Raman Spectroscopy Study of Phosphorites Combined with PCA-HCA and OPLS-DA Models

Abstract

Phosphorite is a nonrenewable strategic resource, a convenient and rapid method of phosphorite grade identification and classification is important to improve phosphate utilization. In this study, Raman spectroscopy has been combined with principal component analysis and hierarchical clustering analysis (PCA-HCA) and orthogonal partial least squares-discriminant analysis (OPLS-DA) models for an investigation of different grade phosphorite samples. Both the PCA-HCA and OPLS-DA models showed that different grade phosphorite samples can be clearly distinguished by statistically analyzing the Raman spectra after smoothing, baseline correction, and first derivation. In addition, the S-line study on the OPLS-DA model clearly demonstrated that the symmetrical stretching vibrational mode of phosphate near 960 cm−1 had a much more significant contribution than other vibrational modes for the differentiation of different grade phosphorite samples.

Synchrotron Radiation FTIR Microspectroscopy Study of Biomolecular Alterations in Vincristine-Treated WRL68 Cells at the Single-Cell Level

“…PCA is an unsupervised technique to reduce the dimension of the high-dimensional spectral original dataset by finding a smaller collection of variables called principal components. The dimensionality reduction of spectral data by PCA not only reflects most of the original spectral information but also reflects the difference between the samples . However, the dimensionality reduction during PCA might overlook important information related to sample difference.…”

“…R 2 X and R 2 Y are the cumulative sum of squares of all x variables and y variables explained by all extracted components. Q 2 is the prediction superiority, which can describe the cumulative prediction degree of the model. , From the OPLS-DA model, the variable influence on projection (VIP) values can also be obtained to identify the important spectral peak signals in the scatter plots. In this research, PCA and OPLS-DA were performed based on the vector-normalized second-derivative spectra of all single cells in the control and treated groups.…”

Multi-wavelength fusion column fingerprint technology combined with chemometric analysis to evaluate the overall quality of the Gardenia jasminoides root

“…20 Orthogonal partial least squares discriminant analysis (OPLS-DA) was set up by using the SIMCA 14.1 software, which is different from PCA because it is a supervised discriminant analysis statistical method, and it is very beneficial to finding relevant information related with particular samples and variables of a dataset. 21 Mathematical model analysis (Origin 2018) was used to draw the multi-wavelength fusion column fingerprint.…”

At-Line Sampling and Characterization of Pyrolytic Vapors from Biomass Feedstock Blends Using SPME-GC/MS-PCA: Influence of Char on Fast Pyrolysis

“…To better understand the overall changes in the composition of biomass pyrolysis vapors, SPME coupled with principal component analysis (PCA) was used to monitor trends of the chromatographic data. PCA is commonly used to detect trends in complex chemical data, including vibrational spectroscopy, NMR, and chromatography. − PCA is particularly useful in detecting related patterns where changes in the major components are highly correlated with changes in minor components, or when changes in one class of compounds, e.g., lignin, are correlated, positively or inversely, with changes in another class of compounds, e.g., carbohydrates.…”