scholarly journals DIFFERENTIATION OF VIETNAMESE COFFEE ORIGIN AND CULTIVARS BY AMINO AND FATTY ACID PROFILE ANALYSIS PRELIMINARY STUDY

2021 ◽  
Vol 58 (6A) ◽  
pp. 288
Author(s):  
Hoang Quoc Tuan ◽  
Lai Quoc Dat ◽  
Cung Thi To Quynh ◽  
Nguyen Hoang Dung ◽  
Nguyen Xuan Loi ◽  
...  
Keyword(s):  
Fatty Acids ◽  
Glutamic Acid ◽  
Geographical Origin ◽  
Profile Analysis ◽  
Principal Component ◽  
Hierarchical Cluster ◽  
Eicosenoic Acid ◽  
Complete Data ◽  
Data Matrix ◽  
Data Set

Compositions of fatty acids and amino acids compound were investigated in coffee beans included Arabica and Robusta cultivars grown in three region of Vietnam. Principal component analysis (PCA) and hierarchical cluster analysis (HCA) were performed on the complete data set to reveal chemical differences among all samples and identify markers characteristic of a particular botanical geographical origin of the coffee. The major fatty acids in the coffee oil analyzed in this study were linoleic acid (C18:2), stearic acid (C18:0), oleic acid (C18:1) palmitic acid (C16:0) and myristic acid (C14:0), followed by small amounts of arachic acid (C20:0), docosanoic acid (C22:0) and eicosenoic acid (C20:1). Glutamic acid and aspartic acid were found at high amount in robusta coffee, from 271 mg/100gDW to 786 mg/100g DW and 373mg/100g DW to 486 mg/100g DW, respectively, whereas alanine and glutamic acid in arabica coffee were in high amount at 268 mg/100g DW to 351 mg/100g DW and 209 mg/100g DW to 285 mg/100g DW, respectively. Leucine (301 to 416 mg/100 g DW), phenylalanine (226 to 305 mg/100 g DW), and lysine (199 to 269 mg/100 g DW). PCA of the complete data matrix demonstrated that there were significant differences among all coffee cultivars and geographical origin, HCA supported the results of PCA and achieved a satisfactory classification performance.

scholarly journals Chemometric approach to quality characterization of milk-based kombucha beverages

Mljekarstvo ◽  
2021 ◽  
Vol 71 (2) ◽  
pp. 83-94
Author(s):  
Jasmina Vitas ◽  
Keyword(s):  
Fatty Acids ◽  
Milk Fat ◽  
Unsaturated Fatty Acids ◽  
Principal Component ◽  
Unknown Origin ◽  
Hierarchical Cluster ◽  
Monounsaturated Fatty Acids ◽  
Data Set ◽  
Linear Discriminant

Milk-based kombucha beverages were obtained conducting kombucha lead fermentation of milk. In order to discriminate the analysed samples and to detect similarities or dissimilarities among them in the space of experimentally determined variables, hierarchical cluster analysis (HCA) and principal component analysis (PCA) were applied. Linear discriminant analysis (LDA) was conducted on the raw data set in order to find a rule for allocating a new sample of unknown origin to the correct group of samples. In the space of the variables analysed by HCA, the dominant discriminating factor for the studied samples of kombucha beverages is the milk fat (MF) content, followed by total unsaturated fatty acids content (TUFA), monounsaturated fatty acids content (MUFA) and polyunsaturated fatty acids content (PUFA). The samples with 0.8 and 1.6% milk fat belong to the same cluster in the space of the analysed variables due to similarities in their AADPPH. It was determined by LDA that there was the biggest difference in quality between the groups of products with winter savoury and stinging nettle, while the highest similarity is between groups of products with wild thyme and peppermint regarding their pH values and antioxidant activity expressed as AADPPH.

scholarly journals Discrimination of the Geographical Origin of the Lateral Roots of Aconitum carmichaelii Using the Fingerprint, Multicomponent Quantification, and Chemometric Methods

Molecules ◽  
2019 ◽  
Vol 24 (22) ◽  
pp. 4124 ◽  
Author(s):  
Lu-Lin Miao ◽  
Qin-Mei Zhou ◽  
Cheng Peng ◽  
Chun-Wang Meng ◽  
Xiao-Ya Wang ◽  
...  
Keyword(s):  
High Performance ◽  
Geographical Origin ◽  
Lateral Roots ◽  
Principal Component ◽  
Hierarchical Cluster ◽  
Similarity Analysis ◽  
Diterpenoid Alkaloids ◽  
Linear Discriminant ◽  
Hplc Fingerprint ◽  
Aconitum Carmichaelii

