scholarly journals Evaluation of wheat/non-traditional flour composite

2014 ◽  
Vol 32 (No. 3) ◽  
pp. 288-295 ◽  
Author(s):  
T. Hofmanová ◽  
M. Hrušková ◽  
I. Švec
Keyword(s):  
Principal Component Analysis ◽  
Wheat Flour ◽  
Dietary Fibre ◽  
Principal Component ◽  
Wheat Sample ◽  
Plant Materials ◽  
Nutritional Improvement ◽  
Nutritional Effect ◽  
Total Dietary Fibre ◽  
High Level

We examine the nutritional effect of selected non-traditional grain samples added into wheat flour. In a form of flour, amaranth, quinoa, lupine, 5 hemp types, 2 teff types and 2 chia types were used for wheat flour substitution on a low and high level. Samples with amaranth and lupine flour showed the best improvement in terms of protein content (in the range between 21.1 and 26.0%). The highest total dietary fibre was found in lupine composites (7.1 and 9.8%). Hemp samples contained a significant amount of minerals in comparison with the control wheat sample (from 1.16% to 1.98%). According to the above-mentioned differences, flour composites containing single tested grains were distinguished by principal component analysis. All examined plant materials could be recommended for wheat flour fortification in terms of nutritional improvement. The addition of non-traditional flours partially changed both the volume and shape of laboratory prepared bread correspondingly to the type and added amount.

scholarly journals A Principal Component Analysis of Acoustic Emission Signals from a Landing Gear Component

2008 ◽  
Vol 13-14 ◽  
pp. 41-47 ◽  
Author(s):  
Rhys Pullin ◽  
Mark J. Eaton ◽  
James J. Hensman ◽  
Karen M. Holford ◽  
Keith Worden ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Acoustic Emission ◽  
Background Noise ◽  
Principal Component ◽  
Component Analysis ◽  
Landing Gear ◽  
Fracture Detection ◽  
Artificial Fracture ◽  
High Level ◽  
Ae Signals

This work forms part of a larger investigation into fracture detection using acoustic emission (AE) during landing gear airworthiness testing. It focuses on the use of principal component analysis (PCA) to differentiate between fracture signals and high levels of background noise. An artificial acoustic emission (AE) fracture source was developed and additionally five sources were used to generate differing AE signals. Signals were recorded from all six artificial sources in a real landing gear component subject to no load. Further to this, artificial fracture signals were recorded in the same component under airworthiness test load conditions. Principal component analysis (PCA) was used to automatically differentiate between AE signals from different source types. Furthermore, successful separation of artificial fracture signals from a very high level of background noise was achieved. The presence of a load was observed to affect the ultrasonic propagation of AE signals.

An energy dispersive X-ray fluorescence spectrometry approach for the identification of geographical origin of wheat flour

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Tong Chen ◽  
Xingpu Qi ◽  
Zaiyong Si ◽  
Qianwei Cheng ◽  
Hui Chen
Keyword(s):  
Principal Component Analysis ◽  
Wheat Flour ◽  
Principal Component ◽  
Component Analysis ◽  
Mineral Elements ◽  
Energy Dispersive ◽  
Fluorescence Spectrometry ◽  
Energy Channel ◽  
X Ray ◽  
Different Origins

Abstract In this work, a method was established for discriminating geographical origins of wheat flour based on energy dispersive X-ray fluorescence spectrometry (ED-XRF) and chemometrics. 68 wheat flour samples from three different origins were collected and analyzed using ED-XRF technology. Firstly, the principal component analysis method was applied to analyze the feasibility of discrimination and reduce data dimensionality. Then, Competitive Adaptive Reweighted Sampling (CARS) was used to further extract feature variables, and 12 energy variables (corresponding to mineral elements) were identified and selected to characterize the geographical attributes of wheat flour samples. Finally, a non-linear model was constructed using principal component analysis and quadratic discriminant analysis (QDA). The CARS-PCA-QDA model showed that the accuracy of five-fold cross-validation was 84.25%. The results showed that the established method was able to select important energy channel variables effectively and wheat flour could be classified based on geographical origins with chemometrics, which could provide a theoretical basis for unveiling the relationship between mineral element composition and wheat origin.

