- 标题
- 摘要
- 关键词
- 实验方案
- 产品
-
Association of 1-hexanol in mixtures with n-hexane: Dielectric, near-infrared and DFT studies
摘要： Association of 1-hexanol in n-hexane has been studied by measurements of nonlinear dielectric effect (NDE) and near-infrared (NIR) spectroscopy. Besides, the dipole moments of selected open and cyclic associates were determined by DFT (density functional theory) calculations. All measurements were performed in the whole range of mole fractions with a step of 0.1. From numerical fitting of the dielectric data we obtained populations of all species present in the mixture. Our results do not support common opinion about the exceptional role of cyclic tetramers. The most abundant cyclic species are trimers and population of the cyclic associates rapidly decreases with increasing size of associates. The variations in population of open associates are more complex and for mole fractions of 1-hexanol greater than 0.1 the relationships have a maximum, which shifts towards higher associates. In pure 1-hexanol this maximum is close to seven. From analysis of the dielectric data it results that at lower alcohol content dominate the cyclic species. When the concentration of 1-hexanol increases the equilibrium shifts towards formation of the linear associates. From NIR spectra we determined the overall population of the free OH in monomers and open associates. Next, these values were used for estimation of an average length of the open associates. It appears that an averaged size of the open associates determined from NIR spectra is higher than that from the dielectric data and these differences are more pronounced when the concentration of 1-hexanol increases. We discuss possible explanations of this observation. Assuming that the actual size of the open associates is between those obtained from both methods, one can estimate that the size of open associates is ranging from 3 to 15 in pure 1-hexanol. It seems that the higher associates are stabilized by interaction of the aliphatic chains.
关键词： NIR spectroscopy,1-hexanol,DFT,hydrogen bonding,associations,non-linear dielectric effect
更新于2025-09-19 17:15:36
-
Quantitative Analysis of Organic Liquid Three-Component Systems: Near-Infrared Transmission versus Raman Spectroscopy, Partial Least Squares versus Classical Least Squares Regression Evaluation and Volume versus Weight Percent Concentration Units
摘要： The band shapes and band positions of near-infrared (NIR) and Raman spectra change depending on the concentrations of specific chemical functionalities in a multicomponent system. To elucidate these effects in more detail and clarify their impact on the analytical measurement techniques and evaluation procedures, NIR transmission spectra and Raman spectra of two organic liquid three-component systems with variable compositions were analyzed by two different multivariate calibration procedures, partial least squares (PLS) and classical least-squares (CLS) regression. Furthermore, the effect of applying different concentration units (volume percent (%V) and weight percent (%W) on the performance of the two calibration procedures have been tested. While the mixtures of benzene/cyclohexane/ethylbenzene (system 1) can be regarded as a blended system with comparatively low molecular interactions, hydrogen bonding plays a dominant role in the blends of ethyl acetate/1-heptanol/1,4-dioxane (system 2). Whereas system 1 yielded equally good calibrations by PLS and CLS regression, for system 2 acceptable results were only obtained by PLS regression. Additionally, for both sample systems, Raman spectra generally led to lower calibration performance than NIR spectra. Finally, volume and weight percent concentration units yielded comparable results for both chemometric evaluation procedures.
关键词： Raman spectroscopy,molecular interactions,organic liquid three-component mixtures,volume/weight percent concentration units,classical least squares (CLS) regression,near-infrared (NIR) spectroscopy,partial least squares (PLS) regression
更新于2025-09-19 17:13:59
-
Enhancement of open circuit voltage of CdTe solar cell
摘要： In the present work, FTO/CdS/CdTe/Te/Al superstrate structured solar cell has been fabricated using thermal evaporation method. A 40 nm thin layer of P-type tellurium has been incorporated between CdTe and back contact to reduce the potential energy barrier by improving the quality of interfaces. The fabricated device characterized using UV–Visible-NIR spectroscopy and I–V measurement. The tellurium interlayer plays a vital role in enhancing the performance of the device. The fabricated device generated open circuit voltage (Voc) of 0.7 V.
