pike autodiff diffuse reflectance accessory (PIKE Technologies)
90
Structured Review
PIKE Technologies
pike autodiff diffuse reflectance accessory
Pike Autodiff Diffuse Reflectance Accessory, supplied by PIKE Technologies, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
https://www.bioz.com/product/pike+diffuse+reflectance+accessory/pike+autodiff+automated+diffuse+reflectance+accessory/10__1016_slash_j__soilbio__2023__108991-73-6-11
Average 90 stars, based on 1 article reviews
Pike Autodiff Diffuse Reflectance Accessory, supplied by PIKE Technologies, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
https://www.bioz.com/product/pike+diffuse+reflectance+accessory/pike+autodiff+automated+diffuse+reflectance+accessory/10__1016_slash_j__soilbio__2023__108991-73-6-11
Average 90 stars, based on 1 article reviews
pike autodiff diffuse reflectance accessory - by Bioz Stars,
2026-11
90/100 stars
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other:Article Title: The ratio of Gram-positive to Gram-negative bacterial PLFA markers as an indicator of carbon availability in organic soils Article Snippet: Diffuse reflectance mid-infrared (MIR) spectra were 295 acquired for all 240 samples using a Thermo Fisher Nicolet 6700 spectrometer (Waltham, MA) 296 equipped with a KBr beam-splitter, a DTGS detector and an automated Article Title: NMR and FTIR characterization of petroleum residues: structural parameters and correlations Article Snippet: Between six and twelve FTIR spectra were obtained for each sample on a Nicolet FTIR Protégé 460 equipment, fitted with a Spectroscopy:Article Title: Simultaneous quantitative analysis of mebendazole polymorphs A-C in powder mixtures by DRIFTS spectroscopy and ANN modeling. Article Snippet: In the present study, a simple method, based on diffuse reflectance FTIR spectroscopy (DRIFTS) and artificial neural network (ANN) modeling is developed for the simultaneous quantitative analysis of mebendazole polymorphs A–C in powder mixtures.. Spectral differences between the polymorphs are elucidated by computationally assisted band assignments on the basis of quantum chemical calculations, and subsequently, the spectra are preprocessed by calculation of 1st and 2nd derivatives.. Then ANN models are fitted after PCA compression of the input space. Fourier Transform Infrared Spectroscopy:Article Title: Simultaneous quantification of carbamazepine crystal forms in ternary mixtures (I, III, and IV) by diffuse reflectance FTIR spectroscopy (DRIFTS) and multivariate calibration. Article Snippet: Diffuse reflectance FTIR spectroscopy (DRIFTS) coupled with modern multivariate calibration methods, namely artificial neural networks (ANNs) in two versions (ANN-raw and ANN-pca), support vector machines (SVMs), lazy learning (LL) and partial least squares (PLS) regression, is used in this study for the quantification of carbamazepine crystal forms in ternary powder mixtures (I, III, and IV).. Two spectral regions (675–1180 and 3400-3600/cm) were selected and the data were partitioned into training and test subsets applying the Kennard-Stone design.. It was found that all the selected algorithms perform better than the PLS regression (root mean squared error of prediction (RMSEP) from 3.0% to 8.2%). Diffuse Reflectance:Article Title: Simultaneous quantification of carbamazepine crystal forms in ternary mixtures (I, III, and IV) by diffuse reflectance FTIR spectroscopy (DRIFTS) and multivariate calibration. Article Snippet: Diffuse reflectance FTIR spectroscopy (DRIFTS) coupled with modern multivariate calibration methods, namely artificial neural networks (ANNs) in two versions (ANN-raw and ANN-pca), support vector machines (SVMs), lazy learning (LL) and partial least squares (PLS) regression, is used in this study for the quantification of carbamazepine crystal forms in ternary powder mixtures (I, III, and IV).. Two spectral regions (675–1180 and 3400-3600/cm) were selected and the data were partitioned into training and test subsets applying the Kennard-Stone design.. It was found that all the selected algorithms perform better than the PLS regression (root mean squared error of prediction (RMSEP) from 3.0% to 8.2%). |