Feasibility study of assessing cotton fiber maturity from near infrared hyperspectral imaging technique

Background Fiber maturity is a key cotton quality property,and its variability in a sample impacts fiber processing and dyeing performance.Currently,the maturity is determined by using established protocols in laboratories under a controlled environment.There is an increasing need to measure fiber maturity using low-cost(in general less than $20000)and small portable systems.In this study,a laboratory feasibility was performed to assess the ability of the shortwave infrared hyperspectral imaging(SWIR HSI)technique for determining the conditioned fiber maturity,and as a comparison,a bench-top commercial and expensive(in general greater than $60000)near infrared(NIR)instrument was used.Results Although SWIR HSI and NIR represent different measurement technologies,consistent spectral characteristics were observed between the two instruments when they were used to measure the maturity of the locule fiber samples in seed cotton and of the well-defined fiber samples,respectively.Partial least squares(PLS)models were established using different spectral preprocessing parameters to predict fiber maturity.The high prediction precision was observed by a lower root mean square error of prediction(RMSEP)(<0.046),higher R_(p)^(2)(>0.518),and greater percentage(97.0%)of samples within the 95% agreement range in the entire NIR region(1000-2500 nm)without the moisture band at 1940 nm.Conclusion SWIR HSI has a good potential for assessing cotton fiber maturity in a laboratory environment.

Shortwave infrared hyperspectral imaging for detection of pH value in Fuji apple

pH value is regarded as one of the most important attributes that affect sensory characteristics and edible quality of apple.The objective of the research was to explore the feasibility of applying shortwave infrared hyperspectral imaging system to detect the pH value of apple.A shortwave infrared hyperspectral imaging system was developed over the wavelength region of 1000-2500 nm and used to acquire hyperspectral images of apple samples.After reflectance calibration,mean reflectance spectral was calculated by averaging the intensity of all pixels within the roundness region of interest(ROI).Synergy interval partial least squares(siPLS)algorithms as an effective multivariable method was conducted on the calibration of regression model to estimate the pH value in Fuji apple.The performance of the final model was back-evaluated according to root mean square error of calibration(RMSEC)and correlation coefficient(Rc)in calibration set,and tested in prediction set.The optimal prediction siPLS model was obtained with correlation coefficient(Rp)of 0.8474 and mean square error of prediction(RMSEP)of 0.0398.The results indicated that shortwave infrared hyperspectral imaging combined with siPLS chemometrics could be an accurate and fast method for nondestructive prediction of pH value in Fuji apple.

Predicting wheat kernels’protein content by near infrared hyperspectral imaging

The objective of this study was to explore the potential of near infrared hyperspectral imaging combined with statistical regression models and neural networks for nondestructive prediction of protein content of wheat kernels.Seventy-nine samples from 11 breeds of wheat kernels were collected.The protein percentage of each sample measured by semimicro-Kjeldahl method was taken as the reference value.After comparing the prediction models of principal components regression(PCR)and partial least squares regression(PLSR)with various pretreatment methods,PLSR preprocessed by zero mean normalization(z score)function of MATLAB was found to obtain better prediction results than other regression models.Based on 10 latent variables of PLSR,the radial basis function(RBF)neural network was applied to improve the prediction,in which the coefficients of determination(R2)were greater than 0.92 for both the calibration set and validation set,while the corresponding RMSE values were 0.3496 and 0.4005,respectively.Therefore,hyperspectral imaging can provide a fast and non-destructive method for predicting the wheat kernels’protein content.

Research on vertical spatial characteristic of satellite infrared hyperspectral atmospheric sounding data

Spatial characteristic is an important indicator of remote sensor performance,and space-borne infrared hyperspectral sounder is the frontier of atmospheric vertical sounding technology.In this paper,the formation mechanism of the vertical spatial characteristics involved in the space-borne infrared hyperspectral sounding data are analyzed in detail,which shows that the vertical spatial characteristics of sounding data depends not only on the spectral channels and their waveband coverage,but also the specific atmospheric parameter and its specific variation interested.The indicators of vertical spatial characteristics are defined and their mathematical models are established based on the mechanism analyses.These models are applied to the vertical spatial characteristic evaluation of atmospheric temperature sounding for FY-4A GIIRS,which is the first space-borne infrared hyperspectral atmospheric sounder in geostationary orbit.It is concluded that FY-4A GIIRS can sound the vertical temperature distribution in whole troposphere and lower stratosphere with height<35 km.This study can provide basic information to support the improvement of infrared hyperspectral sounder and the trace of vertical spatial characteristics of atmospheric inversion products.

Sunglint Impact on Atmospheric Soundings from Hyperspectral Resolution Infrared Radiances

The mid-wave infrared band （3-5 #rn） has been widely used for atmospheric soundings. The sunglint impact on the atmospheric parameter retrieval using this band has been neglected because the reflected radiances in this band are significantly less than those in the visible band. In this study, an investigation of sunglint impact on the atmospheric soundings was conducted with Atmospheric InfraRed Sounder ob- servation data from 1 July to 7 July 2007 over the Atlantic Ocean. The impact of sunglint can lead to a brightness temperature increase of 1.0 K for the surface sensitive sounding channels near 4.58 #m. This contamination can indirectly cause a positive bias of 4 g kg-1 in the water vapor retrieval near the ocean surface, and it can be corrected by simply excluding those contaminated channels.

