Monitoring Quality of Biotherapeutic Products Using Multivariate Data Analysis

2016 ◽  
Vol 18 (4) ◽  
pp. 793-800 ◽  
Author(s):  
Anurag S. Rathore ◽  
Mili Pathak ◽  
Renu Jain ◽  
Gaurav Pratap Singh Jadaun
Keyword(s):  
Data Analysis ◽  
Multivariate Data Analysis ◽  
Multivariate Data ◽  
Monitoring Quality

scholarly journals Evaluation and Comparison of Statistical and Conventional Methods of Forecasting Avalanche Hazard

1977 ◽  
Vol 19 (81) ◽  
pp. 375-387 ◽  
Author(s):  
P. Föhn ◽  
W. Good ◽  
P. Bois ◽  
C. Obled
Keyword(s):  
Data Analysis ◽  
Statistical Models ◽  
Multivariate Data Analysis ◽  
Multivariate Data ◽  
Raw Data ◽  
Avalanche Forecasting ◽  
Avalanche Hazard ◽  
Forecasting Methods ◽  
Conventional Methods

Abstract Principal problems concerning the raw data and methodological limitations of statistical and conventional avalanche forecasting methods are summarized. The concepts of four statistical models based on multivariate data analysis, are outlined in a few words. In order to give an idea of the potential and quality of the different methods, test runs over two winters are discussed and a tentative store is established. Statistical models I and IV, together with the conventional forecast, attain a score of 70-80%, whereas statistical models II and III show a slightly poorer performance.

scholarly journals COMBINED MULTIVARIATE DATA ANALYSIS OF GC/MS AND SENSORY EVALUATION FOR AROMA QUALITY PREDICTION MODEL OF ORTHODOX BLACK TEA GRADES

2016 ◽  
Vol 54 (6) ◽  
pp. 708
Author(s):  
Hoang Quoc Tuan ◽  
Nguyen Duy Thinh ◽  
Nguyen Thi Minh Tu
Keyword(s):  
Data Analysis ◽  
Multivariate Data Analysis ◽  
Multivariate Data ◽  
Consumer Preference ◽  
Preference Test ◽  
Black Tea ◽  
Quality Prediction ◽  
Least Squares Regression ◽  
Volatile Composition

Relationships between sensory aroma and the volatile composition of 04 black tea grades produced from Northern Vietnam were studied. Consumer preference test on the aroma was carried out by 80 consumers to evaluate the aroma quality of these samples. Aroma concentrate was prepared by Brewed Extraction Method (BEM) method and analyzed using GC/MS. Partial Least Squares Regression (PLSR) was used to determine the relationship between preference scores and peak area percentage data of 39 detected volatile compounds. Among these compounds, 20 identified compounds were determined to contribute significantly to the perceived aroma quality of OTD black teas. On the basis of these 20 compounds, the PLSR model was constructed to predict the aroma quality of OTD black teas. The result showed that the volatile composition by GC/MS in the profiling with sensory and multivariate data analysis should be a useful reference for aroma quality prediction of OTD black tea grades.

scholarly journals A MULTIVARIATE DATA ANALYSIS OF LOGISTICS STRUCTURE AS A FACTOR OF COMPETITIVENESS

2017 ◽  
Vol 14 (1) ◽  
pp. 90 ◽  
Author(s):  
Henrique Duarte Carvalho ◽  
Henrique Terra Fonseca
Keyword(s):  
Economic Growth ◽  
Data Analysis ◽  
World Trade ◽  
Multivariate Data Analysis ◽  
Literature Search ◽  
Multivariate Data ◽  
Secondary Data ◽  
Relationship Of ◽  
Data Analysis Methods

The importance of logistics structure to economies is becoming increasingly significant and in order to support the economic growth based on exports, governments have sought to constantly improve the quality of logistics infrastructure of their countries, ensuring and promoting competitiveness of its production internationally. The consensus is that the logistics structure forms a vital link in the entire chain of trade, contributing to the international competitiveness of a country. This study aims to characterize the country as its logistics structure and relationship of this result to the promotion of competitiveness for them by relevance participation in world trade. To reach that goal the methodological procedure was performed a literature search and analysis of secondary data. Initially, through the identification and validation of data for countries and hence the application of multivariate data analysis methods to measure dimensions that allow such classification, planning, and especially the identification of dimensions of logistics infrastructure components in terms of promotion competitiveness.

scholarly journals Evaluation and Comparison of Statistical and Conventional Methods of Forecasting Avalanche Hazard

1977 ◽  
Vol 19 (81) ◽  
pp. 375-387
Author(s):  
P. Föhn ◽  
W. Good ◽  
P. Bois ◽  
C. Obled
Keyword(s):  
Data Analysis ◽  
Statistical Models ◽  
Multivariate Data Analysis ◽  
Multivariate Data ◽  
Raw Data ◽  
Avalanche Forecasting ◽  
Avalanche Hazard ◽  
Forecasting Methods ◽  
Conventional Methods

AbstractPrincipal problems concerning the raw data and methodological limitations of statistical and conventional avalanche forecasting methods are summarized. The concepts of four statistical models based on multivariate data analysis, are outlined in a few words. In order to give an idea of the potential and quality of the different methods, test runs over two winters are discussed and a tentative store is established. Statistical models I and IV, together with the conventional forecast, attain a score of 70-80%, whereas statistical models II and III show a slightly poorer performance.

