The Savitzky-Golay(SG)filter,which employs polynomial least-squares approximations to smooth data and estimate derivatives,is widely used for processing noisy data.However,noise suppression by the SG filter is recogni...The Savitzky-Golay(SG)filter,which employs polynomial least-squares approximations to smooth data and estimate derivatives,is widely used for processing noisy data.However,noise suppression by the SG filter is recognized to be limited at data boundaries and high frequencies,which can significantly reduce the signal-to-noise ratio(SNR).To solve this problem,a novel method synergistically integrating Principal Component Analysis(PCA)with SG filtering is proposed in this paper.This approach avoids the is-sue of excessive smoothing associated with larger window sizes.The proposed PCA-SG filtering algorithm was applied to a CO gas sensing system based on Cavity Ring-Down Spectroscopy(CRDS).The perform-ance of the PCA-SG filtering algorithm is demonstrated through comparison with Moving Average Filtering(MAF),Wavelet Transformation(WT),Kalman Filtering(KF),and the SG filter.The results demonstrate that the proposed algorithm exhibits superior noise reduction capabilities compared to the other algorithms evaluated.The SNR of the ring-down signal was improved from 11.8612 dB to 29.0913 dB,and the stand-ard deviation of the extracted ring-down time constant was reduced from 0.037μs to 0.018μs.These results confirm that the proposed PCA-SG filtering algorithm effectively improves the smoothness of the ring-down curve data,demonstrating its feasibility.展开更多
Crassostrea gigas has good taste and high nutritional value;however,there are few assessments of comprehensive and panoramic analyses of the nutritional quality of the northern oyster.To study the nutritional characte...Crassostrea gigas has good taste and high nutritional value;however,there are few assessments of comprehensive and panoramic analyses of the nutritional quality of the northern oyster.To study the nutritional characteristics of C.gigas from different sources(ploidy,region,size,and culture mode),C.gigas from various ploidy(diploid and triploid),regions(Rushan,Off-site fattening,and Rongcheng),sizes(small,medium,and large)and culture modes(nearshore and offshore)were selected for comparative analyses.The nutritional components(moisture,protein,fat,and mineral),flavor substances(taste amino acids,nucleotides,and succinic acid),and functional indices(eicosapentaenoic acid(EPA),docosahexaenoic acid(DHA),and taurine)of C.gigas were determined.Principal component analysis(PCA)was used to comprehensively evaluate the oysters and investigate the variations in nutritional quality.The PCA results indicate that protein,essential fatty acids,selenium,zinc,taste amino acids,taurine,EPA,and DHA were core components contributing to 82.25%of the cumulative variance,providing a more comprehensive reflection of the nutrient composition of C.gigas.The extensive quality rankings for the C.gigas were as follows:diploid>triploid,Rushan>fattening>Rongcheng,medium>large>small,and offshore>nearshore.The score rank revealed that diploid oysters of medium-size from Rushan demonstrated superior nutritional quality compared to other tested samples.This is the first comprehensive and systematic investigation of C.gigas in northern China to reveal the feature of nutrients,flavor,and functional components.The study provided data support for the culture,consumption,processing,research,and nutritional quality improvement of oyster industry.展开更多
Restoration of phase aberrations is crucial for addressing atmospheric turbulence in light propagation.Traditional restoration algorithms based on Zernike polynomials(ZPs)often encounter challenges related to high com...Restoration of phase aberrations is crucial for addressing atmospheric turbulence in light propagation.Traditional restoration algorithms based on Zernike polynomials(ZPs)often encounter challenges related to high computational complexity and insufficient capture of high-frequency phase aberration components,so we proposed a Principal-Component-Analysis-based method for representing phase aberrations.This paper discusses the factors influencing the accuracy of restoration,mainly including the sample space size and the sampling interval of D/r_(0),on the basis of characterizing phase aberrations by Principal Components(PCs).The experimental results show that a larger D/r_(0)sampling interval can ensure the generalization ability and robustness of the principal components in the case of a limited amount of original data,which can help to achieve high-precision deployment of the model in practical applications quickly.In the environment with relatively strong turbulence in the test set of D/r_(0)=24,the use of 34 terms of PCs can improve the corrected Strehl ratio(SR)from 0.007 to 0.1585,while the Strehl ratio of the light spot after restoration using 34 terms of ZPs is only 0.0215,demonstrating almost no correction effect.The results indicate that PCs can serve as a better alternative in representing and restoring the characteristics of atmospheric turbulence induced phase aberrations.These findings pave the way to use PCs of phase aberrations with fewer terms than traditional ZPs to achieve data dimensionality reduction,and offer a reference to accelerate and stabilize the model and deep learning based adaptive optics correction.展开更多
Purpose:This study analyzes the profiles of elite Brazilian researchers,recognized through the prestigious CNPq productivity scholarships.By identifying distinct researcher clusters,the study sheds light on different ...Purpose:This study analyzes the profiles of elite Brazilian researchers,recognized through the prestigious CNPq productivity scholarships.By identifying distinct researcher clusters,the study sheds light on different academic strategies,levels of productivity,and academic contributions within the Brazilian higher education system.Design/methodology/approach:The research analyzes a comprehensive dataset of 14,003 researchers,employing principal component analysis(PCA)followed by cluster analysis to group researchers based on their academic attributes.The clusters reflect diverse aspects of research productivity,graduate supervisions,and publication patterns.Findings:The analysis reveals the existence of three distinct researcher profiles(the Advanced Supervisors,the Book Publishers/Organizers,and the Generalists).The study also highlights the characteristics of highcaliber scientists,representing the upper echelon of Brazilian researchers in terms of productivity and impact.Research limitations:Although the study provides a robust analysis of the Brazilian system,the results reflect specific characteristics of the Brazilian academic context.Furthermore,the analysis was restricted to normalized annual data,which may overlook temporal variations in researcher productivity.Pratical implications:The findings provide valuable insights for policy makers,funding agencies(such as CNPq),and university administrators who aim to develop tailored support programs for different researcher profiles.Originality/value:The cluster-based profiling offers a novel perspective on how different academic trajectories coexist within a national science system,offering lessons for other emerging economies.展开更多
The Global Navigation Satellite System(GNSS)is vital for monitoring terrestrial water storage(TWS).However,effectively extracting hydrological load deformation from GNSS observations poses a significant challenge.This...The Global Navigation Satellite System(GNSS)is vital for monitoring terrestrial water storage(TWS).However,effectively extracting hydrological load deformation from GNSS observations poses a significant challenge.This study proposes a novel strategy;the seasonal hydrological load signals are removed from the raw data,and the remaining signals use principal component analysis(PCA).Simulation results from Yunnan Province demonstrate that the spatial distribution of the root mean square error(RMSE)is improved by approximately 15% compared with traditional PCA extraction from raw data.From January 2013 to December 2022,TWS was inverted from 24 GNSS stations in Yunnan Province.The spatial distribution and time series of TWS inverted from GNSS align well with those TWS inferred from the Gravity Recovery and Climate Experiment(GRACE),GRACE Follow-On(GFO),and the Global Land Data Assimilation System(GLDAS)land surface model.However,the amplitude of the GNSS-inverted TWS is slightly higher.Since GNSS ground stations are more sensitive to hydrological load signals,they show correlations with precipitation data that are 8.6%and 6.0%higher than those of GRACE and GLDAS,respectively.In the power spectral density analysis of GRACE/GFO,GLDAS,and GNSS,the signal strength of GNSS is much higher than that of GRACE/GFO and GLDAS in the June and February cycles.These findings suggest that the new data extraction strategy can capture higher frequency hydrological signals in TWS,and GNSS observations can help address limitations in GRACE/GFO observations.This study demonstrates the potential of GNSS TWS in capturing higher-frequency hydrological signals and climate extremes application.展开更多
