Rapid Discrimination of Indonesian Herbal Medicines by Using Electronic Nose Based on Array of Commercial Gas Sensors

2015 ◽  
Vol 771 ◽  
pp. 209-212 ◽  
Author(s):  
Fajar Hardoyono ◽  
Kuwat Triyana ◽  
Bambang Heru Iswanto
Keyword(s):  
Electronic Nose ◽  
Gas Sensors ◽  
Herbal Medicines ◽  
Principal Component ◽  
Statistical Analyses ◽  
Local Market ◽  
Multivariate Statistical ◽  
Good Repeatability ◽  
And Cluster Analysis ◽  
Rapid Discrimination

The aim of this study is to discriminate herbal medicines (here after referred to as herbals) by an electronic nose (e-nose) based on an array of eight commercially gas sensors and multivariate statistical analyses. Seven kinds of herbal essential oils purchased from local market in Yogyakarta Indonesia, including zingiberofficinale (ZO), kaempferiagalanga (KG), curcuma longa (CL), curcuma zedoaria (CZ), languasgalanga (LG), pogostemoncablin (PO), and curcuma xanthorrizharoxb (CX) were measured by using this e-nose consecutively. Due to the use of dynamic headspace in this e-nose, data for one cycle (sampling and purging) were recorded every five second for 10 cycles. Each kind of herbals was analyzed for five replications and relative amplitude of the responses was extracted as a feature. The statistical analyses of principal component analysis (PCA) and cluster analysis (CA) were used for discriminating samples. The PCA score plot shows that these 35 essential oil samples were separated into 7 groups based on similarity of patterns. The first two components, PC1 and PC2, capture 96.2% of data variance. Meanwhile, by using 80% similarity, the CA clusters 7 herbals into 3 classes. In this case, the first class consists of ZO and CZ and the second class consists of KG, CL, LG and CX, while the PO sample is clustered in the third class. These classes need to be validated using a standard analytical instrument such as GC/MS. The technique shows some advantages including easy in operation because of without any sample preparation, rapid detection, and good repeatability.

scholarly journals Discrimination between Alternative Herbal Medicines from Different Categories with the Electronic Nose

Sensors ◽  
2018 ◽  
Vol 18 (9) ◽  
pp. 2936 ◽  
Author(s):  
Xianghao Zhan ◽  
Xiaoqing Guan ◽  
Rumeng Wu ◽  
Zhan Wang ◽  
You Wang ◽  
...  
Keyword(s):  
Herbal Medicine ◽  
Electronic Nose ◽  
Nearest Neighbor ◽  
Herbal Medicines ◽  
Principal Component ◽  
Support Vector ◽  
Linear Discriminant ◽  
Convenient Means ◽  
Electronic Nose System ◽  
Insight Into

As alternative herbal medicine gains soar in popularity around the world, it is necessary to apply a fast and convenient means for classifying and evaluating herbal medicines. In this work, an electronic nose system with seven classification algorithms is used to discriminate between 12 categories of herbal medicines. The results show that these herbal medicines can be successfully classified, with support vector machine (SVM) and linear discriminant analysis (LDA) outperforming other algorithms in terms of accuracy. When principal component analysis (PCA) is used to lower the number of dimensions, the time cost for classification can be reduced while the data is visualized. Afterwards, conformal predictions based on 1NN (1-Nearest Neighbor) and 3NN (3-Nearest Neighbor) (CP-1NN and CP-3NN) are introduced. CP-1NN and CP-3NN provide additional, yet significant and reliable, information by giving the confidence and credibility associated with each prediction without sacrificing of accuracy. This research provides insight into the construction of a herbal medicine flavor library and gives methods and reference for future works.

scholarly journals Evaluation of the anthropogenic impact in Suat Ugurlu Dam lake using multivariate statistical techniques

2018 ◽  
Vol 20 (1) ◽  
pp. 161-168 ◽  
Keyword(s):  
Anthropogenic Impact ◽  
Sediment Quality ◽  
Principal Component ◽  
Statistical Techniques ◽  
Multivariate Statistical Techniques ◽  
Cluster Number ◽  
Multivariate Statistical ◽  
Oxidation Reduction ◽  
Oxidation Reduction Potential ◽  
And Cluster Analysis

Sediments play an important role in the quality of aquatic ecosystems in the Dam Lake where they can either be a sink or a source of contaminants, depending on the management. This purpose of this study is to identify the sediment quality in order to find out the causes for the malodor and the eutrophication that is causing a bad scenario. Solutions for improving the dam are proposed. Multivariate statistical techniques, such as a principal component analysis (PCA) and cluster analysis (CA), were applied to the data regarding sediment quality in relation to anthropogenic impact in Suat Ugurlu Dam Lake. This data was generated during 2014-2015, with monitoring at four sites for 11 parameters. A PCA and CA were used in the study of the samples. The total variance of 84.1%, 74.3%, 87.4% and 91.5% suggest 4, 3, 3 and 4 principle components (PCs) in the four locations: LC1, LC2, LC3 and LC4, respectively. Also, a CA was applied to both the variables and the observations. Some variables and observations showed a high similarity based on the results of variables in the CA. Also, the similarity ratio of temperature-mercury (Hg) and oxidation reduction potential (ORP) was high and generally, the cluster number of variables was 5, according to the selected similarity level.

