Analysis of Forest Foliage Spectra Using a Multivariate Mixture Model

1997 ◽  
Vol 5 (3) ◽  
pp. 167-173 ◽  
Author(s):  
Christine A. Hlavka ◽  
David L. Peterson ◽  
Lee F. Johnson ◽  
Barry Ganapol
Keyword(s):  
Mixture Model ◽  
Near Infrared ◽  
Photon Absorption ◽  
Chemical Measurements ◽  
Near Infrared Spectra ◽  
Photon Absorption And Scattering ◽  
Linear Mixture Model ◽  
Wet Chemical ◽  
Nitrogen Bonds ◽  
Reflectance Measurements

Wet chemical measurements and near infrared spectra of dry ground leaf samples were analysed to test a multivariate regression technique for estimating component spectra. The technique is based on a linear mixture model for log(1/ R) pseudoabsorbance derived from diffuse reflectance measurements. The resulting unmixed spectra for carbohydrates, lignin and protein resemble the spectra of extracted plant carbohydrates, lignin and protein. The unmixed protein spectrum has prominent absorption peaks at wavelengths that have been associated with nitrogen bonds. It therefore appears feasible to incorporate the linear mixture model in whole leaf models of photon absorption and scattering so that effects of varying nitrogen and carbon concentration on leaf reflectance may be simulated.

scholarly journals Dual Near Infrared Two-Photon Microscopy for Deep-Tissue Dopamine Nanosensor Imaging

2017 ◽  
Author(s):  
Jackson T. Del Bonis-O’Donnell ◽  
Ralph H. Page ◽  
Abraham G. Beyene ◽  
Eric G. Tindall ◽  
Ian McFarlane ◽  
...  
Keyword(s):  
Near Infrared ◽  
Photon Absorption ◽  
Deep Tissue ◽  
Biologically Relevant ◽  
Two Photon Microscopy ◽  
Two Photon ◽  
Tissue Phantom ◽  
Photon Excitation ◽  
Photon Absorption And Scattering ◽  
Two Photon Excitation

A key limitation for achieving deep imaging in biological structures lies in photon absorption and scattering leading to attenuation of fluorescence. In particular, neurotransmitter imaging is challenging in the biologically-relevant context of the intact brain, for which photons must traverse the cranium, skin and bone. Thus, fluorescence imaging is limited to the surface cortical layers of the brain, only achievable with craniotomy. Herein, we describe optimal excitation and emission wavelengths for through-cranium imaging, and demonstrate that near-infrared emissive nanosensors can be photoexcited using a two-photon 1560 nm excitation source. Dopamine-sensitive nanosensors can undergo two-photon excitation, and provide chirality-dependent responses selective for dopamine with fluorescent turn-on responses varying between 20% and 350%. We further calculate the two-photon absorption cross-section and quantum yield of dopamine nanosensors, and confirm a two-photon power law relationship for the nanosensor excitation process. Finally, we show improved image quality of the nanosensors embedded 2 mm deep into a brain-mimetic tissue phantom, whereby one-photon excitation yields 42% scattering, in contrast to 4% scattering when the same object is imaged under two-photon excitation. Our approach overcomes traditional limitations in deep-tissue fluorescence microscopy, and can enable neurotransmitter imaging in the biologically-relevant milieu of the intact and living brain.

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

Mid and near Infrared Study of Carbohydrates by Canonical Correlation Analysis

1993 ◽  
Vol 1 (2) ◽  
pp. 99-108 ◽  
Author(s):  
P. Robert ◽  
M.F. Devaux ◽  
A. Qannari ◽  
M. Safar
Keyword(s):  
Correlation Analysis ◽  
Infrared Spectra ◽  
Canonical Correlation ◽  
Near Infrared ◽  
Sugar Content ◽  
Infrared Region ◽  
Total Sugar ◽  
Mid Infrared ◽  
Total Sugar Content ◽  
Near Infrared Spectra

Multivariate data treatments were applied to mid and near infrared spectra of glucose, fructose and sucrose solutions in order to specify near infrared frequencies that characterise each carbohydrate. As a first step, the mid and near infrared regions were separately studied by performing Principal Component Analyses. While glucose, fructose and sucrose could be clearly identified on the similarity maps derived from the mid infrared spectra, only the total sugar content of the solutions was observed when using the near infrared region. Characteristic wavelengths of the total sugar content were found at 2118, 2270 and 2324 nm. In a second step, the mid and near infrared regions were jointly studied by a Canonical Correlation Analysis. As the assignments of frequencies are generally well known in the mid infrared region, it should be useful to study the relationships between the two infrared regions. Thus, the canonical patterns obtained from the near infrared spectra revealed wavelengths that characterised each carbohydrate. The OH and CH combination bands were observed at: 2088 and 2332 nm for glucose, 2134 and 2252 nm for fructose, 2058 and 2278 nm for sucrose. Although a precise assignment of the near infrared bands to chemical groups within the molecules was not possible, the present work showed that near infrared spectra of carbohydrates presented specific features.

Analysis of the near-infrared spectra of C60−

1995 ◽  
Vol 247 (1-2) ◽  
pp. 57-62 ◽  
Author(s):  
Robert D. Bolskar ◽  
Sean H. Gallagher ◽  
Robert S. Armstrong ◽  
Peter A. Lay ◽  
Christopher A. Reed
Keyword(s):  
Infrared Spectra ◽  
Near Infrared ◽  
Near Infrared Spectra
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