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The continuous wavelet projections algorithm: A practical spectral-feature-mining approach for crop detection

The Crop Journal 2022年第5期10卷 1264-1273页

作者： Xiaohu Zhao Jingcheng Zhang Ruiliang Pu Zaifa Shu Weizhong He Kaihua Wu College of Artificial Intelligence Hangzhou Dianzi UniversityHangzhou 310018ZhejiangChina School of Geosciences University of South FloridaTampaFL 33620USA Lishui Institute of Agriculture and Forestry Sciences Lishui 323000ZhejiangChina

Spectroscopy can be used for detecting crop characteristics. A goal of crop spectrum analysis is to extract effective features from spectral data for establishing a detection model. An ideal spectral feature set should have high sensitivity to target parameters but low information redundancy among ***, feature-selection methods that satisfy both requirements are lacking. To address this issue,in this study, a novel method, the continuous wavelet projections algorithm(CWPA), was developed,which has advantages of both continuous wavelet analysis(CWA) and the successive projections algorithm(SPA) for generating optimal spectral feature set for crop detection. Three datasets collected for crop stress detection and retrieval of biochemical properties were used to validate the CWPA under both classification and regression scenarios. The CWPA generated a feature set with fewer features yet achieving accuracy comparable to or even higher than those of CWA and SPA. With only two to three features identified by CWPA, an overall accuracy of 98% in classifying tea plant stresses was achieved, and high coefficients of determination were obtained in retrieving corn leaf chlorophyll content(R^(2)= 0.8521)and equivalent water thickness(R^(2)= 0.9508). The mechanism of the CWPA ensures that the novel algorithm discovers the most sensitive features while retaining complementarity among features. Its ability to reduce the data dimension suggests its potential for crop monitoring and phenotyping with hyperspectral data.

关键词： Hyperspectral Crop parameters Crop phenotyping Continuous wavelet analysis successive projections algorithm

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Nondestructive determination of IMP content in chilled chicken based on hyperspectral data combined with chemometrics

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International Journal of Agricultural and Biological Engineering 2022年第1期15卷 277-284页

作者： Yangyang Wang Hongju He Shengqi Jiang Hanjun Ma School of Food Science Henan Institute of Science and TechnologyXinxiang 453003HenanChina

This study was conducted to investigate the potential of hyperspectral imaging technique(900-1700 nm)for nondestructive determination of inosinic acid(IMP)in *** images of chicken flesh samples were acquired,and their mean spectra within the images were *** quantitative relationship between the mean spectra and reference IMP value was fitted by partial least squares(PLS)regression algorithm.A PLS model(MAS-PLS)built with moving average smoothing(MAS)spectra showed better performance in predicting IMP content,leading to correlation coefficients(RP)of 0.951,root mean square error(RMSEP)of 0.046 mg/g,and residual predictive deviation(RPD)of *** coefficient(RC),successive projections algorithm(SPA),stepwise,competitive adaptive reweighted sampling(CARS),and uninformative variable elimination(UVE)were used to select the optimal wavelengths to optimize the MAS-PLS *** on the 18 optimal wavelengths(907.14,917.02,918.67,926.90,930.20,936.78,956.54,1004.28,1135.89,1211.56,1302.07,1367.94,1397.60,1488.31,1680.17,1683.49,1686.80 and 1695.10 nm)selected from MAS spectra by SPA,the MAS-SPA-PLS model was built with R_(P) of 0.920,RMSEP of 0.056 mg/g and RPD of 3.220,which was similar to the MAS-PLS *** overall study indicated that hyperspectral imaging in the 900-1700 nm range combined with PLS and SPA could be used to predict the IMP content in chicken flesh.

关键词： near-infrared hyperspectral imaging chicken inosinic acid partial least squares successive projections algorithm

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Rice Origin Tracing Technology Based on Fluorescence Spectroscopy and Stoichiometry

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SENSORS 2024年第10期24卷 2994页

作者： Li, Changming Tan, Yong Liu, Chunyu Guo, Wenjing Changchun Univ Sci & Technol Sch Phys Changchun 130022 Peoples R China Changchun Guanghua Univ Engn Technol R&D Ctr Changchun 130033 Peoples R China

