Organic matter pores in the chang 7 lacustrine shales from the Ordos Basin and its effect on reflectance measurement

To quantify the pore characteristics of various macerals in Chang 7 lacustrine shales,macerals were effectively identified according to their optical and morphological characteristics,and the nanoscale pore structure of macerals was observed by scanning electron microscope.Meanwhile,the reflectances of different positions in the same pieces of vitrinite or solid bitumen with heterogeneous pores development were measured.The results showed that the average contents of sapropelinite,liptinite,vitrinite,inertinite and solid bitumen are 42.7%,8.7%,13.6%,13.8% and 21.2%,respectively,which suggests that the source of the organic matter of the Chang 7 shales is a mixed source input.The organic pores of Chang 7 shales are enriched,and the pore shapes are mostly round or elliptical.The pore size of organic pores has a wide distribution,mainly concentrate in the range of 100-400 nm,and the average plane porosity of organic pores is 10.13%.The size order of the organic pores in various macerals is:solid bitumen<bituminite<alginite<vitrinite<fusinite<liptinite.The abundance order of organic matter pores of each maceral is as follows:alginite>fusinite>bituminite>solid bitumen>vitrinite>liptinite.OM pores are mainly contributed by bituminite,solid bitumen and fusinite.The plane porosity of bituminite increases with maturity.In the process of thermal evolution,the plane porosity of fusinite is distributed in the two ranges of 20%-28% and 1%-7%.The former is mainly the primary pores of the fusinite itself,and the latter is the secondary pores formed in the thermal evolution.As for the organic pores of other macerals,no obvious thermal evolution law was found.Meanwhile,the surface imperfections of vitrinite or solid bitumen is enhanced by the enrichment of organic pores(an increase in pore size or pore number),which may result in the underestimation of their reflectances.

Development of a portable reflectance confocal microscope and its application in the noninvasive in vivo evaluation of mesenchymal stem cell-promoted cutaneous wound healing

The process of wound healing is routinely evaluated by histological evaluation in the clinic,which may cause scarring and secondary injury.Reflectance confocal microscopy(RCM)represents a noninvasive,real-time imaging technique that allows in vivo evaluation of the skin.Traditional RCM was wide-probe-based,which limited its application on uneven and covered skin.In this study,we report the development of a portable reflectance confocal microscope(PRCM)in which all components were assembled in a handheld shell.Although the size and weight of the PRCM were reduced based on the use of a microelectromechanical system,the resolution was kept at 0.91μm,and the field of view of the system was 343μm×532μm.When used in vivo,the PRCM was able to visualize cellular and nuclear morphology for both mouse and human skin.PRCM evaluations were then performed on wounds after topically applied mesenchymal stem cells(MSCs)or saline treatment.The PRCM allowed visualization of the formation of collagen bundles,re-epithelization from the wound edge to the wound bed,and hair follicle regeneration,which were consistent with histological evaluations.Therefore,we offer new insights into monitoring the effects of topically applied MSCs on the process of wound healing by using PRCM.This study illustrates that the newly developed PRCM represents a promising device for real-time,noninvasive monitoring of the dynamic process of wound healing,which demonstrates its potential to diagnose,monitor,or predict disease in clinical wound therapy.

Detection of Rice Yellow Mottle at the Asymptomatic Stage by Hyperspectral Fluorescence and Reflectance Spectroscopies

Rice yellow mottle is considered the most destructive disease threatening rice production in Africa. Early detection of this infection in rice is essential to limit its expansion and proliferation. However, there is no research devoted to the spectral detection of rice yellow mottle virus (RYMV) infection, especially in the asymptomatic or early stages. This work proposes the use of hyperspectral fluorescence and reflectance data at leaf level for the detection of this disease in asymptomatic stages. A greenhouse experiment was therefore conducted to collect hyperspectral fluorescence and reflectance data at different stages of infection. These data allowed to calculate nine vegetation indices: one from fluorescence spectra and eight from reflectance spectra. A t-test made it possible to identify, from the second day after infection, four relevant reflectance vegetation indices to discriminate healthy leaves from those infected: these are Photochemical Reflectance Index (PRI), Transformed Chlorophyll Absorption in Reflectance Index (TCARI), Structure Intensive Pigment Index (SIPI) and Simple Ratio Pigment Index (SRPI). The fluorescence index was less sensitive in detecting infection. The four significant vegetation indices for the detection of RYMV were then used to build and evaluate models for discriminating plants according to their health status by the supervised classification of support vector machine (SVM) at different stages of infection. The maximum overall accuracy is 92.5% six days after inoculation (6 DAI). The sixth day after inoculation would be the adequate day to detect RYMV. This plants discrimination was validated by the mean reflectance spectra and by the histograms showing the differences between the average reflectance vegetation indices values of the two types of plants. Our results demonstrate the feasibility of differentiating RYMV-infected samples. They suggest that support vector machine learning models could be developed to diagnose RYMV-infected plants based on vegetation indices derived from spectral profiles at early stages of disease development.