Wood anatomical variability in Early Permian ‘araucarioids’

IAWA Journal ◽  
2014 ◽  
Vol 35 (3) ◽  
pp. 307-331 ◽  
Author(s):  
Menno Booi ◽  
Isabel M. van Waveren ◽  
Johanna H.A. van Konijnenburg-van Cittert
Keyword(s):  
Principal Component Analysis ◽  
Wood Species ◽  
Principal Component ◽  
Statistical Analyses ◽  
Late Paleozoic ◽  
Individual Species ◽  
Early Permian ◽  
Multivariate Statistical Analyses ◽  
Multivariate Statistical ◽  
Anatomical Analysis

Although araucarioid wood is poor in diagnostic characters, well in excess of 200 Late Paleozoic species have been described. This study presents a largescale anatomical analysis of this wood type based on the fossil wood collections from the Early Permian Mengkarang Formation of Sumatra, Indonesia. Principal Component Analysis visualisation, in conjunction with uni- and multivariate statistical analyses clearly show the wood from the Mengkarang Formation to be a contiguous micromorphological unit in which no individual species can be distinguished. Pycnoxylic wood species described previously from this collection or other collections from the Mengkarang Formation fall within the larger variability described here. Based on comparison with wood from modern-day Araucariaceae, the Early Permian specimens can be differentiated from extant (but unrelated) “araucarioids” by a few (continuous) characters.

scholarly journals DSC, FTIR and Raman Spectroscopy Coupled with Multivariate Analysis in a Study of Co-Crystals of Pharmaceutical Interest

Molecules ◽  
2018 ◽  
Vol 23 (9) ◽  
pp. 2136 ◽  
Author(s):  
Patrycja Garbacz ◽  
Marek Wesolowski
Keyword(s):  
Principal Component ◽  
Water Soluble ◽  
Crystal Formation ◽  
Multivariate Statistical ◽  
Molar Ratios ◽  
Pharmaceutical Ingredients ◽  
Multivariate Statistical Approach ◽  
And Cluster Analysis ◽  
Dsc Curves ◽  
Physical Mixtures

Co-crystals have garnered increasing interest in recent years as a beneficial approach to improving the solubility of poorly water soluble active pharmaceutical ingredients (APIs). However, their preparation is a challenge that requires a simple approach towards co-crystal detection. The objective of this work was, therefore, to verify to what extent a multivariate statistical approach such as principal component analysis (PCA) and cluster analysis (CA) can be used as a supporting tool for detecting co-crystal formation. As model samples, physical mixtures and co-crystals of indomethacin with saccharin and furosemide with p-aminobenzoic acid were prepared at API/co-former molar ratios 1:1, 2:1 and 1:2. Data acquired from DSC curves and FTIR and Raman spectroscopies were used for CA and PCA calculations. The results obtained revealed that the application of physical mixtures as reference samples allows a deeper insight into co-crystallization than is possible with the use of API and co-former or API and co-former with physical mixtures. Thus, multivariate matrix for PCA and CA calculations consisting of physical mixtures and potential co-crystals could be considered as the most profitable and reliable way to reflect changes in samples after co-crystallization. Moreover, complementary interpretation of results obtained using DSC, FTIR and Raman techniques is most beneficial.

scholarly journals Lithofacies Identification and Multivariate Analysis of Groundwater Chemistry in Coastal Aquifers in Koko Area of the Western Niger Delta

Hydrology ◽  
2019 ◽  
Vol 6 (2) ◽  
pp. 31 ◽  
Author(s):  
Oghenero Ohwoghere-Asuma ◽  
Kizito Aweto ◽  
Chukwuma Ugbe
Keyword(s):  
Groundwater Quality ◽  
Principal Component ◽  
Organic Pollution ◽  
Clayey Sand ◽  
Multivariate Statistical ◽  
Subsurface Geology ◽  
Dissolved Ions ◽  
Considerable Impact ◽  
And Cluster Analysis

