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Research of Nitrate Concentration Prediction Based on GA

Research of Nitrate Concentration Prediction Based on GA

Conference on Ocean Optics and Information Technology / International Symposium on Optoelectronic Technology and Application (OTA)

作者： Wang Xueji Hu Bingliang Yu Tao Liu Qingsong Li Hongbo Fan Yao Chinese Acad Sci Xian Inst Opt & Precis Mech Lab Spectral Imaging Tech Xian 710119 Shaanxi Peoples R China Univ Chinese Acad Sci Beijing 100049 Peoples R China

ISBN: (数字)9781510623439

ISBN: (纸本)9781510623439

High concentration of nitrate will cause many problems, such as water eutrophication and compromise of human beings' health. A fast and stable approach was applied to predict nitrate concentration in solutions using the dual optical active correction continuous spectrum analyzer designed by our research group. Firstly, standard normal variate (SNV) was used to pretreat the spectral data. Then characteristic wavelengths of spectral curve were selected by using successive projections algorithm (SPA) and genetic algorithm (GA) respectively. Finally, partial least-squares regression (PLSR) was applied to build the spectral prediction model to predict nitrate concentration, and coefficient of determination (R-2) and root mean square error of prediction (RMSEP) were introduced as the evaluation indicators of prediction models. For SNV-GA-PLS model, R-2=0.9966 and RMSEP=0.1712, which outperformed than SNV-UVE-SPA-PLS model (R-2=0.9896, RMSEP=0.3952). It demonstrated that he model which selects spectral characteristic wavelengths by GA can decrease the complexity of prediction model building and ensure the accuracy as well. Hence, SNV-GA-PLS model can be used to predict nitrate concentration in water with quick and steady performance.

关键词： Nitrate concentration Ultraviolet/ visible spectral technology Genetic algorithm successive projections algorithm

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Preliminary Study of Determination of Dinophysis Concentration Using Hyperspectral Imaging

Preliminary Study of Determination of <i>Dinophysis</i> Conc...

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OCEANS MTS/IEEE Conference

作者： Shen, Ye Huang, Hui Song, Hong Zhan, Shuyue Han, Jun Chen, Ying Zhejiang Univ Ocean Coll Hangzhou 310058 Zhejiang Peoples R China

ISBN: (纸本)9780933957435

Conventional measurement methods were based on microscope, which was time-consuming and complex operation. In this paper, the aim of this work was to preliminary study the feasibility of the rapid and precision determination of Dinophysis concentration at the wavelength of 495-1030 nm. The transmittance spectra of algae liquid were obtained by hyperspectral transmittance image system. The region of interest (ROI) of each hyperspectral images was segmented and the mean spectra data were extracted from the ROI, then the data was arranged in a matrix and applied to further analysis. Partial least square regression (PLSR) was then used to build model with full spectral wavelengths. Nine wavelengths were chosen as the optimal wavelengths using the successive projections algorithm (SPA) to establish the new simplified model (SPA-PLSR) to correlate the selected spectral feature with Dinophysis concentration. By comparing the performance of different models, the best result was by PLSR based on optimal spectral wavelengths with correlation coefficient (R) of 0.9811. The preliminary study revealed the possibility of hyperspectral transmittance imaging as a rapid method to determine Dinophysis concentration.

关键词： Dinophysis concentration Image processing Hyperspectral transmittance imaging successive projections algorithm Preliminary study

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Hyperspectral Characteristics of Celosia Argentea Which Lived in Manganese Stress Environment and Inversion Model for Concentration Effect of Manganese

Hyperspectral Characteristics of Celosia Argentea Which Live...

