Principal Components Analysis

Alkarkhi, Abbas F.M.; Alqaraghuli, Wasin A.A.

doi:10.1016/b978-0-12-814262-2.00008-x

Cited by 19 publications

(13 citation statements)

References 1 publication

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Multivariate data analysis based on the principal component analysis (PCA) and cluster analysis was conducted using a customized code written in Matlab ® R2021a. The main use of the PCA in this study was to find relationships between variables and samples as they are constructed using covariance methods as a parameter engineering justification for the ANN modelling presented [ 51 , 52 , 53 , 54 , 55 ]. Besides, cluster analysis helps to visualize the relative grouping of commercial rice samples according to these parameters.…”

Section: Methodsmentioning

confidence: 99%

Computer Vision and Machine Learning Analysis of Commercial Rice Grains: A Potential Digital Approach for Consumer Perception Studies

Aznan

Viejo

Pang

et al. 2021

Sensors

View full text Add to dashboard Cite

Rice quality assessment is essential for meeting high-quality standards and consumer demands. However, challenges remain in developing cost-effective and rapid techniques to assess commercial rice grain quality traits. This paper presents the application of computer vision (CV) and machine learning (ML) to classify commercial rice samples based on dimensionless morphometric parameters and color parameters extracted using CV algorithms from digital images obtained from a smartphone camera. The artificial neural network (ANN) model was developed using nine morpho-colorimetric parameters to classify rice samples into 15 commercial rice types. Furthermore, the ANN models were deployed and evaluated on a different imaging system to simulate their practical applications under different conditions. Results showed that the best classification accuracy was obtained using the Bayesian Regularization (BR) algorithm of the ANN with ten hidden neurons at 91.6% (MSE = <0.01) and 88.5% (MSE = 0.01) for the training and testing stages, respectively, with an overall accuracy of 90.7% (Model 2). Deployment also showed high accuracy (93.9%) in the classification of the rice samples. The adoption by the industry of rapid, reliable, and accurate methods, such as those presented here, may allow the incorporation of different morpho-colorimetric traits in rice with consumer perception studies.

show abstract

Section: Methodsmentioning

confidence: 99%

Computer Vision and Machine Learning Analysis of Commercial Rice Grains: A Potential Digital Approach for Consumer Perception Studies

Aznan

Viejo

Pang

et al. 2021

Sensors

View full text Add to dashboard Cite

show abstract

“…Hydrology-soil-vegetation interaction is the most fundamental process in coastal wetland ecosystems (Alkarkhi & Alqaraghuli, 2019;Cronk & Fennessy, 2001;, and most of the previous studies highlighted the impacts of soil chemistry on plants. In salt marsh wetlands, soil salinity is one of the main stresses for plant traits, especially for glycophytic species.…”

Section: Influence Of Environment Factors On Plantsmentioning

confidence: 99%

A threshold‐like effect on the interaction between hydrological connectivity and dominant plant population in tidal marsh wetlands

Liu

Xie

et al. 2021

Land Degrad Dev

View full text Add to dashboard Cite

Tidal marsh wetlands in the Yellow River Delta provide valuable eco‐services to the local population and global ecology. However, this area is suffering from serious degradation under the stresses of social development and climate change. Hydrological connectivity, a new framework in hydrology and ecology, has been proposed as the main factor affecting the ecological processes in coastal wetlands; however, its role in hydrology–soil–vegetation interactions remains unclear. In this study, we parametrically quantified the hydrological connectivity in the tidal marsh wetlands and analyzed its relationship with Phragmites australis, one of the dominant species in this area. Our results showed threshold‐like effects on the interaction between hydrological connectivity and P. australis on the plot scale. When biomass is lower than 2.2 kg m−2, the population density and structure size were found to increase with hydrological connectivity. When the biomass is higher than the threshold, the plots disconnected hydrologically because of high water consumption. Compared with soil chemistry, salinity, and water soil content, hydrological connectivity in the surface soil layer is more strongly linked to the plant traits and spatial structure in the tidal marsh wetlands due to the narrow ranges of other variables. Based on the Best on previous study and our analysis, we do not recommend dense plantation of P. australis, especially near the freshwater sources in the tidal marsh, because of its high reproduction ability and competitive nature, which may cut the freshwater connectivity off, lowering the richness of plant species and habitat diversity.

show abstract

“…Since PCA functions as a linear projection of high dimensional data into a lower dimensional space, it lacks the ability to explain complex non-linear relationships that are likely present in the SHS-GC-IMS data. 33 Hence, advanced methods that could recognise non-linear variable relationships were tested. Although the underlying principles of these chemometric methods are essential in understanding how the models could transform the input data into classification results, they will not be discussed elaborately in this article for the sake of conciseness.…”

Section: Principal Component Analysis (Pca) Of Shs-gc-ims Datamentioning

confidence: 99%

Section: Principal Component Analysis (Pca) Of Shs-gc-ims Datamentioning

confidence: 99%

Assessment of Sauvignon Blanc Aroma and Quality Gradings Based on Static Headspace-Gas Chromatography-Ion Mobility Spectrometry (SHS-GC-IMS): Merging Analytical Chemistry with Machine Learning

Zhu

Benkwitz²,

Kilmartin³

2020

Preprint

View full text Add to dashboard Cite

<div>In this paper, we report on the application of the static headspace-gas chromatography-ion mobility spectrometry (SHS-GC-IMS) instrument in the field of wine aroma analysis and its potential in constructing a prediction model for the quality gradings of wines. The easy-to-operate, cost effective SHS-GC-IMS instrument was innovatively used for a non-targeted search for volatile compounds in Sauvignon Blanc wine, with the identification of volatiles seldom before reported. The wine aroma profile acquired by the instrument was organically and innovatively combined with advanced classification models, inspired by the computer science community, to produce high classification accuracy in terms of wine quality gradings. Useful insights were also extracted by using advanced interpretation methods on complex models to learn the important volatiles correlated with wine quality grading.</div>

show abstract

Principal Components Analysis

Cited by 19 publications

References 1 publication

Computer Vision and Machine Learning Analysis of Commercial Rice Grains: A Potential Digital Approach for Consumer Perception Studies

Computer Vision and Machine Learning Analysis of Commercial Rice Grains: A Potential Digital Approach for Consumer Perception Studies

A threshold‐like effect on the interaction between hydrological connectivity and dominant plant population in tidal marsh wetlands

Assessment of Sauvignon Blanc Aroma and Quality Gradings Based on Static Headspace-Gas Chromatography-Ion Mobility Spectrometry (SHS-GC-IMS): Merging Analytical Chemistry with Machine Learning

Contact Info

Product

Resources

About