scholarly journals FTIR and HPLC-Based Metabolomics of Yacon Leaves Extracts (Smallanthus sonchifolius [Poepp & Endl.] H. Robinson) from Two Locations in Indonesia

2020 ◽  
Vol 20 (3) ◽  
pp. 567
Author(s):  
Zulhelmi Aziz ◽  
Nancy Dewi Yuliana ◽  
Partomuan Simanjuntak ◽  
Mohamad Rafi ◽  
Syamsudin Abdillah
Keyword(s):  
Principal Component Analysis ◽  
Cross Validation ◽  
Permutation Tests ◽  
Principal Component ◽  
Pca Analysis ◽  
Multivariate Models ◽  
Smallanthus Sonchifolius ◽  
Postharvest Handling ◽  
Prominent Peak ◽  
Hplc Data

Smallanthus sonchifolius [Poepp. & Endl.] H. Robinson (Asteraceae) also known as Yacon or insulin plant, is traditionally used for treating diabetes. Varying geographical origins and postharvest handling, however, seem to affect quantitative and qualitative metabolites in the leaves of Smallanthus sonchifolius [Poepp. & Endl.] H. Robinson (Yacon). The study was conducted to compare and differentiate metabolites profile/fingerprint of Yacon leaves which were grown and obtained from different locations in Pulau Jawa i.e. Lembang (Jawa Barat) and Wonosobo (Jawa Tengah). Three different solvents (95% ethanol, 50% ethanol and water) were used to synthesize Yacon leaves extracts, in order to determine the suitable solvent to produce discernable differentiation through FTIR and HPLC-based metabolomics. Principal Component Analysis (PCA) of FTIR data (4000–400 cm1 wavenumber) indicated that Yacon leaves extracted with ethanol at 95%, had a distinctive FTIR fingerprint profile when compared to others. However, the FTIR-based PCA could not differentiate the extracts based on their geographical origins, although PCA analysis of HPLC-data successfully differentiated the extracts based on their geographical origins. Furthermore, the prominent peak for the leaves extract from Lembang and Wonosobo as regards retention time, was observed at 21.59–25.10 min and 20.69–21.695 min respectively. Notably, R2Y and Q2 value obtained by cross-validation and permutation tests showed all multivariate models were statistically reliable. Overall, there is the need to conduct further research using a more sophisticated tool such as LC-MS, to identify which metabolites represented by the aforementioned FTIR and HPLC data.

scholarly journals Exploring the Innovative Personality Characteristics among Teachers

2016 ◽  
Vol 9 (4) ◽  
pp. 1 ◽  
Author(s):  
Nooraini Othman
Keyword(s):  
Principal Component Analysis ◽  
Secondary School ◽  
Goodness Of Fit ◽  
Secondary School Teachers ◽  
Principal Component ◽  
Threshold Value ◽  
Personality Characteristics ◽  
Pca Analysis ◽  
School Teachers ◽  
Confirmatory Factor

<p class="apa">The aim of this study is to explore the characteristics of innovative personality among teachers in Malaysia. Samples of the research were randomly selected among secondary school teachers in three districts in Malaysia. Research instrument was self-developed by the researchers based on interviews carried out with some resource persons who are both experts and authoritative in their fields, as well as through literature review. A pilot study was carried out among 30 respondents. Cronbach’s Alpha value for the whole instrument is .952, indicating that it is reliable and suitable for actual data collection. A total of 484 sets of questionnaires were completed and gathered to form the data for this research. The data were then analysed using an advanced statistical method called Principal Component Analysis (PCA). Findings of the research concluded three constructs, namely, Leadership, Openness and Braveness. The constructs were labelled based on groups of items which were formed as a result of the PCA analysis. Meanwhile, Confirmatory Factor Analysis (CFA) was used to validate each dimension and to analyse the coherence of data based on model hypothesis. The findings of CFA indicated the goodness-of-fit values of the revised model, as follows: CMIN/DF=2.56; CFI=.935; and RMSEA=.057; with each figure above the threshold value. <strong></strong></p>

scholarly journals Leaf geometric morphometrics among populations of Dalbergia ecastaphyllum (L.) Taub.