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International Conference on Intelligent Earth Observing and Applications (IEOAs)

作者： Chen, Sanming Lin, Gang Yin, Xianyang Sun, Xiaolin Xu, Jiasheng Liu, Zhiying Guilin Univ Technol Coll Earth Sci Guilin 541004 Guangxi Peoples R China State Key Lab Continental Tecton & Dynam Beijing 100037 Peoples R China

ISBN: (纸本)9781510600492

Sedimentary manganese deposits widely distribute in North Guangxi with the characteristic existing Celosia argentea. Celosia argentea is a kind of plant which has a strong ability to enrich manganese. In order to study the relationship between the hyperspectral characteristics of Celosia argentea and the concentration effect of manganese in the soil, we used soil of B layer in mining area, background soil and the soil adding reagent of MnCl4 to make up experimental sample soil with 10 levels Manganese content for the same batch Celosia argentea. The levels are 0mg/kg, 4500mg/kg, 9000mg/kg, 13500mg/kg, 18000mg/kg, 18020mg/kg, 18040mg/kg, 18080mg/kg, 18160mg/kg. ASD FieldSpec-4 has been used to measure the abnormal spectrums of these Celosia argentea through a whole growth cycle. After pretreating the spectral data, we used successive projections algorithm (SPA) to extract the characteristic variables for extracting 1603 bands into 8 bands. Finally, the relationship between the spectral variables and the concentration of manganese was predicted by the Model of Partial Least Squares Regression (PLSR). The results show that the correlation coefficient-r(2) are 0.8714 and 0.9141 in two sets of data. The prediction results are satisfactory, but the front 5 groups are closer to the regression line than the last 5 groups.

关键词： Celosia argentea spectral reflectance stress vegetation successive projections algorithm partial least squares regression.

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Quantitative Monitoring of Leaf Area Index in Rice Based on Hyperspectral Feature Bands and Ridge Regression algorithm

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REMOTE SENSING 2022年第12期14卷 2777-2777页

作者： Ji, Shu Gu, Chen Xi, Xiaobo Zhang, Zhenghua Hong, Qingqing Huo, Zhongyang Zhao, Haitao Zhang, Ruihong Li, Bin Tan, Changwei Yangzhou Univ Jiangsu Key Lab Crop Genet & Physiol Jiangsu Co Innovat Ctr Modern Prod Technol Grain Joint Int Res Lab Agr & Agriprod SafetyMinist Ed Yangzhou 225009 Jiangsu Peoples R China

Leaf area index (LAI) is one of the indicators measuring the growth of rice in the field. LAI monitoring plays an important role in ensuring the stable increase of grain yield. In this study, the canopy reflectance spectrum of rice was obtained by ASD at the elongation, booting, heading and post-flowering stages of rice, and the correlations between the original reflectance (OR), first-derivative transformation (FD), reciprocal transformation (1/R), and logarithmic transformation (LOG) with LAI were analyzed. Characteristic bands of spectral data were then selected based on the successive projections algorithm (SPA) and Pearson correlation. Moreover, ridge regression (RR), partial least squares (PLS), and multivariate stepwise regression (MSR) were conducted to establish estimation models based on characteristic bands and vegetation indices. The research results showed that the correlation between canopy spectrum and LAI was significantly improved after FD transformation. Modeling using SPA to select FD characteristic bands performed better than using Pearson correlation. The optimal modeling combination was FD-SPA-VI-RR, with the coefficient of determination (R-2) of 0.807 and the root-mean-square error (RMSE) of 0.794 for the training set, R-2 of 0.878 and RMSE of 0.773 for the validation set 1, and R-2 of 0.705 and RMSE of 1.026 for the validation set 2. The results indicated that the present model may predict the rice LAI accurately, meeting the requirements of large-scale statistical monitoring of rice growth indicators in the field.

关键词： leaf area index hyperspectral successive projections algorithm ridge regression rice

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Estimation of foliar nitrogen of rubber trees using hyperspectral reflectance with feature bands

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INFRARED PHYSICS & TECHNOLOGY 2019年 102卷 103021-000页

作者： Guo, Peng-Tao Li, Mao-Fen Luo, Wei Cha, Zheng-Zao Chinese Acad Trop Agr Sci Rubber Res Inst Haikou 571101 Hainan Peoples R China Chinese Acad Trop Agr Sci Hainan Prov Key Lab Pract Res Trop Crops Informat Inst Sci & Tech Informat Haikou 571101 Hainan Peoples R China