Comprehensive evaluation based on mineral element content is one of the effective methods for the exploration and utilization of wild tulip germplasm resources.In this study,Tulipa iliensis,Tulipa tianschanica and Tul...Comprehensive evaluation based on mineral element content is one of the effective methods for the exploration and utilization of wild tulip germplasm resources.In this study,Tulipa iliensis,Tulipa tianschanica and Tulipa heterophylla distributed in China were used as the research objects.The contents of 10mineral elements(N,K,P,S,Ca,Mg,Cu,Zn,Fe,Mn)in roots,bulbs and leaves were determined,and the three wild tulips were comprehensively evaluated by correlation analysis,principal component analysis and cluster analysis.The results showed distinct variations in mineral element content among different organs of T.iliensis,T.tianschanica and T.heterophylla,with T.heterophylla exhibiting significantly higher mineral content across all organs compared to the other two wild tulips.Correlation analysis revealed significant(p<0.05)inter-element relationships in T.iliensis,T.tianschanica and T.heterophylla,with positive correlations between N and P,Ca and Zn in roots,P and Mg,P and Cu,Mg and Cu in bulbs,K and Mg,K and Fe,Zn and Mn,Mg and Fe in leaves,alongside a negative S and Fe correlation in leaves.The comprehensive evaluation identified N,S,Ca,and Zn as representative elements for assessing the three wild tulips,with their abundance ranking as follows:T.heterophylla>T.iliensis>T.tianschanica.The results of cluster analysis showed that T.heterophylla was clustered into one category in the roots because of the rich content of mineral elements.T.iliensis and T.tianschanica were clustered into one category in the bulbs because the accumulation of S element was higher than T.heterophylla.T.iliensis and T.heterophylla were clustered into one category in the leaves because of the rich content of mineral elements.The distribution of diverse mineral elements enables wild tulip germplasm resources to adapt to varied natural habitats,playing a decisive role in their response to specific environmental stresses.Studying mineral elements is an important way to gain an in-depth understanding of tulip germplasm resources.The results are of practical significance for conserving wild tulip resources and achieving sustainable utilization.展开更多
Traditional beamforming techniques may not accurately locate sources in scenarios with both stationary and rotating sound sources.The existence of rotating sound sources can cause blurring in the stationary beamformin...Traditional beamforming techniques may not accurately locate sources in scenarios with both stationary and rotating sound sources.The existence of rotating sound sources can cause blurring in the stationary beamforming map.Current algorithms for separating different moving sound sources have limited effectiveness,leading to significant residual noise,especially when the rotating source is strong enough to mask stationary sources completely.To overcome these challenges,a novel solution utilizing a virtual rotating array in the modal domain combined with robust principal component analysis is proposed to separate sound sources with different rotational speeds.This approach,named Robust Principal Component Analysis in the Modal domain(RPCA-M),investigates the performance of convex nuclear norm and non-convex Schatten-p norm to distinguish stationary and rotating sources.By comparing the errors in Cross-Spectral Matrix(CSM)recovery and acoustic imaging across different algorithms,the effectiveness of RPCA-M in separating stationary and moving sound sources is demonstrated.Importantly,this method effectively separates sound sources,even when there are significant variations in their amplitudes at different rotation speeds.展开更多
This study employs Principal Component Analysis(PCA)and 13 years of SD-WACCM-X model data(2007-2019)to investigate the characteristics and mechanisms of Inter-hemispheric Coupling(IHC)triggered by sudden stratospheric...This study employs Principal Component Analysis(PCA)and 13 years of SD-WACCM-X model data(2007-2019)to investigate the characteristics and mechanisms of Inter-hemispheric Coupling(IHC)triggered by sudden stratospheric warming(SSW)events.IHC in both hemispheres leads to a cold anomaly in the equatorial stratosphere,a warm anomaly in the equatorial mesosphere,and increased temperatures in the mesosphere and lower thermosphere(MLT)region of the summer hemisphere.However,the IHC features during boreal winter period are significantly weaker than during the austral winter period,primarily due to weaker stationary planetary wave activity in the Southern Hemisphere(SH).During the austral winter period,IHC results in a warm anomaly in the polar mesosphere of the SH,which does not occur in the NH during boreal winter period.This study also examines the possible influence of quasi-two-day waves(QTDWs)on IHC.We found that the largest temperature anomaly in the summer polar MLT region is associated with a large wind instability area,and a well-developed critical layer structure of QTDW in January.In contrast,during July,despite favorable conditions for QTDW propagation in the Northern Hemisphere,weaker IHC response is observed,suggesting that IHC features and the relationship with QTDWs during July would be more complex than during January.展开更多
Groundwater is a crucial water source for urban areas in Africa, particularly where surface water is insufficient to meet demand. This study analyses the water quality of five shallow wells (WW1-WW5) in Half-London Wa...Groundwater is a crucial water source for urban areas in Africa, particularly where surface water is insufficient to meet demand. This study analyses the water quality of five shallow wells (WW1-WW5) in Half-London Ward, Tunduma Town, Tanzania, using Principal Component Analysis (PCA) to identify the primary factors influencing groundwater contamination. Monthly samples were collected over 12 months and analysed for physical, chemical, and biological parameters. The PCA revealed between four and six principal components (PCs) for each well, explaining between 84.61% and 92.55% of the total variance in water quality data. In WW1, five PCs captured 87.53% of the variability, with PC1 (33.05%) dominated by pH, EC, TDS, and microbial contamination, suggesting significant influences from surface runoff and pit latrines. In WW2, six PCs explained 92.55% of the variance, with PC1 (36.17%) highlighting the effects of salinity, TDS, and agricultural runoff. WW3 had four PCs explaining 84.61% of the variance, with PC1 (39.63%) showing high contributions from pH, hardness, and salinity, indicating geological influences and contamination from human activities. Similarly, in WW4, six PCs explained 90.83% of the variance, where PC1 (43.53%) revealed contamination from pit latrines and fertilizers. WW5 also had six PCs, accounting for 92.51% of the variance, with PC1 (42.73%) indicating significant contamination from agricultural runoff and pit latrines. The study concludes that groundwater quality in Half-London Ward is primarily affected by a combination of surface runoff, pit latrine contamination, agricultural inputs, and geological factors. The presence of microbial contaminants and elevated nitrate and phosphate levels underscores the need for improved sanitation and sustainable agricultural practices. Recommendations include strengthening sanitation infrastructure, promoting responsible farming techniques, and implementing regular groundwater monitoring to safeguard water resources and public health in the region.展开更多
