Fourier transform infrared spectroscopic imaging and multivariate regression for prediction of proteoglycan content of articular cartilage.

Fourier Transform Infrared (FT-IR) spectroscopic imaging has been earlier applied for the spatial estimation of the collagen and the proteoglycan (PG) contents of articular cartilage (AC). However, earlier studies have been limited to the use of univariate analysis techniques. Current analysis metho...

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Autores principales:	Lassi Rieppo, Jarno Rieppo, Jukka S Jurvelin, Simo Saarakkala
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2012
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/20b1df29f7f34975b04c294f8ccab0a1
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spelling	oai:doaj.org-article:20b1df29f7f34975b04c294f8ccab0a12021-11-18T07:27:51ZFourier transform infrared spectroscopic imaging and multivariate regression for prediction of proteoglycan content of articular cartilage.1932-620310.1371/journal.pone.0032344https://doaj.org/article/20b1df29f7f34975b04c294f8ccab0a12012-01-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/22359683/pdf/?tool=EBIhttps://doaj.org/toc/1932-6203Fourier Transform Infrared (FT-IR) spectroscopic imaging has been earlier applied for the spatial estimation of the collagen and the proteoglycan (PG) contents of articular cartilage (AC). However, earlier studies have been limited to the use of univariate analysis techniques. Current analysis methods lack the needed specificity for collagen and PGs. The aim of the present study was to evaluate the suitability of partial least squares regression (PLSR) and principal component regression (PCR) methods for the analysis of the PG content of AC. Multivariate regression models were compared with earlier used univariate methods and tested with a sample material consisting of healthy and enzymatically degraded steer AC. Chondroitinase ABC enzyme was used to increase the variation in PG content levels as compared to intact AC. Digital densitometric measurements of Safranin O-stained sections provided the reference for PG content. The results showed that multivariate regression models predict PG content of AC significantly better than earlier used absorbance spectrum (i.e. the area of carbohydrate region with or without amide I normalization) or second derivative spectrum univariate parameters. Increased molecular specificity favours the use of multivariate regression models, but they require more knowledge of chemometric analysis and extended laboratory resources for gathering reference data for establishing the models. When true molecular specificity is required, the multivariate models should be used.Lassi RieppoJarno RieppoJukka S JurvelinSimo SaarakkalaPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 7, Iss 2, p e32344 (2012)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Lassi Rieppo Jarno Rieppo Jukka S Jurvelin Simo Saarakkala Fourier transform infrared spectroscopic imaging and multivariate regression for prediction of proteoglycan content of articular cartilage.
description	Fourier Transform Infrared (FT-IR) spectroscopic imaging has been earlier applied for the spatial estimation of the collagen and the proteoglycan (PG) contents of articular cartilage (AC). However, earlier studies have been limited to the use of univariate analysis techniques. Current analysis methods lack the needed specificity for collagen and PGs. The aim of the present study was to evaluate the suitability of partial least squares regression (PLSR) and principal component regression (PCR) methods for the analysis of the PG content of AC. Multivariate regression models were compared with earlier used univariate methods and tested with a sample material consisting of healthy and enzymatically degraded steer AC. Chondroitinase ABC enzyme was used to increase the variation in PG content levels as compared to intact AC. Digital densitometric measurements of Safranin O-stained sections provided the reference for PG content. The results showed that multivariate regression models predict PG content of AC significantly better than earlier used absorbance spectrum (i.e. the area of carbohydrate region with or without amide I normalization) or second derivative spectrum univariate parameters. Increased molecular specificity favours the use of multivariate regression models, but they require more knowledge of chemometric analysis and extended laboratory resources for gathering reference data for establishing the models. When true molecular specificity is required, the multivariate models should be used.
format	article
author	Lassi Rieppo Jarno Rieppo Jukka S Jurvelin Simo Saarakkala
author_facet	Lassi Rieppo Jarno Rieppo Jukka S Jurvelin Simo Saarakkala
author_sort	Lassi Rieppo
title	Fourier transform infrared spectroscopic imaging and multivariate regression for prediction of proteoglycan content of articular cartilage.
title_short	Fourier transform infrared spectroscopic imaging and multivariate regression for prediction of proteoglycan content of articular cartilage.
title_full	Fourier transform infrared spectroscopic imaging and multivariate regression for prediction of proteoglycan content of articular cartilage.
title_fullStr	Fourier transform infrared spectroscopic imaging and multivariate regression for prediction of proteoglycan content of articular cartilage.
title_full_unstemmed	Fourier transform infrared spectroscopic imaging and multivariate regression for prediction of proteoglycan content of articular cartilage.
title_sort	fourier transform infrared spectroscopic imaging and multivariate regression for prediction of proteoglycan content of articular cartilage.
publisher	Public Library of Science (PLoS)
publishDate	2012
url	https://doaj.org/article/20b1df29f7f34975b04c294f8ccab0a1
work_keys_str_mv	AT lassirieppo fouriertransforminfraredspectroscopicimagingandmultivariateregressionforpredictionofproteoglycancontentofarticularcartilage AT jarnorieppo fouriertransforminfraredspectroscopicimagingandmultivariateregressionforpredictionofproteoglycancontentofarticularcartilage AT jukkasjurvelin fouriertransforminfraredspectroscopicimagingandmultivariateregressionforpredictionofproteoglycancontentofarticularcartilage AT simosaarakkala fouriertransforminfraredspectroscopicimagingandmultivariateregressionforpredictionofproteoglycancontentofarticularcartilage
_version_	1718423396116594688

Fourier transform infrared spectroscopic imaging and multivariate regression for prediction of proteoglycan content of articular cartilage.

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