Evaluation of selectivity and robustness of near-infrared glucose measurements based on short-scan Fourier transform infrared interferograms

Wabomba, Mukire J.; Small, Gary W.; Arnold, Mark A.

doi:10.1016/s0003-2670(03)00337-4

Cited by 22 publications

(17 citation statements)

References 8 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The IMF needs to meet two natures, one is that the number of extreme value points and passing zero points is equal or differs at most by one point, another one is that the mean of upper and lower envelope at any point is zero. (6) The r 1 (t) = s(t) − c 1 (t), as a new signal to be analyzed, repeat the steps (1)- (5) to obtain the second IMF and the r 2 (t) = s(t) − c 2 (t). (7) Repeat the above steps and the decomposition ends when the residue r n (t) is a monotonic function.…”

Section: Introductionmentioning

confidence: 99%

Pretreatment and Wavelength Selection Method for Near-Infrared Spectra Signal Based on Improved CEEMDAN Energy Entropy and Permutation Entropy

2017

Entropy

View full text Add to dashboard Cite

Abstract:The noise of near-infrared spectra and spectral information redundancy can affect the accuracy of calibration and prediction models in near-infrared analytical technology. To address this problem, the improved Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) and permutation entropy (PE) were used to propose a new method for pretreatment and wavelength selection of near-infrared spectra signal. The near-infrared spectra of glucose solution was used as the research object, the improved CEEMDAN energy entropy was then used to reconstruct spectral data for removing noise, and the useful wavelengths are selected based on PE after spectra segmentation. Firstly, the intrinsic mode functions of original spectra are obtained by improved CEEMDAN algorithm. The useful signal modes and noisy signal modes were then identified by the energy entropy, and the reconstructed spectral signal is the sum of useful signal modes. Finally, the reconstructed spectra were segmented and the wavelengths with abundant glucose information were selected based on PE. To evaluate the performance of the proposed method, support vector regression and partial least square regression were used to build the calibration model using the wavelengths selected by the new method, mutual information, successive projection algorithm, principal component analysis, and full spectra data. The results of the model were evaluated by the correlation coefficient and root mean square error of prediction. The experimental results showed that the improved CEEMDAN energy entropy can effectively reconstruct near-infrared spectra signal and that the PE can effectively solve the wavelength selection. Therefore, the proposed method can improve the precision of spectral analysis and the stability of the model for near-infrared spectra analysis.

show abstract

Section: Introductionmentioning

confidence: 99%

Pretreatment and Wavelength Selection Method for Near-Infrared Spectra Signal Based on Improved CEEMDAN Energy Entropy and Permutation Entropy

2017

Entropy

View full text Add to dashboard Cite

show abstract

“…NIR spectroscopy is faster and provides a reasonable signal-to-noise ratio as compared to other methods. The prediction of the concentration of glucose from a NIR spectra remains a challenge due to underlying noise and necessitates the development of advanced and efficient multivariate data analysis algorithms [2][3][4][5].…”

Section: Introductionmentioning

confidence: 99%

Local linear embedded regression in the quantitative analysis of glucose in near infrared spectra

et al. 2015

View full text Add to dashboard Cite

show abstract

“…Research over the years has identified near infra red (NIR) spectroscopy coupled with chemometrics and signal processing methods as one of the most promising techniques for non-invasive blood glucose measurement [1][2][3] to help with the management of diabetes. NIR spectroscopy is fast, easy and accurate in comparison to other techniques.…”

Section: Introductionmentioning

confidence: 99%

Determination of glucose concentration from near-infrared spectra using locally weighted partial least square regression

Malik

Benaïssa

2012

2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society

View full text Add to dashboard Cite

This paper proposes the use of locally weighted partial least square regression (LW-PLSR) as an alternative multivariate calibration method for the prediction of glucose concentration from NIR spectra. The efficiency of the proposed model is validated in experiments carried out in a noncontrolled environment or sample conditions using mixtures composed of glucose, urea and triacetin. The collected data spans the spectral region from 2100nm to 2400nm with spectra resolution of 1nm. The results show that the standard error of prediction (SEP) decreases to 23.85 mg/dL when using LW-PLSR in comparison to the SEP values of 49.40 mg/dL, and 27.56 mg/dL using Principal Component Regression (PCR) and Partial Least Square (PLS) regression respectively.

show abstract

Evaluation of selectivity and robustness of near-infrared glucose measurements based on short-scan Fourier transform infrared interferograms

Cited by 22 publications

References 8 publications

Pretreatment and Wavelength Selection Method for Near-Infrared Spectra Signal Based on Improved CEEMDAN Energy Entropy and Permutation Entropy

Pretreatment and Wavelength Selection Method for Near-Infrared Spectra Signal Based on Improved CEEMDAN Energy Entropy and Permutation Entropy

Local linear embedded regression in the quantitative analysis of glucose in near infrared spectra

Determination of glucose concentration from near-infrared spectra using locally weighted partial least square regression

Contact Info

Product

Resources

About