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Discussion on the validity of NIR spectral data in non-invasive blood glucose sensing.

Zhang W, Liu R, Zhang W, Jia H, Xu K - Biomed Opt Express (2013)

Bottom Line: The aim is to evaluate the validity of the spectral data set, instead of assessing the calibration models, and then to provide a complementary procedure for better verifying or rejecting the data set.It includes tracing back to the source of the chance correlation on the chemical basis, selecting appropriate preprocessing methods before building multivariate calibration models, and therefore may avoid invalid models.Results show that, spectral variations from chance correlations induced by those experimental factors can be determined by the 2DCOS method, which develops avenues for prospectively accurate prediction in clinical application of this technology.

View Article: PubMed Central - PubMed

Affiliation: State Key Laboratory of Precision Measuring Technology and Instruments, Tianjin University, Tianjin 300072, China.

ABSTRACT
In this paper, the effects of two-dimensional correlation spectroscopy (2DCOS) on chance correlations in the spectral data, generated from the correlations between glucose concentration and some undesirable experimental factors, such as instrument drift, sample temperature variations, and interferent compositions in the sample matrix, are investigated. The aim is to evaluate the validity of the spectral data set, instead of assessing the calibration models, and then to provide a complementary procedure for better verifying or rejecting the data set. It includes tracing back to the source of the chance correlation on the chemical basis, selecting appropriate preprocessing methods before building multivariate calibration models, and therefore may avoid invalid models. The utility of the proposed analysis is demonstrated with a series of aqueous solutions using near-infrared spectra over the overtone band of glucose. Results show that, spectral variations from chance correlations induced by those experimental factors can be determined by the 2DCOS method, which develops avenues for prospectively accurate prediction in clinical application of this technology.

No MeSH data available.


Spectra of pure water (the FT-IR spectrometer).
© Copyright Policy - open-access
Related In: Results  -  Collection


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g003: Spectra of pure water (the FT-IR spectrometer).

Mentions: To investigate the autopeak differences originated from instrument to instrument, similar repetitive experiment is conducted on the FT-IR spectrometer. Figure 3Fig. 3


Discussion on the validity of NIR spectral data in non-invasive blood glucose sensing.

Zhang W, Liu R, Zhang W, Jia H, Xu K - Biomed Opt Express (2013)

Spectra of pure water (the FT-IR spectrometer).
© Copyright Policy - open-access
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC3675860&req=5

g003: Spectra of pure water (the FT-IR spectrometer).
Mentions: To investigate the autopeak differences originated from instrument to instrument, similar repetitive experiment is conducted on the FT-IR spectrometer. Figure 3Fig. 3

Bottom Line: The aim is to evaluate the validity of the spectral data set, instead of assessing the calibration models, and then to provide a complementary procedure for better verifying or rejecting the data set.It includes tracing back to the source of the chance correlation on the chemical basis, selecting appropriate preprocessing methods before building multivariate calibration models, and therefore may avoid invalid models.Results show that, spectral variations from chance correlations induced by those experimental factors can be determined by the 2DCOS method, which develops avenues for prospectively accurate prediction in clinical application of this technology.

View Article: PubMed Central - PubMed

Affiliation: State Key Laboratory of Precision Measuring Technology and Instruments, Tianjin University, Tianjin 300072, China.

ABSTRACT
In this paper, the effects of two-dimensional correlation spectroscopy (2DCOS) on chance correlations in the spectral data, generated from the correlations between glucose concentration and some undesirable experimental factors, such as instrument drift, sample temperature variations, and interferent compositions in the sample matrix, are investigated. The aim is to evaluate the validity of the spectral data set, instead of assessing the calibration models, and then to provide a complementary procedure for better verifying or rejecting the data set. It includes tracing back to the source of the chance correlation on the chemical basis, selecting appropriate preprocessing methods before building multivariate calibration models, and therefore may avoid invalid models. The utility of the proposed analysis is demonstrated with a series of aqueous solutions using near-infrared spectra over the overtone band of glucose. Results show that, spectral variations from chance correlations induced by those experimental factors can be determined by the 2DCOS method, which develops avenues for prospectively accurate prediction in clinical application of this technology.

No MeSH data available.