查看更多>>摘要：We formally describe a 1- or 2-ligands fluorescence quenching or exhaustion model that takes ionic strength into account. We give ready-to-use formulas, which are easy to implement on a common spreadsheet, to determine complexing capacities and apparent stability constants of fluorescence ligands by adjusting quenching or enhancing experimental curves. The strength of our model is to consider parameters that have rarely taken in account in the literature, resulting in a significant improvement in the quality of the modeling: the charge associated with one or two ligands, and ionic strength. The model predicted fluorescence at various ionic strengths from parameters determined at a given ionic strength. This model is suitable for many applications, such as complexation of dissolved natural organic matter with metal ions, even in sea water, or biologic media.
查看更多>>摘要：Raman spectroscopy, a "fingerprint" spectrum of substances, can be used to characterize various biological and chemical samples. To allow for blood classification using single-cell Raman spectroscopy, several machine learning algorithms were implemented and compared. A single-cell laser optical tweezer Raman spectroscopy system was established to obtain the Raman spectra of red blood cells. The Boruta algorithm extracted the spectral feature frequency shift, reduced the spectral dimension, and determined the essential features that affect classification. Next, seven machine learning classification models are analyzed and compared based on the classification accuracy, precision, and recall indicators. The results show that support vector machines and artificial neural networks are the two most appropriate machine learning algorithms for single-cell Raman spectrum blood classification, and this finding provides essential guidance for future research studies. (c) 2022 Elsevier B.V. All rights reserved.
查看更多>>摘要：A new action mechanism for the fluorescent detection on the Al3+ ion of the sensitive 1-((2-hydroxynaph thalen-1-yl)methylene)urea(OCN) is theoretically studied. The extensive theoretical calculations on the OCN and the isomer structure OCN-T are performed. The emission and absorption spectra consistent with the experiment value. The absorption spectra peaks (362 nm and 326 nm) of OCN and OCN-T molecules are attributed to the experimentally observed absorption spectra at 356 nm and 314 nm, respectively. The calculated fluorescence value of the OCN-AL structure is 460 nm, while the OCN-T-AL structure has no fluorescence. These results better explain that OCN and its isomers OCN-T are involved in the absorption, and the detection spectrum signal is emitted from the OCN-AL complex. The OCN and OCN-T molecules are obvious hydrogen bonding systems. The excited state intramolecular proton transfer photochemical behaviors and detecting Al3+ ion photophysical changes were explained for the first time at the molecular level. As driving force of excited state intramolecular proton transfer (ESIPT) reaction, the bond parameters and vibrational frequencies of intramolecular hydrogen bond were analyzed by optimizing structures and calculating infrared spectra, analysis of frontier molecular orbitals. To further elucidate the proton transfer reactive paths, the scanned the potential energy curves (PECs) of OCN andOCN-T chemosensor in different electronic states are plotted. This work proposes a reasonable explanation for the detection mechanism of the OCN sensor. (C) 2022 Elsevier B.V. All rights reserved.
查看更多>>摘要：The tautomerism of curcumin (Cur) in water-ethanol solutions in the presence of fumed silica was studied by UV-visible spectroscopy. The results showed that the enol tautomer exists at an ethanol concentration in solution > 50%, and with an increase in the water content, the tautomeric equilibrium shifts towards the formation of the keto tautomer. Quantum-chemical calculations (solvation model SM 5.42/6-31G (d), GAMESSPLUS) of various curcumin isomers confirmed that the existence of curcumin keto tautomer in aqueous solution is more thermodynamically favorable. The ratio of keto and enol forms also depends on the dielectric constant of water-ethanol solutions: at epsilon < 45, only the enol form of cur cumin exists, while at e > 45, the relative amount of the keto tautomer increases in proportion to the dielectric constant. Curcumin tautomers adsorb on fumed silica in different ways. At a low curcumin concentration in the initial solutions (< 1.5 x 10(-4) M), only the enol tautomer forms a monolayer on the sorbent surface, apparently due to its planar structure. The keto tautomer, characterized by a bent structure, begins to adsorb only at a concentration of Cur > 1.5 x 10(-4) M, being a component of molecular aggregates with coplanar geometry. (c) 2022 Elsevier B.V. All rights reserved.