Simple method for extracting the seasonal signals of photochemical reflectance index and normalized difference vegetation index measured using a spectral reflectance sensor

A spectral reflectance sensor(SRS)fixed on the near-surface ground was developed to support the continuous monitoring of vegetation indices such as the normalized difference vegetation index(NDVI)and photochemical reflectance index(PRI).NDVI is useful for indicating crop growth/phenology,whereas PRI was developed for observing physiological conditions.Thus,the seasonal change patterns of NDVI and PRI are two valuable pieces of information in a crop-monitoring system.However,capturing the seasonal patterns is considered challenging because the vegetation index values estimated by the reflection from vegetation are often governed by meteorological conditions,such as solar irradiance and precipitation.Further,unlike growth/phenology,the physiological condition has diurnal changes as well as seasonal characteristics.This study proposed a novel filtering method for extracting the seasonal signals of SRS-based NDVI and PRI in paddy rice,barley,and garlic.First,the measurement accuracy of SRSs was compared with handheld spectrometers,and the R^(2)values between the two devices were 0.96 and 0.81 for NDVI and PRI,respectively.Second,the experimental study of threshold criteria with respect to meteorological variables(i.e.,insolation,cloudiness,sunshine duration,and precipitation)was conducted,and sunshine duration was the most useful one for excluding distorted values of the vegetation indices.After data processing based on sunshine duration,the R^(2)values between the measured vegetation indices and the extracted seasonal signals of vegetation indices increased by approximately 0.002–0.004(NDVI)and 0.065–0.298(PRI)on the three crops,and the seasonal signals of vegetation indices became noticeably improved.This method will contribute to an agricultural monitoring system by identifying the seasonal changes in crop growth and physiological conditions.

Characterizing and Estimating Fungal Disease Severity of Rice Brown Spot with Hyperspectral Reflectance Data

Large-scale farming of agriculture crops requires real-time detection of disease for field pest management. Hyperspectral remote sensing data generally have high spectral resolution, which could be very useful for detecting disease stress in green vegetation at the leaf and canopy levels. In this study, hyperspectral reflectances of rice in the laboratory and field were measured to characterize the spectral regions and wavebands, which were the most sensitive to rice brown spot infected by Bipolaris oryzae （Helminthosporium oryzae Breda. de Hann）. Leaf reflectance increased at the ranges of 450 to 500 nm and 630 to 680 nm with the increasing percentage of infected leaf surface, and decreased at the ranges of 520 to 580 nm, 760 to 790 nm, 1550 to 1750 nm, and 2080 to 2350 nm with the increasing percentage of infected leaf surface respectively. The sensitivity analysis and derivative technique were used to select the sensitive wavebands for the detection of rice brown spot infected by B. oryzae. Ratios of rice leaf reflectance were evaluated as indicators of brown spot. R669/R746 （the reflectance at 669 nm divided by the reflectance at 746 nm, the following ratios may be deduced by analogy）, R702/R718, R692/R530, R692/R732, R535/R746, R521/R718, and R569/R718 increased significantly as the incidence of rice brown spot increased regardless of whether it＇s at the leaf or canopy level. R702/R718, R692/R530, R692/R732 were the best three ratios for estimating the disease severity of rice brown spot at the leaf and canopy levels. This result not only confirms the capability of hyperspectral remote sensing data in characterizing crop disease for precision pest management in the real world, but also testifies that the ratios of crop reflectance is a useful method to estimate crop disease severity.

An effective reflectance method for designing broadband antireflection films coupled with solar cells

The solar spectrum covers a broad wavelength range, which requires that antireflection coating （ARC） is effective over a relatively wide wavelength range for more incident light coming into the cell. In this paper, we present two methods to measure the composite reflection of SiO2/ZnS double-layer ARC in the wavelength ranges of 300-870 nm （duaI- junction） and 300-1850 nm （triple-junction）, under the solar spectrum AM0. In order to give sufficient consideration to the ARC coupled with the window layer and the dispersion effect of the refractive index of each layer, we use multidimensional matrix data for reliable simulation. A comparison between the results obtained from the weighted-average reflectance （WAR） method commonly used and that from the effective-average reflectance （EAR） method introduced here shows that the optimized ARC through minimizing the effective-average reflectance is convenient and available.