The origin of agricultural products is crucial to their quality and safety. This study explored the differences in chemical composition and structure of rice from different origins using fluorescence detection technology. These differences are mainly affected by climate, environment, geology and other factors. By identifying the fluorescence characteristic absorption peaks of the same rice seed varieties from different origins, and comparing them with known or standard samples, this study aims to authenticate rice, protect brands, and achieve traceability. The study selected the same variety of rice seed planted in different regions of Jilin Province in the same year as samples. Fluorescence spectroscopy was used to collect spectral data, which was preprocessed by normalization, smoothing, and wavelet transformation to remove noise, scattering, and burrs. The processed spectral data was used as input for the long short-term memory (LSTM) model. The study focused on the processing and analysis of rice spectra based on NZ-WT-processed data. To simplify the model, uninformative variable elimination (UVE) and successive projections algorithm (SPA) were used to screen the best wavelengths. These wavelengths were used as input for the support vector machine (SVM) prediction model to achieve efficient and accurate predictions. Within the fluorescence spectral range of 475-525 nm and 665-690 nm, absorption peaks of nicotinamide adenine dinucleotide (NADPH), riboflavin (B2), starch, and protein were observed. The origin tracing prediction model established using SVM exhibited stable performance with a classification accuracy of up to 99.5%.The experiment demonstrated that fluorescence spectroscopy technology has high discrimination accuracy in tracing the origin of rice, providing a new method for rapid identification of rice origin.

关键词： fluorescence spectroscopy rice origin identification successive projections algorithm support vector machine spectral analysis

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Rapid discrimination of urine specific gravity using spectroscopy and a modified combination method based on SPA and spectral index

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JOURNAL OF BIOPHOTONICS 2024年第1期17卷 e202300323-e202300323页

作者： Yang, Cheng-bo Cai, Zhi-long Li, Qing-zhi Tang, Feng Wu, Jing-jun Yang, Jia Zhang, Yu-rong Li, Bo Yang, Ping Ye, Xin Yang, Li-ming China Acad Engn Phys Res Ctr Laser Fus Mianyang Peoples R China Sichuan Sci City Hosp Mianyang Peoples R China China Acad Engn Phys Res Ctr Laser Fus Mianyang 621900 Peoples R China Sichuan Sci City Hosp Mianyang 621900 Peoples R China

To achieve high-accuracy urine specific gravity discrimination and guide the design of four-waveband multispectral sensors. A modified combination strategy was attempted to be proposed based on the successive projections algorithm (SPA) and the spectral index (SI) in the present study. First, the SPA was used to select four spectral variables in the full spectra. Second, the four spectral variables were mathematically transformed by SI to obtain SI values. Then, SPA gradually fusions the SI values and establishes models to identify USG. The results showed that the SPA can screen out the four characteristic wavelengths related to the measured sample attributes. SIs can be used to improve the performance of constructed prediction models. The best model only involves four spectral variables and 1 SI value, with high accuracy (91.62%), sensitivity (0.9051), and specificity (0.9667). The results reveal that m-SPA-SI can effectively distinguish USG and provide design guidance for 4-wavelength multispectral sensors.

关键词： random forest spectral analysis spectral index successive projections algorithm urine specific gravity

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Hyperspectral band selection based on dual evaluation measures and improved nondominated sorting genetic algorithm

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JOURNAL OF APPLIED REMOTE SENSING 2021年第2期15卷

作者： Yang, Yu Wang, Xin Huang, Min Zhu, Qibing Guo, Ya Xu, Libing Zhou, Zheng Jiangnan Univ Key Lab Adv Proc Control Light Ind Minist Educ Wuxi Jiangsu Peoples R China Natl Supercomp Ctr Wuxi Wuxi Jiangsu Peoples R China Tsinghua Univ Dept Earth Syst Sci Beijing Peoples R China

Awide variety of swarm intelligence algorithm-based approaches have been recently developed for selecting the near-optimal bands from hyperspectral images (HSIs). However, such methods [including the nondominated sorting genetic algorithm (NSGA)] for HSIs pixel classification are limited by the lack of effective initialization and directional evolution. This research proposed a successive projections algorithm (SPA) and individual repair operation for NSGA (named Sr-NSGA) for band selection. Specifically, Sr-NSGA used the SPA to initialize the population and construct repair sequences that optimize new generated individuals in evolution. Meanwhile, with the guidance of two mutually restricted fitness functions, i.e., average mutual information and classification accuracy, Sr-NSGA searched for the near-optimal band set in an iterative way. Different combinations obtained by SR-NSGA and three effective band selection methods were tested and compared on the Botswana, KSC, and Indian Pines datasets. The results show that Sr-NSGA yielded better performance than the other three methods. Furthermore, support vector machine was used as the classifier for the pixel classification of the Indian Pines dataset to test Sr-NSGA. Experimental result show that Sr-NSGA achieves an overall accuracy of 95.57% and adapts to different classifiers. (C) 2021 Society of PhotoOptical Instrumentation Engineers (SPIE)