We study the influence of disorder on the Moore–Read state by principal component analysis(PCA),which is one of the ground state candidates for the 5/2 fractional Hall state.By using PCA,the topological features of t...We study the influence of disorder on the Moore–Read state by principal component analysis(PCA),which is one of the ground state candidates for the 5/2 fractional Hall state.By using PCA,the topological features of the ground state wave functions with different disorder strengths can be distilled.As the disorder strength increases,the Moore–Read state will be destroyed.We explore the phase transition by analyzing the overlaps between the random sample wave functions and the topologically distilled state.The cross-point between the amplitudes of the principal component and its counterpart is the phase transition point.Additionally,the origin of the second component comes from the excited states,which is different from the Laughlin state.展开更多
[Objective] This study aimed to explore the related mechanisms of the breaking of flue-cured tobacco leaves. [Method] Anti-breaking models of the main veins of flue-cured tobacco leaves were constructed for principal ...[Objective] This study aimed to explore the related mechanisms of the breaking of flue-cured tobacco leaves. [Method] Anti-breaking models of the main veins of flue-cured tobacco leaves were constructed for principal component analysis on the anti-breaking index, leaf traits and cellulose contents. [Result] The results showed that the growth traits had certain relevance with the cellulose contents while the leaf weight assumed a significant negative correlation with the anti-breaking index, indicating that the heavier the leaf weight was, the weaker the anti-breaking capacity of flue-cured tobacco would be; the cross-sectional area of main veins and the cellulose contents had shown a positive correlation with the anti-breaking index, indicating that the thicker the main vein of flue-cured tobacco was, the higher the cellulose contents would be, and the stronger the anti-breaking capacity of flue-cured tobacco leaves would be. [Conclusion] This study provided theoretical basis and reference to improve tobacco production and enhance the quality of flue-cured tobacco.展开更多
[Objective] This study was conducted to provide certain theoretical reference for the comprehensive evaluation and breeding of new fresh waxy corn vari- eties. [Method] With 5 good fresh waxy corn varieties as experim...[Objective] This study was conducted to provide certain theoretical reference for the comprehensive evaluation and breeding of new fresh waxy corn vari- eties. [Method] With 5 good fresh waxy corn varieties as experimental materials, correlation analysis and principal component anatysis were performed on 13 agronomic traits, i.e., plant height, ear position, ear weight, ear diameter, axis diameter, ear length, bald tip length, ear row number, number of grains per row, 100-kernel weight, fresh ear yield, tassel length, and tassel branch number. [Result] The principal component analysis performed to the 13 agronomic traits showed that the first three principal components, i.e., the fresh ear yield factors, the tassel factors and the bald top factors, had an accumulative contribution rate over 87.2767%, and could basically represent the genetic information represented by the 13 traits. The first principal component is the main index for the selection and evaluation of good corn varieties which should have large ear, large ear diameter but small axis diameter, i.e., longer grains, larger number of grains per ear, higher, 100-grain weight and higher plant height. As to the second principal component, the plants of fresh corn varieties are best to have longer tassel and not too many branches, and under the premise of ensuring enough pollen for the female spike, the varieties with fewer tassel branches shoud be selected as far as possible. From the point of the third principal component, bald tip length affects the marketing quality of fresh corn, and during fariety evaluation and breeding, the bald top length should be control at the Iowest standard. [Conclusion] The fresh ear yield of corn is in close positive correlation with ear weight, 100-grain weight, ear diameter, number of grains per row and ear length, and plant height also affects fresh ear yield.展开更多
Ground roll is an interference wave that severely degrades the signal-to-noise ratio of seismic data and affects its subsequent processing and interpretation.In this study,according to differences in morphological cha...Ground roll is an interference wave that severely degrades the signal-to-noise ratio of seismic data and affects its subsequent processing and interpretation.In this study,according to differences in morphological characteristics between ground roll and reflected waves,we use morphological component analysis based on two-dimensional dictionaries to separate ground roll and reflected waves.Because ground roll is characterized by lowfrequency,low-velocity,and dispersion,we select two-dimensional undecimated discrete wavelet transform as a sparse representation dictionary of ground roll.Because of a strong local correlation of the reflected wave,we select two-dimensional local discrete cosine transform as the sparse representation dictionary of reflected waves.A sparse representation model of seismic data is constructed based on a two-dimensional joint dictionary then a block coordinate relaxation algorithm is used to solve the model and decompose seismic record into reflected wave part and ground roll part.The good effects for the synthetic seismic data and application of real seismic data indicate that when using the model,strong-energy ground roll is considerably suppressed and the waveform of the reflected wave is effectively protected.展开更多
In principal component analysis （PCA） algorithms for face recognition, to reduce the influence of the eigenvectors which relate to the changes of the illumination on abstract features, a modified PCA （MPCA） algori...In principal component analysis （PCA） algorithms for face recognition, to reduce the influence of the eigenvectors which relate to the changes of the illumination on abstract features, a modified PCA （MPCA） algorithm is proposed. The method is based on the idea of reducing the influence of the eigenvectors associated with the large eigenvalues by normalizing the feature vector element by its corresponding standard deviation. The Yale face database and Yale face database B are used to verify the method. The simulation results show that, for front face and even under the condition of limited variation in the facial poses, the proposed method results in better performance than the conventional PCA and linear discriminant analysis （LDA） approaches, and the computational cost remains the same as that of the PCA, and much less than that of the LDA.展开更多
In order to overcome the shortcomings that the reconstructed spectral reflectance may be negative when using the classic principal component analysis （PCA）to reduce the dimensions of the multi-spectral data, a nonne...In order to overcome the shortcomings that the reconstructed spectral reflectance may be negative when using the classic principal component analysis （PCA）to reduce the dimensions of the multi-spectral data, a nonnegative constrained principal component analysis method is proposed to construct a low-dimensional multi-spectral space and accomplish the conversion between the new constructed space and the multispectral space. First, the reason behind the negative data is analyzed and a nonnegative constraint is imposed on the classic PCA. Then a set of nonnegative linear independence weight vectors of principal components is obtained, by which a lowdimensional space is constructed. Finally, a nonlinear optimization technique is used to determine the projection vectors of the high-dimensional multi-spectral data in the constructed space. Experimental results show that the proposed method can keep the reconstructed spectral data in [ 0, 1 ]. The precision of the space created by the proposed method is equivalent to or even higher than that by the PCA.展开更多