关键词： Open-circuit voltage (Voc),I–V characteristics,Thermal evaporator,Solar cell,Cadmium Telluride (CdTe),UV–Visible-NIR spectroscopy
更新于2025-09-19 17:13:59
-
An Overview on the Applications of Typical Non-linear Algorithms Coupled With NIR Spectroscopy in Food Analysis
摘要： Near-infrared (NIR) spectroscopy as a low-cost technique with its non-destructive fast nature, precision, control, accuracy, repeatability, and reproducibility has been extensively employed in most industries for food quality measurements. Its coupling to different modeling techniques has been identified as a way of improving the accuracy and robustness of non-destructive measurement of foodstuffs. This review provides an overview of the application of non-linear algorithms in food quality and safety specific to NIR spectroscopy. The review also provides in-depth knowledge about the principle of NIR spectroscopy along with different non-linear models such as artificial neural network (ANN), AdaBoost, local algorithm (LA), support vector machine (SVM), and extreme learning machine (ELM). Moreover, non-linear algorithms coupled with NIR spectroscopy for ensuring food quality and their future perspective has been discussed.
关键词： BP-ANN,NIR spectroscopy,Non-linear applications,Non-linear algorithm,AdaBoost
更新于2025-09-19 17:13:59
-
Robust Fourier transformed infrared spectroscopy coupled with multivariate methods for detection and quantification of urea adulteration in fresh milk samples
摘要： Urea is added as an adulterant to give milk whiteness and increase its consistency for improving the solid not fat percentage, but the excessive amount of urea in milk causes overburden and kidney damages. Here, an innovative sensitive methodology based on near‐infrared spectroscopy coupled with multivariate analysis has been proposed for the robust detection and quantification of urea adulteration in fresh milk samples. In this study, 162 fresh milk samples were used, those consisting 20 nonadulterated samples (without urea) and 142 with urea adulterant. Eight different percentage levels of urea adulterant, that is, 0.10%, 0.30%, 0.50%, 0.70%, 0.90%, 1.10%, 1.30%, and 1.70%, were prepared, each of them prepared in triplicates. A Frontier NIR spectrophotometer (BSEN60825‐1:2007) by Perkin Elmer was used for scanning the absorption of each sample in the wavenumber range of 10,000–4,000 cm-1, using 0.2 mm path length CaF2 sealed cell at resolution of 2 cm-1. Principal components analysis (PCA), partial least‐squares discriminant analysis (PLS‐DA), and partial least‐squares regressions (PLSR) methods were applied for the multivariate analysis of the NIR spectral data collected. PCA was used to reduce the dimensionality of the spectral data and to explore the similarities and differences among the fresh milk samples and the adulterated ones. PLS‐DA also showed the discrimination between the nonadulterated and adulterated milk samples. The R‐square and root mean square error (RMSE) values obtained for the PLS‐DA model were 0.9680 and 0.08%, respectively. Furthermore, PLSR model was also built using the training set of NIR spectral data to make a regression model. For this PLSR model, leave‐one‐out cross‐validation procedure was used as an internal cross‐validation criteria and the R‐square and the root mean square error (RMSE) values for the PLSR model were found as 0.9800 and 0.56%, respectively. The PLSR model was then externally validated using a test set. The root means square error of prediction (RMSEP) obtained was 0.48%. The present proposed study was intended to contribute toward the development of a robust, sensitive, and reproducible method to detect and determine the urea adulterant concentration in fresh milk samples.
关键词： urea,principal components analysis,partial least‐squares regressions,milk adulteration,NIR spectroscopy,partial least‐squares discriminant analysis
更新于2025-09-19 17:13:59
-
Surface-Enhanced Absorption Spectroscopy for Optical Fiber Sensing
摘要： Visible and near-infrared spectroscopy are widely used for sensing applications but suffer from poor signal-to-noise ratios for the detection of compounds with low concentrations. Enhancement by surface plasmon resonance is a popular technique that can be utilized to increase the signal of absorption spectroscopy due to the increased near-field created close to the plasmons. Despite interest in surface-enhanced infrared absorption spectroscopy (SEIRAS), the method is usually applied in lab setups rather than real-life sensing situations. This study aimed to achieve enhanced absorption from plasmons on a fiber-optic probe and thus move closer to applications of SEIRAS. A tapered coreless fiber coated with a 100 nm Au film supported signal enhancement at visible wavelengths. An increase in absorption was shown for two dyes spanning concentrations from 5 × 10?8 mol/L to 8 × 10?4 mol/L: Rhodamine 6G and Crystal Violet. In the presence of the Au film, the absorbance signal was 2–3 times higher than from an identically tapered uncoated fiber. The results confirm that the concept of SEIRAS can be implemented on an optical fiber probe, enabling enhanced signal detection in remote sensing applications.
