Investigation of the discrimination and characterization of blood serum structure in patients with opioid use disorder using IR spectroscopy and PCA-LDA analysis

Diagnosis of Lung Cancer by ATR-FTIR Spectroscopy and Chemometrics

“…The peak at 1740cm −1 was attributed to the C=O stretching vibration from ester carbonyl in triglycerides ( 25 ). The spectral band of 3000-2800cm −1 was mainly correlated with the lipid-related C-H asymmetric stretching vibration of CH 3 at 2959cm -1 and CH 2 at 2930cm -1 ( 30 , 31 ). The spectral band of 1250-1000cm −1 was correlated with the P=O asymmetric stretching vibration at 1243cm -1 and symmetric stretching vibration at 1079cm -1 of in nucleic acids ( 32 ).…”

“…Although the increased area in lipid band was not significant compared with the other three bands, there was still a statistical difference between the two groups of serum samples (p < 0.05). According to Beer-Lambert law, the increase of the absorbance of the spectral band indicates the increase of the corresponding functional group concentration ( 31 ). Therefore, the concentrations of protein, lipid and nucleic acids molecules in serum of patients with lung cancer were increased compared with those in healthy people.…”

Combinatorial and machine learning approaches for the analysis of Cu2ZnGeSe4: influence of the off-stoichiometry on defect formation and solar cell performance

“…This type of algorithm is widely used for spectral data analysis in different methods and elds of application. [56][57][58][59][60][61] This is because of the high dimensionality nature of the spectroscopic data analysed in this work and the ability of LDA to reduce these dimensions to just a few while preserving most of the information, which allows for feature extraction rather than feature selection. Using the Raman spectra of each cell obtained with different wavelengths as input, the LDA algorithm was used to classify the different cells of the combinatorial sample according to the following classi-cation targets: efficiency, V oc and [Zn/Ge] ratio.…”

Comparison of chemical constituents of Bupleurum marginatum var. stenophyllum and Bupleurum chinense DC. using UHPLC–Q‐TOF–MS based on a metabonomics approach

“…Principal component analysis is a multivariate statistical analysis method that retains as much information of the original variable as possible while reducing the dimensions of multiple variables with a specific correlation index into a handful of comprehensive indices. The PCA plot visually displays the scattered points of the overall differences between different samples, and through scoring, identifies the main component that has a more significant influence—a greater absolute value of projection in the main component (Guleken et al, 2020; Xu et al, 2020). As shown in Figure 4, the total ion flow diagram obtained under the negative ion mode was more abundant; therefore, PCA pattern recognition was conducted using the negative ion mode to obtain the score diagram for B. marginatum var.…”