Moving-window bis-correlation coefficients method for visible and near-infrared spectral discriminant analysis with applications

The moving window bis corelation coefficients(MW BiCC)was proposed and employed for the discriminant analysis of transgenic sugarcane leaves and B-thalassemia with visible and near-infrared(Vis NIR)spectroscopy.The well-performed moving window principal component analysis linear discriminant analysis(MWPCA-LDA)was also conducted for comparison.A total of 306 transgenic(positive)and 150 nont ransgenic(negative)leave samples of sugarcane were collected and divided to calibration,prediction,and validation.The diffuse reflection spectra were corected using Savitzky-Golay(SG)smoothing with first-order derivative(d=1),third-degree polynomial(p=3)and 25 smpothing points(m=25).The selected waveband was 736-1054nm with MW-BiCC,and the positive and negative validation recognition rates(V_REC^(+),VREC^(-))were 100%,98.0%,which achieved the same effect as MWPCA-LDA.Another example,the 93 B-thalassemia(positive)and 148 nonthalassemia(negative)of human hemolytic samples were colloctod.The transmission spectra were corrected using SG smoothing withd=1,p=3 and m=53.Using M W-BiCC,many best wavebands were selected(e.g.,1116-1146,17941848 and 22842342nm).The V_REC^(+)and V_REC^(-)were both 100%,which achieved the same effect as MW-PCA-LDA.Importantly,the BICC only required ca lculating correlation cofficients between the spectrum of prediction sample and the average spectra of two types of calibration samples.Thus,BiCC was very simple in algorithm,and expected to obtain more applications.The results first confirmed the feasibility of distinguishing B-thalassemia and normal control samples by NIR spectroscopy,and provided a promising simple tool for large population thalassemia screening.

Measurement of soluble solids content in watermelon by Vis/NIR diffuse transmittance technique

Watermelon is a popular fruit in the world with soluble solids content （SSC） being one of the major characteristics used for assessing its quality. This study was aimed at obtaining a method for nondestructive SSC detection of watermelons by means of visible/near infrared （Vis/NIR） diffuse transmittance technique. Vis/NIR transmittance spectra of intact watermelons were acquired using a low-cost commercially available spectrometer operating over the range 350-1000 nm. Spectra data were analyzed by two multivariate calibration techniques： partial least squares （PLS） and principal component regression （PCR） methods. Two experiments were designed for two varieties of watermelons [Qilin （QL）, Zaochunhongyu （ZC）], which have different skin thickness range and shape dimensions. The influences of different data preprocessing and spectra treatments were also investigated. Performance of different models was assessed in terms of root mean square errors of calibration （RMSEC）, root mean square errors of prediction （RMSEP） and correlation coefficient （r） between the predicted and measured parameter values. Results showed that spectra data preprocessing influenced the performance of the calibration models. The first derivative spectra showed the best results with high correlation coefficient of determination [r=0.918 （QL）; r=0.954 （ZC）], low RMSEP [0.65 °Brix （QL）; 0.58 °Brix （ZC）], low RMSEC [0.48 °Brix （QL）; 0.34°Brix （ZC）] and small difference between the＇RMSEP and the RMSEC by PLS method. The nondestructive Vis/NIR measurements provided good estimates of SSC index of watermelon, and the predicted values were highly correlated with destructively measured values for SSC. The models based on smoothing spectra （Savitzky-Golay filter smoothing method） did not enhance the performance of calibration models obviously. The results indicated the feasibility of Vis/NIR diffuse transmittance spectral analysis for predicting watermelon SSC in a nondestructive way.

Determining the Zn Content of Cherry in Field Using VNIR Spectroscopy

Visible and near-infrared(VNIR)spectroscopy is an eco-friendly method used for estimating plant nutrient deficiencies.The aim of this study was to investigate the possibility of using VNIR method for estimating Zn content in cherry orchard leaves under field conditions.The study was conducted in 3different locations in Isparta region of Turkey.Fifteen cherry orchards containing normal and Zn deficient plants were chosen,and 60 leaf samples were collected from each location.The reflectance spectra of the leaves were measured with an ASD FieldSpec HandHeld spectroradiometer and a plant probe.The Zn contents of leaf samples were predicted through laboratory analysis.The spectral reflectance measurements were used to estimate the Zn levels using stepwise multiple linear regression analysis method.Prediction models were created using the highest coefficient of determination value.The results show that Zn content of cherry trees can be estimated using the VNIR spectroscopic method(87.5<r2<96.79).Moreover,plant nutrient contents can be estimated without using chemicals.However,further research is necessary to develop a standard method for field conditions.Because spectral reflectance is affected by ecological conditions,agricultural applications and nutrient interactions,more effective models must be developed depending on the geographical location,period and plant type.