Understanding aquifer lithofacies and depth of occurrence, and what factors influence its quality and chemistry are of paramount importance to the management of groundwater resource. Subsurface lithofacies distribution was characterized by resistivity and validated with available subsurface geology. Resistivity values varied from less than 100 Ωm to above 1000 Ωm. Lithofacies identified includes clay, clayey sand, sand and peat. Shallow unconfined and confined aquifers occurred at depths ranging from 0 to 12 m and 18 to 63 m, respectively. Geochemistry and multivariate statistical analysis consisting of principal component analysis (PCA) and cluster analysis (CA) were used for the determination of quality and groundwater evolution. Groundwater types depicted by Piper plots were Ca3+, Cl− and Na+, Cl−, which was characterized by low dissolved ions, slightly acidic and Fe2+. The dominant variables influencing groundwater quality as returned by PCA were organic pollution resulting from swampy depositional environment, anthropogenic effects resulting from septic and leachates from haphazard dumpsites mixing with groundwater from diffuse sources. In addition, the weathering and dissolution of aquifer sediments rich in feldspar and clay minerals have considerable impact on groundwater quality. CA depicted two distinct types of groundwater that are significantly comparable to those obtained from Piper plots.

Characterization of Balsamic Vinegar by Multivariate Statistical Analysis of Trace Element Content

1998 ◽  
Vol 81 (5) ◽  
pp. 1087-1095 ◽  
Author(s):  
Antonella Del Signore ◽  
Barbara Campisi ◽  
Franco Di Giacomo
Keyword(s):  
Principal Component Analysis ◽  
Principal Component ◽  
Trace Element Content ◽  
The Other ◽  
Statistical Techniques ◽  
Multivariate Statistical ◽  
Linear Discriminant ◽  
Balsamic Vinegar ◽  
And Cluster Analysis

Abstract To characterize vinegars according to the types prescribed by Italian regulations, 8 trace elements (Cr, Mn, Co, Ni, Cu, Zn, Cd, and Pb) were determined. The data collected were successively elaborated by 3 statistical techniques: linear principal component analysis (LPCA), linear discriminant analysis (LDA), and cluster analysis (CA). LDA and LPCA best classified and discriminated the 3 types of vinegar under study, separating traditional balsamic vinegars from the other 2 types, nontraditionally aged balsamic vinegars and common vinegars. The latter 2 types were appreciably distinguished only by LDA through bidimensional analysis of discriminant scores

Quality Control of Orange Juice Using Electronic Nose

2015 ◽  
Vol 1131 ◽  
pp. 242-245
Author(s):  
Rungroj Maolanon ◽  
Winadda Wongwiriyapan ◽  
Sirapat Pratontep
Keyword(s):  
Quality Control ◽  
Electronic Nose ◽  
Gas Sensors ◽  
Orange Juice ◽  
Fermentation Process ◽  
Control Process ◽  
Principal Component ◽  
Alcohol Concentration ◽  
Ethanol Level ◽  
Quality Control Process

Applications of electronic noses to classify the freshness of food and beverages by mimicking the olfactory perception are becoming widely recognized in food industries. For pasteurized orange juice, packaging and shelf-life are key factors for the quality control, which are generally inspected by the sensory stability and quality (odor, color, texture and taste) of the orange juice. An electronic nose based on five different commercial metal oxide gas sensors, a temperature sensor and a humidity sensor has been designed and constructed to examine the quality of orange juice as subjected to the fermentation process. The duration for a single measurement from an orange juice sample was approximately two minutes. The data acquisition of the voltage responses of the gas sensors were achieved via a microcontroller unit. The data classification was statistically analyzed by the “Principal Component Analysis (PCA)”. The Euclidean distance between two PCA groups was used as an indicator of ethanol concentration. The orange juice was laced with various concentrations of ethanol from 0.1 to 1.0% ethanol to simulate fermented orange juice at different stages. The objective was to characterize the freshness of orange juice by means of the ethanol level from the fermentation process. The results show a distinctive classification of the orange juice for an alcohol concentration lower than 0.1%. Thus the electronic nose offers a rapid, highly sensitive alternative for the quality control process.

The climatic regions of New Brunswick: a multivariate analysis of meteorological data

1984 ◽  
Vol 14 (3) ◽  
pp. 389-394 ◽  
Author(s):  
H. van Groenewoud
Keyword(s):  
New Brunswick ◽  
Classification Scheme ◽  
Meteorological Data ◽  
Principal Component ◽  
Site Classification ◽  
Forest Site ◽  
Multivariate Statistical ◽  
Climatic Regions ◽  
Geological Maps ◽  
And Cluster Analysis

New Brunswick was divided into 11 climatic regions by means of three multivariate statistical analyses (principal component analysis, R and Q type, and cluster analysis) of data on precipitation, various temperature parameters, elevation, latitude, and longitude for 76 climatological stations. These regions form the first-level division for a forest site classification scheme being implemented in New Brunswick. Comparison of the climatic and geological maps of New Brunswick with the plant community distribution shows that either climatic or geologic parameters may control the distribution of the vegetation.

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