[Objective] This study aimed to analyze the morphological diversity of red- seed watermelon （Citrullus lanatus ssp. vulgaris var. megalaspermus Lin et Chao） germplasm resources. [Method] Multiple cluster analysis an...[Objective] This study aimed to analyze the morphological diversity of red- seed watermelon （Citrullus lanatus ssp. vulgaris var. megalaspermus Lin et Chao） germplasm resources. [Method] Multiple cluster analysis and principal components analysis on the morphological traits of 51 red-seed watermelon germplasm resources were carried out. [Result] The coefficient of variations （CVs） of 39 morphological traits in 51 red-seed watermelon idioplasm resources ranged from 5.37% to 66.95%, with an average of 22.87%. The average of Shannon diversity information indices was 1.55. Among them, the Shannon diversity information index of seed length was the highest （2.16） and that of seed shell figure pattern was the lowest （0.32）. In ad- dition, the morphological diversity information indices of quantity characters were higher than that of quality characters. The principal components analysis revealed that the variance contribution rates of the first, second and third principal compo- nents were 19.49%, 15.32% and 9.55%, respectively. Cluster analysis divided the 51 materials into three broad branches based on the morphological traits. There was only one material in the fist branch and two in the second branch, and all the three materials were wild. The other 48 materials were divided into the third branch and all of them were cultivars. [Conclusion] This study provided a theoretical basis for the protection and utilization of red-seed watermelon resources.展开更多
Matrix principal component analysis （MatPCA）, as an effective feature extraction method, can deal with the matrix pattern and the vector pattern. However, like PCA, MatPCA does not use the class information of sampl...Matrix principal component analysis （MatPCA）, as an effective feature extraction method, can deal with the matrix pattern and the vector pattern. However, like PCA, MatPCA does not use the class information of samples. As a result, the extracted features cannot provide enough useful information for distinguishing pat- tern from one another, and further resulting in degradation of classification performance. To fullly use class in- formation of samples, a novel method, called the fuzzy within-class MatPCA （F-WMatPCA）is proposed. F-WMatPCA utilizes the fuzzy K-nearest neighbor method（FKNN） to fuzzify the class membership degrees of a training sample and then performs fuzzy MatPCA within these patterns having the same class label. Due to more class information is used in feature extraction, F-WMatPCA can intuitively improve the classification perfor- mance. Experimental results in face databases and some benchmark datasets show that F-WMatPCA is effective and competitive than MatPCA. The experimental analysis on face image databases indicates that F-WMatPCA im- proves the recognition accuracy and is more stable and robust in performing classification than the existing method of fuzzy-based F-Fisherfaces.展开更多
[Objective] This study was conducted to explore the internal relationship among root biological traits of sweetpotato, as well as the regularity in their formation and differentiation. [Method] The root traits of 10 s...[Objective] This study was conducted to explore the internal relationship among root biological traits of sweetpotato, as well as the regularity in their formation and differentiation. [Method] The root traits of 10 sweetpotato cultivars were measured through hydroponic culture in a greenhouse and field survey, and then their correlations were analyzed by statistical methods. [Result] The root morphological traits of sweetpotato at seedling stage such as projected area, surface area, average diameter and volume processed the highest contribution rate （80.56%） 10 d after transplanting, and the contribution rate of root average diameter reached 27.79% 20 d after transplanting. Storage root fresh weight per plant shared extremely significant positive correlations with storage root fresh weight of penultimate node and storage root fresh weight of antepenultimate node, and a significant positive corre- lation with commercial storage root number, and a significant negative correlation with storage root number of penultimate node. Among them, the correlation coeffi- cient of storage root fresh weight per plant with storage root fresh weight of antepenultimate node was the highest （0.659 5）. Fifteen days after transplanting, storage root fresh weight per plant had significant negative correlations with root projected area, surface area and volume. There was a significant positive correlation between root dry weight and storage root fresh weight per plant 25 d after transplanting. Root dry weight, volume, length, average diameter of sweetpotato seedlings had higher relational degrees with storage root fresh weight per plant. Ten and twenty days after transplanting were important time for the growth and differentiation of sweetpotato roots. In addition, node length and planting depth had certain influence on sweetpotato yield, and direct relationship existed between the seedling root biological traits and storage root yield of sweetpotato. [Conclusion] The results provide theoretical support for standard cultivation and new variety breeding of sweetpotato.展开更多
In order to distinguish 8 kinds of rhizome crops, the 40 samples were studied by Fourier transform infrared spectroscopy （FTIR） combined with wavelet transform （WT）, principal component analysis （PCA） and hieram...In order to distinguish 8 kinds of rhizome crops, the 40 samples were studied by Fourier transform infrared spectroscopy （FTIR） combined with wavelet transform （WT）, principal component analysis （PCA） and hieramhical cluster analysis （HCA）. The results showed that the infrared spectra were similar on the whole, but there were differences in peak position, peak shape and peak absorption intensity in the range of 1 800-700 cm-1. The infrared spectra in the range of 1 800-700 cm-1 were selected to perform continuous wavelet transform （CWT） and discrete wavelet transform （DWT）. The 15th-Ievel decomposition coefficients of CWT and the 5=-level detail coefficients of DWT were classified by PCA and HCA. The cumulative contri- bution rates of the first three principal components of CWT and DWT were 93.12% and 89.78%, respectively. The accurate recognition rates of PCA and HCA were all 100%. It is proved that FTIR combined with WT can be used to distinguish different kinds of rhizome crops.展开更多
Objective] The alm was to survey 10 characters of 8 fresh edibIe soy-bean varieties, analyze maln Ioading factors using principal component analysis, and estabIish muItipIe regression equation on fresh pod yield. [Met...Objective] The alm was to survey 10 characters of 8 fresh edibIe soy-bean varieties, analyze maln Ioading factors using principal component analysis, and estabIish muItipIe regression equation on fresh pod yield. [Methods] Through princi-pal component analysis on 10 characters of 8 fresh edibIe soybean varieties, char-acters reIated to fresh pod yield of fresh edibIe soybean were cIarified. [Results] Af-ter the principal components analysis, pod weight per pIant, 100-seed weight and pod number per pIant of fresh edibIe soybean were chosen to study their reIation with the yield of fresh edibIe soybean, moreover, it was demonstrated that the reIa-tion was Iinear reIation, thus it was suitabIe for muItivariate regression analysis. Fi-nal y, the mathematical expression formuIa about fresh pod yield was estabIished. [Conclusions] There were three characters affecting fresh pod yield, nameIy, pod weight per pIant, 100-seed weight and pod number per pIant, the mathematical equation was y=816.732＋4.145X6-0.718X8-0.985X9 （X6： pod weight per pIant; X8： 100-seed weight; X9： pod number per pIant）.展开更多
文摘The Savitzky-Golay(SG)filter,which employs polynomial least-squares approximations to smooth data and estimate derivatives,is widely used for processing noisy data.However,noise suppression by the SG filter is recognized to be limited at data boundaries and high frequencies,which can significantly reduce the signal-to-noise ratio(SNR).To solve this problem,a novel method synergistically integrating Principal Component Analysis(PCA)with SG filtering is proposed in this paper.This approach avoids the is-sue of excessive smoothing associated with larger window sizes.The proposed PCA-SG filtering algorithm was applied to a CO gas sensing system based on Cavity Ring-Down Spectroscopy(CRDS).The perform-ance of the PCA-SG filtering algorithm is demonstrated through comparison with Moving Average Filtering(MAF),Wavelet Transformation(WT),Kalman Filtering(KF),and the SG filter.The results demonstrate that the proposed algorithm exhibits superior noise reduction capabilities compared to the other algorithms evaluated.The SNR of the ring-down signal was improved from 11.8612 dB to 29.0913 dB,and the stand-ard deviation of the extracted ring-down time constant was reduced from 0.037μs to 0.018μs.These results confirm that the proposed PCA-SG filtering algorithm effectively improves the smoothness of the ring-down curve data,demonstrating its feasibility.