Leaf nitrogen is an indispensable parameter for simulating biogeochemical cycling in ecosystems. However, detailed information about leaf nitrogen of rubber trees (Hevea brasiliensis) in the tropical regions is rare. Aims of the current study were to determine the feature bands of foliar nitrogen of rubber trees and to examine the ability of these bands to estimate leaf nitrogen concentration (LNC). In this work, a hybrid variable selection method namely competitive adaptive reweighted sampling (CARS) combined with successive projections algorithm (SPA) (CARS-SPA) was used to extract feature bands for predicting LNC of rubber trees. Two hundred leaf samples were gathered from five fields through April to November (tapping season) in 2014. These samples were divided into calibration dataset (n = 140) and prediction dataset (n = 60) using the Kennard-Stone algorithm. Sixty bands were determined from the first derivative spectrum using the CARS-SPA. Among these bands, not only the commonly used absorption features of chlorophyll, protein, and the red edge position were included, but also those of starch, cellulose, and lignin were selected. All the 60 bands were used as input variables for the partial least squares regression (PLSR) and artificial neural networks (ANN) models to estimate LNC of rubber trees. The determination coefficient of calibration (r(c)(2)) and prediction (r(p)(2)), and root mean square error of calibration (RMSEC) and prediction (RMSEP), as well as normalized RMSEC (nRMSEC) and normalized RMSEP (nRMSEP) were employed to evaluate performances of these models. Results indicated that CARS-SPA-ANN (r(c)(2) = 0.82, RMSEC = 0.24%, nRMSEC = 7.76%;r(p)(2) = 0.78, RMSEP = 0.22%, nRMSEP = 6.73%) outperformed the other selected models except CARS-PLSR (r(c)(2) = 0.91, RMSEC = 0.17%, nRMSEC = 5.46%;r(p)(2) = 0.80, RMSEP = 0.21%, nRMSECP = 6.44%). However, CARS-SPA-ANN contained much less bands and was more stable than CARS-PLSR. In conclusion, hyperspectral

关键词： Leaf nitrogen concentration Artificial neural networks Competitive adaptive reweighted sampling successive projections algorithm

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Simultaneous determination of goat milk adulteration with cow milk and their fat and protein contents using NIR spectroscopy and PLS algorithms

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LWT-FOOD SCIENCE AND TECHNOLOGY 2020年 127卷 109427-109427页

作者： dos Santos Pereira, Elainy Virginia de Sousa Fernandes, David Douglas Ugulino de Araujo, Mario Cesar Goncalves Dias Diniz, Paulo Henrique Sucupira Maciel, Maria Ines Univ Fed Paraiba Technol Ctr Food Sci & Technol Postgrad Program PPGCTA Joao Pessoa 58051900 Paraiba Brazil State Univ Paraiba CCT Dept Chem NUPEA Campina Grande 58429500 Paraiba Brazil Univ Fed Paraiba CCEN Dept Chem Joao Pessoa 58051970 Paraiba Brazil Fed Univ West Bahia Pure & Appl Chem Postgrad Program Barreiras 47810059 BA Brazil Univ Fed Rural Pernambuco Food Sci & Technol Postgrad Program Recife 52171900 PE Brazil

This work employed NIR spectroscopy and PLS algorithms for the identification and quantification of goat milk adulteration by adding cow milk, besides the determination of their fat and protein contents. Since cow milk can represent a health risk to allergic consumers regardless its amount, PLS-DA was able to identify cow milk additions in goat milk as low as 1.0154 g/100g, likewise the non-adulterated goat and cow milk samples, achieving a 100% of correct classification. For quantification purposes, the successive projections algorithm for interval selection in PLS (iSPA-PLS) provided the best results for the determination of both adulteration and fat contents, while PLS gave better results for the protein quantification. Despite the great similarity of both natural dairy matrices and their intrinsic variability, the prediction results provided suitable values with high correlation coefficients and low RMSEP and REP values, with RPD values higher than 3. Therefore, the proposed methodology proved to be a useful, fast and non-destructive tool for screening the quality of goat milk in terms of its adulteration with cow milk, in addition to the quantification of its fat and protein contents.