2019 ◽  
Vol 35 (6) ◽  
Author(s):  
Daniel Vieira de Morais ◽  
Lorena Andrade Nunes ◽  
Vandira Pereira da Mata ◽  
Maria Angélica Pereira de Carvalho Costa ◽  
Geni da Silva Sodré ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Geometric Morphometrics ◽  
Cross Validation ◽  
Abiotic Factors ◽  
Principal Component ◽  
Canonical Variate ◽  
Validation Test ◽  
Significant Difference ◽  
Variate Analysis ◽  
Geographical Distances

Leaves are plant structures that express important traits of the environment where they live. Leaf description has allowed identification of plant species as well as investigation of abiotic factors effects on their development, such as gases, light, temperature, and herbivory. This study described populations of Dalbergia ecastaphyllum through leaf geometric morphometrics in Brazil. We evaluated 200 leaves from four populations. The principal component analysis (PCA) showed that the first four principal components were responsible for 97.81% of variation. The non-parametric multivariate analysis of variance (NPMANOVA) indicated significant difference between samples (p = 0.0001). The Mentel test showed no correlation between geographical distances and shape. The canonical variate analysis (CVA) indicated that the first two variables were responsible for 96.77 % of total variation, while the cross-validation test showed an average of 83.33%. D. ecastaphyllum leaves are elliptical and ovate.

scholarly journals Analisis Metode Pengenalan Wajah Two Dimensial Principal Component Analysis (2DPCA) dan Kernel Fisher Discriminant Analysis Menggunakan Klasifikasi KNN (K- Nearest Neighbor)

2020 ◽  
Vol 2 (2) ◽  
pp. 29-38
Author(s):  
Abdur Rohman Harits Martawireja ◽  
Hilman Mujahid Purnama ◽  
Atika Nur Rahmawati
Keyword(s):  
Principal Component Analysis ◽  
Discriminant Analysis ◽  
Cross Validation ◽  
Nearest Neighbor ◽  
Principal Component ◽  
Component Analysis ◽  
K Nearest Neighbor ◽  
Fisher Discriminant Analysis ◽  
Fisher Discriminant ◽  
Kernel Fisher Discriminant Analysis

Pengenalan wajah manusia (face recognition) merupakan salah satu bidang penelitian yang penting dan belakangan ini banyak aplikasi yang menerapkannya, baik di bidang komersil ataupun di bidang penegakan hukum. Pengenalan wajah merupakan sebuah sistem yang berfungsikan untuk mengidentifikasi berdasarkan ciri-ciri dari wajah seseorang berbasis biometrik yang memiliki keakuratan tinggi. Pengenalan wajah dapat diterapkan pada sistem keamanan. Banyak metode yang dapat digunakan dalam aplikasi pengenalan wajah untuk keamanan sistem, namun pada artikel ini akan membahas tentang dua metode yaitu Two Dimensial Principal Component Analysis dan Kernel Fisher Discriminant Analysis dengan metode klasifikasi menggunakan K-Nearest Neigbor. Kedua metode ini diuji menggunakan metode cross validation. Hasil dari penelitian terdahulu terbukti bahwa sistem pengenalan wajah metode Two Dimensial Principal Component Analysis dengan 5-folds cross validation menghasilkan akurasi sebesar 88,73%, sedangkan dengan 2-folds validation akurasi yang dihasilkan sebesar 89,25%. Dan pengujian metode Kernel Fisher Discriminant dengan 2-folds cross validation menghasilkan akurasi rata rata sebesar 83,10%.

scholarly journals Comparative analysis of shell occupation by two southern populations of the hermit crab Loxopagurus loxochelis (Decapoda, Diogenidae)

2012 ◽  
Vol 60 (3) ◽  
pp. 299-310 ◽  
Author(s):  
Luciane Ayres-Peres ◽  
Aline Ferreira Quadros ◽  
Fernando L. Mantelatto
Keyword(s):  
Principal Component Analysis ◽  
Comparative Analysis ◽  
Hermit Crab ◽  
Regression Tree ◽  
Principal Component ◽  
Hermit Crabs ◽  
Pca Analysis ◽  
Internal Volume ◽  
The Relationship ◽  
Two Populations

