scholarly journals Analysis of Distribution of Ingredients in Commercially Available Clarithromycin Tablets Using Near-Infrared Chemical Imaging with Principal Component Analysis and Partial Least Squares

2015 ◽  
Vol 63 (9) ◽  
pp. 663-668 ◽  
Author(s):  
Tatsuo Koide ◽  
Yoshihisa Yamamoto ◽  
Toshiro Fukami ◽  
Noriko Katori ◽  
Haruhiro Okuda ◽  
...  
2017 ◽  
Vol 29 (1) ◽  
pp. 140
Author(s):  
K. R. Counsell ◽  
C. L. Durfey ◽  
J. M. Feugang ◽  
S. T. Willard ◽  
P. L. Ryan ◽  
...  

In vitro fertilization is optimized when there is a homogenous population of viable spermatozoa, not subjected toxic waste products of apoptotic cells. In a previous study, we developed a “nanopurification” technique to magnetically target and remove non-viable spermatozoa from a boar insemination dose. Nanopurified semen has successfully been studied with IVF in swine and bovine but lacks health data regarding offspring produced from exposed semen. Developmental health performance in mammals is typically assessed through measurements of immune related biomolecules in plasma (e.g. immunoglobulins), quantifying each variable with a specific analytical assay. Recent developments in aqueous based near infrared spectroscopy (NIR), aquaphotomics, have been shown to distinguish reproductive stages (e.g. oestrus, diestrus) in blood serum. Thus, application of aquaphotomics may be ideal for analysis of offspring resulting from fertilization with nanopurified semen, using serum or plasma. Our study objective was to identify holistic differences in blood plasma by characterising NIR spectral profiles in offspring produced from nanopurified semen. Extended boar semen doses were mixed with or without specific nanoparticles to target non-viable spermatozoa. Semen doses were exposed to an electromagnetic field, noninvasively separating non-viable spermatozoa from the insemination dose. Six gilts were bred with (n = 3) or without (n = 3) nanopurified semen. Following birth and weaning, 20 offspring of equal sexes were randomly selected from control and nanopurified litters (10/group) for growth and developmental measurements up until market weight. Blood plasma was collected from offspring at market weight for NIR analysis. Spectral data were collected with a quartz cuvette and ASD FieldSpec® 3 spectrophotometer (ASD Inc., Boulder, CO, USA). Chemometric analysis (Unscrambler® X version 10.4; CAMO Software, Oslo, Norway) included a Savistsky-Golay 1st and 2nd derivative for detection of distinct spectral features. Principal component analysis and partial least-squares block-discrimination were used to examine treatment effects, in a blind experiment. Plasma spectral profiles from control and nanopurified offspring contained 6 shared peaks at 1360, 1373, 1402, 1404, 1422, and 1428 nm. Principle components 1 and 2 accounted for 96.26% of the total variance, with no separation of principal component analysis scores for plasma spectra between groups. Partial least-squares discriminant analysis metrics (slope = 0.026, SECV = 0.52) and Students t-test showed no significant difference (P = 0.57) between groups. Results indicate blood plasma content is not influenced in nanopurified offspring when compared with the control. In addition, solute NIR has shown to be a valuable promising tool for assessing complex aqueous solutions in swine. Further effects on growth and development from offspring born from nanopurified continue to be investigated. This work was supported by USDA-ARS Biophotonics Initiative grant #58–6402–3-018.


SaberEs ◽  
2010 ◽  
Author(s):  
María Susana Vitelleschi ◽  
Directora: Marta Beatriz Quaglino

En este trabajo se aborda la problemática de la construcción de modelos PCA (Principal Component Analysis) a partir de conjuntos de datos con información faltante. Se trabaja sobre tres situaciones diferentes con relación a la matriz de datos originales. En cada situación se generaron pérdidas a través de mecanismos aleatorios y no aleatorios, en diferentes porcentajes en una sola variable por vez, seleccionada mediante dos criterios: la que más contribuye y menos contribuye en la formación de la primera componente principal. A partir de cada conjunto de datos incompletos se construye el modelo PCA utilizando: Casos Completos, Nonlinear Iterative Partial Least Squares (NIPALS) y Expectation Maximization (EM). Se comparan los resultados con los obtenidos a través del conjunto de datos originales. Se definen una serie de medidas para estudiar cómo se afectan los resultados según la dimensión de la matriz de datos, el porcentaje y el mecanismo de pérdida, con relación a: bondad del ajuste, bondad de predicción, vectores cargas, ortonormalidad de la matriz de cargas y ortogonalidad de la matriz de “scores”.


2019 ◽  
Vol 102 (6) ◽  
pp. 1814-1821 ◽  
Author(s):  
Long Guo ◽  
Dan Zhang ◽  
Lei Wang ◽  
Zijing Xue ◽  
Mei Guo ◽  
...  

Abstract Background: Artemisia argyi and A. lavandulifolia are two morphologically similar herbal medicines derived from the Artemisia genus. Although the two Artemisia herbs have been used as herbal medicines for a long time, studies on their phytochemicals and bioactive compositions are still limited, and no research has been devoted to compare the volatile compounds in A. argyi and A. lavandulifolia. Objective: To compare the volatile constituents in A. argyi and A. lavandulifolia and to explore chemical markers for discrimination and quality evaluation of the two Artemisia herbal medicines. Methods: A GC-MS-based metabolomic approach was employed to compare and discriminate A. argyi and A. lavandulifolia from the aspect of volatile compounds. Multivariate statistical methods, including principal component analysis and orthogonal partial least-squares discriminate analysis, were applied to explore chemical markers for discrimination of the two Artemisia herbal medicines. Results: Thirty volatile compounds were identified, and the chemical profiles of volatile compounds in A. argyi and A. lavandulifolia were quite similar. Principal component analysis and orthogonal partial least-squares discrimination analysis results indicated that the two Artemisia herbal medicines could be distinguished effectively from each other. Ten volatile compounds were selected as potential chemical markers for discrimination of the two Artemisia herbal medicines. Conclusions: The GC-MS-based metabolomics could be an acceptable strategy for comparison and discrimination of A. argyi and A. lavandulifolia as well as authentication of herbal medicines derived from other closely related species. Highlights: GC-MS based metabolomic approach was firstly applied to compare and discriminate Artemisia argyi and Artemisia lavandulifolia.


2012 ◽  
Vol 443-444 ◽  
pp. 731-737 ◽  
Author(s):  
Li Jing ◽  
Xiao Qiang Wen

An experimental platform of arc pipe was built to measure the corresponding parameters of fouling and the equation of fouling characteristics was built based on principal component analysis and partial least squares, in which there are several input variables such as input and output temperature, wall temperature, flow rate and so on, and there is only one output variables—fouling resistance. In order to compare with the five-input-variables equation, the six-input-variables equation was also built. The prediction results show the model built in this paper is reasonable and feasible.


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