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.

Comprehensive Evaluation and Prediction of the Effectiveness of H_(2)O_(2)- assisted Na_(2)CO_(3)Pretreatment of Corn Stover Using Multivariate Analysis

In this study,multivariate analysis methods,including a principal component analysis(PCA)and partial least square(PLS)analysis,were applied to reveal the inner relationship of the key variables in the process of H_(2)O_(2)-assisted Na_(2)CO_(3)(HSC)pretreatment of corn stover.A total of 120 pretreatment experiments were implemented at the lab scale under different conditions by varying the particle size of the corn stover and process variables.The results showed that the Na_(2)CO_(3) dosage and pretreatment temperature had a strong influence on lignin removal,whereas pulp refining instrument(PFI)refining and Na_(2)CO_(3) dosage played positive roles in the final total sugar yield.Furthermore,it was found that pretreatment conditions had a more significant impact on the amelioration of pretreatment effectiveness compared with the properties of raw corn stover.In addition,a prediction of the effectiveness of the corn stover HSC pretreatment based on a PLS analysis was conducted for the first time,and the test results of the predictability based on additional pretreatment experiments proved that the developed PLS model achieved a good predictive performance(particularly for the final total sugar yield),indicating that the developed PLS model can be used to predict the effectiveness of HSC pretreatment.Therefore,multivariate analysis can be potentially used to monitor and control the pretreatment process in future large-scale biorefinery applications.

Qualitative and Quantitative Analysis for the Quality Control of Rhizoma Coptidis by HPLC-DAD and HPLC-ESI-MS

For quality control purpose, an approach of fingerprinting and simultaneous quantification of five major bioactive constituents of Rhizoma Coptidis was established via a high-performance liquid chromatograph coupled with a photodiode array UV detector（HPLC-DAD） and an electrospray ionization mass spectrometer（HPLC-ESI/MS） The compounds were identified on the basis of the comparison of their mass spectra with literature data and those of standard samples and quantified by the HPLC-DAD method. Baseline separation was achieved on an XTerra C18 column（5 μm, 250 mm×4.6 mm i. d.） with linear gradient elution of formate buffer（consisting of 0.5% formic acid, adjusted to pH=4.5 with ammonia） and acetonitrile（consisting of 0.2% formic acid and 0.2% triethylamine）. The me- thod was validated for linearity（r^2〉0.9995）, repeatability（RSD〈3.1%）, intra- and inter-day precision（RSD〈1.8%） with recovery（99.9%-105.1%）, limits of detection（0.15-0.35 μg/mL）, and limits of quantification（0.53-0.82 μg/mL）. The similarities of 32 batches of Rhizoma Coptidis and their classification according to their manufacturers were based on the retention time and peak areas of the characteristic compounds. The five compounds were selected for quality assessment ofRhizoma coptidis via partial least squares analysis（PLS）.

The Time Series Spectral Analysis of Satellite Altimetry and Coastal Tide Gauges and Tide Modeling in the Coast of Caspian Sea

This study endeavors to deal with the least square spectral analysis on the time series, to find present significant frequencies, to analyze 40 tide components using harmonic methods and to show relationship between discovered frequencies and 40 components of tide. For the purpose of collecting data of altimetry satellites of Topex/Poseidon (T/P), Jason 1, Jason 2 and coastal tide gauges of Bandar Anzali, Noshahr, and Nekah were utilized. In this time series formed by cross over points of altimetry satellite and then using least square spectral analysis on time series derived from altimetry satellite and coastal tide gauges the significant components were found and annual, biannual, and monthly components were discovered. Then, analysis of 40 tide components was conducted using harmonic method to find the amplitude and phase. It represented that solar annual (Sa) plays the most significant role on Caspian Sea corresponded to the least square spectral analysis of the time series. The results shows that the annual (Sa) and semi-annual Solar (Ssa) constituents on all of the ports listed have the highest amplitude in comparison with the other constituents which are respectively 16 cm, 18 cm and 15 cm for annual constituent and 2.8 cm, 5.4 cm and 3.7 cm for semi-annual constituent.