查看更多>>摘要：Nitric oxide (NO) plays various physiological and pathological roles in lots of biological processes. It is crucial to detect NO sensitively and selectively in vivo and in vitro as homeostasis of NO is closely related to various diseases. Herein, a novel dual-channel fluorescent dye (ENNH2) based on dicarboxyimide anthracene was developed as a highly sensitive and selective probe to detect NO in living systems using the dual-channel fluorescence. ENNH2 can emit bright red fluorescence due to the intramolecular charge transfer (ICT) from the amino group at the 6-position of 1,2-dicarboxyimide anthracene to the conjugated aromatic ring, and the ICT is effectively inhibited by the reductive deamination of the amino in the pres-ence of NO to obtain the remarkable strong green emission with the excellent sensitivity (5.52 nM). Promisingly, ENNH2 exhibits an excellent performance in endogenous NO dual-channel fluorescence imaging of RAW 264.7 cells and zebrafish. (C)& nbsp;2022 Elsevier B.V. All rights reserved.
查看更多>>摘要：The rapid and accurate detection of diesel multiple properties is an important research topic in petrochemical industry that is conducive to diesel quality assessment and environmental pollution mitigation. To that end, this paper developed a new machine learning model for near infrared (NIR) spectroscopy capable of simultaneously determining diesel density, viscosity, freezing point, boiling point, cetane number and total aromatics. The model combined improved XY co-occurrence distance (ISPXY) and differential evolution-gray wolf optimization support vector machine (DEGWO-SVM) to attain the goal of rapidity and accuracy. Experimental results indicated that the average recovery, mean square error, mean absolute percentage error and determination coefficient of the presented method outperformed those of the existing machine learning methods. The proposed hybrid model provides superior solution to the problem of low efficiency and high cost of diesel quality detection, and has the potential to be utilized as a promising tool for diesel routine monitoring. (c) 2022 Elsevier B.V. All rights reserved.
查看更多>>摘要：Sensor arrays use pattern recognition for the identification and quantification of analytes. In the presented work, a gold nanoparticle (GNP) based optical sensor array was employed to classify and quantify seven toxic metals (arsenic, barium, cadmium, cerium, chromium, lead, and mercury). The sensor array receptors were GNPs functionalized by mercaptoundecanoic acid, 2-mercaptoethanesulfonate, and a 1:1 mixture of the two ligands. The mixed-ligand particle responds to the same analytes as the mono-ligand particles but in a distinctive way. This behavior demonstrates the high potential of mixed-ligand particles in the fabrication of sensor array receptors. The responses of the GNPs to different concentrations of the seven metal ions were analyzed, and a unique "classification trajectory" was produced for every metal. Samples of different metal concentrations were then measured and identified using the "classification trajectories". Once sample composition has been identified, a PLSR model, produced from the concatenated sensor array spectra of four calibration samples for each nanoparticle, was used to determine the metal concentration. (c) 2022 Elsevier B.V. All rights reserved.
查看更多>>摘要：This study presents a comprehensive comparative study of different green spectrophotometric approaches without any physical separation on processing a ternary mixture of Aceclofenac (ACE), Paracetamol (PAR) and Rabeprazole (RAB) in combined medicine for managing tough symptoms in the COVID-19 Pandemic. The different univariate complementary resolutions according to the response used for the assay of the cited drugs after applying the processing steps were implemented using successive insilico sample enrichment for resolving the ternary mixture via different windows of spectrophotometric platform using sticking -pulling strategy (SPS). Window I; based on manipulation of the data of zero order absorption spectrum of the mixture using novel Extended absorbance difference (EAD) and Absorbance difference (AD) methods coupled with corresponding spectrum subtraction method (SS). Window III; based on manipulation of the data of ratio spectra via Constant value coupled with constant subtraction (CV-CS) and novel Induced dual amplitude difference (IDAD) method coupled with corresponding spectrum subtraction method (SS). Finally, window IV; based on manipulation of the data of derivative of the ratio spectrum of the mixture via novel Factorized derivative ratio null contribution (FDD-NC) and Factorized unlimited derivative ratio (FUDD) methods coupled with corresponding spectrum subtraction method (SS). Synthetic mixtures and commercial medicine were constructively analyzed using the proposed methods while maintaining calibration graphs to be linear over ranges; 4.0-40.0 lg/mL for ACE, 2.0-14.0 lg/mL for PAR and 4.0-30.0 lg/mL for RAB. Moreover, methods' validation was confirmed via performing exhaustive statistical treatment of the experimental findings. The proposed methodologies can be used for the routine analysis of the cited drugs in quality control laboratories. Additionally, Spectral Similarity Index (SSI) was calculated to detect counterfeit products and methods' greenness profile was finally guaranteed through analytical greenness (AGREE) metric assessment tool. (C)& nbsp;2022 Elsevier B.V. All rights reserved.