The present study aimed to comparatively verify the relation between the hermit crabs and the shells they use in two populations of Loxopagurus loxochelis. Samples were collected monthly from July 2002 to June 2003, at Caraguatatuba and Ubatuba Bay, São Paulo, Brazil. The animals sampled had their sex identified, were weighed and measured; their shells were identified, measured and weighed, and their internal volume determined. To relate the hermit crab's characteristics and the shells' variables, principal component analysis (PCA) and a regression tree were used. According to the PCA analysis, the three gastropod shells most frequently used by L. loxochelis varied in size. The regression tree successfully explained the relationship between the hermit crab's characteristics and the internal volume of the inhabited shell. It can be inferred that the relationship between the morphometry of an individual hermit crab and its shell is not straightforward and it is impossible to explain only on the basis of direct correlations between the body's and the shell's attributes. Several factors (such as the morphometry and the availability of the shell, environmental conditions and inter- and intraspecific competition) interact and seem to be taken into consideration by the hermit crabs when they choose a shell, resulting in the diversified pattern of shell occupancy shown here and elsewhere.

scholarly journals Morphometry and Distribution of Senecio Nemorensis agg. Species (Asteraceae) in Poland

2014 ◽  
Vol 59 (1) ◽  
pp. 37-54
Author(s):  
Kaja Rola
Keyword(s):  
Principal Component Analysis ◽  
Principal Component ◽  
Involucral Bract ◽  
Canonical Discriminant Analysis ◽  
Herbarium Specimens ◽  
Distribution Map ◽  
Pca Analysis ◽  
Leaf Base ◽  
Quantitative Characters ◽  
Base Width

Abstract A morphometric analysis based on 316 herbarium specimens of Senecio nemorensis agg. indicated the occurrence of the following four species in Poland: S. germanicus Wallr., S. hercynicus Herborg, S. ovatus (G. Gaertn. et al.) Willd. and S. ucranicus Hodálová. Principal component analysis (PCA), analysis of variance (ANOVA)/Kruskal-Wallis test and canonical discriminant analysis (CDA) were applied. Quantitative characters such as supplementary bract length, leaf base width, ligule length and the supplementary/involucral bract length ratio clearly discriminated taxa within S. nemorensis agg. Included is a distribution map of the investigated species based on the examined material, with particular emphasis on the course of the northeastern boundary of S. hercynicus and the northwestern boundary of S. ucranicus. Also given is a determination key for species within S. nemorensis agg. in Poland, together with morphological descriptions of particular species

scholarly journals Distinguishing Coconut Oil from Coconut Paring Oil using Principle Component Analysis of Fatty Acid Data

CORD ◽  
2012 ◽  
Vol 28 (1) ◽  
pp. 5
Author(s):  
J.M.N. Marikkar
Keyword(s):  
Principal Component Analysis ◽  
Fatty Acid ◽  
Iodine Value ◽  
Coconut Oil ◽  
Principal Component ◽  
Component Analysis ◽  
Pca Analysis ◽  
Fatty Acid Data ◽  
Influencing Parameters ◽  
Iodine Values

A study was carried out to distinguish coconut oil from coconut pairing oil by the application of principal component analysis (PCA) to fatty acid compositional and iodine value data. Five samples of ordinary coconut oil extracted from five different batches of copra and five samples of coconut pairing oil obtained from five batches of dried coconut pairings were employed. Fatty acid composition and iodine values of oil samples were determined individually and the data were analyzed statistically. PCA analysis showed that lauric and oleic acid contents and iodine value data are the most influencing parameters to discriminate coconut oil from coconut pairing oil. Hence, the application of PCA to fatty acid compositional and iodine value data was successful in distinguishing coconut oil from coconut pairing oil.

scholarly journals IPCARF: improving lncRNA-disease association prediction using incremental principal component analysis feature selection and a random forest classifier

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Rong Zhu ◽  
Yong Wang ◽  
Jin-Xing Liu ◽  
Ling-Yun Dai
Keyword(s):  
Principal Component Analysis ◽  
Random Forest ◽  
Cross Validation ◽  
Search Algorithm ◽  
Principal Component ◽  
Component Analysis ◽  
Biological Data ◽  
Learning Technology ◽  
Disease Associations ◽  
Fold Cross Validation