基金Supported by the Central Public-interest Scientific Institution Basal Research Fund,YSFRI,CAFS(No.20603022024016)the Central Public-interest Scientific Institution Basal Research Fund,CAFS(Nos.2023TD52,2023TD76)the earmarked fund for CARS(No.CARS-49)。
文摘Crassostrea gigas has good taste and high nutritional value;however,there are few assessments of comprehensive and panoramic analyses of the nutritional quality of the northern oyster.To study the nutritional characteristics of C.gigas from different sources(ploidy,region,size,and culture mode),C.gigas from various ploidy(diploid and triploid),regions(Rushan,Off-site fattening,and Rongcheng),sizes(small,medium,and large)and culture modes(nearshore and offshore)were selected for comparative analyses.The nutritional components(moisture,protein,fat,and mineral),flavor substances(taste amino acids,nucleotides,and succinic acid),and functional indices(eicosapentaenoic acid(EPA),docosahexaenoic acid(DHA),and taurine)of C.gigas were determined.Principal component analysis(PCA)was used to comprehensively evaluate the oysters and investigate the variations in nutritional quality.The PCA results indicate that protein,essential fatty acids,selenium,zinc,taste amino acids,taurine,EPA,and DHA were core components contributing to 82.25%of the cumulative variance,providing a more comprehensive reflection of the nutrient composition of C.gigas.The extensive quality rankings for the C.gigas were as follows:diploid>triploid,Rushan>fattening>Rongcheng,medium>large>small,and offshore>nearshore.The score rank revealed that diploid oysters of medium-size from Rushan demonstrated superior nutritional quality compared to other tested samples.This is the first comprehensive and systematic investigation of C.gigas in northern China to reveal the feature of nutrients,flavor,and functional components.The study provided data support for the culture,consumption,processing,research,and nutritional quality improvement of oyster industry.
文摘Restoration of phase aberrations is crucial for addressing atmospheric turbulence in light propagation.Traditional restoration algorithms based on Zernike polynomials(ZPs)often encounter challenges related to high computational complexity and insufficient capture of high-frequency phase aberration components,so we proposed a Principal-Component-Analysis-based method for representing phase aberrations.This paper discusses the factors influencing the accuracy of restoration,mainly including the sample space size and the sampling interval of D/r_(0),on the basis of characterizing phase aberrations by Principal Components(PCs).The experimental results show that a larger D/r_(0)sampling interval can ensure the generalization ability and robustness of the principal components in the case of a limited amount of original data,which can help to achieve high-precision deployment of the model in practical applications quickly.In the environment with relatively strong turbulence in the test set of D/r_(0)=24,the use of 34 terms of PCs can improve the corrected Strehl ratio(SR)from 0.007 to 0.1585,while the Strehl ratio of the light spot after restoration using 34 terms of ZPs is only 0.0215,demonstrating almost no correction effect.The results indicate that PCs can serve as a better alternative in representing and restoring the characteristics of atmospheric turbulence induced phase aberrations.These findings pave the way to use PCs of phase aberrations with fewer terms than traditional ZPs to achieve data dimensionality reduction,and offer a reference to accelerate and stabilize the model and deep learning based adaptive optics correction.
文摘Purpose:This study analyzes the profiles of elite Brazilian researchers,recognized through the prestigious CNPq productivity scholarships.By identifying distinct researcher clusters,the study sheds light on different academic strategies,levels of productivity,and academic contributions within the Brazilian higher education system.Design/methodology/approach:The research analyzes a comprehensive dataset of 14,003 researchers,employing principal component analysis(PCA)followed by cluster analysis to group researchers based on their academic attributes.The clusters reflect diverse aspects of research productivity,graduate supervisions,and publication patterns.Findings:The analysis reveals the existence of three distinct researcher profiles(the Advanced Supervisors,the Book Publishers/Organizers,and the Generalists).The study also highlights the characteristics of highcaliber scientists,representing the upper echelon of Brazilian researchers in terms of productivity and impact.Research limitations:Although the study provides a robust analysis of the Brazilian system,the results reflect specific characteristics of the Brazilian academic context.Furthermore,the analysis was restricted to normalized annual data,which may overlook temporal variations in researcher productivity.Pratical implications:The findings provide valuable insights for policy makers,funding agencies(such as CNPq),and university administrators who aim to develop tailored support programs for different researcher profiles.Originality/value:The cluster-based profiling offers a novel perspective on how different academic trajectories coexist within a national science system,offering lessons for other emerging economies.
