Sensitivity of model-based quantitative FTIR to instrumental and spectroscopic database error sources

Coleman, Marc D.; Gardiner, Tom

doi:10.1016/j.vibspec.2009.04.005

Cited by 7 publications

(10 citation statements)

References 36 publications

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“…There are several potential sources of error in the current studies, including technical errors, such as Beer's law non-linearity, instrumental misalignment, inaccurate weighing of the pure component samples, and scattering in the KBr pellets [41], which may have contributed to broadening of the peak positions in the Model B spectra. However, similar results for prediction of type II collagen in tissues were obtained with both Models A and B, indicating that the spectral artifacts did not interfere significantly with the analyses.…”

Section: Discussionmentioning

confidence: 99%

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Fourier Transform Infrared Imaging and Infrared Fiber Optic Probe Spectroscopy Identify Collagen Type in Connective Tissues

et al. 2013

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Hyaline cartilage and mechanically inferior fibrocartilage consisting of mixed collagen types are frequently found together in repairing articular cartilage. The present study seeks to develop methodology to identify collagen type and other tissue components using Fourier transform infrared (FTIR) spectral evaluation of matrix composition in combination with multivariate analyses. FTIR spectra of the primary molecular components of repair cartilage, types I and II collagen, and aggrecan, were used to develop multivariate spectral models for discrimination of the matrix components of the tissues of interest. Infrared imaging data were collected from bovine bone, tendon, normal cartilage, meniscus and human repair cartilage tissues, and composition predicted using partial least squares analyses. Histology and immunohistochemistry results were used as standards for validation. Infrared fiber optic probe spectral data were also obtained from meniscus (a tissue with mixed collagen types) to evaluate the potential of this method for identification of collagen type in a minimally-invasive clinical application. Concentration profiles of the tissue components obtained from multivariate analysis were in excellent agreement with histology and immunohistochemistry results. Bone and tendon showed a uniform distribution of predominantly type I collagen through the tissue. Normal cartilage showed a distribution of type II collagen and proteoglycan similar to the known composition, while in repair cartilage, the spectral distribution of both types I and II collagen were similar to that observed via immunohistochemistry. Using the probe, the outer and inner regions of the meniscus were shown to be primarily composed of type I and II collagen, respectively, in accordance with immunohistochemistry data. In summary, multivariate analysis of infrared spectra can indeed be used to differentiate collagen type I and type II, even in the presence of proteoglycan, in connective tissues, using both imaging and fiber optic methodology. This has great potential for clinical in situ applications for monitoring tissue repair.

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Section: Discussionmentioning

confidence: 99%

“…The quality of the model was evaluated by assessment of the root mean square error (RMSE), and the regression coefficient (R 2 ) of the cross validation model. RMSE measures the precision of the model and is calculated according to the equation below:(i: number of samples = 1 to N) [40], [41].…”

Section: Methodsmentioning

confidence: 99%

Fourier Transform Infrared Imaging and Infrared Fiber Optic Probe Spectroscopy Identify Collagen Type in Connective Tissues

et al. 2013

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“…RMSE measures the precision of the model and is calculated according to the equation below:

RMSE = \sqrt{\frac{\sum {(Y prediction (i) - Y experiment (i))}^{2}}{N}}

(i: number of samples = 1 to N). 2, 15 …”

Section: Methodsmentioning

confidence: 99%

Infrared Fiber Optic Probe Evaluation of Degenerative Cartilage Correlates to Histological Grading

Hanifi

Yang

et al. 2012

Am J Sports Med

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Background Osteoarthritis (OA), a degenerative cartilage disease, results in alterations of the chemical and structural properties of the tissue. Arthroscopic evaluation of full-depth tissue composition is limited and would require tissue harvesting, which is inappropriate in daily routine. Fourier transform infrared (FT-IR) spectroscopy is a modality based on molecular vibrations of matrix components that can be used in conjunction with a fiber optic to acquire quantitative compositional data from the cartilage matrix. Purpose To develop a model based on infrared spectra of articular cartilage to predict the histological Mankin score as an indicator of tissue quality. Study Design Descriptive laboratory study Methods Infrared fiber optic probe (IFOP) spectra were collected from nearly normal and more degraded regions of tibial plateau articular cartilage harvested during knee arthroplasty (N = 61). Each region was graded using a modified Mankin score (MMS). A multivariate partial least squares (PLS) algorithm using second derivative spectra was developed to predict histological MMS. Results The PLS model derived from IFOP spectra predicted the MMS with a prediction error of ~1.4, which resulted in ~72% of the Mankin scored tissues being predicted correctly, and 96% being predicted within one grade of their true modified Mankin score. Conclusion These data demonstrate that IFOP spectral parameters correlate with histological tissue grade, and can be used to provide information on tissue composition. Clinical Relevance IFOP studies have significant potential for evaluation of cartilage tissue quality without the need for tissue harvest. Combined with arthroscopy, IFOP analysis could facilitate definition of tissue margins in debridement procedures.

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“…Errors due to a lack of knowledge about ILS during synthetic calibration have been considered in detail in the literature [32]. In the simplest approximation, ILS is a Fourier transform of apodisation functions [33], also called windows [34].…”

Section: Modeling Of Gas Spectramentioning

confidence: 99%

A Local Model and Calibration Set Ensemble Strategy for Open-Path FTIR Gas Measurement with Varying Temperature

Cięszczyk¹

2013

Metrology and Measurement Systems

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Open-Path Fourier Transform Infrared OP-FTIR spectrometers are commonly used for the measurement of atmospheric pollutants and of gases in industrial processes. Spectral interpretation for the determination of gas concentrations is based on the HITRAN database line-by-line modeling method. This article describes algorithms used to model gas spectra and to determine gas concentration under variable temperatures. Integration of individual rotational lines has been used to reduce the impact of spectrometer functions on the comparison of both measured and synthetic modeled spectra. Carbon monoxide was used as an example. A new algorithm for gas concentration retrieval consisting of two ensemble methods is proposed. The first method uses an ensemble of local models based on linear and non-linear PLS (partial least square) regression algorithms, while the second is an ensemble of a calibration set built for different temperatures. It is possible to combine these methods to decrease the number of regression models in the first ensemble. These individual models are appropriate for specific measurement conditions specified by the ensemble of the calibration set. Model selection is based on comparison of gas spectra with values determined from each local model.

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Sensitivity of model-based quantitative FTIR to instrumental and spectroscopic database error sources

Cited by 7 publications

References 36 publications

Fourier Transform Infrared Imaging and Infrared Fiber Optic Probe Spectroscopy Identify Collagen Type in Connective Tissues

Fourier Transform Infrared Imaging and Infrared Fiber Optic Probe Spectroscopy Identify Collagen Type in Connective Tissues

Infrared Fiber Optic Probe Evaluation of Degenerative Cartilage Correlates to Histological Grading

A Local Model and Calibration Set Ensemble Strategy for Open-Path FTIR Gas Measurement with Varying Temperature

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