Assessing canopy nitrogen and carbon content in maize by canopy spectral reflectance and uninformative variable elimination

Assessing canopy nitrogen content(CNC) and canopy carbon content(CCC) of maize by hyperspectral remote sensing data permits estimating cropland productivity, protecting farmland ecology, and investigating the nitrogen and carbon cycles in the atmosphere. This study aimed to assess maize CNC and CCC using canopy hyperspectral information and uninformative variable elimination(UVE). Vegetation indices(VIs) and wavelet functions were adopted for estimating CNC and CCC under varying water and nitrogen regimes. Linear, nonlinear, and partial least squares(PLS) regression models were fitted to VIs and wavelet functions to estimate CNC and CCC, and were evaluated for their prediction accuracy.UVE was used to eliminate uninformative variables, improve the prediction accuracy of the models, and simplify the PLS regression models(UVE-PLS). For estimating CNC and CCC, the normalized difference vegetation index(NDVI, based on red edge and NIR wavebands) yielded the highest correlation coefficients(r > 0.88). PLS regression models showed the lowest root mean square error(RMSE) among all models. However, PLS regression models required nine VIs and four wavelet functions, increasing their complexity. UVE was used to retain valid spectral parameters and optimize the PLS regression models.UVE-PLS regression models improved validation accuracy and resulted in more accurate CNC and CCC than the PLS regression models. Thus, canopy spectral reflectance integrated with UVE-PLS can accurately reflect maize leaf nitrogen and carbon status.

Understanding the thermal stability of human serum proteins with the related near-infrared spectral variables selected by Monte Carlo-uninformative variable elimination

Understanding the thermal stability of the proteins in human serum is essential since human serum is the important source of pharmaceutical proteins. Near-infrared（NIR） spectroscopy was applied to the investigation of thermal changes in secondary structure and hydration of human serum proteins.However, as a multicomponent system, the overlap of the broad NIR bands makes the structural analysis very difficult directly using the spectra of serum samples. Therefore, continuous wavelet transform（CWT） was used to improve the resolution of NIR spectra, and Monte Carlo-uninformative variable elimination（MC-UVE） method was applied to the selection of the variables associated with the proteins for the structural analysis. The variables（5956, 5867, 5815, 5747, 4525, 4401, 4359 and 4328 cm^-1） related to protein secondary structures and those（7074, 6951, 6827 and 6700 cm 1） connected with water species were selected. Then, the thermal stability was analyzed through the intensity variations of the selected variables with temperature from 30℃ to 80 ℃. It was found that the variation of the spectral variables related to both a-helix and b-sheet changes apparently around 60 ℃, indicating the beginning of the thermal denaturation and the transition from a-helix to b-sheet. Moreover, an obvious change was found around 60℃for the content of the water specie S3, i.e., the water cluster containing three hydrogen bonds. The result demonstrates that MC-UVE can identify the protein-related NIR spectral variables, and the water species may be a marker for investigation of the structural change of proteins in biochemical systems.

Online quantitative analysis of soluble solids content in navel oranges using visible-nearinfrared spectroscopy and variable selection methods

Variable selection is applied widely for visible-near infrared(Vis-NIR)spectroscopy analysis of internal quality in fruits.Different spectral variable selection methods were compared for online quantitative analysis of soluble solids content(SSC)in navel oranges.Moving window partial least squares(MW-PLS),Monte Carlo uninformative variables elimination(MC-UVE)and wavelet transform(WT)combined with the MC-UVE method were used to select the spectral variables and develop the calibration models of online analysis of SSC in navel oranges.The performances of these methods were compared for modeling the Vis NIR data sets of navel orange samples.Results show that the WT-MC-UVE methods gave better calibration models with the higher correlation cofficient(r)of 0.89 and lower root mean square error of prediction(RMSEP)of 0.54 at 5 fruits per second.It concluded that Vis NIR spectroscopy coupled with WT-MC-UVE may be a fast and efective tool for online quantitative analysis of SSC in navel oranges.

Modeling and Analysis of Mesh Tree Hybrid Power／Ground Networks with Multiple Voltage Supply in Time Domain

This paper proposes a novel algorithm, which can be used to model and analyzemesh tree hybrid power/ground distribution networks with multiple voltage supply in time domain.Not only this algorithm enhances common method''s ability on analysis of power/ground network withirregular topology, but also very high accuracy it keeps. The accuracy and stability of thisalgorithm is proved using strict math method in this paper. Also, the usage of both preconditiontechnique based on Incomplete Choleskey Decomposition and fast variable elimination technique hasimproved the algorithm''s efficiency a lot. Experimental results show that it can finish the analysisof power/ground network with enormous, size within very short time. Also, this algorithm can beapplied to analyze the clock network, bus network, and signal network without buffer under highworking frequency because of the independence of the topology.