关键词： hyperspectral image band selection successive projections algorithm repair operation Sr-NSGA

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Short wave-near infrared spectroscopy for predicting soluble solid content in intact mango with variable selection algorithms and chemometric model

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JOURNAL OF FOOD COMPOSITION AND ANALYSIS 2024年 136卷

作者： Khatun, Sapna Al Masum, Abdullah Islam, Md Hamidul Ashik-E-Rabbani, Muhammad Rahman, Anisur Bangladesh Rice Res Inst BRRI Farm Machinery & Post Harvest Technol Div Gazipur 1701 Bangladesh Habiganj Agr Univ Dept Agr Mechanizat Habiganj 3300 Bangladesh Bangladesh Agr Univ Dept Farm Power & Machinery Mymensingh 2202 Bangladesh

Mango sweetness is one of the most prominent internal quality constituents that attract consumer's attention. The current common methods used to determine the sweetness of mango have significant disadvantages in that labor-intensive, time-consuming, and damaging techniques. This research aims to predict sweetness in mangoes using short wave-near infrared (SW-NIR) spectroscopy in the ranges 900-1650 nm, along with variable selection algorithms and chemometric model. A total of 120 mango samples were used to collect the spectra using a fibre module SW-NIR spectrometer. The partial least squares (PLS) regression with several spectral preprocessing methods was employed to develop the calibration model, and the best preprocessing technique was selected. The Savitzky-Golay second-derivative preprocessing technique performed better among the other preprocessing techniques with a correlation coefficient of prediction (r(pred)) of 0.74 and standard error of prediction (SEP) is 0.78 %Brix. After that, two variable selection techniques were used to select effective wavelength variables, including regression coefficient and successive projections algorithm (SPA). For SSC prediction in the range 900-1650 nm, the SPA-PLS model obtained a r(pred) of 0.78 and SEP of 0.67 %Brix. The current study unequivocally shows that the proposed SW-NIR spectroscopy coupled with a suitable chemometrics method can evaluate mango sweetness nondestructively.

关键词： Spectroscopy Mango Spectral pre-processing Partial least squares regression Variable selection techniques successive projections algorithm

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Qualitative and quantitative detection of microplastics in soil based on LIF technology combined with OOA-ELM/SPA-PLS

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MICROCHEMICAL JOURNAL 2024年 201卷

作者： Yan, Pengcheng Li, Guodong Wang, Wenchang Zhao, Yuting Wang, Jingbao Wen, Ziming Anhui Univ Sci & Technol Sch Elect & Informat Engn Huainan 232001 Peoples R China Anhui Univ Sci & Technol State Key Lab Min Response & Disaster Prevent & Co Huainan 232001 Peoples R China Anhui Univ Sci & Technol Collaborat Innovat Ctr Mine Intelligent Equipment Huainan 232001 Peoples R China

Traditional methods for detecting soil microplastics include chemical digestion, density separation, staining, etc. These methods often only provide qualitative analysis of microplastics and are difficult to accurately quantify. To address this issue, laser induced fluorescence (LIF) technology is introduced, which can achieve high sensitivity, non-destructive, and rapid detection, and improve the accuracy and efficiency of qualitative and quantitative analysis of soil microplastics. Therefore, based on LIF technology, a qualitative prediction model for optimizing Extreme Learning Machine (ELM) using Osprey Optimization algorithm (OOA) and a quantitative prediction model combining Continuous Projection algorithm (SPA) with Partial Least Squares algorithm (PLS) were established. Qualitative analysis used eight types of microplastics, with AS, PA66, ABS, PS, POM, PBT, PET, and PVC as experimental samples. Initially, spectral data is obtained using LIF technology and the raw spectral data is pre-processed using polynomial smoothing algorithm (SG) and moving average (MA) methods. Subsequently, factor analysis (FA) and local linear embedding (LLE) techniques are applied to dimensionality reduction and then input into the ELM model. At the same time, the ELM parameter is optimized by using OOA algorithm. In the quantitative analysis, PA66 powder and soil are mixed in different proportions (10:0, 8:2, 5:5, 2:8, 0:10), SG and MA pre-processing are performed on the spectral data obtained by LIF technology, and SPA is used to select the spectral characteristic wavelength for the original data and the pre-processed spectral data, and then a linear regression model is established combined with PLS. In order to facilitate the comparison of data, the experimental results are reserved to 5 decimal places, the experimental results indicate that LIF technology is faster than traditional methods in extracting raw data. The convergence of the OOA-optimized ELM model is better than tha