关键词： surface enhancement,SEIRAS,sensing,optical fiber,Vis/NIR spectroscopy,gold
更新于2025-09-16 10:30:52
-
A New Plant Indicator ( <i>Artemisia lavandulaefolia</i> DC.) of Mercury in Soil Developed by Fourier-Transform Near-Infrared Spectroscopy Coupled with Least Squares Support Vector Machine
摘要： A rapid indicator of mercury in soil using a plant (Artemisia lavandulaefolia DC., ALDC) commonly distributed in mercury mining area was established by fusion of Fourier-transform near-infrared (FT-NIR) spectroscopy coupled with least squares support vector machine (LS-SVM). The representative samples of ALDC (stem and leaf ) were gathered from the surrounding and distant areas of the mercury mines. As a reference method, the total mercury contents in soil and ALDC samples were determined by a direct mercury analyzer incorporating high-temperature decomposition, catalytic adsorption for impurity removal, amalgamation capture, and atomic absorption spectrometry (AAS). Based on the FT-NIR data of ALDC samples, LS-SVM models were established to distinguish mercury-contaminated and ordinary soil. The results of reference analysis showed that the mercury level of the areas surrounding mercury mines (0–3 kilometers, 7.52–88.59 mg/kg) was significantly higher than that of the areas distant from mercury mines (>5 kilometers, 0–0.75 mg/kg). The LS-SVM classification model of ALDC samples was established based on the original spectra, smoothed spectra, second-derivative (D2) spectra, and standard normal transformation (SNV) spectra, respectively. The prediction accuracy of D2-LS-SVM was the highest (0.950). FT-NIR combined with LS-SVM modeling can quickly and accurately identify the contaminated ALDC. Compared with traditional methods which rely on naked eye observation of plants, this method is objective and more sensitive and applicable.
关键词： soil,LS-SVM,FT-NIR spectroscopy,Artemisia lavandulaefolia DC.,mercury
更新于2025-09-16 10:30:52
-
A GA-based stacking algorithm for predicting soil organic matter from vis-NIR spectral data
摘要： It has been demonstrated that diffuse reflectance spectroscopy in the visible and near-infrared (vis–NIR) can be exploited to predict chemical and physical soil properties. Immense soil spectral libraries (SSL) are being developed, therefore more elaborate tools that capitalize on contemporary knowledge and techniques need to be established to provide accurate predictions. In this paper, we propose a novel genetic algorithm-based stacking model that makes synergetic use of multiple models developed from different pre-processed spectral sources (termed L1 models). This is a form of ensemble learning where multiple hypotheses are combined to create a more robust and more accurate ensemble hypothesis. The genetic algorithm automatically defines the configuration of the stacked model, by selecting the best cooperating subset of the initial models. Our methodology was tested on the newly developed GEO-CRADLE SSL to predict soil organic matter (SOM). Results showed that the accuracy of prediction of the proposed method ( =0.76, and ratio of performance to inter quartile range RPIQ=2.22) was better than the one attained by the best L1 model ( =0.65, RPIQ=1.93). This approach can thus be effectively utilized to enhance the predictions of soil properties in small and large soil spectral libraries alike.
关键词： model stacking,North Africa,GEO-CRADLE,vis–NIR spectroscopy,soil spectroscopy,Middle East,Balkans
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
-
Prototype of the Near-Infrared Spectroscopy Expert System for Particleboard Identification
摘要： The overall goal of this work was to develop a prototype expert system assisting quality control and traceability of particleboard panels on the production floor. Four different types of particleboards manufactured at the laboratory scale and in industrial plants were evaluated. The material differed in terms of panel type, composition, and adhesive system. NIR spectroscopy was employed as a pioneer tool for the development of a two-level expert system suitable for classification and traceability of investigated samples. A portable, commercially available NIR spectrometer was used for nondestructive measurements of particleboard panels. Twenty-five batches of particleboards, each containing at least three independent replicas, was used for the original system development and assessment of its performance. Four alternative chemometric methods (PLS-DA, kNN, SIMCA, and SVM) were used for spectroscopic data classification. The models were developed for panel recognition at two levels differing in terms of their generality. In the first stage, four among twenty-four tested combinations resulted in 100% correct classification. Discrimination precision with PLS-DA and SVMC was high (>99%), even without any spectra preprocessing. SNV preprocessed spectra and SVMC algorithm were used at the second stage for panel batch classification. Panels manufactured by two producers were 100% correctly classified, industrial panels produced by different manufacturing plants were classified with 98.9% success, and the experimental panels manufactured in the laboratory were classified with 63.7% success. Implementation of NIR spectroscopy for wood-based product traceability and quality control may have a great impact due to the high versatility of the production and wide range of particleboards utilization.
关键词： traceability,quality control,expert system,NIR spectroscopy,particleboard
更新于2025-09-10 09:29:36