Prediction of emission spectra of fluorescence materials using principal component analysis

Abstract

Fluorescent materials are now a critical field of research due to their unique excitation and emission properties that can be tailored to specific fluorescence detection technologies. In this work, a procedure is described to approximate the emission spectral data of fluorescent materials of different types from their excitation spectral data using principal component analysis (PCA) technique. First, PCA as a statistical and mathematical method was used to reconstruct the excitation and emission spectra of training dataset and then, the approximation was accomplished by multiple linear regression (MLR).The performance of obtained function was examined on testing dataset. Afterward, CIE tristimulus values of the fluorescent samples were calculated based on ASTM, E2152-12 standard test method. The colorimetric accuracy was then evaluated by calculating the geometric differences in CIE tristimulus values X, Y, and Z for the 1964 standard colorimetric observer under illuminant D65. The obtained results show a good curve fit between the actual emission spectra and recovered emission spectra. In addition, based on cumulative variance and root mean square (RMS), eight principal components were selected as optimum number of principal components for prediction of emission spectra data.

Computer-aided diagnosis of abnormal breasts in mammogram images by weighted-type fractional Fourier transform

“…Note that PCA in online prediction used the coefficient matrix generated by the PCA in offline learning. [46][47][48] Preprocessing result Figure 5 shows the result of each procedure during the preprocessing. Figure 5(a) shows an original image of a normal breast with dense glandular background.…”

“…Note that PCA in online prediction used the coefficient matrix generated by the PCA in offline learning. [46][47][48]…”

Effect of classification by competitive neural network on reconstruction of reflectance spectra using principal component analysis

“…The first basis vector represents the maximum variance in the reflectance spectra data and the later basis vectors maximally represent the remaining variance. Thus, more than 95% of the variance in a reflectance spectra set can be recovered by using only the first three basis functions …”

“…Thus, more than 95% of the variance in a reflectance spectra set can be recovered by using only the first three basis functions. [25][26][27][28][29][30] In recent years, some researchers used the PCA in reconstruction of reflectance spectra. As an example, Ansari et al 14 improved recovery of reflectance spectra from CIE tristimulus values using a progressive database selection technique.…”