- 标题
- 摘要
- 关键词
- 实验方案
- 产品
-
ON THE COOPERATIVITY EFFECT IN WATSON&CRICK AND WOBBLE PAIRS FOR A HALOURACIL SERIES AND ITS POTENTIAL QUANTITATIVE APPLICATION STUDIED THROUGH SERS
摘要： The nature of the cooperativity effect of hydrogen bonds in Watson & Crick and wobble base pairs formed with thymine, uracil and its 5-halogenated derivatives (5-fluoro, -chloro and -bromouracil) have been studied through SERS, and using chemometric tools to process data and extract relevant information. Remarkable differences between the two kinds of pairs were clearly observed and the behavior correlated to the withdrawing character of different substituents at the 5- position of uracil was verified. Multivariate analyses have also unveiled information about pair’s stability and a stronger cooperativity effect seems to rule the Watson & Crick pairs when compared to wobble pairs. Defined patterns in the behavior of Watson & Crick pairs allowed the design of an indirect methodology for quantifying 5-bromouracil using a PLS method with variable selection. LOD values of 0.037 and 0.112 mmol L-1 in absence and presence of structurally similar interferences were reached, while its direct SERS quantification is only possible at around 45 mmol L-1.
关键词： SERS,5-halogenated derivatives,Watson & Crick pairs,wobble pairs,multivariate analyses,quantitative application,chemometric tools,cooperativity effect,hydrogen bonds
更新于2025-09-23 15:21:01
-
Hyperspectral images: a qualitative approach to evaluate the chemical profile distribution of Ca, K, Mg, Na and P in edible seeds employing laser-induced breakdown spectroscopy
摘要： In the present study, laser-induced breakdown spectroscopy (LIBS) combined with chemometric tools was used to investigate the metal composition in nine seed samples. The samples were directly analyzed, and a matrix with 9 rows and 9 columns (81 points) and 10 consecutive pulses were analyzed in each point. A total of 810 emission spectra were collected from 186 to 1042 nm from the surface and bulk of the sample. The dataset was normalized by Euclidian norm and principal component analysis (PCA) was used for the initial exploratory investigation. Calcium, Mg, Na, K and P were mainly identified in all samples; the distribution of metals in these samples is not completely homogeneous, however, i.e., composition of the elements change from one layer to another. This fact can be probably related to the absorption capability of nutrients resulting from different factors such as soil characteristics, physiology of the plant, water source composition and fertilizers which can influence the distribution of the elements in different seeds. To confirm the elements observed by LIBS, the samples were digested using microwave-assisted digestion, and Ca, K, Mg, Na and P were determined by inductively coupled plasma-optical emission spectrometry (ICP-OES). In addition, some minor nutrients such as S and Zn were also investigated and the relationships between elements were observed through the Pearson correlation graph, and some of them, such as Mg and Na, P and Na, S and P, S and Zn, are extremely correlated; it means that, for example, when the concentration of Mg increases, that of Na also increases.
关键词： Chemometric tools,Laser-induced breakdown spectroscopy,Edible seeds,Chemical profile distribution,Hyperspectral images
更新于2025-09-12 10:27:22
-
Quantitative analysis of stibnite content in raw ore by Raman spectroscopy and chemometric tools
摘要： This paper presents a non‐destructive and fast methodology to quantify the stibnite content in raw ore samples using Raman spectroscopy. A total of 120 raw ore reference samples were obtained from a froth flotation process, and multipoint collection and averaging were used to obtain Raman spectra from the raw ore samples. Our aim was to create the best multivariate calibration model for the quantitative analysis of stibnite contents in the raw ore samples. Several strategies were evaluated to generate a robust model; these strategies included preprocessing methods (de‐nosing, baseline correction, and vector normalization), selecting the key wavenumbers for the quantitative analysis of stibnite, and building a multivariate calibration model. Synergy interval partial least squares (PLS), backward interval PLS, stability competitive adaptive reweighted sampling, and PLS with a genetic algorithm (GA) were investigated to build the linear multivariable models, whereas artificial neural network (ANN) and support vector regression (SVR) preceded by GAs (GA‐ANN and GA‐SVR) were implemented to build the non‐linear multivariable models. Experimental results showed that the best multivariable calibration model for stibnite was obtained by a combination of an ANN and GA (GA‐ANN). In contrast to the PLS model based on the full spectrum, the root mean square error of calibration and root mean square error of prediction of the GA‐ANN method for the calibration and validation sets were reduced to 0.2038 from 0.3552 and to 0.2196 from 0.3927, respectively, and the squares of the correlation coefficients of the calibration and validation sets were increased to 0.9369 from 0.8053 and 0.9219 from 0.7561, respectively. The above results indicate that the multivariable calibration model for stibnite is stable. Furthermore, this method could be used in the froth flotation process to precisely determine the stibnite content in raw ore samples.
关键词： Raman spectroscopy,stibnite content,multivariate regression,chemometric tools,wavenumber selection
更新于2025-09-09 09:28:46