Using the Enzymatic Growth of Nanoparticles To Create a Biosensor. An Undergraduate Quantitative Analysis Experiment

2009 ◽  
Vol 86 (6) ◽  
pp. 712 ◽  
Author(s):  
James Bai ◽  
Kaleyhia Flowers ◽  
Salil Benegal ◽  
Milagros Calizo ◽  
Vrudhdhi Patel ◽  
...  
Keyword(s):  
Quantitative Analysis ◽  
Analysis Experiment

Separation of an APC mixture. A quantitative analysis experiment

1976 ◽  
Vol 53 (11) ◽  
pp. 731
Author(s):  
Paul Haddad ◽  
Malcolm Rasmussen
Keyword(s):  
Quantitative Analysis ◽  
Analysis Experiment

Modeling with Multiple Correlated Spectral Data Based on Approximating the Nonlinear Spectrum Induced by Scattering

2021 ◽  
pp. 000370282110365
Author(s):  
Yongshun Luo ◽  
Gang Li ◽  
Guosong Shan ◽  
Ling Lin
Keyword(s):  
Quantitative Analysis ◽  
Spectral Data ◽  
Prediction Accuracy ◽  
Data Set ◽  
Analysis Experiment ◽  
Scattering Material ◽  
Modeling Data ◽  
Highly Correlated ◽  
Equal Space ◽  
Strong Scattering

In the spectral quantitative analysis of scattering solution, the improvement of accuracy is seriously restricted by the nonlinearity caused by scattering, and even the measurement will fail due to the influence of scattering. The important reasons are that the modeling variables are greatly affected by nonlinearity, and the information contained in the modeling data cannot represent the scattering characteristics. In this paper, a method is proposed, in which the spectral data of several optical pathlengths with equal space are combined as the modeling data set of a sample. These highly correlated spectral data contain relatively nonlinear information. The addition of the spectral data provides more options for the selection of principal components in modeling with PLS method. By giving lower weight to the corresponding wavelength which is greatly affected by scattering, the model is insensitive to scattering and the prediction accuracy is improved. Through the spectral quantitative analysis experiment on strong scattering material, the prediction accuracy of the model was 61.7% higher than that of the traditional method and was 58.5% higher than that of the variable sorting for normalization method. The feasibility of the method is verified.

An Improved Quantitative Image Analyser

1977 ◽  
Vol 35 ◽  
pp. 226-227
Author(s):  
J.P. Fallon ◽  
P.J. Gregory ◽  
C.J. Taylor
Keyword(s):  
Feature Extraction ◽  
Quantitative Analysis ◽  
Frequency Content ◽  
General Sense ◽  
Physical Measurements ◽  
Quantitative Image ◽  
Image Analyser ◽  
Automatic Methods ◽  
Quantitative Results

Quantitative image analysis systems have been used for several years in research and quality control applications in various fields including metallurgy and medicine. The technique has been applied as an extension of subjective microscopy to problems requiring quantitative results and which are amenable to automatic methods of interpretation.Feature extraction. In the most general sense, a feature can be defined as a portion of the image which differs in some consistent way from the background. A feature may be characterized by the density difference between itself and the background, by an edge gradient, or by the spatial frequency content (texture) within its boundaries. The task of feature extraction includes recognition of features and encoding of the associated information for quantitative analysis.Quantitative Analysis. Quantitative analysis is the determination of one or more physical measurements of each feature. These measurements may be straightforward ones such as area, length, or perimeter, or more complex stereological measurements such as convex perimeter or Feret's diameter.

Quantification of Silica Uptake by Alveolar Macrophages - An Emperical Scanning Electron Microprobe Method

1982 ◽  
Vol 40 ◽  
pp. 332-333
Author(s):  
V. V. Damiano ◽  
R. P. Daniele ◽  
H. T. Tucker ◽  
J. H. Dauber
Keyword(s):  
Quantitative Analysis ◽  
Alveolar Macrophages ◽  
Bulk Sample ◽  
Silica Particles ◽  
Empirical Method ◽  
Phagocytic Cells ◽  
Calibration Standards ◽  
X Ray ◽  
Accurate Quantitative Analysis ◽  
Scanning Electron

An important example of intracellular particles is encountered in silicosis where alveolar macrophages ingest inspired silica particles. The quantitation of the silica uptake by these cells may be a potentially useful method for monitoring silica exposure. Accurate quantitative analysis of ingested silica by phagocytic cells is difficult because the particles are frequently small, irregularly shaped and cannot be visualized within the cells. Semiquantitative methods which make use of particles of known size, shape and composition as calibration standards may be the most direct and simplest approach to undertake. The present paper describes an empirical method in which glass microspheres were used as a model to show how the ratio of the silicon Kα peak X-ray intensity from the microspheres to that of a bulk sample of the same composition correlated to the mass of the microsphere contained within the cell. Irregular shaped silica particles were also analyzed and a calibration curve was generated from these data.

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