基金supported by the Natural Science Foundation of China(Grant Nos.42374032,42174103,42004073)Provincial Natural Science Foundation(2024JJ8348)the Key Laboratory of Natural Resources Monitoring and Supervision in Southern Hilly Region,Ministry of Natural Resources(NRMSSHR2023Y01)。
文摘The Global Navigation Satellite System(GNSS)is vital for monitoring terrestrial water storage(TWS).However,effectively extracting hydrological load deformation from GNSS observations poses a significant challenge.This study proposes a novel strategy;the seasonal hydrological load signals are removed from the raw data,and the remaining signals use principal component analysis(PCA).Simulation results from Yunnan Province demonstrate that the spatial distribution of the root mean square error(RMSE)is improved by approximately 15% compared with traditional PCA extraction from raw data.From January 2013 to December 2022,TWS was inverted from 24 GNSS stations in Yunnan Province.The spatial distribution and time series of TWS inverted from GNSS align well with those TWS inferred from the Gravity Recovery and Climate Experiment(GRACE),GRACE Follow-On(GFO),and the Global Land Data Assimilation System(GLDAS)land surface model.However,the amplitude of the GNSS-inverted TWS is slightly higher.Since GNSS ground stations are more sensitive to hydrological load signals,they show correlations with precipitation data that are 8.6%and 6.0%higher than those of GRACE and GLDAS,respectively.In the power spectral density analysis of GRACE/GFO,GLDAS,and GNSS,the signal strength of GNSS is much higher than that of GRACE/GFO and GLDAS in the June and February cycles.These findings suggest that the new data extraction strategy can capture higher frequency hydrological signals in TWS,and GNSS observations can help address limitations in GRACE/GFO observations.This study demonstrates the potential of GNSS TWS in capturing higher-frequency hydrological signals and climate extremes application.
基金supported by the“Natural Science Foundation Project of Qinghai Province,China[2025-ZJ-950M]”and“West Light Foundation.Chinese Academy of Science[1–7]”.
文摘Comprehensive evaluation based on mineral element content is one of the effective methods for the exploration and utilization of wild tulip germplasm resources.In this study,Tulipa iliensis,Tulipa tianschanica and Tulipa heterophylla distributed in China were used as the research objects.The contents of 10mineral elements(N,K,P,S,Ca,Mg,Cu,Zn,Fe,Mn)in roots,bulbs and leaves were determined,and the three wild tulips were comprehensively evaluated by correlation analysis,principal component analysis and cluster analysis.The results showed distinct variations in mineral element content among different organs of T.iliensis,T.tianschanica and T.heterophylla,with T.heterophylla exhibiting significantly higher mineral content across all organs compared to the other two wild tulips.Correlation analysis revealed significant(p<0.05)inter-element relationships in T.iliensis,T.tianschanica and T.heterophylla,with positive correlations between N and P,Ca and Zn in roots,P and Mg,P and Cu,Mg and Cu in bulbs,K and Mg,K and Fe,Zn and Mn,Mg and Fe in leaves,alongside a negative S and Fe correlation in leaves.The comprehensive evaluation identified N,S,Ca,and Zn as representative elements for assessing the three wild tulips,with their abundance ranking as follows:T.heterophylla>T.iliensis>T.tianschanica.The results of cluster analysis showed that T.heterophylla was clustered into one category in the roots because of the rich content of mineral elements.T.iliensis and T.tianschanica were clustered into one category in the bulbs because the accumulation of S element was higher than T.heterophylla.T.iliensis and T.heterophylla were clustered into one category in the leaves because of the rich content of mineral elements.The distribution of diverse mineral elements enables wild tulip germplasm resources to adapt to varied natural habitats,playing a decisive role in their response to specific environmental stresses.Studying mineral elements is an important way to gain an in-depth understanding of tulip germplasm resources.The results are of practical significance for conserving wild tulip resources and achieving sustainable utilization.
基金supported by the National Key Research and Development Plan of China(No.2023YFB3406500)the National Natural Science Foundation of China(No.52475132)+2 种基金the Aeronautical Science Foundation of China(No.20200015053001)the Shaanxi Key Research Program Project,China(No.2024GX-ZDCYL-01–16)the Xi’an Key Industrial Chain Technology Research Project,China(No.2023JH-RGZNGG-0033)。
文摘Traditional beamforming techniques may not accurately locate sources in scenarios with both stationary and rotating sound sources.The existence of rotating sound sources can cause blurring in the stationary beamforming map.Current algorithms for separating different moving sound sources have limited effectiveness,leading to significant residual noise,especially when the rotating source is strong enough to mask stationary sources completely.To overcome these challenges,a novel solution utilizing a virtual rotating array in the modal domain combined with robust principal component analysis is proposed to separate sound sources with different rotational speeds.This approach,named Robust Principal Component Analysis in the Modal domain(RPCA-M),investigates the performance of convex nuclear norm and non-convex Schatten-p norm to distinguish stationary and rotating sources.By comparing the errors in Cross-Spectral Matrix(CSM)recovery and acoustic imaging across different algorithms,the effectiveness of RPCA-M in separating stationary and moving sound sources is demonstrated.Importantly,this method effectively separates sound sources,even when there are significant variations in their amplitudes at different rotation speeds.
基金supported by the National Natural Science Foundation of China(Grant Numbers 42374195 and 42188101)the fellowship of China National Postdoctoral Program for Innovative Talents(Grant Number BX20230273)+1 种基金the Hubei Provincial Natural Science Foundation of China(Grant Number 2024AFB-097)the Postdoctor Project of Hubei Province(Grant Number 2024HBBHCXA054).
文摘This study employs Principal Component Analysis(PCA)and 13 years of SD-WACCM-X model data(2007-2019)to investigate the characteristics and mechanisms of Inter-hemispheric Coupling(IHC)triggered by sudden stratospheric warming(SSW)events.IHC in both hemispheres leads to a cold anomaly in the equatorial stratosphere,a warm anomaly in the equatorial mesosphere,and increased temperatures in the mesosphere and lower thermosphere(MLT)region of the summer hemisphere.However,the IHC features during boreal winter period are significantly weaker than during the austral winter period,primarily due to weaker stationary planetary wave activity in the Southern Hemisphere(SH).During the austral winter period,IHC results in a warm anomaly in the polar mesosphere of the SH,which does not occur in the NH during boreal winter period.This study also examines the possible influence of quasi-two-day waves(QTDWs)on IHC.We found that the largest temperature anomaly in the summer polar MLT region is associated with a large wind instability area,and a well-developed critical layer structure of QTDW in January.In contrast,during July,despite favorable conditions for QTDW propagation in the Northern Hemisphere,weaker IHC response is observed,suggesting that IHC features and the relationship with QTDWs during July would be more complex than during January.