LS-SVM and Monte Carlo methods based reliability analysis for settlement of soft clayey foundation

A method which adopts the combination of least squares support vector machine（LS-SVM） and Monte Carlo（MC） simulation is used to calculate the foundation settlement reliability.When using LS-SVM,choosing the training dataset and the values for LS-SVM parameters is the key.In a representative sense,the orthogonal experimental design with four factors and five levels is used to choose the inputs of the training dataset,and the outputs are calculated by using fast Lagrangian analysis continua（FLAC）.The decimal ant colony algorithm（DACA） is also used to determine the parameters.Calculation results show that the values of the two parameters,and δ2 have great effect on the performance of LS-SVM.After the training of LS-SVM,the inputs are sampled according to the probabilistic distribution,and the outputs are predicted with the trained LS-SVM,thus the reliability analysis can be performed by the MC method.A program compiled by Matlab is employed to calculate its reliability.Results show that the method of combining LS-SVM and MC simulation is applicable to the reliability analysis of soft foundation settlement.

Plate reinforced square hollow section X-joints subjected to in-plane moment

The static test of 13 square hollow section(SHS) X-joints with different β and different types of plate reinforcement under in-plane moment in brace was carried out. Experimental test schemes, failure modes of specimens, moment-vertical displacement curves, moment-deformation of the chord, and strain strength distribution curves were presented. The effect of β and plate reinforcement types on in-plane flexural property of SHS X-joints was studied. Results show that punching shear of chord face disappears, brace material fracture appears and concave and convex deformation of chord decrease when either collar plates or doubler plates were welded on chord face. Moment-vertical displacement curves of all specimens have obvious elastic, elastic-plastic and plastic stages. As β increases, the in-plane flexural ultimate capacity and initial stiffness of joints of the same plate reinforcement type increase, but ductility of joints decreases. With the same β, the in-plane flexural initial stiffness and ultimate capacity of doubler plate reinforced joints, collar plate reinforced joints, and unreinforced joints decrease progressively. Thickness of reinforcement plate has no obvious effect on in-plane flexural initial stiffness and ultimate capacity of joints. As thickness of reinforcement plate increases, the ductility of reinforced X-joints decreases. The concave and convex deformation of every specimen has good symmetry;as β increases, the yield and ultimate deformation of chord decrease.

Optimization of wavelength range and data interval in chemometric analysis of complex pharmaceutical mixtures

The performance of different chemometric approaches was evaluated in the spectrophotometric determination of pharmaceutical mixtures characterized by having the amount of components with a very high ratio. Principal component regression （PCR）, partial least squares with one dependent variable （PLS1） or multi-dependent variables （PLS2）, and multivariate curve resolution （MCR） were applied to the spectral data of a ternary mixture containing paracetamol, sodium ascorbate and chlorpheniramine （150：140：1, m/m/m）, and a quaternary mixture containing paracetamol, caffeine, phenylephrine and chlorpheniramine （125：6. 25：1.25：1, m/m/m/m）. The UV spectra of the calibration samples in the range of 200-320 nm were pre-treated by removing noise and useless data, and the wavelength regions having the most useful analytical information were selected using the regression coefficients calculated in the multivariate modeling. All the defined chemometric models were validated on external sample sets and then applied to commercial pharmaceutical formulations. Different data intervals, fixed at 0.5, 1.0, and 2.0 point/nm, were tested to optimize the prediction ability of the models. The best results were obtained using the PLSlcalibration models and the quantification of the species of a lower amount was sig- nificantly improved by adopting 0.5 data interval, which showed accuracy between 94.24% and 107.76%.

Theoretical convergence analysis of complex Gaussian kernel LMS algorithm

With the vigorous expansion of nonlinear adaptive filtering with real-valued kernel functions,its counterpart complex kernel adaptive filtering algorithms were also sequentially proposed to solve the complex-valued nonlinear problems arising in almost all real-world applications.This paper firstly presents two schemes of the complex Gaussian kernel-based adaptive filtering algorithms to illustrate their respective characteristics.Then the theoretical convergence behavior of the complex Gaussian kernel least mean square（LMS） algorithm is studied by using the fixed dictionary strategy.The simulation results demonstrate that the theoretical curves predicted by the derived analytical models consistently coincide with the Monte Carlo simulation results in both transient and steady-state stages for two introduced complex Gaussian kernel LMS algonthms using non-circular complex data.The analytical models are able to be regard as a theoretical tool evaluating ability and allow to compare with mean square error（MSE） performance among of complex kernel LMS（KLMS） methods according to the specified kernel bandwidth and the length of dictionary.

