- 标题
- 摘要
- 关键词
- 实验方案
- 产品
-
X-ray irradiation effects on nuclear and membrane regions of single SH-SY5Y human neuroblastoma cells investigated by Raman micro-spectroscopy
摘要： Raman micro-spectroscopy was performed in vitro on nuclear and membrane regions of single SH-SY5Y human neuroblastoma cells after irradiation by graded X-ray doses (2, 4, 6, 8 Gy). The acquired spectra were analyzed by principal component analysis (PCA) and interval-PCA (i-PCA) methods. Biochemical changes occurring in the different regions of single cells as a consequence of the radiation exposure were observed in cells fixed immediately after the irradiation. The most relevant effects arose from the analysis of the spectra from the cell nucleus region. The observed changes were discussed in terms of the modifications in the cell cycle, resulting in an increase in the DNA-related signal, a protein rearrangement and changes in lipid and carbohydrates profiles within the nucleus. Potential markers of an apoptotic process in cell population irradiated with 6 and 8-Gy X-ray doses could have been singled out. No significant effects were found in spectra from cells fixed 24 h after the irradiation, thus suggesting the occurrence of repairing processes of the X-ray induced damage.
关键词： X-ray effects on DNA, lipids, proteins and carbohydrates,Single SH-SY5Y human cancer cells,Raman micro-spectroscopy,Cellular nucleus and membrane,Multivariate analysis
更新于2025-09-11 14:15:04
-
Classification of Sand Grains by Terahertz Time-Domain Spectroscopy and Chemometrics
摘要： A novel method combining terahertz time-domain spectroscopy (THz-TDS) with chemometrics was proposed for classification of sand samples from different deserts and grain sizes. THz-TDS combined with chemometrics was firstly utilized for identifying the source of sand samples and analyzing the sizes of sand granule. Terahertz time-domain transmittance spectra were collected and then Savitzky–Golay smoothing and orthogonal signal correction (OSC) approaches were used for spectral pretreatment. Principal component analysis, partial least squares discriminant analysis, and support vector machine were used to establish classification models of the sand samples. The prediction correctness ranged between 94 and 97%. Scanning electron microscopy was used to investigate the morphology of the sand samples. The sand samples were analyzed by energy dispersive spectroscopy and the elemental content distributions were obtained. Fourier transform infrared absorption spectra of the sand samples were also collected. THz-TDS combined with multivariate modeling methods provided reliable and useful information to determine the provenance of the sands originated from different deserts in northern China. This work reveals that THz-TDS can be a useful tool for classification of sand from different deserts.
关键词： Terahertz spectroscopy,Chemometrics,Multivariate analysis,Sand source identification
更新于2025-09-10 09:29:36
-
Sparse NIR Optimization method (SNIRO) to quantify analyte composition with visible (VIS)/near infrared (NIR) spectroscopy (350nm-2500nm)
摘要： Visual-Near-Infra-Red (VIS/NIR) spectroscopy has led the revolution in high-throughput phenotyping methods used to determine chemical and structural elements of organic materials. In the current state of the art, spectrophotometers used for imaging techniques are either very expensive or too large to be used as a field-operable device. In this study we developed a Sparse NIR Optimization method (SNIRO) that selects a pre-determined number of wavelengths that enable quantification of analytes in a given sample using linear regression. We compared the computed complexity time and the accuracy of SNIRO to Marten’s test, to forward selection test and to LASSO all applied to the determination of protein content in corn flour and meat and octane number in diesel using publicly available datasets. In addition, for the first time, we determined the glucose content in the green seaweed Ulva sp., an important feedstock for marine biorefinery. The SNIRO approach can be used as a first step in designing a spectrophotometer that can scan a small number of specific spectral regions, thus decreasing, potentially, production costs and scanner size and enabling the development of field-operable devices for content analysis of complex organic materials.