关键词： Goat milk Cow milk Adulteration NIR spectroscopy successive projections algorithm

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Moisture spectral characteristics and hyperspectral inversion of fly ash-filled reconstructed soil

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SPECTROCHIMICA ACTA PART A-MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2021年 253卷 119590-119590页

作者： Xia, Ke Xia, Shasha Shen, Qiang Yang, Bin Song, Qiang Xu, Yunfei Zhang, Shiwen Zhou, Xu Zhou, Yan Anhui Univ Sci & Technol Sch Geomat Huainan 232001 Peoples R China Anhui Univ Sci & Technol Sch Earth & Environm Huainan 232001 Peoples R China Hohai Univ Sch Earth Sci & Engn Nanjing 211100 Peoples R China Minist Nat Resource Land Consolidat & Rehabil Ctr Beijing 100035 Peoples R China

To explore problems with the fast estimation method of moisture content (MC) in reconstructed soil under human disturbance, this paper used a fly ash-filled reconstructed soil region as the research object and obtained experimental data by Fieldspec4 high spectrometry and the laboratory drying method. The response characteristics of MC were analyzed from the original spectral data that underwent mathematical transformation and the spectral index data, and a corresponding inversion model was established. Combined with the successive projections algorithm (SPA), the model was optimized with a better fitting effect, and the optimal inversion model was obtained. The results showed that the composition of soil and fly ash were different, resulting in obvious differences in the shape of the spectral curve, but both had large moisture absorption peaks near 1420 nm and 1920 nm. After mathematical transformation, the correlation between the spectral reflectance and MC was enhanced, in which the absolute value of the maximum correlation between the soil moisture content (SMC) was 0.839, and the absolute value of the maximum correlation between the fly ash moisture content (FMC) was 0.801. Among them, the first-order differential of multivariate scattering correction (MSC') and the first-order differential of logarithm ((lgR)') had higher fitting accuracy for FMC and SMC, respectively. The scale and sensitivity of significance variables were greatly improved based on the spectral index of two-band operation. Better FMC and SMC models were constructed based on the difference soil index (DSI) under mathematical transformation, and R-2 were 0.73 and 0.87, respectively. After SPA optimization, the predictive ability of the model was further improved, in which the predictive accuracy R-2 of FMC and SMC reached up to 0.87 and 0.96, respectively, and the RPD was greater than 3. This shows that the DSI model based on MSC' and (lgR)' combined with the SPA method can be used as an effec

关键词： Hyperspectral Fly ash-filled reconstructed soil Moisture content Mathematical transformation Spectral index successive projections algorithm

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A hybrid model of bald eagle search and relevance vector machine for dam safety monitoring using long-term temperature

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ADVANCED ENGINEERING INFORMATICS 2023年 55卷

作者： Yu, Xin Li, Junjie Kang, Fei Dalian Univ Technol Fac Infrastruct Engn Sch Hydraul Engn Dalian 116024 Peoples R China Hohai Univ Coll Water Conservancy & Hydropower Engn Nanjing 210098 Peoples R China

Accurate simulation of temperature effect is a major challenge for computational (intelligent) prediction models used for monitoring health of high concrete dams, especially in regions with long freezing periods and distinct seasons, occasional extreme weather. A Hydrostatic-Temperature long-term-Time (HTLT) model was proposed for better temperature effect simulation using long-term measured environment temperatures and explored the influence of temperatures data sets of different time lengths on dam displacement prediction accuracy with the traditional Hydrostatic-Season-Time model as control. The Bald Eagle Search algorithm was coupled with the Relevance Vector Machine to form a novel hybrid model (BES-RVM) for predicting concrete gravity dam displacement response and comparison of nonlinear mapping capability between different kernel functions was conducted. Further optimized by successive projections algorithm (SPA) for feature selection, sensitive features were selected from the proposed HTLT variable sets to improve the prediction model's accuracy. The prediction model was experimented on a real concrete dam with results showing that the BES-RVM model gave excellent prediction performance. Using the HTLT variable sets significantly reduced the prediction errors and the best performed result came from variables of the two-year long temperatures data. The SPA optimized BES-RVM model largely brought down the dimension and the collinearity of the input variables and further improved the prediction performance.