Abstract Background Identifying lncRNA-disease associations not only helps to better comprehend the underlying mechanisms of various human diseases at the lncRNA level but also speeds up the identification of potential biomarkers for disease diagnoses, treatments, prognoses, and drug response predictions. However, as the amount of archived biological data continues to grow, it has become increasingly difficult to detect potential human lncRNA-disease associations from these enormous biological datasets using traditional biological experimental methods. Consequently, developing new and effective computational methods to predict potential human lncRNA diseases is essential. Results Using a combination of incremental principal component analysis (IPCA) and random forest (RF) algorithms and by integrating multiple similarity matrices, we propose a new algorithm (IPCARF) based on integrated machine learning technology for predicting lncRNA-disease associations. First, we used two different models to compute a semantic similarity matrix of diseases from a directed acyclic graph of diseases. Second, a characteristic vector for each lncRNA-disease pair is obtained by integrating disease similarity, lncRNA similarity, and Gaussian nuclear similarity. Then, the best feature subspace is obtained by applying IPCA to decrease the dimension of the original feature set. Finally, we train an RF model to predict potential lncRNA-disease associations. The experimental results show that the IPCARF algorithm effectively improves the AUC metric when predicting potential lncRNA-disease associations. Before the parameter optimization procedure, the AUC value predicted by the IPCARF algorithm under 10-fold cross-validation reached 0.8529; after selecting the optimal parameters using the grid search algorithm, the predicted AUC of the IPCARF algorithm reached 0.8611. Conclusions We compared IPCARF with the existing LRLSLDA, LRLSLDA-LNCSIM, TPGLDA, NPCMF, and ncPred prediction methods, which have shown excellent performance in predicting lncRNA-disease associations. The compared results of 10-fold cross-validation procedures show that the predictions of the IPCARF method are better than those of the other compared methods.

scholarly journals Physicochemical and Techno-Functional Characterization of Native Corn Reintroduced in the Andean Zone of Jujuy, Argentina

Proceedings ◽  
2020 ◽  
Vol 53 (1) ◽  
pp. 7
Author(s):  
María Alejandra Giménez ◽  
Cristina Noemí Segundo ◽  
Manuel Oscar Lobo ◽  
Norma Cristina Sammán
Keyword(s):  
Principal Component Analysis ◽  
Functional Properties ◽  
Food Industry ◽  
Functional Characterization ◽  
Principal Component ◽  
Component Analysis ◽  
High Variability ◽  
Pca Analysis

The chemical and techno-functional properties of nine maize races from the Andean zone of Jujuy, Argentina, in the process of reintroduction, were determined. Principal component analysis (PCA) was applied to establish the differences between them. The breeds studied showed high variability in their chemical and techno-functional properties, which would indicate that their applications in the food industry will also be differentiated. The PCA analysis allowed us to group them into four groups, and the Capia Marron and Culli races showed unique properties, mainly in the formation of gels.

Note on the Use of Principal Component Analysis (PCA) and Clustering for the Analysis of Wafer Level ATPG data

2006 ◽  
Author(s):  
Kartik Ramanujachar ◽  
Satish Draksharam
Keyword(s):  
Principal Component Analysis ◽  
Hierarchical Clustering ◽  
Principal Component ◽  
Component Analysis ◽  
Pattern Generation ◽  
Automatic Test Pattern Generation ◽  
Pca Analysis ◽  
Wafer Level ◽  
Failure Data ◽  
Commonality Analysis

Abstract This article explores the use of principal component analysis (PCA) and hierarchical clustering in the analysis of wafer level automatic test pattern generation (ATPG) failure data. The principle of commonality is extended by utilizing hierarchical clustering to collect die that are more similar to one another in their manner of failure than to others. Similarity is established by PCA of the patterns that the die in a wafer fail. Results demonstrated that PCA analysis and clustering are useful tools for dimensionality reduction and commonality analysis of wafer level ATPG data. The utility of PCA analysis and clustering in the extraction of die for physical failure analysis is also illustrated.

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