关键词： Imaging,VIS/NIR spectroscopy,Ulva sp.,Chemometrics,Multivariate Analysis,Diesel Octane Number,seaweeds,Sparse Linear Regression
更新于2025-09-10 09:29:36
-
Strength in Numbers: Development of a Fluorescence Sensor Array for Secondary Structures of DNA
摘要： High-throughput assessment of secondary structures adopted by DNA oligonucleotides is currently hampered by the lack of suitable biophysical methods. Fluorescent sensors hold great potential in this respect; however, the moderate selectivity that they display for one DNA conformation over the others constitutes a major drawback to the development of accurate assays. Moreover, the use of single sensors impedes a comprehensive classification of the tested sequences. Herein, we propose to overcome these limitations through the development of a fluorescence sensor array constituted by easily accessible, commercial dyes. Multivariate analysis of the emission data matrix produced by the array allows to explore the conformational preferences of DNA sequences of interest, either by calculating the probability of group membership in the six predefined structural categories (three G-quadruplex groups, double-stranded, and two groups of single-stranded forms), or by revealing their particular structural features. The assay enables rapid screening of synthetic DNA oligonucleotides in a 96-well plate format.
关键词： fluorescence sensor array,G-quadruplex,high-throughput conformational analysis,multivariate analysis,DNA secondary structures
更新于2025-09-09 09:28:46
-
Unveiling the Chemical Composition of Halide Perovskite Films Using Multivariate Statistical Analyses
摘要： The local chemical composition of halide perovskites is a crucial factor in determining their macroscopic properties and their stability. While the combination of scanning transmission electron microscopy (STEM) and energy-dispersive X-ray spectroscopy (EDX) is a powerful and widely used tool for accessing such information, electron-beam-induced damage and complex formulation of the films make this investigation challenging. Here we demonstrate how multivariate analysis – including statistical routines derived from “big data” research, such as Principal Component Analysis, PCA – can be used to dramatically improve the signal recovery from fragile materials. We also show how a similar decomposition algorithm (Non-negative Matrix Factorisation, NMF) can unravel elemental composition at the nanoscale in perovskite films, highlighting the presence of segregated species and identifying the local stoichiometry at the nanoscale.
关键词： hybrid perovskite,big data,multivariate analysis,chemical composition,STEM-EDX,nanoscale
更新于2025-09-09 09:28:46
-
Comparison and Identification for Rhizomes and Leaves of Paris yunnanensis Based on Fourier Transform Mid-Infrared Spectroscopy Combined with Chemometrics
摘要： Paris polyphylla, as a traditional herb with long history, has been widely used to treat diseases in multiple nationalities of China. Nevertheless, the quality of P. yunnanensis fluctuates among from different geographical origins, so that a fast and accurate classification method was necessary for establishment. In our study, the geographical origin identification of 462 P. yunnanensis rhizome and leaf samples from Kunming, Yuxi, Chuxiong, Dali, Lijiang, and Honghe were analyzed by Fourier transform mid infrared (FT-MIR) spectra, combined with partial least squares discriminant analysis (PLS-DA), random forest (RF), and hierarchical cluster analysis (HCA) methods. The obvious cluster tendency of rhizomes and leaves FT-MIR spectra was displayed by principal component analysis (PCA). The distribution of the variable importance for the projection (VIP) was more uniform than the important variables obtained by RF, while PLS-DA models obtained higher classification abilities. Hence, a PLS-DA model was more suitably used to classify the different geographical origins of P. yunnanensis than the RF model. Additionally, the clustering results of different geographical origins obtained by HCA dendrograms also proved the chemical information difference between rhizomes and leaves. The identification performances of PLS-DA and the RF models of leaves FT-MIR matrixes were better than those of rhizomes datasets. In addition, the model classification abilities of combination datasets were higher than the individual matrixes of rhizomes and leaves spectra. Our study provides a reference to the rational utilization of resources, as well as a fast and accurate identification research for P. yunnanensis samples.
关键词： chemometrics,Paris polyphylla Smith var. yunnanensis,Fourier transform infrared,multivariate analysis
更新于2025-09-04 15:30:14