The Statistical Prediction of the Vitrinite Reflectance and Study of the Ancient Geothermal Field in Songliao Basin,China

The resource of the gas from coal and coal measures deep in Songliao Basin has been drawing more and more attention to . It is necessary to find out the evolution regularity of the geothermal field of the basin in addition to a series of geological studies in order to predict its resources because the ancient geothermal field of the basin is one of the main factors controlling the generation , evolution and disappearance of oil and gas . In the recent twenty years , it is generally believed that vitrinite reflectance is the best quantitative marker for the ancient geothermal field . In the present paper , a systematic study of the vitrinite reflectance value of Songliao Basin and its influence factors is made by multiple statistical analysis so as to reconstruct the evolutional process of the Moho and the corresponding geothermal field . Then , an overall prediction is made of the vitrinite reflectance and the distribution of J3-K1 fault basin group at the bottom of Songliao Basin , which provides the evidence for the further prediction of the gas potentiality from coal and coal measures deep in the basin .

A Method for Spaceborne Synthetic Remote Sensing of Atmospheric Aerosol Optical Depth and Vegetation Reflectance

Spaceborne synthetic remote sensing of atmospheric aerosol optical depth and vegetation reflectance is very significant, but it remains to be a question unresolved yet. Based on the property of vegetation reflectance spectra from near ultra violet to near infrared and the sensitivity of outgoing radiance to vegetation reflectance and atmospheric aerosol optical depth, a new method for spaceborne synthetic remote sensing of the reflectance and the depth is proposed, and an iteration correlation inversion algorithm is developed in this paper. According to numerical experiment, effects of radiance error, error in aerosol imaginary index and vegetation medium inhomogeneity on retrieved result are analyzed. Inversion results show that the effect of error in aerosol imaginary index is very important. As the error of aerosol imaginary index is within 0.01, standard errors of aerosol optical depth and vegetation reflectance solutions for 14 spectral channels from 410 nm to 900 nm are respectively less than 0.063 and 0.023. And as the radiance error is within 2%, the standard errors are less than 0.023 and 0.0056.

Monitoring Protein and Starch Accumulation in Wheat Grains with Leaf SPAD and Canopy Spectral Reflectance

The research was conducted to determine the relationships of protein and starch accumulation dynamics in grains of wheat to post-heading leaf SPAD values and canopy spectral reflectance. The results showed that leaf nitrogen accumulation was exponentially related to leaf SPAD values and linearly related to canopy spectral reflectance, and that there was negative linear relationship between leaf nitrogen accumulation and grain protein accumulation, but positive linear relationship between post-heading leaf nitrogen transloca-tion and grain protein accumulation at maturity. In addition, leaf SPAD values were parabolically related with and ratio indices R(l 500,610)and R(l 220,560)were exponentially related with protein and starch accumulation in grains. These results indicate that leaf SPAD values and canopy spectral reflectance should be good indicators of quality formation dynamics in wheat grains.

Diagnosis of Nitrogen Status in Rice Leaves with Canopy Spectral Reflectance

The investigation was made on the relationship of seasonal time-course canopy spectral reflectance and ratio index to total leaf nitrogen accumulation (leaf nitrogen content per unit ground area) in rice under different nitrogen treatments. The results showed there was a close correlation between the canopy spectral reflectance and total leaf nitrogen accumulation. Ratio of near infrared to green band (R810/R560) was linearly related with total leaf nitrogen accumulation. independent of nitrogen levels and development stages. Different datasets were used to test the linear regression equation, with average estimation accuracy of 91. 22%, RMSE of 1.09 and average relative error of 0. 026. Thus, the ratio index R810/R560 of canopy spectral reflectance should be useful for non-destructive monitoring and diagnosis of nitrogen status in rice plants.