Fuzi is a well-known traditional Chinese medicine developed from the lateral roots of Aconitum carmichaelii Debx. It is rich in alkaloids that display a wide variety of bioactivities, and it has a strong cardiotoxicity and neurotoxicity. In order to discriminate the geographical origin and evaluate the quality of this medicine, a method based on high-performance liquid chromatography (HPLC) was developed for multicomponent quantification and chemical fingerprint analysis. The measured results of 32 batches of Fuzi from three different regions were evaluated by chemometric analysis, including similarity analysis (SA), hierarchical cluster analysis (HCA), principal component analysis (PCA), and linear discriminant analysis (LDA). The content of six representative alkaloids of Fuzi (benzoylmesaconine, benzoylhypaconine, benzoylaconine, mesaconitine, hypaconitine, and aconitine) were varied by geographical origin, and the content ratios of the benzoylmesaconine/mesaconitine and diester-type/monoester-type diterpenoid alkaloids may be potential traits for classifying the geographical origin of the medicine. In the HPLC fingerprint similarity analysis, the Fuzi from Jiangyou, Sichuan, was distinguished from the Fuzi from Butuo, Sichuan, and the Fuzi from Yunnan. Based on the HCA and PCA analyses of the content of the six representative alkaloids, all of the batches were classified into two categories, which were closely related to the plants’ geographical origins. The Fuzi samples from Jiangyou were placed into one category, while the Fuzi samples from Butuo and Yunnan were put into another category. The LDA analysis provided an efficient and satisfactory prediction model for differentiating the Fuzi samples from the above-mentioned three geographical origins. Thus, the content of the six representative alkaloids and the fingerprint similarity values were useful markers for differentiating the geographical origin of the Fuzi samples.

scholarly journals Impurity profile analysis of drug products containing acetylsalicylic acid: a chemometric approach

2013 ◽  
Vol 11 (7) ◽  
pp. 1091-1100 ◽  
Author(s):  
Joanna Ronowicz ◽  
Bogumiła Kupcewicz ◽  
Joanna Mydłowska ◽  
Elżbieta Budzisz
Keyword(s):  
Acetylsalicylic Acid ◽  
Profile Analysis ◽  
Principal Component ◽  
Analytical Techniques ◽  
Hierarchical Cluster ◽  
Data Sets ◽  
Impurity Profile ◽  
Hierarchical Segmentation ◽  
Drug Products ◽  
Impurity Profiles

AbstractIn this work attention is focused on impurity profile analysis in combination with infrared spectroscopy and chemometric methods. This approach is considered as an alternative to generally complex and time-consuming classic analytical techniques such as liquid chromatography. Various strategies for constructing descriptive models able to identify relations among drug impurity profiles hidden in multivariate chromatographic data sets are also presented and discussed. The hierarchical (cluster analysis) and non-hierarchical segmentation algorithms (k-means method) and principal component analysis are applied to gain an overview of the similarities and dissimilarities among impurity profiles of acetylsalicylic acid formulations. A tree regression algorithm based on infrared spectra is used to predict the relative content of impurities in the drug products investigated. Satisfactory predictive abilities of the models derived indicate the possibility of implementing them in the quality control of drug products.

scholarly journals Association Plots: Visualizing associations in high-dimensional correspondence analysis biplots

2020 ◽  
Author(s):  
Elzbieta Gralinska ◽  
Martin Vingron
Keyword(s):  
Correspondence Analysis ◽  
Principal Component ◽  
Projection Methods ◽  
Three Dimensions ◽  
Data Matrix ◽  
Small Data ◽  
Complex Data ◽  
Science Data ◽  
Data Set ◽  
The Matrix

SummaryIn molecular biology, just as in many other fields of science, data often come in the form of matrices or contingency tables with many measurements (rows) for a set of variables (columns). While projection methods like Principal Component Analysis or Correspondence Analysis can be applied for obtaining an overview of such data, in cases where the matrix is very large the associated loss of information upon projection into two or three dimensions may be dramatic. However, when the set of variables can be grouped into clusters, this opens up a new angle on the data. We focus on the question which measurements are associated to a cluster and distinguish it from other clusters. Correspondence Analysis employs a geometry geared towards answering this question. We exploit this feature in order to introduce Association Plots for visualizing cluster-specific measurements in complex data. Association Plots are two-dimensional, independent of the size of data matrix or cluster, and depict the measurements associated to a cluster of variables. We demonstrate our method first on a small data set and then on a genomic example comprising more than 10,000 conditions. We will show that Association Plots can clearly highlight those measurements which characterize a cluster of variables.

scholarly journals Hydrochemical assessment of Semarang area using multivariate statistics: A sample based dataset

2016 ◽  
Author(s):  
Dasapta Erwin Irawan ◽  
Thomas Triadi Putranto
Keyword(s):  
Cluster Analysis ◽  
Statistical Treatment ◽  
Principal Component ◽  
Hierarchical Cluster ◽  
Data Set ◽  
Anal Ysis ◽  
R Packages ◽  
Point Data ◽  
Depth Water ◽  
Water Elevation

