Phantom Glucose Calibration Models from Simulated Noninvasive Human Near-Infrared Spectra

1998 ◽  
Vol 70 (9) ◽  
pp. 1773-1781 ◽  
Author(s):  
Mark A. Arnold ◽  
Jason J. Burmeister ◽  
Gary W. Small
Keyword(s):  
Infrared Spectra ◽  
Near Infrared ◽  
Near Infrared Spectra ◽  
Calibration Models

Pure Component Selectivity Analysis of Multivariate Calibration Models from Near-Infrared Spectra

2004 ◽  
Vol 76 (9) ◽  
pp. 2583-2590 ◽  
Author(s):  
Mark A. Arnold ◽  
Gary W. Small ◽  
Dong Xiang ◽  
Jiang Qui ◽  
David W. Murhammer
Keyword(s):  
Infrared Spectra ◽  
Near Infrared ◽  
Multivariate Calibration ◽  
Pure Component ◽  
Near Infrared Spectra ◽  
Calibration Models

Multi-Product Calibration Models of near Infrared Spectra of Foods

2006 ◽  
Vol 14 (6) ◽  
pp. 395-402 ◽  
Author(s):  
Elisabeth Micklander ◽  
Karin Kjeldahl ◽  
Max Egebo ◽  
Lars Nørgaard
Keyword(s):  
Infrared Spectra ◽  
Near Infrared ◽  
Near Infrared Spectra ◽  
Calibration Models

Fuzzy Optimal Associative Memory for Background Prediction of Near-Infrared Spectra

1996 ◽  
Vol 50 (1) ◽  
pp. 35-42 ◽  
Author(s):  
Busolo Wa Wabuyele ◽  
Peter De B. Harrington
Keyword(s):  
Associative Memory ◽  
Infrared Spectra ◽  
Plasma Glucose ◽  
Near Infrared ◽  
Second Derivative ◽  
Derivative Spectra ◽  
Near Ir ◽  
Near Infrared Spectra ◽  
Calibration Models ◽  
Second Derivative Spectra

A fuzzy optimal associative memory (FOAM) has been devised for background correction of near-infrared spectra. The FOAM yields improved predicted background scans for calculation of near-IR absorbance spectra of glucose in plasma matrices from single-beam data. The FOAM is an enhanced optimal associative memory (OAM) that uses a fuzzy function for encoding the spectra. The FOAM can predict a matching reference spectrum for a near-IR absorbance spectrum with low glucose absorbances by using second-derivative spectra. Glucose concentrations were predicted from calibration models furnished by partial least-squares (PLS). The FOAM stored reference spectra obtained from either water/phosphate buffer or plasma/glucose solutions. Both of these associative memories were evaluated. The standard error of prediction (SEP) for glucose concentration from an optimal PLS calibration model based on FOAM-corrected spectra was 0.60 mM for the water/phosphate buffer spectra. For FOAM-corrected spectra from plasma/glucose reference spectra, the SEP was 0.68 mM. The SEP of conventionally corrected double-beam second-derivative spectra was 0.81 mM. FOAM-corrected spectra generally furnish improved calibration models.

Rapid Determining Contents of the Rhubarb Anthraquinones Compounds by Support Vector Machines Modeling based on Near Infrared Spectra

2020 ◽  
Vol 16 ◽  
Author(s):  
Linqi Liu ◽  
JInhua Luo ◽  
Chenxi Zhao ◽  
Bingxue Zhang ◽  
Wei Fan ◽  
...  
Keyword(s):  
Infrared Spectra ◽  
Near Infrared ◽  
Mean Squared Error ◽  
Rapid Determination ◽  
Partial Least Square ◽  
Least Square ◽  
Support Vector ◽  
Near Infrared Spectra ◽  
Aloe Emodin ◽  
Relative Differences

BACKGROUND: Measuring medicinal compounds to evaluate their quality and efficacy has been recognized as a useful approach in treatment. Rhubarb anthraquinones compounds (mainly including aloe-emodin, rhein, emodin, chrysophanol and physcion) are its main effective components as purgating drug. In the current Chinese Pharmacopoeia, the total anthraquinones content is designated as its quantitative quality and control index while the content of each compound has not been specified. METHODS: On the basis of forty rhubarb samples, the correlation models between the near infrared spectra and UPLC analysis data were constructed using support vector machine (SVM) and partial least square (PLS) methods according to Kennard and Stone algorithm for dividing the calibration/prediction datasets. Good models mean they have high correlation coefficients (R2) and low root mean squared error of prediction (RMSEP) values. RESULTS: The models constructed by SVM have much better performance than those by PLS methods. The SVM models have high R2 of 0.8951, 0.9738, 0.9849, 0.9779, 0.9411 and 0.9862 that correspond to aloe-emodin, rhein, emodin, chrysophanol, physcion and total anthraquinones contents, respectively. The corresponding RMSEPs are 0.3592, 0.4182, 0.4508, 0.7121, 0.8365 and 1.7910, respectively. 75% of the predicted results have relative differences being lower than 10%. As for rhein and total anthraquinones, all of the predicted results have relative differences being lower than 10%. CONCLUSION: The nonlinear models constructed by SVM showed good performances with predicted values close to the experimental values. This can perform the rapid determination of the main medicinal ingredients in rhubarb medicinal materials.

Discrimination of transgenic tomatoes based on visible/near-infrared spectra

2007 ◽  
Vol 584 (2) ◽  
pp. 379-384 ◽  
Author(s):  
Lijuan Xie ◽  
Yibin Ying ◽  
Tiejin Ying ◽  
Haiyan Yu ◽  
Xiaping Fu
Keyword(s):  
Infrared Spectra ◽  
Near Infrared ◽  
Near Infrared Spectra ◽  
Transgenic Tomatoes
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