Discrimination of Transgenic Rice Based on Near Infrared Reflectance Spectroscopy and Partial Least Squares Regression Discriminant Analysis

Near infrared reflectance spectroscopy （NIRS）, a non-destructive measurement technique, was combined with partial least squares regression discrimiant analysis （PLS-DA） to discriminate the transgenic （TCTP and mi166） and wild type （Zhonghua 11） rice. Furthermore, rice lines transformed with protein gene （OsTCTP） and regulation gene （Osmi166） were also discriminated by the NIRS method. The performances of PLS-DA in spectral ranges of 4 000-8 000 cm-1 and 4 000-10 000 cm-1 were compared to obtain the optimal spectral range. As a result, the transgenic and wild type rice were distinguished from each other in the range of 4 000-10 000 cm-1, and the correct classification rate was 100.0% in the validation test. The transgenic rice TCTP and mi166 were also distinguished from each other in the range of 4 000-10 000 cm-1, and the correct classification rate was also 100.0%. In conclusion, NIRS combined with PLS-DA can be used for the discrimination of transgenic rice.

Assessment of Photochemical Reflectance Index as a Tool for Evaluation of Chlorophyll Fluorescence Parameters in Cotton and Peanut Cultivars Under Water Stress Condition

The relationships between photochemical reflectance index （PRI） and the chlorophyll fluorescence parameters were examined to assess suitability of PRI as a remote-sensing tool for the chlorophyll fluorescence parameters. A greenhouse experiment was conducted using cotton and peanut crops under water stress condition. Five cotton and six peanut cultivars were grown using Andosole soil in pots maintained at two water levels; the control and water stress treatment （WS） of 100 and 50% of the daily transpiration, respectively. Higher non-photochemical quenching （NPQ） was exhibited by peanut than that of cotton by the water stress. On the other hand, the decreases of the actual quantum yield of photosystem II （△F/F＇m） and PRI by the water stress in cotton were larger than those in peanut. There were positively significant correlation coefficients between PRI and △F/F＇m in cotton at noon and in the afternoon including the control and WS. The correlations of PRI with NPQ were negatively significant at noon and in the afternoon for cotton, and in the afternoon for peanut. No clear relationship was found among these parameters in the morning probably due to the diurnal increase in global solar radiation. It was concluded that there would be a possibility to detect the effects of water stress on △F/F＇m and NPQ by PRI with some exceptions, although PRI could not note varietals differences in △F/F＇m and NPQ for each treatment.

Derivation of salt content in salinized soil from hyperspectral reflectance data: A case study at Minqin Oasis, Northwest China

Soil salinization is a serious ecological and environmental problem because it adversely affects sustainable development worldwide, especially in arid and semi-arid regions. It is crucial and urgent that advanced technologies are used to efficiently and accurately assess the status of salinization processes. Case studies to determine the relations between particular types of salinization and their spectral reflectances are essential because of the distinctive characteristics of the reflectance spectra of particular salts. During April 2015 we collected surface soil samples(0–10 cm depth) at 64 field sites in the downstream area of Minqin Oasis in Northwest China, an area that is undergoing serious salinization. We developed a linear model for determination of salt content in soil from hyperspectral data as follows. First, we undertook chemical analysis of the soil samples to determine their soluble salt contents. We then measured the reflectance spectra of the soil samples, which we post-processed using a continuum-removed reflectance algorithm to enhance the absorption features and better discriminate subtle differences in spectral features. We applied a normalized difference salinity index to the continuum-removed hyperspectral data to obtain all possible waveband pairs. Correlation of the indices obtained for all of the waveband pairs with the wavebands corresponding to measured soil salinities showed that two wavebands centred at wavelengths of 1358 and 2382 nm had the highest sensitivity to salinity. We then applied the linear regression modelling to the data from half of the soil samples to develop a soil salinity index for the relationships between wavebands and laboratory measured soluble salt content. We used the hyperspectral data from the remaining samples to validate the model. The salt content in soil from Minqin Oasis were well produced by the model. Our results indicate that wavelengths at 1358 and 2382 nm are the optimal wavebands for monitoring the concentrations of chlorine and sulphate compounds, the predominant salts at Minqin Oasis. Our modelling provides a reference for future case studies on the use of hyperspectral data for predictive quantitative estimation of salt content in soils in arid regions. Further research is warranted on the application of this method to remotely sensed hyperspectral data to investigate its potential use for large-scale mapping of the extent and severity of soil salinity.