Abstract. The following paper describes in brief the data set related to our project "Hydrochemical assessment of Semarang Groundwater Quality". All of 58 samples were taken in 1992, 1993, 2003, 2006, and 2007 using well point data from several reports from Ministry of Energy and Min- eral Resources and independent consultants. We provided 20 parameters in each samples (sample id, coord X, coord Y, well depth, water level, water elevation, TDS, pH, EC, K, Ca, Na, Mg, Cl, SO4, HCO3, year, ion balance, screen location, and chemical facies). The chemical composi- tion were tested in the Water Quality Laboratory, Universitas Diponegoro using mas spectrofotometer method. The statistical treatment for the dataset (available on Zenodo doi:10.5281/zenodo.57293) were described as follows: (1) data preparation in to csv file format, load it in to R environment; (2) data treatment, including: correlation matrix, cluster analysis using kmeans and hierarchical cluster analysis, and principal component analysis. For anal- ysis and visualizations, We used the following R packages: ggplot2, dplyr, factomineR, factoExtra, cluster, ggcorrplot, and ape.

scholarly journals Applying Chemometrics to Evaluate Mine Tailings’ Potential As Partial Cement Replacement

2020 ◽  
Author(s):  
Anne Mette T. Simonsen ◽  
Kristine B. Pedersen ◽  
Pernille E. Jensen
Keyword(s):  
Mine Tailings ◽  
Principal Component ◽  
Large Data ◽  
Hierarchical Cluster ◽  
Physical Characteristics ◽  
Mine Tailing ◽  
Data Set ◽  
Cement Replacement ◽  
Partial Cement Replacement ◽  
Physical And Chemical

This study investigates the utilization of mine tailings, the by-product originating from metal- and mineral-based ore mining, as a new cement replacement material. This paper is based on the chemical and physical characteristics of 13 mine tailing samples. In this study, Chemometrics were applied to consider all parameters simultaneously and obtain a thorough screening of potential relations in the large data set. Hierarchical Cluster Analysis (HCA) groups samples according to (dis)similar features and Principal Component Analysis (PCA) visualizes predominating variables and relations to samples. The application of HCA highlighted a clear grouping between mine tailings according to characteristics. Meanwhile, PCA identified the predominant chemical and physical characteristics in the mine tailing samples. Chemometrics therefore provided a thorough overview of mine tailings’ physical and chemical characteristics. Keywords: mine tailings, chemometrics, cement replacement

scholarly journals Effect of Repetitive Applications of the Biocontrol Bacterium Pseudomonas putida 06909-rif/nal on Citrus Soil Microbial Communities

2002 ◽  
Vol 92 (8) ◽  
pp. 857-862 ◽  
Author(s):  
K. Steddom ◽  
J. A. Menge ◽  
D. Crowley ◽  
J. Borneman
Keyword(s):  
Fatty Acids ◽  
Pseudomonas Putida ◽  
Microbial Communities ◽  
Soil Microbial Communities ◽  
Acid Methyl Ester ◽  
Intergenic Spacer ◽  
Principal Component ◽  
Analysis Of Covariance ◽  
Data Set ◽  
Soil Microbial

The effects of repetitive applications of Pseudomonas putida 06909-rif/nal on the resident microbial communities within a citrus orchard were studied with fatty acid methyl-ester (FAME) profiles and ribosomal intergenic spacer analysis. The data set from FAME was large and very complex, requiring 23 factors from principal component analysis to explain 91% of variability in the data. Spatial and temporal effects on variation within microbial communities were much greater than the effects of either yearly applications of Pseudomonas putida 06909-rif/nal, weekly repetitive applications of Pseudomonas putida 06909-rif/nal, or yearly applications of the fungicide metalaxyl and the nematicide phenamiphos. Multivariate analysis of covariance showed much of the variability between treatments could be accounted for by populations of Pseudomonas putida 06909-rif/nal. Soil fatty acids that showed significant changes between treatments were not related to fatty acids found in Pseudomonas putida 06909-rif/nal, suggesting applications of Pseudomonas putida 06909-rif/nal altered the soil microbial community.

scholarly journals Multi-Sensor Characterization of Sparkling Wines Based on Data Fusion

Chemosensors ◽  
2021 ◽  
Vol 9 (8) ◽  
pp. 200
Author(s):  
Anais Izquierdo-Llopart ◽  
Javier Saurina
Keyword(s):  
Data Fusion ◽  
Organic Acids ◽  
Principal Component ◽  
Hierarchical Cluster ◽  
Sensor Data ◽  
Data Set ◽  
Total Antioxidant ◽  
Red Grape ◽  
Grape Varieties

