Hyperspectral imaging (HSI) is a powerful, non-invasive analytical technique extensively utilized in chemistry as it simultaneously captures morphological and chemical information from samples across a broad spectrum ...
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Hyperspectral imaging (HSI) is a powerful, non-invasive analytical technique extensively utilized in chemistry as it simultaneously captures morphological and chemical information from samples across a broad spectrum of chemically informative wavelengths. In this context, morphological information refers to the spatial structure, shape, texture, and distribution of elements within the image. Enhancing its already widespread application requires reducing the computational load of the voluminous hyperspectral images while unmixing signals from different chemical species with unknown spectral fingerprints. Endmember extraction, which involves finding the purest spectral signatures within the data, is needed for decomposing these mixed signals. By resolving mixed pixels into their constituent endmembers, HSI enables accurate quantification and spatial mapping of chemical components, even when prior knowledge is limited. Current methods for end-member extraction, such as NFINDR, VCA, PPI, SIMPLISMA, and AMEE, are limited by issues including computational slowness, the requirement for extensive parameter optimization, and a lack of hierarchical consistency. Consequently, there is a pressing need for a method that is both faster and more accurate. successive projection algorithm (SPA) is developed for forward wavelength selection to improve the predictive accuracy of regression models under strong collinearity. SPA emerges as a rapid and accurate endmember extraction technique, with applications extending beyond chemistry to areas such as food safety, environmental monitoring, and material analysis. Comparative analyses using both simulated and experimental datasets illustrate SPA's superior robustness, repeatability, absence of parameter tuning requirements, and computational efficiency when compared with the methods in current use. These findings show the value of SPA as a robust tool for computationally efficient hyperspectral image analysis in chemical applications and
Plant nitrogen concentration (PNC) is a traditional standard index to measure the nitrogen nutritional status of winter wheat. Rapid and accurate diagnosis of PNC performs an important role in mastering the growth sta...
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Plant nitrogen concentration (PNC) is a traditional standard index to measure the nitrogen nutritional status of winter wheat. Rapid and accurate diagnosis of PNC performs an important role in mastering the growth status of winter wheat and guiding field precision fertilization. In this study, the in situ hyperspectral reflectance data were measured by handheld SVC HR-1024I (SVC) passive field spectroradiometer and PNC were determined by the modified Kjeldahl digestion method. Continuous wavelet transform (CWT), successive projection algorithm (SPA) and partial least square (PLS) regression were combined to construct an efficient method for estimating winter wheat PNC. The main objectives of this study were to (1) use CWT to extract various wavelet coefficients under different decomposition scales, (2) use SPA to screen sensitive wavelet coefficients as independent variables and combine with PLS regression to establish winter wheat PNC estimation models, respectively, and (3) compare the precision of PLS regression models to find a reliable model for estimating winter wheat PNC during the growing season. The results of this paper showed that properly increasing the decomposition scale of CWT could weaken the impact of high-frequency noise on the prediction model. The number of wavelet coefficients has been significantly reduced after screened by SPA. The PNC estimation model (CWT-Scale6-SPA-PLS) based on the wavelet coefficients of the sixth decomposition scale most accurately predicted the PNC (the determination coefficient of the calibration set (R-c(2)) was 0.85. Root mean square error of the calibration set (RMSEc) was 0.27. The determination coefficient of the validation set (R-v(2)) was 0.84. Root mean square error of the validation set (RMSEv) was 0.28 and relative prediction deviation (RPD) was 2.47). CWT-Scale6-SPA-PLS can be used to predict PNC. The optimal winter wheat PNC prediction model based on CWT proposed in this study is a reliable method for rapid
Hyperspectral imaging technology (HSI) is considered to be a promising technology to detect and predict seed viability quickly and without damage. This research aimed to use HSI combined with deep learning methods to ...