Impacts on Initial Condition Modification from Hyperspectral Infrared Sounding Data Assimilation: Comparisons between Full-Spectrum and Channel-Selection Scheme Based on Two-Month Experiments Using CrIS and IASI Observation

This paper discusses the performance difference between full-spectrum and channel-selection assimilation scheme of hyperspectral infrared observation, e.g. CrIS and IASI, on improving the accuracy of initial condition in numerical weather prediction. To accomplish this, we develop a 3D-Variational data assimilation system whose observation operator is a principal-component based fast radiative transfer model, which equips the direct assimilation of full-channel radiance from hyperspectral infrared sounders with high computational efficiency. This project’s primary goal is to demonstrate that assimilation of infrared observation in a full-channel mode could improve the accuracy of initial condition compared to selected-channel assimilation. Results show that full-channel assimilation performs better than selected-channel assimilation in modifying low and middle troposphere (1000 - 700 hPa, 700 - 400 hPa) temperature and water vapor field, while marginal improvements from temperature and water vapor field could be found over upper troposphere (400 - 100 hPa). This research also proves the feasibility of an alternative path to data assimilation for the full usage of hyperspectral infrared sounding observation in numerical weather prediction.

Evaluation of growth characteristics of Aspergillus parasiticus inoculated in different culture media by shortwave infrared(SWIR) hyperspectral imaging

The growth characteristics of Aspergillus parasitic us incubated on two culture media were ex-amined using shortwave infrared(SWIR,1000-2500 nm)hyperspectral imaging(HSI)in this work.HSI images of the A.parasiticus colonies growing on rose bengal medium(RBM)and maize agar medium(MAM)were recorded daily for 6 days.The growth phases of A.parasiticus were indicated through the pixel number and average spectra of colonies.On score plot of the first principal component(PC1)and PC2,four growth zones with varying mycelium densities were identified.Eight characteristic wavelengths(1095,1145,1195,1279,1442,1655,1834 and 1929 nm)were selected from PC1 loading,average spectra of each colony as well as each growth zone.F urthermore,support vector machine(S VM)classifier based on the eight wavelengths was built,and the classification accuracies for the four zones(from outer to inner zones)on the colonies on RBM were 99.77%,9935%,99.75%and 99.60%and 99.77%,9939%,99.31%and 98.22%for colonies on MAM.In addition,a new score plot of PC2 and PC3 was used to differ-entiate the colonies incubated on RBM and MAM for 6 days.Then characteristic wavelengths of 1067,1195,1279,1369,1459,1694,1834 and 1929 nm were selected from the loading of PC2 and PCg.Based on them,a new SVM model was developed to diferentiate colonies on RBM and MAM with accuracy of 100.00%and 9999%,respectively.In conclusion,SWIR hyperspectral image is a powerful tool for evaluation of growth characteristics of A.parasiticus incubated in diferent culture media.

NIR Hyperspectral Imaging Measurement of Sugar Content in Peach Using PLS Regression

Near infrared （NIR） hyperspectral imaging measurement of sugar content in peach was introauced. NIR spectral images （650～1 000 nm, resolution： 2 nm） of peach samples were captured with developed hyperspectral imaging setup. Partial least square （PLS） regression prediction model was developed to estimate the sugar content in peach; step-wise backward method was utilized to determine optimal wavelength subsets. Experimental results show that the calibration model with optimal wavelength subsets has a correlation coefficient of prediction of 0.97 and a standard error of prediction of 0.19, the prediction accuracy is higher than the calibration model applied over the whole wavelength, which proves that variable selection plays an important role in improving the prediction accuracy of PLS regression model.

A Neural Network Based Single Footprint Temperature Retrieval for Atmospheric Infrared Sounder Measurements and Its Application to Study on Stratospheric Gravity Wave

Satellite hyperspectral infrared sounder measurements have better horizontal resolution than other sounding techniques as it boasts the stratospheric gravity wave(GW)analysis.To accurately and efficiently derive the three-dimensional structure of the stratospheric GWs from the single-field-of-view(SFOV)Atmospheric Infra Red Sounder(AIRS)observations,this paper firstly focuses on the retrieval of the atmospheric temperature profiles in the altitude range of 20-60 km with an artificial neural network approach(ANN).The simulation experiments show that the retrieval bias is less than 0.5 K,and the root mean square error(RMSE)ranges from 1.8 to 4 K.Moreover,the retrieval results from 20 granules of the AIRS observations with the trained neural network(AIRS_SFOV)and the corresponding operational AIRS products(AIRS_L2)as well as the dual-regression results from the Cooperative Institute for Meteorological Satellite Studies(CIMSS)(AIRS_DR)are compared respectively with ECMWF T799 data.The comparison indicates that the standard deviation of the ANN retrieval errors is significantly less than that of the AIRS_DR.Furthermore,the analysis of the typical GW events induced by the mountain Andes and the typhoon"Soulik"using different data indicates that the AIRS_SFOV results capture more details of the stratospheric gravity waves in the perturbation amplitude and pattern than the operational AIRS products do.