Optical detection of Prorocentrum donghaiense blooms based on multispectral reflectance

Prorocentrum donghaiense is one of the most common red tide causative dinoflagellates in the Changjiang （Yangtze） River Estuary and the adjacent area of the East China Sea. It causes large-scale blooms in late spring and early summer that lead to widespread ecologic and economic damage. A means for distinguish- ing dinoflagellate blooms from diatom （Skeletonema costatum） blooms is desired. On the basis of measure- ments of remote sensing refectance [Rrs（λ）] and inherent optical parameters, the potential of using a mul- tispectral approach is assessed for discriminating the algal blooms due to P. donghaiense from those due to S. costatum. The behavior of two reflectance ratios [R1 = Rrs（560）/Rrs（532） and Re = Rrs（708）/Rrs（665）], suggests that differentiation of P. donghaiense blooms from diatom bloom types is possible from the current band setup of ocean color sensors. It is found that there are two reflectance ratio regimes that indicate a bloom is dominated by P. donghaiense; （1） R1 〉 1.55 and R2 〈 1.0 or （2） R1 〉 1.75 and R2 ≥ 1.0. Various sensitivity analyses are conducted to investigate the effects of the variation in varying levels of chlorophyll concentration and colored dissolved organic matter （CDOM） as well as changes in the backscattering ratio （bbp/bp） on the efficacy of this muitispectral approach. Results indicate that the intensity and inherent op- tical properties of the algal species explain much of the behavior of the two ratios. Although backscattering influences the amplitude of Rrs（λ）, especially in the 530 and 560 nm bands, the discrimination between P. donghaiense and diatoms is not significantly affected by the variation of bbp/bp. Since aCDOM （440） in coastal areas of the ECS is typically lower than 1.0 m-1 in most situations, the presence of CDOM does not interfere with this discrimination, even as SCDOM varies from 0.01 to 0.026 nm-1. Despite all of these effects, the dis- crimination of P. donghaiense blooms from diatom blooms based on multispectral measurements of Rrs（λ） is feasible.

Application of Near Infrared Diffuse Reflectance Spectroscopy with Radial Basis Function Neural Network to Determination of Rifampincin Isoniazid and Pyrazinamide Tablets

Partial least squares（PLS）,back-propagation neural network（BPNN）and radial basis function neural network（RBFNN）were respectively used for estalishing quantative analysis models with near infrared（NIR）diffuse reflectance spectra for determining the contents of rifampincin（RMP）,isoniazid（INH）and pyrazinamide（PZA）in rifampicin isoniazid and pyrazinamide tablets.Savitzky-Golay smoothing,first derivative,second derivative,fast Fourier transform（FFT）and standard normal variate（SNV）transformation methods were applied to pretreating raw NIR diffuse reflectance spectra.The raw and pretreated spectra were divided into several regions,depending on the average spectrum and RSD spectrum.Principal component analysis（PCA）method was used for analyzing the raw and pretreated spectra in different regions in order to reduce the dimensions of input data.The optimum spectral regions and the models＇ parameters were chosen by comparing the root mean square error of cross-validation（RMSECV）values which were obtained by leave-one-out cross-validation method.The RMSECV values of the RBFNN models for determining the contents of RMP,INH and PZA were 0.00288,0.00226 and 0.00341,respectively.Using these models for predicting the contents of INH,RMP and PZA in prediction set,the RMSEP values were 0.00266,0.00227 and 0.00411,respectively.These results are better than those obtained from PLS models and BPNN models.With additional advantages of fast calculation speed and less dependence on the initial conditions,RBFNN is a suitable tool to model complex systems.

Graptolite, Chitinozoan and Scolecodont Reflectances and Their Use as Indicators of Thermal Maturity

Based on studies of the reflectance of Ordovician and Silurian zooclasts from 11 sections around the Huangling Anticline, Hubei, China, combined with determination of the conodont colour alteration indices (CAI) and of infrared spectroscopy of dispersed organic matter from the same or adjacent horizons, it is further proved that reflectances of graptolites, chitinozoans and scolecldonts, like vitrinite and natural bitumen, increase with increasing maturation, but follow different reflectance paths. They may be used, therefore, as thermal maturation indicators. Graptolites display optically strong anisotropy. The maximum reflectance measured from graptolite on polished section cut parallel to the bedding plane is the most suitable for determination of maturation of their host rocks. In polished section chitinozoan and scolecodont show reflectance, but no bireflectance owing to their isotropy. The random reflectance of chitinozoans is higher than that of scolecodonts in the case of the same maturity. By analysing the distribution trends of graptolite, chitionzoan and scolecodont reflectances, the Ordovician and Silurian strata can be subdivided into three areas of maturation in the present region. The potential of oil or gas generation is discussed for each area. The result is basically coincident with the distribution pattern of regional organic maturation presented by CAI and infrared spectroscopic determination of organic matter.

