Rapid Detection of Five Estrogens Added Illegally to Dietary Supplements by Combining TLC with Raman Imaging Microscope

Estrogens added illegally to dietary supplements are hazardous to human health. Traditional detection and analysis methods have many limitations, and we have developed an assay that combines thin-layer chromatography with Raman imaging microscopy (TLC-RIM). The five estrogens (estrone, estradiol, estriol, ethinyl estradiol, and diethylstilbestrol) were initially separated by TLC, then detected by area scanning Raman imaging with a 532 nm laser under a microscope. Raman spectra were obtained for each estrogen, which were used for detecting estrogen illegally added to botanical dietary supplements. The LOD of each estrogen was 0.4, 1.0, 0.8, 0.2, and 0.2 mg/mL, respectively. The matrix in the real sample did not interfere with the detection of estrogens. The method was fast, sensitive, stable, specific, and reliable.     

Learnable three-dimensional Gabor convolutional network with global affinity attention for hyperspectral image classification

Benefiting from the development of hyperspectral imaging technology, hyperspectral image (HSI) classification has become a valuable direction in remote sensing image processing. Recently, researchers have found a connection between convolutional neural networks (CNNs) and Gabor filters. Therefore, some Gabor-based CNN methods have been proposed for HSI classification. However, most Gabor-based CNN methods still manually generate Gabor filters whose parameters are empirically set and remain unchanged during the CNN learning process. Moreover, these methods require patch cubes as network inputs. Such patch cubes may contain interference pixels, which will negatively affect the classification results. To address these problems, in this paper, we propose a learnable three-dimensional (3D) Gabor convolutional network with global affinity attention for HSI classification. More precisely, the learnable 3D Gabor convolution kernel is constructed by the 3D Gabor filter, which can be learned and updated during the training process. Furthermore, spatial and spectral global affinity attention modules are introduced to capture more discriminative features between spatial locations and spectral bands in the patch cube, thus alleviating the interfering pixels problem. Experimental results on three well-known HSI datasets (including two natural crop scenarios and one urban scenario) have demonstrated that the proposed network can achieve powerful classification performance and outperforms widely used machine-learning-based and deep-learning-based methods.     

The Classification of Rice Blast Resistant Seed Based on Ranman Spectroscopy and SVM

Rice blast is a serious threat to rice yield. Breeding disease-resistant varieties is one of the most economical and effective ways to prevent damage from rice blast. The traditional identification of resistant rice seeds has some shortcoming, such as long possession time, high cost and complex operation. The purpose of this study was to develop an optimal prediction model for determining resistant rice seeds using Ranman spectroscopy. First, the support vector machine (SVM), BP neural network (BP) and probabilistic neural network (PNN) models were initially established on the original spectral data. Second, due to the recognition accuracy of the Raw-SVM model, the running time was fast. The support vector machine model was selected for optimization, and four improved support vector machine models (ABC-SVM (artificial bee colony algorithm, ABC), IABC-SVM (improving the artificial bee colony algorithm, IABC), GSA-SVM (gravity search algorithm, GSA) and GWO-SVM (gray wolf algorithm, GWO)) were used to identify resistant rice seeds. The difference in modeling accuracy and running time between the improved support vector machine model established in feature wavelengths and full wavelengths (200–3202 cm⁻¹) was compared. Finally, five spectral preproccessing algorithms, Savitzky–Golay 1-Der (SGD), Savitzky–Golay Smoothing (SGS), baseline (Base), multivariate scatter correction (MSC) and standard normal variable (SNV), were used to preprocess the original spectra. The random forest algorithm (RF) was used to extract the characteristic wavelengths. After different spectral preproccessing algorithms and the RF feature extraction, the improved support vector machine models were established. The results show that the recognition accuracy of the optimal IABC-SVM model based on the original data was 71%. Among the five spectral preproccessing algorithms, the SNV algorithm’s accuracy was the best. The accuracy of the test set in the IABC-SVM model was 100%, and the running time was 13 s. After SNV algorithms and the RF feature extraction, the classification accuracy of the IABC-SVM model did not decrease, and the running time was shortened to 9 s. This demonstrates the feasibility and effectiveness of IABC in SVM parameter optimization, with higher prediction accuracy and better stability. Therefore, the improved support vector machine model based on Ranman spectroscopy can be applied to the fast and non-destructive identification of resistant rice seeds.     

Structure and bonding in simple thiazyl compounds

The molcules NSF, NSO⁻, NSCl, HNSF⁺, HNSO, CH₃NSF⁺, CH₃NSO are investigated at the SCF level and partly with inclusion of electron correlation effects. Equilibrium geometries and relative stabilities of isomers and conformers are reported. Structure constants and relative energies are discussed in connection with the molecular electronic structure as characterized by the results of population analyses.     

The synthesis and stereoselective conjugate addition reactions of α-alkoxyorganocuprate reagents

α-Alkoxyorganocuprate reagents have been prepared from α-alkoxyorganostannanes. The cuprates undergo diastereoselective conjugate addition reactions with cyclohexenone with up to 8:92 selectivity. The effects of trimethylsilyl chloride on the chemical yields and the diastereoselectivity of the reaction are described.     

δ-Jordan李超代数的交换扩张

通过δ-Jordan李超代数T的表示和上同调理论,构造δ-Jordan李超代数T■V.证明了δ-Jordan李超代数的等价交换扩张给出相同的表示.通过δ-Jordan李超代数的表示和其交换扩张得到2-上圈.     

Preparation of a zeolite imidazole skeleton–silica hybrid monolithic column for amino acid analysis via capillary electrochromatography

We developed a novel, convenient and low-cost one-pot strategy for preparing a zeolitic imidazolate framework-8 (ZIF-8)–silica hybrid monolithic column by adding ZIF-8 directly to a polymer solution of the silica matrix. The simulated stationary phase and monolithic column prepared under optimal conditions were characterized using X-ray diffraction, scanning electron microscopy, Fourier-transform infrared spectroscopy, thermogravimetric analysis nitrogen physisorption and zeta potential. The results obtained confirmed the successful introduction of ZIF-8 into the silica monolithic column, and the prepared monolithic column exhibited good permeability and physicochemical stability. A capillary electrochromatography method was developed based on a ZIF-8–silica hybrid monolithic column through which 15 mixed amino acids, 4 neutral compounds, 4 nipagin esters and 2 chlorinated fungicides were separated in 14, 5, 7 and 6 min, respectively, under optimal conditions. The relative standard deviations retention times and column efficiencies in run-to-run, day-to-day and column-to-column varied in the ranges of 1.90%–2.21%, 2.13%–2.51% and 3.08%–6.65%, respectively, which demonstrated that ZIF-8–silica hybrid monolithic column exhibited satisfactory reproducibility and stability. The incorporation of ZIF-8 into a silica monolithic column is a promising method for preparing novel monolithic columns composed of a metal–organic framework.     

A Sensitive Fluorescent Sensor for Highly Sensitive Detection of Water in Organic Solvents Based on Substituted Imidazole

Journal of Fluorescence - A novel substituted imidazole derivative 1a with carboxyl and quinoline structure has been designed and synthesized. And our initial discovery is that this compound can...