Application Fourier transform near infrared spectrometer in rapid estimation of soluble solids content of intact citrus fruits

Nondestructive method of measuring soluble solids content (SSC) of citrus fruits was developed using Fourier transform near infrared reflectance (FT-NIR) measurements collected through optics fiber. The models describing the relationship between SSC and the NIR spectra of citrus fruits were developed and evaluated. Different spectra correction algorithms (standard normal variate (SNV), multiplicative signal correction (MSC)) were used in this study. The relationship between laboratory SSC and FT-NIR spectra of citrus fruits was analyzed via principle component regression (PCR) and partial least squares (PLS) re- gression method. Models based on the different spectral ranges were compared in this research. The first derivative and second derivative were applied to all spectra to reduce the effects of sample size, light scattering, instrument noise, etc. Different baseline correction methods were applied to improve the spectral data quality. Among them the second derivative method after baseline correction produced best noise removing capability and yielded optimal calibration models. A total of 170 NIR spectra were acquired; 135 NIR spectra were used to develop the calibration model; the remaining spectra were used to validate the model. The developed PLS model describing the relationship between SSC and NIR reflectance spectra could predict SSC of 35 samples with correlation coefficient of 0.995 and RMSEP of 0.79 °Brix.

Rock and Soil Classification Using PLS-DA and SVM Combined with a Laser-Induced Breakdown Spectroscopy Library

Laser-induced breakdown spectroscopy （LIBS） has become a powerful technology in geological applications. The correct identification of rocks and soils is critical to many geological projects. In this study, LIBS database software with a user-friendly and intuitive interface is developed based on Windows, consisting of a database module and a sample identification module. The database module includes a basic database containing LIBS persistent lines for elements and a dedicated geological database containing LIBS emission lines for several rock and soil reference standards. The module allows easy use of the data. A sample identification module based on partial least squares discriminant analysis （PLS-DA） or support vector machine （SVM） algorithms enables users to classify groups of unknown spectra. The developed system was used to classify rock and soil data sets in a dedicated database and the results demonstrate that the system is capable of fast and accurate classification of rocks and soils, and is thus useful for the detection of geological materials.

Measurement of sugar content in Fuji apples by FT-NIR spectroscopy

To evaluate the potential of FT-NIR spectroscopy and the influence of the distance between the light source/detection probe and the fruit for measuring the sugar content (SC) of Fuji apples, diffuse reflectance spectra were measured in the spectral range from 12500 to 4000 cm^-1 at 0 mm, 2 mm, 4 mm and 6 mm distances. Four calibration models at four distances were established between diffused reflectance spectra and sugar content by partial least squares (PLS) analysis. The correlation coefficients (R) of calibrations ranged from 0.982 to 0.997 with SEC values from 0.138 to 0.453 and the SECV values from 0.74 to 1.58. The best model of original spectra at 0 mm distance yielded high correlation determination of 0.918, a SEC of 0.092, and a SEP of 0.773. The results showed that different light/detection probe-fruit distances influence the apple reflective spectra and SC predictions.

Preparation of dendritic bismuth film electrodes and their application for detection of trace Pb(Ⅱ)and Cd(Ⅱ)

In this paper,dendritic Bi film electrodes with porous structure had successfully been prepared on glassy carbon electrode using a constant current electrolysis method based on hydrogen bubble dynamic templates.The electrode prepared using a large applied current density showed an increased internal electroactive area and a significantly improved electrochemical performance.The analytical utility of the prepared dendritic Bi film electrodes for the determination of Pb(Ⅱ)and Cd(Ⅱ)in the range of 5–50 μg·L^(-1)were presented in combination with square wave stripping voltammetry in model solution.Compared with non-porous Bi film electrode,the dendritic Bi film electrode exhibited higher sensitivity and lower detection limit.The prepared Bi film electrode with dendritic structure was also successfully applied to real water sample analysis.

The classification of plants by laser-induced breakdown spectroscopy based on two chemometric methods

The applications of laser-induced breakdown spectroscopy(LIBS) on classifying complex natural organics are relatively limited and their accuracy still requires improvement.In this work,to study the methods on classification of complex organics,three kinds of fresh leaves were measured by LIBS.100 spectra from 100 samples of each kind of leaves were measured and then they were divided into a training set and a test set in a ratio of 7:3.Two algorithms of chemometric methods including the partial least squares discriminant analysis(PLS-DA) and principal component analysis Mahalanobis distance(PCA-MD) were used to identify these leaves.By using 23 lines from 16 elements or molecules as input data,these two methods can both classify these three kinds of leaves successfully.The classification accuracies of training sets are both up to 100% by PCA-MD and PLS-DA.The classification accuracies of the test set are 93.3% by PCA-MD and 97.8% by PLS-DA.It means that PLS-DA is better than PCA-MD in classifying plant leaves.Because the components in PLS-DA process are more suitable for classification than those in PCA-MD process.We think that this work can provide a reference for plant traceability using LIBS.