关键词： Laser induced fluorescence technique Qualitative and quantitative detection Extreme learning machine Osprey optimization algorithm Partial least squares successive projections algorithm

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A multispectral imaging system developed based on the spectral feature selection method for identification of housefly pupae

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MICROCHEMICAL JOURNAL 2024年 200卷

作者： Yang, Cheng-bo Li, Qing-zhi Tang, Feng Wu, Jing-jun Li, Bo Ye, Xin Yang, Li-ming China Acad Engn Phys Res Ctr Laser Fus Mianyang 621900 Peoples R China

Hyperspectral imaging systems can simultaneously obtain spectral and spatial information of fly pupae, especially achieving a large area of spectral information collection in a short period. However, hyperspectral imaging systems are expensive and require much computation. This study attempts to develop a four-channel spectral imaging system composed of a camera and four filters to achieve spectral discrimination of housefly pupae over a large area. It also aims to verify the feasibility of using spectral feature selection methods to guide the development of low-cost specialized spectral systems. Meanwhile, a strategy combining spectral degradation with the traversal method was proposed for determining the parameter indicators of optical components. Firstly, use a microspectrometer to acquire the spectra of fly pupae. Secondly, the successive projections algorithm (SPA) extracts four feature wavelengths related to fly pupa recognition from the full spectrum variables. Then, based on the strategy of spectral degradation combined with the traversal method, the parameters of the four filters are determined. Finally, a 4-channel spectral imaging system was constructed, and a prediction model was built with random forest (RF), while the spectral index (SI) method was used to improve model performance. The results indicate that this low-cost multispectral imaging system can achieve high-performance discrimination of housefly pupae (accuracy = 97.44 %). At the same time, SPA, spectral degradation, and traversal methods can provide effective central wavelength and resolution indicators for constructing low-cost multispectral imaging systems, offering a practical and feasible example to guide the development of specialized low-cost spectral devices using spectral feature selection methods.

关键词： Housefly pupae successive projections algorithm Spectral degradation Multispectral imaging system Spectral resolution

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The identification of aged-rice adulteration by support vector machine classification combined with characteristic wavelength variables

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MICROCHEMICAL JOURNAL 2024年 199卷

作者： Song, Jiahui Yu, Yue Wang, Ruoni Chen, Menting Li, Zhanming He, Xueming Ren, Zhongyang Dong, Hao Jiangsu Univ Sci & Technol Sch Grain Sci & Technol Zhenjiang 212100 Peoples R China Nanjing Univ Finance & Econ Coll Food Sci & Engn Nanjing 210023 Peoples R China Jimei Univ Coll Ocean Food & Biol Engn Xiamen 361021 Peoples R China Zhongkai Univ Agr & Engn Coll Light Ind & Food Sci Guangzhou 510225 Peoples R China

The aging of rice during long-term storage leads to a decline in quality and a decrease in consumption value. Therefore, the efficient and rapid detection of aged-rice adulteration is crucial for rice quality control. This paper aims to utilize near-infrared spectroscopy (NIRS) in conjunction with chemometrics to identify two types of rice adulteration: Late japonica rice and Southern japonica rice. To achieve this, competitive adaptive reweighted sampling (CARS) and successive projections algorithm (SPA) were utilized to select characteristic wavelengths from the spectral data, both with and without pretreatment methods. In order to establish a support vector machine classification (SVC) model with exceptional predictive capability, cross validation (CV), genetic algorithm (GA), and particle swarm optimization (PSO) were adopted to find the best combination of parameters (C, g). Notably, the prediction accuracy achieved by MMN-CARS-CV-SVC, SNV-CARS-CV-SVC, SNV-CARS-GA-SVC, and SNV-CARS-PSO-SVC models was 98% for Southern japonica rice adulteration, with varying proportions of aging rice. Conversely, the prediction accuracy of the MSC-CARS-CV-SVC model reached 96% for Late japonica rice adulteration, with varying proportions of aged rice. In conclusion, the combination of NIRS and SVC enhances the discriminative analysis of aged-rice adulteration, and the outstanding generalization ability of the SVC model offers new possibilities for the analysis of adulteration in other cereal grains.

关键词： Adulteration successive projections algorithm Competitive adaptive reweighted sampling Chemometrics Support vector machine classification

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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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