This paper is focused on the assessment of a multi-sensor approach to improve the overall characterization of sparkling wines (cava wines). Multi-sensor, low-level data fusion can provide more comprehensive and more accurate vision of results compared with the study of simpler data sets from individual techniques. Data from different instrumental platforms were combined in an enriched matrix, integrating information from spectroscopic (UV/Vis and FTIR), chromatographic, and other techniques. Sparkling wines belonging to different classes, which differed in the grape varieties, coupages, and wine-making processes, were analyzed to determine organic acids (e.g., tartaric, lactic, malic, and acetic acids), pH, total acidity, polyphenols, total antioxidant capacity, ethanol, or reducing sugars. The resulting compositional values were treated chemometrically for a more efficient recovery of the underlaying information. In this regard, exploratory methods such as principal component analysis showed that phenolic compounds were dependent on varietal and blending issues while organic acids were more affected by fermentation features. The analysis of the multi-sensor data set provided a more comprehensive description of cavas according to grape classes, blends, and vinification processes. Hierarchical Cluster Analysis (HCA) allowed specific groups of samples to be distinguished, featuring malolactic fermentation and the chardonnay and red grape classes. Partial Least Squares-Discriminant Analysis (PLS-DA) also classified samples according to the type of grape varieties and fermentations. Bar charts and complementary statistic test were performed to better define the differences among the studied samples based on the most significant markers of each cava wine type. As a conclusion, catechin, gallic, gentisic, caftaric, caffeic, malic, and lactic acids were the most remarkable descriptors that contributed to their discrimination based on varietal, blending, and oenological factors.

scholarly journals PHENETIC AND UNSUPERVISED MULTIVARIATE ANALYSIS SYZYGIUM POLYANTHUM (WIGHT) WALP

2021 ◽  
Vol 52 (1) ◽  
pp. 249-258
Author(s):  
Abdulrahman & et al.
Keyword(s):  
Multivariate Analysis ◽  
Woody Plants ◽  
De Novo ◽  
Morphological Variability ◽  
Principal Component ◽  
Hierarchical Cluster ◽  
Peninsular Malaysia ◽  
Data Matrix ◽  
Documented Information ◽  
Study Species

Myrtaceae family is widely distributed in Asia has been the largest group of plant; mainly trees and few shrubs. Distributed all over the world in tropical and subtropical areas. Syzygium is the largest genus with economical value found all over the Malaysian Peninsular. Evolutionary relationships within the Syzygium is unclear and there are currently no reliable criteria to divide the genus into manageable entities for systematic study. Species of Syzygium is the richest genus of woody plants in South East Asia with approximately 1000 or more species but little is known about the genus. Syzygium polyanthum Wight is one of the favourites Ulam that have been consumed for ages in Peninsular Malaysia and also as herbal medicine. The species is widely misunderstood due to extreme morphological variability, similarity in aroma and flavor. The species is substituted or adulterated with several other species. The study was aimed to construct phenetic tree and unsupervised multivariate analysis from morphological and anatomical the data matrix.  Phenetic analysis, Principal component and hierarchical cluster analysis revealed they are two different cultivars species  but inter variation exited among cultivars of same species.  The above documented information has added new  taxonomic information with regard to the identification of the cultivars in Peninsular Malaysia. The study recommends further study on de novo sequence of Serai kayu and Serai kayu hutan.

scholarly journals Identification of Rainfall Patterns on Hydrological Simulation Using Robust Principal Component Analysis

2018 ◽  
Vol 11 (3) ◽  
pp. 1162 ◽  
Author(s):  
S.M. Shaharudin ◽  
N. Ahmad ◽  
N.H. Zainuddin ◽  
N.S. Mohamed
Keyword(s):  
Principal Component Analysis ◽  
Simulated Data ◽  
Principal Component ◽  
Breakdown Point ◽  
Component Analysis ◽  
Data Matrix ◽  
Robust Pca ◽  
Data Set ◽  
Number Of Components ◽  
Rainfall Patterns

A robust dimension reduction method in Principal Component Analysis (PCA) was used to rectify the issue of unbalanced clusters in rainfall patterns due to the skewed nature of rainfall data. A robust measure in PCA using Tukey’s biweight correlation to downweigh observations was introduced and the optimum breakdown point to extract the number of components in PCA using this approach is proposed. A set of simulated data matrix that mimicked the real data set was used to determine an appropriate breakdown point for robust PCA and  compare the performance of the both approaches. The simulated data indicated a breakdown point of 70% cumulative percentage of variance gave a good balance in extracting the number of components .The results showed a  more significant and substantial improvement with the robust PCA than the PCA based Pearson correlation in terms of the average number of clusters obtained and its cluster quality.

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