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Hyperspectral imaging technology (HSI) is considered to be a promising technology to detect and predict seed viability quickly and without damage. This research aimed to use HSI combined with deep learning methods to identify the seed viability of Sophora japonica. After simulating natural aging, high-viability, low-viability and non-viability seeds were distinguished, and then data collection continued for the first 10 h of seed imbibition. The validation set accuracy of machine learning methods (support vector machine (SVM) and random forest (RF)) without preprocessing was less than 80%, while the one-dimensional deep learning methods (convolutional neural network (CNN), recurrent neural network (RNN) and its superposition) were above 95%. An improvement was proposed on the basis of the original successive projection algorithm (SPA) and compared with three typical algorithms. When Autoregressive locally optimized successive projection algorithm (ALO-SPA) was combined with RNN, the detection model worked best. Therefore, based on this model, the vitality prediction was estab-lished using the data of swelling 0 h, and its effect was better than that of detection model. The results show that hyperspectral imaging combined with deep learning model can more accurately predict the seed viability of Sophora japonica.
The successive projection algorithm (SPA) is a fast algorithm to tackle separable nonnegative matrix factorization (NMF). Given a nonnegative data matrix X, SPA identifies an index set K such that there exists a nonne...
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ISBN:
(纸本)9781728155494
The successive projection algorithm (SPA) is a fast algorithm to tackle separable nonnegative matrix factorization (NMF). Given a nonnegative data matrix X, SPA identifies an index set K such that there exists a nonnegative matrix H with X approximate to X(:, K)H. SPA has been successfully used as a pure-pixel search algorithm in hyperspectral unmixing and for anchor word selection in document classification. Moreover, SPA is provably robust in low-noise settings. The main draw-backs of SPA are that it is not robust to outliers and does not take the data fitting term into account when selecting the indices in K. In this paper, we propose a new SPA variant, dubbed Robust SPA (RSPA), that is robust to outliers while still being provably robust in low-noise settings, and that takes into account the reconstruction error for selecting the indices in K. We illustrate the effectiveness of RSPA on synthetic data sets and hyperspectral images.
Soluble solid content of apple is one of the important indexes to evaluate taste of apple. In order to eliminate collinearity between original spectral variables, reduce calculation of modeling variables, and improve ...
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ISBN:
(纸本)9781728134802
Soluble solid content of apple is one of the important indexes to evaluate taste of apple. In order to eliminate collinearity between original spectral variables, reduce calculation of modeling variables, and improve correction speed and modeling accuracy, this paper applies successive projection algorithm (SPA) to the establishment of near-infrared correction model of soluble solid content of apple. The sample set partitioning based on be x-y distances method (SPXY) selected representative of the calibrating samples, then it is processed by window smoothing method and standard normal variate transformation method (SNV), and variable selection is conducted by SPA. 19 optimal characteristic variables were selected for modeling, and the root mean square error of prediction and predictive correlation coefficient were 0.4071 and 0.9351 respectively. The results show that the continuous projectionalgorithm can effectively improve the correction speed, and this algorithm is effective and feasible.
The application of near infrared spectroscopy for quantitative analysis of cotton-polyester textile was investigated in the present work. A total of 214 cotton-polyester fabric samples, covering the range from 0% to 1...
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The application of near infrared spectroscopy for quantitative analysis of cotton-polyester textile was investigated in the present work. A total of 214 cotton-polyester fabric samples, covering the range from 0% to 100% cotton were measured and analyzed. Partial least squares and least-squares support vector machine models with all variables as input data were established. Furthermore, successive projection algorithm was used to select effective wavelengths and establish the successive projection algorithm-least-squares support vector machine models, with the comparison of two other effective wavelength selection methods: loading weights analysis and regression coefficient analysis. The calibration and validation results show that the successive projection algorithm-least-squares support vector machine model outperformed not only the partial least squares and least-squares support vector machine models with all variables as inputs, but also the least-squares support vector machine models with loading weights analysis and regression coefficient analysis effective wavelength selection. The root mean squared error of calibration and root mean squared error of prediction values of the successive projection algorithm-least-squares support vector machine regression model with the optimal performance were 0.77% and 1.17%, respectively. The overall results demonstrated that near infrared spectroscopy combined with least-squares support vector machine and successive projection algorithm could provide a simple, rapid, economical and non-destructive method for determining the composition of cotton-polyester textiles.
The successive projection algorithm (SPA) has been known to work well for separable nonnegative matrix factorization (NMF) problems arising in applications, such as topic extraction from documents and endmember detect...