Comparative study of the physicochemical, compositional and functional properties of eight flours obtained from different plant materials found in Colombia

2016 ◽  
Vol 22 (8) ◽  
pp. 699-707 ◽  
Author(s):  
Seneida Lopera-Cardona ◽  
Cecilia Gallardo ◽  
Jairo Umaña-Gallego ◽  
Lina María Gil
Keyword(s):  
Principal Component Analysis ◽  
Functional Properties ◽  
Principal Component ◽  
Component Analysis ◽  
Ananas Comosus ◽  
Whole Grain ◽  
Plant Materials ◽  
Pineapple Peel ◽  
Yellow Peas ◽  
Oyster Mushrooms

The physicochemical, compositional and functional properties of flour from green plantains ( Musa acuminata) of the large green plantain variety, oyster mushrooms ( Pleorotus ostreatus), pineapple peel ( Ananas comosus) of the ‘apple pineapple’ variety, yellow peas ( Pisum sativum), chickpeas ( Cicer arietinum), whole grain rice ( Oryza sativa), whole grain corn ( Zea mays) and whole grain white quinoa (Chenopodium quinoa) were evaluated by using one-way analysis of variance, Pearson correlations and principal component analysis chemical composition of the eight flours, statistically differed ( p < 0.05). Oyster mushroom and yellow pea flours had the greatest protein content (28.92 and 21.02%, respectively), whereas the pineapple peel, peas and corn stood out for their high contents of Fe and Zn. All flours exhibited emulsifying and foaming activities, while hydration and interfacial properties showed statistically significant negative correlations. There was a clear relationship between levels of protein and carbohydrates and gelation and syneresis phenomena in thermally treated flour suspensions. According to principal component analysis of functional, physicochemical and compositional properties, flours were classified into five groups of raw materials: (1) yellow peas, (2) chickpeas, rice, corn and quinoa, (3) green plantain, (4) pineapple peel and (5) oyster mushrooms. Results are promising to formulate mixes and composite flours for fortification and/or enrichment of food products by using different technological processes.

scholarly journals Principal Component Analysis on Soil Fertility Parameters of Vegetable Growing Panchayats/ Locations in Ernakulam District of Kerala

2020 ◽  
pp. 58-65
Author(s):  
A. Muhsina ◽  
Brigit Joseph ◽  
Vijayaraghava Kumar
Keyword(s):  
Principal Component Analysis ◽  
Soil Fertility ◽  
Soil Ph ◽  
Principal Component ◽  
Component Analysis ◽  
Organic Carbon Content ◽  
Score Plot ◽  
Five Factors ◽  
Total Variability ◽  
High Level

The present paper used Principal Component Analysis (PCA) on 13 soil fertility parameters including soil pH and electrical conductivity of 17 vegetable growing panchyat/locations in Ernakulam district of Kerala based on 583 soil samples. Soil pH of panchayats varied from 4.2- 5.8 with a coefficient of variation 3.16-12.23 per cent and it was inferred that most of the panchayats in the district had very strongly acidic (pH: 4.2-5) and strongly acidic soils (pH: 5-5.5). High level of organic carbon content was noticed in most of the panchayats except in four panchayats. The results of PCA revealed that five PC’s together explained a total variability of 80 per cent and the remaining PCs accounted for 20 per cent of the variability in the data which has been discarded from further analysis. First principal component accounted for 25 per cent variance followed by PC 2(21%), PC 3(14%), PC 4(10%) and PC 5(10%). Factor analysis generated five factors and they explained 85 per cent of variability. Score plot drawn as part of PCA showed that Chengamanadu, Manjapra and Thirumaradi panchayats had high content of soil available S and B. EC was also found to be higher in these panchayats. Amount of OC, Fe and Mn were more in Kalady, Keerampara and Mudakkuzha of Ernakulam district whereas Thuravur, Piravom and Pothanikkad had highly acidic and Mg rich soils. Amount of Zn was more in Vengoor panchayat. Available K, Ca, P and Cu were found to be higher in Kakkad, Nedumbassery, Vengola and Kadungalloor. Based on the fertility status of each panchayats, they could be classified into different groups.

scholarly journals Application of Characteristic Model-Based Principal Component Analysis in Optimization of Flowmeter Parameters

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Wenping Jiang ◽  
Zhencun Jiang ◽  
Lingyang Wang ◽  
Jun Min ◽  
Yi Zhu ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Measurement Error ◽  
Measurement Accuracy ◽  
Structural Parameters ◽  
Principal Component ◽  
Component Analysis ◽  
Characteristic Model ◽  
Industrial Systems ◽  
Model Based ◽  
High Level

In complex industrial processes, it is necessary to perform modeling analysis on some industrial systems and find and optimize the factors that have the greatest impact on the results, in order to achieve the optimization of the industrial systems. However, due to the high-level nature or complex working mechanism of complex industrial systems, traditional principal component analysis methods are difficult to apply. Therefore, this paper proposes a characteristic model-based principal component analysis (CMPCA) to perform principal component analysis on complex industrial systems. The differential pressure flowmeter is taken as an example to verify the effectiveness of the method. Flowmeter is an indispensable instrument in measurement, and its accuracy depends on its own structural parameters. However, the measurement accuracy of some flow meters is not high, and the measurement error is large, which affects the normal industrial production process. This method is used to analyze the influence of the structural parameters of the flowmeter on its measurement accuracy, and the four most important structural parameters are found and optimized. The measurement error of the Bitoba flowmeter is reduced from 1% to 0.2%, and the measurement repeatability is reduced from 0.3 to 0.06, which proves the effectiveness of the method.

