demonstrates that bisphosphonates have a direct influence on protein structure, warranting further investigation of these effects.

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Multivariate Analysis for the Processing of Signals

Cited by 6 publications

References 54 publications

A Preliminary Evaluation of the Ability of Keratotic Tissue to Act as a Prognostic Indicator of Hip Fracture Risk

A Preliminary Evaluation of the Ability of Keratotic Tissue to Act as a Prognostic Indicator of Hip Fracture Risk

Development and Integration of Block Operations for Data Invariant Automation of Digital Preprocessing and Analysis of Biological and Biomedical Raman Spectra

Raman spectroscopy as a tool for monitoring osteoporosis therapy in postmenopausal osteoporosis

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