Encyclopedia of Sensors and Biosensors 2023
DOI: 10.1016/b978-0-12-822548-6.00025-x
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Sensing Materials: Optical Sensing Based on Carbon Quantum Dots

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“…25 In a typical PCA analysis, two results are generated, loadings and score plots. The loading vectors explain the correlation between variables and components by measuring the specific effects of variables on the components, on the other hand, the score vectors define the trajectory of the principal components with respect to the observations.- 26 All factors whose eigenvalues (squared factor loadings that load on a factor taken together) exceed one, as well as the inflection point of a scree plot, are well-known standard criteria (graph of each eigenvalue against the respective factors) to determine the number of factors to be extracted. Theoretically, the first factor in PCA accounts for the maximum variation between the factors, and the subsequent components do not correlate with the previous PCs and express as much of the remaining information as possible.…”
Section: Data and Research Methodologymentioning
confidence: 99%
“…25 In a typical PCA analysis, two results are generated, loadings and score plots. The loading vectors explain the correlation between variables and components by measuring the specific effects of variables on the components, on the other hand, the score vectors define the trajectory of the principal components with respect to the observations.- 26 All factors whose eigenvalues (squared factor loadings that load on a factor taken together) exceed one, as well as the inflection point of a scree plot, are well-known standard criteria (graph of each eigenvalue against the respective factors) to determine the number of factors to be extracted. Theoretically, the first factor in PCA accounts for the maximum variation between the factors, and the subsequent components do not correlate with the previous PCs and express as much of the remaining information as possible.…”
Section: Data and Research Methodologymentioning
confidence: 99%