Karl Fischer Water Titration−Principal Component Analysis Approach on Wheat Flour

2013 ◽  
Vol 7 (6) ◽  
pp. 1353-1358 ◽  
Author(s):  
Laura Corpaş ◽  
Nicoleta G. Hădărugă ◽  
Ioan David ◽  
Paul Pîrşan ◽  
Daniel I. Hădărugă ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Wheat Flour ◽  
Principal Component ◽  
Component Analysis ◽  
Analysis Approach ◽  
Karl Fischer ◽  
Karl Fischer Water Titration

scholarly journals An Efficient and High Accuracy P300 Detection for Brain Computer Interface System Based on Kernel Principal Component Analysis

2021 ◽  
Author(s):  
Mohammad Farukh Hashmi Mohammad Farukh Hashmi ◽  
Jagdish D.Kene Jagdish D.Kene ◽  
Deepali M.Kotambkar Deepali M.Kotambkar ◽  
Praveen Matte Praveen Matte ◽  
Avinash G.Keskar Avinash G.Keskar
Keyword(s):  
Principal Component Analysis ◽  
Brain Computer Interface ◽  
Principal Component ◽  
Component Analysis ◽  
Computer Interface ◽  
Kernel Principal Component Analysis ◽  
Support Vector ◽  
Discrete Wavelet ◽  
Single Trial ◽  
High Level

Abstract Human machine interaction with the use of brain signals has been made possible by the advent of the technology popularly known as brain computer interface (BCI). P300 is one such brain signal which is used in many BCI systems. The problems associated with most of the existing P300 detection methods are that they are time consuming and computationally complex as they follow the procedure of averaging the values obtained from multiple trials. Also the existing single trial methods have been able to obtain only moderate accuracy levels. In this paper, a novel approach which for achieving a high level of accuracy has been proposed for single trial P300 signal detection amidst noise and artifacts. In this method features were obtained by applying Discrete Wavelet Transform followed by a technique making use of the obtained wavelet coefficients. Kernel Principal Component Analysis (KPCA) was used for reducing the feature dimension. Classification of the P300 signal using the reduced features was done using Support Vector Machine (SVM). The Dataset used was the Dataset II of the third BCI Competition. An accuracy of 98.53% was achieved for Subject S1 (signal obtained from the first person) and 99.25% for Subject S2 (signal obtained from the second person) by using the proposed method. A high level of accuracy was obtained, as compared to many existing techniques. Also the speed of classification was improved with the use of reduced feature dimensions.

scholarly journals Multivariate Analysis and its Application for Screening Mungbean [Vigna radiata (L.) Wilczek] Landraces

2022 ◽  
Author(s):  
Berk Benlioglu ◽  
Ugur Ozkan
Keyword(s):  
Principal Component Analysis ◽  
Cluster Analysis ◽  
Multivariate Analysis ◽  
Vigna Radiata ◽  
Morphological Traits ◽  
Principal Component ◽  
Component Analysis ◽  
Multivariate Statistical ◽  
Breeding Programs ◽  
High Level

Background: Mungbean [Vigna radiata (L.) Wilczek] is known as one of the important crop of the Vigna group. In order to determine morphological traits of mungbean, multivariate analysis will provide important advantages in the selection phase of future breeding programs. Multivariate statistical analysis was used to determine and classify these traits. Multivariate analysis, that includes principal component analysis (PCA) and cluster analysis (CA), is considered the best tool for selecting promising genotypes in the future breeding programs. Methods: Eighteen landraces and two species were used to classify morphological traits in this study. Nine different morphological traits were observed during the research period. These are; days to 50% flowering (DFT), plant height (PH), branches per plant (BPP), clusters per plant (CPP), number of pods per cluster (PPC), seed yield per plot (SYPP), biomass yield per plot (BYPP), harvest index (HI), 1000 seed weight (SW). Result: Principal component analysis (PCA) revealed a high level of variation among the genotypes. Therefore, high variability was observed in DFT (36-59 day), PH (39-76 cm), BPP (3-7), CPP (4-21), SYPP (231-824 g), BYPP (3300-10300 g), HI (6.77-11.25%) and 1000 SW (19.95-50.50 g). According to cluster analysis, landraces with the least genetic diversity distance between them in terms of morphological traits examined were determined as 2 and 3.

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