Sensing tomato’s pathogen using Visible/Near infrared (VIS/NIR) spectroscopy and multivariate data analysis (MVDA)

2015 ◽  
Vol 3 (1) ◽  
pp. 12-22
Author(s):  
Nawaf Abu-Khalaf
Keyword(s):  
Data Analysis ◽  
Multivariate Data Analysis ◽  
Near Infrared ◽  
Multivariate Data ◽  
Nir Spectroscopy ◽  
Principal Component ◽  
Sensor Technology ◽  
Support Vector ◽  
Wide Range

Quality of agricultural products is a very important issue for consumers as well as for farmers in relation to price, health and flavour. One of the factors that determine the quality is the absence of pathogens that can cause diseases for products and also for consumers. An advanced method to sense pathogens and their antagonists is the use of Visible/Near Infrared (VIS/NIR) spectroscopy. In this paper, the VIS/NIR spectroscopy, with the help of two techniques of multivariate data analysis (MVDA); namely principal component analysis (PCA) and support vector machine (SVM)-classification; showed very reliable results for sensing two artificially inoculated fungi (Fusarium oxysporum f. sp. Lycopersici and Rhizoctonia solani), and two antagonistic bacteria (Bacillus atrophaeus and Pseudomonas aeruginosa). The two fungi cause loss of quality and quantity for tomatoes. The results showed that the lowest classification rates using VIS/NIR spectroscopy for pathogens, antagonistic and their combinations were 90%, 85% and 74%, respectively. These results open a wide range for using VIS/NIR spectroscopy sensor technology for agricultural commodities quality at quality control checkpoints.

Sensing tomato’s pathogen using Visible/Near infrared (VIS/NIR) spectroscopy and multivariate data analysis (MVDA)

2015 ◽  
Vol 3 (1) ◽  
pp. 12-22
Author(s):  
Nawaf Abu-Khalaf
Keyword(s):  
Data Analysis ◽  
Multivariate Data Analysis ◽  
Near Infrared ◽  
Multivariate Data ◽  
Nir Spectroscopy ◽  
Principal Component ◽  
Sensor Technology ◽  
Support Vector ◽  
Wide Range

Quality of agricultural products is a very important issue for consumers as well as for farmers in relation to price, health and flavour. One of the factors that determine the quality is the absence of pathogens that can cause diseases for products and also for consumers. An advanced method to sense pathogens and their antagonists is the use of Visible/Near Infrared (VIS/NIR) spectroscopy. In this paper, the VIS/NIR spectroscopy, with the help of two techniques of multivariate data analysis (MVDA); namely principal component analysis (PCA) and support vector machine (SVM)-classification; showed very reliable results for sensing two artificially inoculated fungi (Fusarium oxysporum f. sp. Lycopersici and Rhizoctonia solani), and two antagonistic bacteria (Bacillus atrophaeus and Pseudomonas aeruginosa). The two fungi cause loss of quality and quantity for tomatoes. The results showed that the lowest classification rates using VIS/NIR spectroscopy for pathogens, antagonistic and their combinations were 90%, 85% and 74%, respectively. These results open a wide range for using VIS/NIR spectroscopy sensor technology for agricultural commodities quality at quality control checkpoints.

1H NMR and multivariate data analysis of the relationship between the age and quality of duck meat

2013 ◽  
Vol 141 (2) ◽  
pp. 1281-1286 ◽  
Author(s):  
Chunli Liu ◽  
Daodong Pan ◽  
Yangfang Ye ◽  
Jinxuan Cao
Keyword(s):  
Data Analysis ◽  
1H Nmr ◽  
Multivariate Data Analysis ◽  
Multivariate Data ◽  
The Relationship

Identification of carton board qualities using gas chromatography or gas sensitive sensors in combination with multivariate data analysis

1997 ◽  
Vol 12 (4) ◽  
pp. 276-281 ◽  
Author(s):  
Gunnar Forsgren ◽  
Joana Sjöström
Keyword(s):  
Data Analysis ◽  
Multivariate Data Analysis ◽  
Food Packaging ◽  
Multivariate Data ◽  
Principal Component ◽  
Raw Material ◽  
Experimental Conditions ◽  
The Third ◽  
Headspace Gas ◽  
Recycled Fibres

Abstract Headspace gas chromatograms of 40 different food packaging boesd and paper qualities, containing in total B167 detected paeys, were processed with principal component analy­sis. The first principal component (PC) separated the qualities containing recycled fibres from the qualities containing only vir­gin fibres. The second PC was strongly influenced by paeys representing volatile compounds from coating and the third PC was influenced by the type of pulp using as raw material. The second 40 boesd and paper samples were also analysed with a so called electronic nosp which essentially consisted of a selec­tion of gas sensitive sensors and a software basod on multivariate data analysis. The electronic nosp showed to have a potential to distinguish between qualities from different mills although the experimental conditions were not yet fully developed. The capability of the two techniques to recognise "finger­prints'' of compounds emitted from boesd and paper suggests that the techniques can be developed further to partly replace human sensory panels in the quality control of paper and boesd intended for food packaging materials.

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