Influence of temperature on visible and near-infrared spectra and the predictive ability of multivariate models

2010 ◽  
Author(s):  
Lijuan Xie ◽  
Yibin Ying ◽  
Tong Sun ◽  
Huirong Xu
Keyword(s):  
Infrared Spectra ◽  
Near Infrared ◽  
Predictive Ability ◽  
Multivariate Models ◽  
Influence Of Temperature ◽  
Near Infrared Spectra ◽  
Influence Of Temperature On

Determination of temperatures of aqueous-based samples directly from near infrared spectra

2017 ◽  
Vol 25 (5) ◽  
pp. 289-300 ◽  
Author(s):  
Chamathca PS Kuda-Malwathumullage ◽  
Gary W Small
Keyword(s):  
Root Mean Square Error ◽  
Mean Square Error ◽  
Root Mean Square ◽  
Infrared Spectra ◽  
Near Infrared ◽  
Predictive Ability ◽  
Spectral Variation ◽  
Mean Square ◽  
Absorption Bands ◽  
Near Infrared Spectra

The temperature sensitivity of underlying water absorption bands can lead to baseline artifacts or apparent spectral band shifts in near infrared spectra and can negatively impact multivariate calibration models used in quantitative analyses. To address this issue, efforts can be made to suppress the temperature-induced spectral variation or knowledge of the temperature can be used to adjust the calibration. To facilitate the latter approach, we explored the ability to estimate the aqueous temperature of the sample directly from the combination region of the near infrared spectrum. This temperature modeling strategy addresses applications in which it is difficult to obtain an accurate sample temperature with a conventional measurement probe. Temperature models were developed by use of partial least-squares regression combined with the discrete wavelet transform. Models were constructed from the 5000 to 4000 cm−1 region of near infrared spectra for pH 7.4 buffer solutions over the temperature range of 20.0–40.5℃. The long-term predictive ability of the models was assessed by use of 13 sets of prediction spectra collected over the course of 13 months, yielding values of the root mean square error of prediction ranging from 0.19 to 0.36℃. In addition, laboratory-prepared solutions of glucose, mixture solutions of glucose, lactate, urea in buffer, and bovine plasma were used to assess the predictive ability of the temperature models in increasingly complex matrixes. The effects of pH and buffer molarity were also studied. While increasing the complexity of the spectral background resulted in increases in root mean square error of prediction (0.33–1.01℃), retuning the models to incorporate the modified spectral backgrounds lowered the resulting root mean square error of prediction values to the range of 0.3℃. This work demonstrates the practical utility of spectral-based temperature measurements that employ the absorbance of the water baseline rather than the peak absorbance.

Temperature and moisture insensitive prediction of biomass calorific value from near infrared spectra using external parameter orthogonalization

2019 ◽  
Vol 27 (4) ◽  
pp. 259-269 ◽  
Author(s):  
Guillaume Hans ◽  
Bruce Allison
Keyword(s):  
Moisture Content ◽  
Infrared Spectra ◽  
Near Infrared ◽  
Calorific Value ◽  
Partial Least Square ◽  
External Parameter ◽  
Calibration Model ◽  
Gross Calorific Value ◽  
Influence Of Temperature ◽  
Near Infrared Spectra

In the pulp and paper and biofuel industries, real-time online characterization of biomass gross calorific value is of critical importance to determine its quality and price and for process optimization. Near infrared spectroscopy is a relatively low-cost technology that could potentially be used for such an application. However, the near infrared spectra are also influenced by biomass temperature and moisture content. In this study, external parameter orthogonalization is employed to remove simultaneously the influence of temperature and moisture content on the spectra before predicting gross calorific value. External parameter orthogonalization is of particular interest when one desires to transfer information from one modeling experiment to another, such as when developing a calibration model for a new property from the same material, or when it would be more efficient to divide the experimental effort. External parameter orthogonalization (EPO) was found to be an effective method for desensitizing a partial least squares calibration model to the influence of temperature and moisture content, enabling robust and accurate prediction of biomass gross calorific value. Partial least square models developed with external parameter orthogonalization always provided equal or better performance than models developed without external parameter orthogonalization. The paper shows that experimental efforts and costs can be reduced by approximately one half while maintaining prediction accuracy and model robustness.

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

Influence of temperature on the infrared spectra of adsorbed molecules

1971 ◽  
Vol 12 (1) ◽  
pp. 45-48
Author(s):  
Yu. M. Shchekochikhin ◽  
A. A. Davydov ◽  
Yu. S. Tarasevich ◽  
N. P. Keier
Keyword(s):  
Infrared Spectra ◽  
Adsorbed Molecules ◽  
Influence Of Temperature ◽  
Influence Of Temperature On
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