Estimating plant crown transpiration and water use efficiency by vegetative reflectance indices associated with chlorophyll fluorescence

This research developed estimates of plant crown transpiration and water-use-efficiency using reflectance and derivative indices extracted from remotely sensed chlorophyll fluorescence measurements under natural conditions. Diurnal changes of leaf-level gas exchange (carbon assimilation rate (A), stomatal conductance (gs), transpiration rate (E)), chlorophyll fluorescence and canopy-scale remote sensing were measured on top crown of valley oak (Quercus lobata) in the foothills of central California, USA. The results indicated Q. lobata experienced saturating irradiance (PAR), which induced photoinhibition indicated by a decrease in the quantum efficiency of photosystem II (r2 = 0.648 with Fv ′/Fm′ and r2 = 0.73 with FPSII) and open reaction centers (qP;r2 = 0.699). The excess absorbed quantum energy was dissipated as heat through the Xanthophyll cycle and other processes (photorespiration and the water-water cycle) rather than energy emission as steady state chlorophyll fluorescence (Fs). An increase in leaf temperature caused by the activity of Xanthophyll cycle was correlated to a decrease in Fs (r2 = 0.381) and an increase in evaporative cooling through E (r2 = 0.800) and water use efficiency (WUE;r2 = 0.872).

Thermal maturity evaluation using Raman spectroscopy for oil shale samples of USA:comparisons with vitrinite reflectance and pyrolysis methods

Thermal maturity is commonly assessed by various geochemical screening methods(e.g.,pyrolysis and organic petrology).In this contribution,we attempt to establish an alternative approach to estimating thermal maturity with Raman spectroscopy,using 24 North American oil shale samples with thermal maturity data generated by vitrinite reflectance(VRo%)and pyrolysis(Tmax)-based maturity calculation(VRe%).The representative shale samples are from the Haynesville(East Texas),Woodford(West Texas),Eagle Ford and Pearsall(South Texas)Formations,as well as Gothic,Mancos,and Niobrara Formation shales(all from Colorado).The Raman spectra of disordered carbonaceous matter(D1 and G bands separation)of these samples were directly obtained from the rock chips without prior sample preparation.Using the Gaussian and Lorentzian distribution approach,thermal maturities from VR were correlated with carbon G and D1.We found that the Raman band separation(RBS)displayed a better correlation for equivalent VRe%than vitrinite reflectance VRo%.The RBS(D1–G)distance versus total organic carbon,free hydrocarbons from thermal extraction(S1),and the remaining hydrocarbon generating potential(S2)indicate that the RBS(D1–G)distance is also related to kerogen type.Data presented here from three methods of maturity determination of shale demonstrate that Raman spectroscopy is a quick and valid approach to thermal maturity assessment.

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.

Use of near-infrared reflectance spectroscopy for the rapid determination of the digestible energy and metabolizable energy content of corn fed to growing pigs

Background： The ability of near-infrared reflectance spectroscopy（NIRS） to determine the digestible energy（DE）and metabolizable energy（ME） content of corn fed to growing pigs was tested. One hundred and seventeen corn samples, comprising different planting regions and varieties were collected from all over China in a three-year period. The samples were randomly split into a calibration set（n = 88） and a validation set（n = 29）. The actual and calculated DE and ME content of the corn samples was determined by digestion-metabolism experiments and the prediction equations of Noblet and Perez（J Anim Sci. 71：3389–98,1993）. The samples were then subjected to NIRS scanning and calibrations were performed by the modified partial least square（MPLS） regression method based on77 different spectral pre-treatments. The NIRS equations based on the actually determined and calculated DE and ME were built separately and then validated using validation samples.Results： The NIRS equations obtained from actually determined DE, the coefficient of determination for calibration（RSQcal）, cross-validation（R^2CV）, and validation（RSQv） were 0.89, 0.87 and 0.86, and these values for determined ME were 0.87, 0.86 and 0.86. For the NIRS equations built from calculated DE, the RSQcal, R^2CV, and RSQvvalues were 0.88, 0.85 and 0.84, and these values for calculated ME were 0.86, 0.84 and 0.82. Except for the equation based on calculated ME（RPD_v= 2.38, 〈 2.50）, the other three equations built from actually determined energy and calculated DE produced good prediction performance（RPD_vranging from 2.53 to 2.69, 〉 2.50） when applied to validation samples.Conclusion： These results indicate that NIRS can be used as a quantitative method for the rapid determination of the available energy in corn fed to growing pigs, and the NIRS equations based on the actually determined energy produced better predictive performance than those built from calculated energy values.