文摘Groundwater is a crucial water source for urban areas in Africa, particularly where surface water is insufficient to meet demand. This study analyses the water quality of five shallow wells (WW1-WW5) in Half-London Ward, Tunduma Town, Tanzania, using Principal Component Analysis (PCA) to identify the primary factors influencing groundwater contamination. Monthly samples were collected over 12 months and analysed for physical, chemical, and biological parameters. The PCA revealed between four and six principal components (PCs) for each well, explaining between 84.61% and 92.55% of the total variance in water quality data. In WW1, five PCs captured 87.53% of the variability, with PC1 (33.05%) dominated by pH, EC, TDS, and microbial contamination, suggesting significant influences from surface runoff and pit latrines. In WW2, six PCs explained 92.55% of the variance, with PC1 (36.17%) highlighting the effects of salinity, TDS, and agricultural runoff. WW3 had four PCs explaining 84.61% of the variance, with PC1 (39.63%) showing high contributions from pH, hardness, and salinity, indicating geological influences and contamination from human activities. Similarly, in WW4, six PCs explained 90.83% of the variance, where PC1 (43.53%) revealed contamination from pit latrines and fertilizers. WW5 also had six PCs, accounting for 92.51% of the variance, with PC1 (42.73%) indicating significant contamination from agricultural runoff and pit latrines. The study concludes that groundwater quality in Half-London Ward is primarily affected by a combination of surface runoff, pit latrine contamination, agricultural inputs, and geological factors. The presence of microbial contaminants and elevated nitrate and phosphate levels underscores the need for improved sanitation and sustainable agricultural practices. Recommendations include strengthening sanitation infrastructure, promoting responsible farming techniques, and implementing regular groundwater monitoring to safeguard water resources and public health in the region.
基金supported by the National Natural Science Foundation of China(Grant Nos.12104075 and 12347101).
文摘We study the influence of disorder on the Moore–Read state by principal component analysis(PCA),which is one of the ground state candidates for the 5/2 fractional Hall state.By using PCA,the topological features of the ground state wave functions with different disorder strengths can be distilled.As the disorder strength increases,the Moore–Read state will be destroyed.We explore the phase transition by analyzing the overlaps between the random sample wave functions and the topologically distilled state.The cross-point between the amplitudes of the principal component and its counterpart is the phase transition point.Additionally,the origin of the second component comes from the excited states,which is different from the Laughlin state.
基金Supported by the Fund of Anhui Provincial Tobacco Monopoly Bureau(AHKJ2008-03)Anhui Provincial University Key Project of Natural Science(KJ2010A114)Undergraduate Student Science and Technology Innovation Fund of Anhui Agricultural University(2010233)~~
文摘[Objective] This study aimed to explore the related mechanisms of the breaking of flue-cured tobacco leaves. [Method] Anti-breaking models of the main veins of flue-cured tobacco leaves were constructed for principal component analysis on the anti-breaking index, leaf traits and cellulose contents. [Result] The results showed that the growth traits had certain relevance with the cellulose contents while the leaf weight assumed a significant negative correlation with the anti-breaking index, indicating that the heavier the leaf weight was, the weaker the anti-breaking capacity of flue-cured tobacco would be; the cross-sectional area of main veins and the cellulose contents had shown a positive correlation with the anti-breaking index, indicating that the thicker the main vein of flue-cured tobacco was, the higher the cellulose contents would be, and the stronger the anti-breaking capacity of flue-cured tobacco leaves would be. [Conclusion] This study provided theoretical basis and reference to improve tobacco production and enhance the quality of flue-cured tobacco.
文摘[Objective] This study was conducted to provide certain theoretical reference for the comprehensive evaluation and breeding of new fresh waxy corn vari- eties. [Method] With 5 good fresh waxy corn varieties as experimental materials, correlation analysis and principal component anatysis were performed on 13 agronomic traits, i.e., plant height, ear position, ear weight, ear diameter, axis diameter, ear length, bald tip length, ear row number, number of grains per row, 100-kernel weight, fresh ear yield, tassel length, and tassel branch number. [Result] The principal component analysis performed to the 13 agronomic traits showed that the first three principal components, i.e., the fresh ear yield factors, the tassel factors and the bald top factors, had an accumulative contribution rate over 87.2767%, and could basically represent the genetic information represented by the 13 traits. The first principal component is the main index for the selection and evaluation of good corn varieties which should have large ear, large ear diameter but small axis diameter, i.e., longer grains, larger number of grains per ear, higher, 100-grain weight and higher plant height. As to the second principal component, the plants of fresh corn varieties are best to have longer tassel and not too many branches, and under the premise of ensuring enough pollen for the female spike, the varieties with fewer tassel branches shoud be selected as far as possible. From the point of the third principal component, bald tip length affects the marketing quality of fresh corn, and during fariety evaluation and breeding, the bald top length should be control at the Iowest standard. [Conclusion] The fresh ear yield of corn is in close positive correlation with ear weight, 100-grain weight, ear diameter, number of grains per row and ear length, and plant height also affects fresh ear yield.
基金supported by the National Scientific Equipment Development Project,"Deep Resource Exploration Core Equipment Research and Development"(Grant No.ZDYZ2012-1)06 Subproject,"Metal Mine Earthquake Detection System"and 05 Subject,"System Integration Field Test and Processing Software Development"
文摘Ground roll is an interference wave that severely degrades the signal-to-noise ratio of seismic data and affects its subsequent processing and interpretation.In this study,according to differences in morphological characteristics between ground roll and reflected waves,we use morphological component analysis based on two-dimensional dictionaries to separate ground roll and reflected waves.Because ground roll is characterized by lowfrequency,low-velocity,and dispersion,we select two-dimensional undecimated discrete wavelet transform as a sparse representation dictionary of ground roll.Because of a strong local correlation of the reflected wave,we select two-dimensional local discrete cosine transform as the sparse representation dictionary of reflected waves.A sparse representation model of seismic data is constructed based on a two-dimensional joint dictionary then a block coordinate relaxation algorithm is used to solve the model and decompose seismic record into reflected wave part and ground roll part.The good effects for the synthetic seismic data and application of real seismic data indicate that when using the model,strong-energy ground roll is considerably suppressed and the waveform of the reflected wave is effectively protected.
文摘In principal component analysis （PCA） algorithms for face recognition, to reduce the influence of the eigenvectors which relate to the changes of the illumination on abstract features, a modified PCA （MPCA） algorithm is proposed. The method is based on the idea of reducing the influence of the eigenvectors associated with the large eigenvalues by normalizing the feature vector element by its corresponding standard deviation. The Yale face database and Yale face database B are used to verify the method. The simulation results show that, for front face and even under the condition of limited variation in the facial poses, the proposed method results in better performance than the conventional PCA and linear discriminant analysis （LDA） approaches, and the computational cost remains the same as that of the PCA, and much less than that of the LDA.