查看更多>>摘要：The identification of soy sauce brands can avoid adulteration and fraud, which is meaningful for food safety screening. Using visible and near-infrared (Vis-NIR) spectroscopy combined with k-nearest neighbor (kNN), the four-category discriminant models of soy sauce brands were established. The soy sauce of three brands (identification) and the other ten brands (interference) were collected, and a total of four categories of samples were obtained. The spectral datasets of two measurement modals (1 mm, 10 mm) were obtained. Based on moving-window (MW) waveband screening and wavelength stepby-step phase-out (WSP), the MW-WSP-kNN algorithm was proposed and applied to the wavelength optimization for the four-category discriminant analysis. Using calibration-prediction-validation experiment design, various high accuracy models with a small number of wavelengths located in NIR region were determined. In the independent validation, for the 1 mm measurement modal, the selected thirty-five dual-wavelength models and one three-wavelength model were located in NIR combined and overtone frequency regions respectively, all achieved 100% total recognition accuracy rate (RARTotal); for the 10 mm measurement modal, the selected seven three-wavelength models located in NIR overtone frequency region all reached more than 96.8% RARTotal, and the optimal RARTotal was 97.8%. The results showed the feasibility of small number of wavelengths' NIR spectroscopy applied to multi-category discriminant of soy sauce brands, with the advantages of rapid, simple and miniaturized. The proposed various small number of wavelengths' models provided a valuable reference for the design of small dedicated spectrometer with different measurement modals. The integrated optimization method and wavelength selection strategy here are also expected to be applied to other fields. (c) 2022 Elsevier B.V. All rights reserved.
查看更多>>摘要：Dendrobium officinale, often used as a kind of tea for daily drinks, has drawn increasing attention for its beneficial effects. Quality evaluation of D. officinale is of great significance to ensure its health care value and safeguard consumers' interest. Given that traditional analytical methods for assessing D. officinale quality are generally time-consuming and laborious, this study developed a comprehensive strategy, with the advantages of being rapid and efficient, enabling the quality evaluation of D. officinale from different geographical origins using near-infrared (NIR) spectroscopy and chemometrics. As the quality indicators, polysaccharides, polyphenols, total flavonoids, and total alkaloids were quantified. Three types of wavelength selection methods were used for model optimization and these were synergy interval (SI), genetic algorithm (GA), and competitive adaptive reweighted sampling (CARS). From the qualitative perspective, the geographical origins of D. officinale were differentiated by NIR spectroscopy combined with partial least squares-discriminant analysis (PLS-DA) and support vector classification (SVC). The PLS models constructed based on the wavelengths selected by CARS yielded the best performance for prediction of the contents of quality indicators in D. officinale. The root mean square error (RMSEP) and coefficient of determination (R-p(2)) in the independent test sets were 12.7768 g kg(-1) and 0.9586, 1.1346 g kg(-1) and 0.9670, 0.3938 g kg(-1) and 0.8803, 0.0825 and 0.7031 and for polysaccharides, polyphenols, total flavonoids, and total alkaloids, respectively. As for the origin identification, the nonlinear SVC was superior to the linear PLS-DA, with the correct recognition rates in calibration and prediction sets up to 100% and 100%, respectively. The overall results demonstrated the potential of NIR spectroscopy and chemometrics in the rapid determination of quality parameters and geographical origin. This study could provide a valuable reference for quality evaluation of D. officinale in a more rapid and comprehensive manner. (C)& nbsp;2022 Elsevier B.V. All rights reserved.
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