Dry film method with ytterbium as the internal standard for near infrared spectroscopic plasma glucose assay coupled with boosting support vector regression

2006 ◽  
Vol 20 (1-2) ◽  
pp. 13-21 ◽  
Author(s):  
Yan-Ping Zhou ◽  
Jian-Hui Jiang ◽  
Hai-Long Wu ◽  
Guo-Li Shen ◽  
Ru-Qin Yu ◽  
...  
Keyword(s):  
Support Vector Regression ◽  
Plasma Glucose ◽  
Near Infrared ◽  
Internal Standard ◽  
Support Vector ◽  
Glucose Assay ◽  
Infrared Spectroscopic ◽  
Film Method ◽  
Dry Film

Spectral Data Modeling Based on Feature Extraction and Extreme Support Vector Regression

2011 ◽  
Vol 128-129 ◽  
pp. 297-300
Author(s):  
Shao Wei Liu ◽  
Dong Yan ◽  
Zhi Hua Liu ◽  
Jian Tang
Keyword(s):  
Feature Extraction ◽  
Support Vector Regression ◽  
Spectral Data ◽  
Latent Variables ◽  
Near Infrared ◽  
Support Vector ◽  
Feature Extraction Method ◽  
Near Infrared Spectra ◽  
Learning Machine ◽  
Elm Kernel

Spectral data such as near-infrared spectrum and frequency spectrum can simply the modeling of the difficulty-to-measured parameters. A novel modeling approach combined the feature extraction with extreme support vector regression (ESVR) is proposed. The latent variables space based feature extraction method can successfully complete the dimension reduction and independent variable extraction. The novel proposed ESVR leaning algorithm is realized by using extreme learning machine (ELM) kernel as SVR kernel, which is used to construct final models with better generalization. The experimental results based on the orange juice near-infrared spectra demonstrate that the proposed approach has better generalization performance and prediction accuracy.

Nanoliter Serum Sample Analysis by Mid-Infrared Spectroscopy for Minimally Invasive Blood-Glucose Monitoring

2005 ◽  
Vol 59 (4) ◽  
pp. 442-451 ◽  
Author(s):  
Edgar Diessel ◽  
Peter Kamphaus ◽  
Klaus Grothe ◽  
Roland Kurte ◽  
Uwe Damm ◽  
...  
Keyword(s):  
Infrared Spectroscopy ◽  
Matrix Protein ◽  
Internal Standard ◽  
Blood Glucose Monitoring ◽  
Absorption Bands ◽  
Mid Infrared ◽  
Mid Infrared Spectroscopy ◽  
Glucose Assay ◽  
Sample Series ◽  
Dry Film

The aim of this study was to demonstrate that mid-infrared spectroscopy is able to quantify glucose in a serum matrix with sample volumes well below 1 μL. For this, we applied mid-infrared attenuated total reflectance (ATR) or transmission-based spectroscopic methods to glucose quantification in microsamples of dry-film sera, either undiluted or diluted 10 times in distilled water. The sample series spanned physiological glucose concentrations between 50 and 600 mg/dL and volumes of 80, 8, and 1 nL. Calibration was carried out using multivariate partial least-squares (PLS) modeling with spectral data between 1180 and 940 cm−1. Best performance was achieved in the ATR experiments. For raw ATR spectra, the optimum standard error of prediction (SEP) of 13.3 mg/dL was obtained for the 8 nL sample series with subsequent 10-fold dilution. With respect to the coefficient of variation of the glucose assay, CVpred, we obtained a value of 3% for the 80 nL volume samples with spectral preprocessing using matrix protein absorption bands as an internal standard, 4% for the 8 nL samples, and 6% for the 1 nL samples with raw data. Spectral standardization resulted in significant improvement, especially for the 80 nL volume sample series. By contrast, the accuracy of the glucose assay for the 1 nL sample volume series could not be improved either by internal standardization or by considering the dry film areas for normalization, which we attribute to varying topographies of the dry films.

scholarly journals Moisture Content Quantization of Masson Pine Seedling Leaf Based on Stacked Autoencoder with Near-Infrared Spectroscopy

2018 ◽  
Vol 2018 ◽  
pp. 1-8 ◽  
Author(s):  
Chao Ni ◽  
Yun Zhang ◽  
Dongyi Wang
Keyword(s):  
Infrared Spectroscopy ◽  
Moisture Content ◽  
Support Vector Regression ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Support Vector ◽  
Spectroscopy Analysis ◽  
Support Vector Regression Model ◽  
Masson Pine ◽  
Stacked Autoencoder

Masson pine is widely planted in southern China, and moisture content of the pine seedling leaves is an important index for evaluating the vigor of seedlings. For precisely predicting leaf moisture content, near-infrared spectroscopy analysis is applied in the experiment, which is a cost-effective, high-speed, and noninvasive material content prediction tool. To further improve the spectroscopy analysis accuracy, in this study, a new analysis model is proposed which integrates a stacked autoencoder for extracting hierarchical output-related features layer by layer and a support vector regression model to leverage these features for precisely predicting moisture contents. Compared with traditional spectroscopy analysis method like partial least squares regression and basic support vector regression, the proposed model shows great superiority for leaf moisture content prediction, with R2 value 0.9946 and root-mean squared error (RMSE) value 0.1636 in calibration set and R2 value 0.9621 and RMSE 0.4249 in prediction set.

Support Vector Regression on Near-Infrared Spectra Analysis for Moisture Determination in Lignitic Coal Samples

2014 ◽  
Vol 494-495 ◽  
pp. 964-967
Author(s):  
Xiao Li Yang ◽  
Yan Fang Li ◽  
Xing Wang Zhang ◽  
Shi Qiang Hu
Keyword(s):  
Support Vector Regression ◽  
Near Infrared ◽  
Parameters Optimization ◽  
Support Vector ◽  
Discrete Wavelet ◽  
Moisture Determination ◽  
Support Vector Regression Model ◽  
Spectra Analysis ◽  
Near Infrared Spectra ◽  
Spectrometry Technique

We studied rapid moisture determination in lignitic coal samples using near-infrared (NIR) spectrometry technique. This research applied support vector regression (SVR) and discrete wavelet transform (DWT) to analyze NIR spectra. Firstly, NIR spectra were pre-processed by DWT for fitting and compression. Then, DWT coefficients were used to build support vector regression model. Through parameters optimization, the results show that DWT-SVR can obtain satisfactory performance for moisture determination in lignitic coal samples.

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