基金The Pre-Research Foundation of National Ministries andCommissions (No9140A16050109DZ01)the Scientific Research Program of the Education Department of Shanxi Province (No09JK701)
文摘In order to overcome the shortcomings that the reconstructed spectral reflectance may be negative when using the classic principal component analysis （PCA）to reduce the dimensions of the multi-spectral data, a nonnegative constrained principal component analysis method is proposed to construct a low-dimensional multi-spectral space and accomplish the conversion between the new constructed space and the multispectral space. First, the reason behind the negative data is analyzed and a nonnegative constraint is imposed on the classic PCA. Then a set of nonnegative linear independence weight vectors of principal components is obtained, by which a lowdimensional space is constructed. Finally, a nonlinear optimization technique is used to determine the projection vectors of the high-dimensional multi-spectral data in the constructed space. Experimental results show that the proposed method can keep the reconstructed spectral data in [ 0, 1 ]. The precision of the space created by the proposed method is equivalent to or even higher than that by the PCA.
基金Supported by the National Program for Space Breeding Special Fund of(2006HT100113)China Agriculture Research System(CARS-26)~~
文摘[Objective] This study aimed to analyze the morphological diversity of red- seed watermelon （Citrullus lanatus ssp. vulgaris var. megalaspermus Lin et Chao） germplasm resources. [Method] Multiple cluster analysis and principal components analysis on the morphological traits of 51 red-seed watermelon germplasm resources were carried out. [Result] The coefficient of variations （CVs） of 39 morphological traits in 51 red-seed watermelon idioplasm resources ranged from 5.37% to 66.95%, with an average of 22.87%. The average of Shannon diversity information indices was 1.55. Among them, the Shannon diversity information index of seed length was the highest （2.16） and that of seed shell figure pattern was the lowest （0.32）. In ad- dition, the morphological diversity information indices of quantity characters were higher than that of quality characters. The principal components analysis revealed that the variance contribution rates of the first, second and third principal compo- nents were 19.49%, 15.32% and 9.55%, respectively. Cluster analysis divided the 51 materials into three broad branches based on the morphological traits. There was only one material in the fist branch and two in the second branch, and all the three materials were wild. The other 48 materials were divided into the third branch and all of them were cultivars. [Conclusion] This study provided a theoretical basis for the protection and utilization of red-seed watermelon resources.
文摘Matrix principal component analysis （MatPCA）, as an effective feature extraction method, can deal with the matrix pattern and the vector pattern. However, like PCA, MatPCA does not use the class information of samples. As a result, the extracted features cannot provide enough useful information for distinguishing pat- tern from one another, and further resulting in degradation of classification performance. To fullly use class in- formation of samples, a novel method, called the fuzzy within-class MatPCA （F-WMatPCA）is proposed. F-WMatPCA utilizes the fuzzy K-nearest neighbor method（FKNN） to fuzzify the class membership degrees of a training sample and then performs fuzzy MatPCA within these patterns having the same class label. Due to more class information is used in feature extraction, F-WMatPCA can intuitively improve the classification perfor- mance. Experimental results in face databases and some benchmark datasets show that F-WMatPCA is effective and competitive than MatPCA. The experimental analysis on face image databases indicates that F-WMatPCA im- proves the recognition accuracy and is more stable and robust in performing classification than the existing method of fuzzy-based F-Fisherfaces.
文摘[Objective] This study was conducted to explore the internal relationship among root biological traits of sweetpotato, as well as the regularity in their formation and differentiation. [Method] The root traits of 10 sweetpotato cultivars were measured through hydroponic culture in a greenhouse and field survey, and then their correlations were analyzed by statistical methods. [Result] The root morphological traits of sweetpotato at seedling stage such as projected area, surface area, average diameter and volume processed the highest contribution rate （80.56%） 10 d after transplanting, and the contribution rate of root average diameter reached 27.79% 20 d after transplanting. Storage root fresh weight per plant shared extremely significant positive correlations with storage root fresh weight of penultimate node and storage root fresh weight of antepenultimate node, and a significant positive corre- lation with commercial storage root number, and a significant negative correlation with storage root number of penultimate node. Among them, the correlation coeffi- cient of storage root fresh weight per plant with storage root fresh weight of antepenultimate node was the highest （0.659 5）. Fifteen days after transplanting, storage root fresh weight per plant had significant negative correlations with root projected area, surface area and volume. There was a significant positive correlation between root dry weight and storage root fresh weight per plant 25 d after transplanting. Root dry weight, volume, length, average diameter of sweetpotato seedlings had higher relational degrees with storage root fresh weight per plant. Ten and twenty days after transplanting were important time for the growth and differentiation of sweetpotato roots. In addition, node length and planting depth had certain influence on sweetpotato yield, and direct relationship existed between the seedling root biological traits and storage root yield of sweetpotato. [Conclusion] The results provide theoretical support for standard cultivation and new variety breeding of sweetpotato.
基金Supported by National Natural Science Foundation of China(30960179)Program for Innovative Research Team in Science and Technology in University of Yunnan Province~~
文摘In order to distinguish 8 kinds of rhizome crops, the 40 samples were studied by Fourier transform infrared spectroscopy （FTIR） combined with wavelet transform （WT）, principal component analysis （PCA） and hieramhical cluster analysis （HCA）. The results showed that the infrared spectra were similar on the whole, but there were differences in peak position, peak shape and peak absorption intensity in the range of 1 800-700 cm-1. The infrared spectra in the range of 1 800-700 cm-1 were selected to perform continuous wavelet transform （CWT） and discrete wavelet transform （DWT）. The 15th-Ievel decomposition coefficients of CWT and the 5=-level detail coefficients of DWT were classified by PCA and HCA. The cumulative contri- bution rates of the first three principal components of CWT and DWT were 93.12% and 89.78%, respectively. The accurate recognition rates of PCA and HCA were all 100%. It is proved that FTIR combined with WT can be used to distinguish different kinds of rhizome crops.
文摘Objective] The alm was to survey 10 characters of 8 fresh edibIe soy-bean varieties, analyze maln Ioading factors using principal component analysis, and estabIish muItipIe regression equation on fresh pod yield. [Methods] Through princi-pal component analysis on 10 characters of 8 fresh edibIe soybean varieties, char-acters reIated to fresh pod yield of fresh edibIe soybean were cIarified. [Results] Af-ter the principal components analysis, pod weight per pIant, 100-seed weight and pod number per pIant of fresh edibIe soybean were chosen to study their reIation with the yield of fresh edibIe soybean, moreover, it was demonstrated that the reIa-tion was Iinear reIation, thus it was suitabIe for muItivariate regression analysis. Fi-nal y, the mathematical expression formuIa about fresh pod yield was estabIished. [Conclusions] There were three characters affecting fresh pod yield, nameIy, pod weight per pIant, 100-seed weight and pod number per pIant, the mathematical equation was y=816.732＋4.145X6-0.718X8-0.985X9 （X6： pod weight per pIant; X8： 100-seed weight; X9： pod number per pIant）.
