spectral data
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2022 ◽  
Vol 185 ◽  
pp. 111810
Author(s):  
Ruirui Yuan ◽  
Mei Guo ◽  
Chengyang Li ◽  
Shoutao Chen ◽  
Guishan Liu ◽  
...  
Keyword(s):  

Agriculture ◽  
2022 ◽  
Vol 12 (1) ◽  
pp. 93
Author(s):  
Chenjie Lin ◽  
Yueming Hu ◽  
Zhenhua Liu ◽  
Yiping Peng ◽  
Lu Wang ◽  
...  

Efficient monitoring of cultivated land quality (CLQ) plays a significant role in cultivated land protection. Soil spectral data can reflect the state of cultivated land. However, most studies have used crop spectral information to estimate CLQ, and there is little research on using soil spectral data for this purpose. In this study, soil hyperspectral data were utilized for the first time to evaluate CLQ. We obtained the optimal spectral variables from dry soil spectral data using a gradient boosting decision tree (GBDT) algorithm combined with the variance inflation factor (VIF). Two estimation algorithms (partial least-squares regression (PLSR) and back-propagation neural network (BPNN)) with 10-fold cross-validation were employed to develop the relationship model between the optimal spectral variables and CLQ. The optimal algorithms were determined by the degree of fit (determination coefficient, R2). In order to estimate CLQ at the regional scale, HuanJing-1A Hyperspectral Imager (HJ-1A HSI) data were transformed into dry soil spectral data using the linkage model of original soil spectral reflectance to dry soil spectral reflectance. This study was conducted in the Guangdong Province, China and the Conghua district within the same province. The results showed the following: (1) the optimal spectral variables selected from the dry soil spectral variables were 478 nm, 502 nm, 614 nm, 872 nm, 966 nm, 1007 nm, and 1796 nm. (2) The BPNN was the optimal model, with an R2(C) of 0.71 and a normalized root mean square error (NRMSE) of 12.20%. (3) The results showed the R2 of the regional-scale CLQ estimation based on the proposed method was 0.05 higher, and the NRMSE was 0.92% lower than that of the CLQ map obtained using the traditional method. Additionally, the NRMSE of the regional-scale CLQ estimation base on dry soil spectral variables from HJ-1A HSI data was 2.00% lower than that of the model base on the original HJ-1A HSI data.


2022 ◽  
Vol 8 (1) ◽  
Author(s):  
Shailesh Mistry ◽  
Akhilesh Kumar Singh

Abstract Background For many years, various drugs have been used for the treatment of infectious diseases but some bacterial microorganisms have induced resistance to several drugs. In a search of new antimicrobial agents, a series of new steroidal hydrazones were designed and synthesized. Result The structures of the compounds were established based on the spectral data. The in vitro antimicrobial activity of some newly synthesized compounds against bacteria and fungi was studied. Conclusion New compounds showed better or similar antimicrobial activity. Designing more efficient steroidal hydrazones from ketosteroid based on the current study may successfully lead to the development of antimicrobial agent. Graphical abstract


2021 ◽  
Vol 12 (1) ◽  
pp. 282
Author(s):  
Andrew Rodger ◽  
Carsten Laukamp

The efficacy of predicting geochemical parameters with a 2-chain workflow using spectral data as the initial input is evaluated. Spectral measurements spanning the approximate 400–25000 nm spectral range are used to train a workflow consisting of a non-negative matrix function (NMF) step, for data reduction, and a random forest regression (RFR) to predict eight geochemical parameters. Approximately 175,000 spectra with their corresponding chemical analysis were available for training, testing and validation purposes. The samples and their spectral and chemical parameters represent 9399 drillcore. Of those, approximately 20,000 spectra and their accompanying analysis were used for training and 5000 for model validation. The remaining pairwise data (150,000 samples) were used for testing of the method. The data are distributed over two large spatial extents (980 km2 and 3025 km2, respectively) and allowed the proposed method to be tested against samples that are spatially distant from the initial training points. Global R2 scores and wt.% RMSE on the 150,000 validation samples are Fe (0.95/3.01), SiO2 (0.96/3.77), Al2O3 (0.92/1.27), TiO (0.68/0.13), CaO (0.89/0.41), MgO (0.87/0.35), K2O (0.65/0.21) and LOI (0.90/1.14), given as Parameter (R2/RMSE), and demonstrate that the proposed method is capable of predicting the eight parameters and is stable enough, in the environment tested, to extend beyond the training sets initial spatial location.


2021 ◽  
Vol 30 (6) ◽  
pp. 586-605
Author(s):  
Satya Narayan Chaulia ◽  

Semi-empirical quantum chemical calculation was made to study the nucleophilicity of the ligand and to study the mode of bonding between the ligand and the metal ions. The natural atomic charge at different atomic sites of the ligand has been calculated along with the electrostatic potential map to predict the reactive sites for electrophilic and nucleophilic attack. The theoretical spectral data such as IR, NMR and electronic have been calculated and compared with the experimentally generated data.


Author(s):  
Ketan Gadani ◽  
Paras Tak ◽  
Mayank Mehta ◽  
Neetu Shorgar

A reproducible isolation method by Reverse Phase (RP) preparative HPLC technique for the isolation of one crucial impurity at 1.65 RRT (Relative Retention Time) in sulfonamide stage of Glyburide API (Active Pharmaceuticals Ingredient) was developed. Preparative chromatography was done on Luna C8, 10µm (250 mm x 21.2mm) preparative HPLC column with acetonitrile: water in 70:30 % v/v proportion as a mobile phase and 8 ml/min as a flow rate. This impurity was detected at 300 nm UV-wavelength maximum. This impurity was isolated from synthesized crude impurity of sulfonamide stage of Glyburide substance by preparative HPLC by injecting 50 mg/ml concentration over 5 ml fixed loop. Isolated impurity was elucidated as N-methyl impurity of sulfonamide intermediate of Glyburide API by means of chromatographic and spectral data. Structural elucidation carried out by spectral data was reviewed. This impurity was analyzed by reverse phase HPLC for purity analysis. A Inertsil C8 (250 x 4.6) mm, 5µ particle size was employed for separation. The mobile phase consisted of Water: Acetonitrile: Methanol in the ratio of 60:15:25 % v/v. The flow rate was set at 1 ml/min. Detection was carried out at 300 nm. 10µL of 2 mg/ml concentration of sample in methanol was injected. The column oven temperature was at 25°C.


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