Spatio-temporal variation of Fraction of Photosynthetically Active Radiation absorbed by vegetation in the Hengduan Mountains, China

The Fraction of Absorbed Photosynthetically Active Radiation(FPAR) is an important indicator of the primary productivity of vegetation. FPAR is often used to estimate the assimilation of carbon dioxide in vegetation. Based on MOD15 A2 H/FPAR data product, the temporal and spatial variation characteristics and variation trend of FPAR in different vegetation types in 2001 to 2018 were analyzed in the Hengduan Mountains. The response of FPAR to climate change was investigated by using Pearson correlation analytical method and partial least squares regression analysis. Results showed that the FPAR in Hengduan Mountains presented an increasing trend with time. Spatially, it was high in the south and low in the north, and it also showed obvious vertical zonality by elevation gradient.The vegetation FPAR was found to be positively correlated with air temperature and sunshine duration but negatively correlated with precipitation. Partial least squares regression analysis showed that the influence of sunshine duration on vegetation FPAR in Hengduan Mountains was stronger than that of air temperature and precipitation.

Comparative study on identi¯cation of healthy and osteoarthritic articular cartilages by fourier transform infrared imaging and chemometrics methods

Two discriminant methods,partial least squares-discriminant analysis(PLS-DA)and Fisher's discriminant analysis(FDA),were combined with Fourier transform infrared imaging(FTIRI)to differentiate healthy and osteoarthritic articular cartilage in a canine model.Osteoarthritic cartilage had been developed for up to two years after the anterior cruciate ligament(ACL)transection in one knee.Cartilage specimens were sectioned into 10μm thickness for FTIRI.A PLS-DA model was developed after spectral pre-processing.All IR spectra extracted from FTIR images were calculated by PLS-DA with the discriminant accuracy of 90%.Prior to FDA,principal component analysis(PCA)was performed to decompose the IR spectral matrix into informative princi pal component matrices.Based on the different discriminant mechanism,the discriminant accuracy(96%)of PCA-FDA with high convenience was higher than that of PLS-DA.No healthy cartilage sample was mis assigned by these two methods.The above mentioned suggested that both integrated technologies of FTIRI-PLS-DA and,especially,FTIRI-PCA-FDA could become a promising tool for the discrimination of healthy and osteoarthritic cartilage specimen as well as the diagnosis of cartilage lesion at microscopic level.The results of the study would be helpful for better understanding the pathology of osteoarthritics.

Assessment of Comprehensive Carrying Capacity of Land Resources Based on Land Functions

Based on relevant research results,from the perspective of land use functions,an evaluation indicator system of carrying capacity of land resources composed of three second-grade indicators( production,living and ecological carrying capacity) including 24 third-grade indicators was established,and the carrying capacity of land resources in ten cities of Shaanxi Province in 2013 was assessed and analyzed by using mean square error analysis method and hierarchical clustering method. The results showed that the three types of carrying capacity in most cities of Shaanxi Province are shown as follows: ecological carrying capacity > living carrying capacity > production carrying capacity,and the differences between various regions in a single type of carrying capacity basically accorded with the actual situation of development in each city; there were obvious differences between various cities in the comprehensive carrying capacity of land resources,which was basically consistent with regional economic and social development.

