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.

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).

Entwicklung, Beobachtung und Steuerung integrierter, quasi-kontinuierlicher pharmazeutischer Produktionsprozesse mit Methoden der Multivariaten Datenverarbeitung

2016 ◽  
Author(s):  
Sven-Oliver Borchert
Keyword(s):  
Principal Component Analysis ◽  
Data Analysis ◽  
Multivariate Data Analysis ◽  
Process Analytical Technology ◽  
Multivariate Data ◽  
Principal Component ◽  
Component Analysis ◽  
Analytical Technology

Die vorliegende Arbeit befasst sich mit Aspekten einer modernen Bioverfahrenstechnik am ­Beispiel von Prozessen zur Herstellung rekombinanter potentieller Malariavakzine. Dabei ­wurden zwei quasi-kontinuierliche Prozesse aus herkömmlichen Batch-Unit Operationen auf­gebaut, in denen die Anwendung von Process Analytical Technology im Vordergrund steht. Das Hauptaugenmerk dieser Arbeit lag dabei auf einer Implementierung der Multivariate Data ­Analysis zum Monitoring und zur Evaluierung des zyklischen Prozessablaufes und seiner Reproduzierbarkeit. Im Bereich der Principal Component Analysis wurde die Methode der Prozessüberwachung mit dem Golden Batch-Tunnel angewendet. Mit dem Golden Batch-Ansatz ­wurden Methoden zur Prozessprädiktion implementiert und mit einer Model Predictive Multi­variate Control auch zur Steuerung von realen Prozesses erprobt. Darüber hinaus wurde die MVDA zur Prädiktion von Medienkomponenten sowie deren zellspezifische Reaktionsraten aus klassischen Onli...

scholarly journals Multivariate statistical analysis of the essential oils of five Beilschmiediaspecies from Peninsular Malaysia

2021 ◽  
Vol 20 (1) ◽  
pp. 61-70
Author(s):  
Wan Mohd Nuzul Hakimi Wan Salleh ◽  
◽  
Shazlyn Milleana Shaharudin ◽  
Keyword(s):  
Principal Component Analysis ◽  
Gas Chromatography ◽  
Data Analysis ◽  
Essential Oils ◽  
Multivariate Data Analysis ◽  
Multivariate Data ◽  
Principal Component ◽  
Component Analysis ◽  
Chemical Components ◽  
Herbal Products

Identification of the chemical compositionof essential oils is very important for ensuring the quality of finished herbal products. The objective of the study was to analyze the chemical components present in the essential oils of five Beilschmiediaspecies (i.e. B. kunstleri, B. maingayi, B. penangiana, B. madang, and B. glabra) by multivariate data analysis using principal component analysis (PCA) and hierarchical clustering analysis (HCA) methods. The essential oils were obtained by hydrodistillation and fully characterized by gas chromatography (GC) and gas chromatography-mass spectrometry (GC-MS). A total of 108 chemical components were successfully identified from the essential oils of five Beilschmiediaspecies. The essential oils were characterized by high proportions of β-caryophyllene (B.kunstleri), δ-cadinene (B. penangianaand B. madang), and β-eudesmol (B. maingayiand B. glabra). Principal component analysis (PCA) and hierarchical cluster analysis (HCA) revealed that chemical similarity was highest for all samples, except for B. madang. The multivariate data analysis may be used for the identification and characterization of essential oils from different Beilschmiediaspecies that are to be used as raw materials of traditional herbal products.

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.

Multivariate micropollutants analysis in marine waters

1995 ◽  
Vol 32 (9-10) ◽  
pp. 341-348
Author(s):  
V. Librando ◽  
G. Magazzù ◽  
A. Puglisi
Keyword(s):  
Multivariate Data Analysis ◽  
Partial Least Squares Regression ◽  
Environmental Chemistry ◽  
Sampling Site ◽  
Principal Component ◽  
Least Squares Regression ◽  
Organic Micropollutants ◽  
Suspended Material ◽  
The Difference

The monitoring of water quality today provides a great quantity of data consisting of the values of the parameters measured as a function of time. In the marine environment, and especially in the suspended material, increasing importance is being given to the presence of organic micropollutants, particularly since some are known to be carcinogenic. As the number of measured parameters increases examining the data and their consequent interpretation becomes more difficult. To overcome such difficulties, numerous chemometric techniques have been introduced in environmental chemistry, such as Multivariate Data Analysis (MVDA), Principal Component Analysis (PCA) and Partial Least Squares Regression (PLSR). The use of the first technique in this work has been applied to the interpretation of the quality of Augusta bay, by measuring the concentration of numerous organic micropollutants, together with the classical water pollution parameters, in different sites and at different times. The MVDA has highlighted the difference between various sampling sites whose data were initially thought to be similar. Furthermore, it has allowed a choice of more significant parameters for future monitoring and more suitable sampling site locations.

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