Structural and Optical Characteristics of Silica Nanotubes Using CNTs as Template

Silica coated multi-wall carbon nanotubes(MWCNTs),silica@MWCNTs and nanocomposites were synthesized by a sol-gel method.By using the synthesized nanocomposites and also CNTs as templates,silica nanotubes(silica-NTs) were prepared.The optical properties of fabricated nanocomposites and nanotubes were characterized by back-scattering micro Raman,UV/Vis/NIR and FT-IR spectra,which show the presence of CNTs structure in the nanocomposites.UV/Vis/NIR and FT-IR spectra also show the presence of silica compounds.The recorded spectra from UV/Vis/NIR and FT-IR also confirm the presence of silica compounds in the nanotubes.The results of FE-SEM imaging data indicate that the synthesized samples are MWCNTs coated uniformly by silica molecules,which act as the template to synthesize silica-NTs.

Physical statistical algorithm for precipitable water vapor inversion on land surface based on multi-source remotely sensed data

Water vapor plays a crucial role in atmospheric processes that act over a wide range of temporal and spatial scales, from global climate to micrometeorology. The determination of water vapor distribution in the atmosphere and its changing pattern is very important. Although atmospheric scientists have developed a variety of means to measure precipitable water vapor(PWV) using remote sensing data that have been widely used, there are some limitations in using one kind satellite measurements for PWV retrieval over land. In this paper, a new algorithm is proposed for retrieving PWV over land by combining different kinds of remote sensing data and it would work well under the cloud weather conditions. The PWV retrieval algorithm based on near infrared data is more suitable to clear sky conditions with high precision. The 23.5 GHz microwave remote sensing data is sensitive to water vapor and powerful in cloud-covered areas because of its longer wavelengths that permit viewing into and through the atmosphere. Therefore, the PWV retrieval results from near infrared data and the indices combined by microwave bands remote sensing data which are sensitive to water vapor will be regressed to generate the equation for PWV retrieval under cloud covered areas. The algorithm developed in this paper has the potential to detect PWV under all weather conditions and makes an excellent complement to PWV retrieved by near infrared data. Different types of surface exert different depolarization effects on surface emissions, which would increase the complexity of the algorithm. In this paper, MODIS surface classification data was used to consider this influence. Compared with the GPS results, the root mean square error of our algorithm is 8 mm for cloud covered area. Regional consistency was found between the results from MODIS and our algorithm. Our algorithm can yield reasonable results on the surfaces covered by cloud where MODIS cannot be used to retrieve PWV.

Design and test of portable comprehensive quality non-destructive detector for grape bunches based on spectrum

Based on the analysis technology of visible/near infrared diffuse reflectance spectroscopy a portable and nondestructive detector was designed to test comprehensive quality of red globe grape bunches in the growth period.The detector included spectrum acquisition probe,spectrometer,lithium battery,halogen lamp light source,advanced RISC machines(ARM)board and peripheral circuit.Based on microsoft foundation classes(MFC)development tool,the real-time analysis and processing software of the detector was written by C++language.The optimal partial least squares regression(PLSR)detection model of multi-quality parameters was implanted into the hardware device.This paper selected the red globe grapes bunches in the growth period as the research samples,collected the visible/near infrared diffuse reflectance spectrum information,and then used the established PLSR model to detect the soluble solid content(SSC),total acid(TA)and pH of the samples to generate comprehensive quality parameter.So as to realize the nondestructive detecting of comprehensive quality of red globe grapes bunches in the growth period.In conclusion,the detector could realize real-time and non-destructive detecting of red globe grapes bunches in growth period aiming at the comprehensive quality.