Earth orientation parameters from VLBI determined with a Kalman filter

This paper introduces the reader to our Kalman filter developed for geodetic VLBI（very long baseline interferometry） data analysis. The focus lies on the EOP（Earth Orientation Parameter） determination based on the Continuous VLBI Campaign 2014（CONT14） data, but also earlier CONT campaigns are analyzed. For validation and comparison purposes we use EOP determined with the classical LSM（least squares method） estimated from the same VLBI data set as the Kalman solution with a daily resolution. To gain higher resolved EOP from LSM we run solutions which yield hourly estimates for polar motion and dUTl = Universal Time（UT1）-Coordinated Universal Time（UTC）. As an external validation data set we use a GPS（Global Positioning System） solution providing hourly polar motion results.Further, we describe our approach for determining the noise driving the Kalman filter. It has to be chosen carefully, since it can lead to a significant degradation of the results. We illustrate this issue in context with the de-correlation of polar motion and nutation.Finally, we find that the agreement with respect to GPS can be improved by up to 50% using our filter compared to the LSM approach, reaching a similar precision than the GPS solution. Especially the power of erroneous high-frequency signals can be reduced dramatically, opening up new possibilities for highfrequency EOP studies and investigations of the models involved in VLBI data analysis.We prove that the Kalman filter is more than on par with the classical least squares method and that it is a valuable alternative, especially on the advent of the VLBI2010 Global Observing System and within the GGOS frame work.

Study on a noninvasive method for rapid screening Human Serum albumin injectables by Raman spectroscopy

Human serum albumin(HSA)injectable product is a severely afflicted area on drug safety due to its high price and restricted supply.Raman spectroscopy performances high specificity on HSA detection and it is even possible to determine HSA injectable products noninvasively.In this study,we developed a noninvasive rapid screening method for of HSA injectable products by using portable Raman spectrometer.Qualitative models were established by using principal component analysis combined with classical least squares(PCA-CLS)algorithm,while quanti-tative model was established by using partial least squares(PLS)algorithm.Model transfer in different instruments of both the same and different apparatus modules was further discussed in this paper.A total of 34 HSA injectable samples collected from markets were used for verification.The identification results showed 100%accuracy and the predicted concentrations of those identified as true HSA were consistent with their labeled concentrations.The quantitative results also indicated that model transfer was excellent in the same apparatus modules of Raman spectrometer at all concentration levels,and still good enough in the different apparatus modules although the relative standard deviation(RSD)value showed a little increasing trend at low HSA concentration level.In conclusion,the method was proved to be feasible and efficient for screening HSA injections,especially on its screening speed and the consideration of glass containers.Moreover,with inspiring results on the model transfer,the method could be used as a universal screening mean to different Raman instruments.

Fast removal of ocular artifacts from electroencephalogram signals using spatial constraint independent component analysis based recursive least squares in brain-computer interface

Ocular artifacts cause the main interfering signals within electroencephalogram （EEG） signal measurements. An adaptive filter based on reference signals from an electrooculogram （EOG） can reduce ocular interference, but collecting EOG signals during a long-term EEG recording is inconvenient and uncomfortable for the subject. To remove ocular artifacts from EEG in brain-computer interfaces （BCIs）, a method named spatial constraint independent component analysis based recursive least squares （SCICA-RLS） is proposed. The method consists of two stages. In the first stage, independent component analysis （ICA） is used to decompose multiple EEG channels into an equal number of independent components （ICs）. Ocular ICs are identified by an automatic artifact detection method based on kurtosis. Then empirical mode decomposition （EMD） is employed to remove any cerebral activity from the identified ocular ICs to obtain exact altifact ICs. In the second stage, first, SCICA applies exact artifact ICs obtained in the first stage as a constraint to extract artifact ICs from the given EEG signal. These extracted ICs are called spatial constraint ICs （SC-ICs）. Then the RLS based adaptive filter uses SC-ICs as reference signals to reduce interference, which avoids the need for parallel EOG recordings. In addition, the proposed method has the ability of fast computation as it is not necessary for SCICA to identify all ICs like ICA. Based on the EEG data recorded from seven subjects, the new approach can lead to average classification accuracies of 3.3% and 12.6% higher than those of the standard ICA and raw EEG, respectively. In addition, the proposed method has 83.5% and 83.8% reduction in time-consumption compared with the standard ICA and ICA-RLS, respectively, which demonstrates a better and faster OA reduction.

Design and analysis of superlens based on complex two-dimensional square lattice photonic crystal

We theoretically demonstrate the imaging properties of a complex two-dimensional(2D) face-centered square lattice photonic crystal(PC) made from germanium cylinders in air background. The finitedifference time-domain(FDTD) method is employed to calculate the band structure and simulate image construction. The band diagram of the complex structure is significantly compressed. Negative refraction occurs in the second energy band with negative phase velocity at a frequency of 0.228(2πc/a), which is lower than results from previous studies. Lower negative refraction frequency leads to higher image resolution. Numerical results show that the spatial resolution of the system reaches 0.7296λ, which is lower than the incident wavelength.