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The successive projection algorithm (SPA) has been known to work well for separable nonnegative matrix factorization (NMF) problems arising in applications, such as topic extraction from documents and endmember detection in hyperspectral images. One of the reasons is in that the algorithm is robust to noise. Gillis and Vavasis showed in [8] that a preconditioner can further enhance its noise robustness. The proof rested on the condition that the dimension d and factorization rank r in the separable NMF problem coincide with each other. However, it may be unrealistic to expect that the condition holds in separable NMF problems appearing in actual applications;in such problems, d is usually greater than r. This paper shows, without the condition d = r, that the preconditioned SPA is robust to noise. (C) 2016 Elsevier Inc. All rights reserved.
The multiple-sets split feasibility problem (MSFP) arises in many areas and it can be unified as a model for many inverse problems where the constraints are required on the solutions in the domain of a linear operator...
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The multiple-sets split feasibility problem (MSFP) arises in many areas and it can be unified as a model for many inverse problems where the constraints are required on the solutions in the domain of a linear operator as well as in the operator's range. Some existing algorithms, in order to get the suitable step size, need to compute the largest eigenvalue of the related matrix, estimate the Lipschitz constant, or use some step-size search scheme, which usually requires many inner iterations. In this article, we introduce a successive projection algorithm for solving the multiple-sets split feasibility problem. In each iteration of this algorithm, the step size is directly computed, which is not needed to compute the largest eigenvalue of the matrix or estimate the Lipschitz constant. It also does not need any step-size search scheme. Its theoretical convergence results are also given.
Rice is susceptible to mold and mildew during storage. Metabolites such as aflatoxin produced during mildew have great harm to the health of consumers. A rapid identification approach of contaminated rice was develope...
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Rice is susceptible to mold and mildew during storage. Metabolites such as aflatoxin produced during mildew have great harm to the health of consumers. A rapid identification approach of contaminated rice was developed based on data fusion of near-infrared spectroscopy and machine vision to satisfy the need for rapid detection of normal rice adulterated with moldy rice. The successive projection algorithm (SPA) was merged with principal component analysis (PCA) and support vector classification (SVC) to create the SPA-PCA-SVC method, which was based on variable selection, feature extraction, and nonlinear modeling methodologies. K-fold cross-validation and the sum of predicted residual squares were used to find the optimal number of main components. The model parameters were tuned using a genetic algorithm. Identification models of adulterated rice was established based on NIR spectroscopy, machine vision, and their fusion data using this method. The identification accuracy of the training set was 92.81%, 86.27%, and 99.35%, and the identification accuracy of the test set was 69.23%, 82.69%, and 96.15%, respectively. Compared to near-infrared spectroscopy and machine vision alone, the identification performance of the model built by fusion data is significantly improved. The findings demonstrate the viability of the near-infrared spectroscopy and machine vision data fusion method for the detection of contaminated rice, providing a theoretical foundation for the creation of online adulterated rice identification tools.
Tomato is an important vegetable that is rich in antioxidants, vitamins, and minerals and has significant economic and health value. In this study, hyperspectral images in the wavelength range of 370 to 1715 nm were f...
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Tomato is an important vegetable that is rich in antioxidants, vitamins, and minerals and has significant economic and health value. In this study, hyperspectral images in the wavelength range of 370 to 1715 nm were first preprocessed to improve the data quality and comparability. Subsequently, the tomatoes were chemically destroyed, and the average activities of peroxidase enzyme, phenylalanine ammonia-lyase enzyme, and alpha-amylase enzyme were 5.108 mU/g, 7.347 U/g, and 35.856 U/g, respectively. Then, two spectral selection algorithms, the genetic algorithm (GA) and the successive projection algorithm, were used to extract effective wavelength bands from high dimensional spectral data. And the extracted effective wavelength variables were combined with partial least squares (PLS) regression to build the optimal spectral selection model GA-PLS. Finally, three additional spectral prediction models were created by combining the GA-selected spectra with three other algorithms: support vector machine, particle swarm optimization-backpropagation neural network, and random forest. After comparing the predictive performance of four models, it was found that the GA-PLS model had the highest prediction accuracy and stability. Furthermore, compared with tomato stems, the near infrared (NIR) bands of tomato leaves were more accurate in predicting the enzyme content of tomato plants. It was found that the GA-PLS model had a better prediction performance for the three enzymes in the NIR band of leaves with Rp (average coefficient of determination for the three enzymes) and RMSEP (average root means square error of the three enzymes) of 0.815 and 1.659, respectively. This provides an effective method for phytochemical composition analysis using hyperspectral imaging.
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