关键词： Structural health monitoring Concrete dam displacement prediction Bald eagle search algorithm Relevance vector machine regression successive projections algorithm Temperature simulation

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Application of NIR transmission spectroscopy with effective wavelength selection in non-destructive determination of essential amino acid content of foxtail millet

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QUALITY ASSURANCE AND SAFETY OF CROPS & FOODS 2017年第2期9卷 237-248页

作者： Chen, J. Ren, X. Shen, Q. China Agr Univ Coll Food Sci & Nutr Engn 17 Tsinghua East Rd Beijing 100083 Peoples R China Natl Engn Res Ctr Fruits & Vegetables Proc 17 Tsinghua East Rd Beijing 100083 Peoples R China

The near infrared (NIR) spectroscopy models developed for rapid and accurate determination of nine essential amino acids and protein in foxtail millet were investigated. The effects of the status of the samples ( intact or ground), amino acid/protein correlation and variable selection methods on the predictive ability of the models were analysed. According to results, although the average spectral patterns of the intact and ground samples were similar, the absolute values of peaks and valleys for the intact samples were slightly higher. The modelling results of intact samples were similar to those obtained with ground samples. In the results of the partial least squares models with full spectra, 81-94% of the amino acid variance could be explained, except for lysine, tryptophan and methionine with the coefficients of determination of cross-validation less than 0.70. The correlation between various amino acids and protein might affect the predictive ability of NIR spectroscopy for essential amino acids. After variable selection, the models for isoleucine, leucine, phenylalanine, threonine, tryptophan and valine were remarkably improved by the successive projections algorithm method. The results indicated that short-wave NIR spectroscopy coupled with variable selection is a promising approach for determination of essential amino acids in foxtail millet.

关键词： near infrared transmission spectroscopy successive projections algorithm interval partial least squares jack-knife algorithm

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Hyperspectral Analysis and Regression Modeling of SPAD Measurements in Leaves of Three Mangrove Species

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FORESTS 2023年第8期14卷 1566页

作者： Li, Huazhe Cui, Lijuan Dou, Zhiguo Wang, Junjie Zhai, Xiajie Li, Jing Zhao, Xinsheng Lei, Yinru Wang, Jinzhi Li, Wei Chinese Acad Forestry Inst Wetland Res Beijing 100091 Peoples R China Beijing Key Lab Wetland Serv & Restorat Beijing 100091 Peoples R China Chinese Acad Forestry Inst Ecol Conservat & Restorat Beijing 100091 Peoples R China Shenzhen Univ Coll Life Sci & Oceanog Shenzhen 518060 Peoples R China Shenzhen Univ MNR Key Lab Geoenvironm Monitoring Great Bay Area Shenzhen 518060 Peoples R China Shenzhen Univ Guangdong Key Lab Urban Informat Shenzhen 518060 Peoples R China Shenzhen Univ Shenzhen Key Lab Spatial Smart Sensing & Serv Shenzhen 518060 Peoples R China

Mangroves have important roles in regulating climate change, and in reducing the impact of wind and waves. Analysis of the chlorophyll content of mangroves is important for monitoring their health, and their conservation and management. Thus, this study aimed to apply four regression models, eXtreme Gradient Boosting (XGBoost), Random Forest (RF), Partial Least Squares (PLS) and Adaptive Boosting (AdaBoost), to study the inversion of Soil Plant Analysis Development (SPAD) values obtained from near-ground hyperspectral data of three dominant species, Bruguiera sexangula (Lour.) Poir. (B. sexangula), Ceriops tagal (Perr.) C. B. Rob. (C. tagal) and Rhizophora apiculata Blume (R. apiculata) in Qinglan Port Mangrove Nature Reserve. The accuracy of the model was evaluated using R-2, RMSE, and MAE. The mean SPAD values of R. apiculata (SPAD(avg) = 66.57), with a smaller dispersion (coefficient of variation of 6.59%), were higher than those of C. tagal (SPAD(avg) = 61.56) and B. sexangula (SPAD(avg) = 58.60). The first-order differential transformation of the spectral data improved the accuracy of the prediction model;R-2 was mostly distributed in the interval of 0.4 to 0.8. The accuracy of the XGBoost model was less affected by species differences with the best stability, with RMSE at approximately 3.5 and MAE at approximately 2.85. This study provides a technical reference for large-scale detection and management of mangroves.

关键词： hyperspectral data successive projections algorithm mangroves SPAD measurements

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