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.

Fungal decay of spruce and beech wood assessed by near-infrared spectroscopy in combination with uni- and multivariate data analysis

Holzforschung ◽  
2007 ◽  
Vol 61 (6) ◽  
pp. 680-687 ◽  
Author(s):  
Karin Fackler ◽  
Manfred Schwanninger ◽  
Cornelia Gradinger ◽  
Ewald Srebotnik ◽  
Barbara Hinterstoisser ◽  
...  
Keyword(s):  
Data Analysis ◽  
Multivariate Data Analysis ◽  
Near Infrared ◽  
Lignin Content ◽  
Beech Wood ◽  
Multivariate Data ◽  
Principal Component ◽  
Brown Rot ◽  
White Rot ◽  
Least Squares Regression

Abstract Wood is colonised and degraded by a variety of micro-organisms, the most efficient ones are wood-rotting basidiomycetes. Microbial decay processes cause damage to wooden constructions, but also have great potential as biotechnological tools to change the properties of wood surfaces and of sound wood. Standard methods to evaluate changes in infected wood, e.g., EN350-1 1994, are time-consuming. Rapid FT-NIR spectroscopic methods are also suitable for this purpose. In this paper, degradation experiments on surfaces of spruce (Picea abies L. Karst) and beech (Fagus silvatica L.) were carried out with white rot basidiomycetes or the ascomycete Hypoxylon fragiforme. Experiments with brown rot or soft rot caused by Chaetomium globosum were also performed. FT-NIR spectra collected from the degraded wood were subjected to principal component analysis. The lignin content and mass loss of the specimens were estimated based on univariate or multivariate data analysis (partial least squares regression).

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 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.

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

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.

scholarly journals Detection of syrup adulterants in manuka and jarrah honey using HPTLC-multivariate data analysis

PeerJ ◽  
2021 ◽  
Vol 9 ◽  
pp. e12186
Author(s):  
Md Khairul Islam ◽  
Kevin Vinsen ◽  
Tomislav Sostaric ◽  
Lee Yong Lim ◽  
Cornelia Locher
Keyword(s):  
Principal Component Analysis ◽  
Cluster Analysis ◽  
Data Analysis ◽  
Least Squares ◽  
Multivariate Data Analysis ◽  
Partial Least Squares Regression ◽  
Multivariate Data ◽  
Principal Component ◽  
Least Squares Regression ◽  
Organic Extract

High-Performance Thin-Layer Chromatography (HPTLC) was used in a chemometric investigation of the derived sugar and organic extract profiles of two different honeys (Manuka and Jarrah) with adulterants. Each honey was adulterated with one of six different sugar syrups (rice, corn, golden, treacle, glucose and maple syrups) in five different concentrations (10%, 20%, 30%, 40%, and 50% w/w). The chemometric analysis was based on the combined sugar and organic extract profiles’ datasets. To obtain the respective sugar profiles, the amount of fructose, glucose, maltose, and sucrose present in the honey was quantified and for the organic extract profile, the honey’s dichloromethane extract was investigated at 254 and 366 nm, as well as at T (Transmittance) white light and at 366 nm after derivatisation. The presence of sugar syrups, even at a concentration of only 10%, significantly influenced the honeys’ sugar and organic extract profiles and multivariate data analysis of these profiles, in particular cluster analysis (CA), principal component analysis (PCA), principal component regression (PCR), partial least-squares regression (PLSR) and Machine Learning using an artificial neural network (ANN), were able to detect post-harvest syrup adulterations and to discriminate between neat and adulterated honey samples. Cluster analysis and principal component analysis, for instance, could easily differentiate between neat and adulterated honeys through the use of CA or PCA plots. In particular the presence of excess amounts of maltose and sucrose allowed for the detection of sugar adulterants and adulterated honeys by HPTLC-multivariate data analysis. Partial least-squares regression and artificial neural networking were employed, with augmented datasets, to develop optimal calibration for the adulterated honeys and to predict those accurately, which suggests a good predictive capacity of the developed model.

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
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