Variable Elimination Research Articles

Hyperspectral Prediction Model of Nitrogen Content in Citrus Leaves Based on the CEEMDAN–SR Algorithm

Nitrogen content is one of the essential elements in citrus leaves (CL), and many studies have been conducted to determine the nutrient content in CL using hyperspectral technology. To address the key problem that the conventional spectral data-denoising algorithms directly discard high-frequency signals, resulting in missing effective signals, this study proposes a denoising preprocessing algorithm, complete ensemble empirical mode decomposition with adaptive noise joint sparse representation (CEEMDAN–SR), for CL hyperspectral data. For this purpose, 225 sets of fresh CL were collected at the Institute of Fruit Tree Research of the Guangdong Academy of Agricultural Sciences, to measure their elemental nitrogen content and the corresponding hyperspectral data. First, the spectral data were preprocessed using CEEMDAN–SR, Stein’s unbiased risk estimate and the linear expansion of thresholds (SURE–LET), sparse representation (SR), Savitzky–Golay (SG), and the first derivative (FD). Second, feature extraction was carried out using principal component analysis (PCA), uninformative variables elimination (UVE), and the competitive adaptive re-weighted sampling (CARS) algorithm. Finally, partial least squares regression (PLSR), support vector regression (SVR), random forest (RF), and Gaussian process regression (GPR) were used to construct a CL nitrogen prediction model. The results showed that most of the prediction models preprocessed using the CEEMDAN–SR algorithm had better accuracy and robustness. The prediction models based on CEEMDAN–SR preprocessing, PCA feature extraction, and GPR modeling had an R2 of 0.944, NRMSE of 0.057, and RPD of 4.219. The study showed that the CEEMDAN–SR algorithm can be effectively used to denoise CL hyperspectral data and reduce the loss of effective information. The prediction model using the CEEMDAN–SR+PCA+GPR algorithm could accurately obtain the nitrogen content of CL and provide a reference for the accurate fertilization of citrus trees.

The significance of senior education in the Internet era for the construction of lifelong education system

Abstract This paper uses Bayesian networks for the state combination of random variables to describe the relationship between prior probability and posterior probability by likelihood. Meanwhile, the elimination of complex variables is achieved by combining the operation results of multivariate non-independent joint conditional probability decomposition. Based on this, Bayes’ theorem is used to calculate the learning efficiency, the comprehensive pass rate of any interactive courseware, and the growing value of the visualized learning process to improve the sharing of resources for senior education. To verify the feasibility of the model, a teaching effectiveness test was conducted, and the results showed that 66.6% of older adults believed that participation in senior education could strengthen their bodies and minds. The percentage of medical and health care knowledge courses was 63.20%. The direct effect of the learning environment on the participation behavior of senior education was 0.143, while the indirect effect of educational resources on the participation behavior of senior education was 0.083. In addition, 61.78% of seniors thought that attitude towards senior education was very important. Therefore, it can be concluded that the model of the elderly education system fills the gap in the structure of the lifelong education system, enriches the uppermost structure of the lifelong education system, and promotes the improvement and development of the lifelong education system.

Relationship between rate of glucose or propionate infusion and milk protein yield and concentration in dairy cows: A meta-regression

Although postruminal glucose infusion into dairy cows has increased milk protein yield in some past experiments, the same trend has not been observed in others. A meta-regression of 64 sets of observations from 29 previously published glucose and propionate infusion studies in dairy cattle, treating study and experiment (study) as random effects, was performed to establish the general effects of glucose equivalent (GlcE) infusion rate on milk true protein (MTP) yield and content, if any, and to identify independent, fixed-effect variables that accounted for the changes in MTP yield and content that were observed. Candidate explanatory variables included rate and site of infusion, diet composition and intake, body weight and lactation stage of the cows, and the change in nutrient intake between GlcE and control treatments. Across all studies, according to a model containing only the random effects of study and experiment, GlcE infusion at an average of 954 g/d increased MTP yield by 26 g/d, on average, whereas mean MTP content was not affected. Backward stepwise elimination of potential explanatory variables from a full mixed model produced a final, reduced model for MTP yield that retained a positive, second-order quadratic effect of infusion rate of GlcE and a positive, linear effect of the change in crude protein intake (CPI) between GlcE treatment and control. This change in CPI due to GlcE infusion ranged from -0.546 to 0.173 kg/d in the dataset. The model fit indicated that when CPI was allowed to drop during GlcE infusion, the effect of GlcE on MTP yield was smaller than when CPI was maintained or increased, in a manifestation of the classic protein:energy interaction. The final reduced model for MTP content contained the same explanatory variables as for MTP yield, plus a negative effect of intravenous compared with gastrointestinal infusion. Overall, the meta-analysis revealed that both MTP yield, and content were positively related to GlcE infusion rate and to the change in CPI between glucose treatment and control.

Modeling of soluble solid content of PE-packaged blueberries based on near-infrared spectroscopy with back propagation neural network and partial least squares (BP-PLS) algorithm.

Blueberries are a nutritious and popular berry worldwide. The physical and chemical properties of blueberries constantly change through the cycle of the supply chain (from harvest to sale). The purpose of this study was to develop a rapid method for detecting the properties of packaged blueberries based on near-infrared (NIR) spectroscopy. NIR was applied to quantitatively determine the soluble solid content (SSC) of polyethylene (PE)-packaged blueberries. An orthogonal partial least squares discriminant analysis model was established to show the correlation between spectral data and the measured SSC. Multiplicative scattering correction, standard normal variable, Savitzky-Golay convolution first derivative, and normalization (Normalize) were used for spectra preprocessing. Uninformative variables elimination, competitive adaptive reweighted sampling, and iteratively retaining informative variables were jointly used for wavelength optimization. NIR-based SSC prediction models for unpacked blueberries and PE-packaged blueberries were developed using partial least squares (PLS). The prediction model for PE-packaged samples (RP 2 =0.876, root mean square error of prediction [RMSEP]=0.632) had less precision than the model for unpacked samples (RP 2 =0.953, RMSEP=0.611). To reduce the effect of PE, the back propagation (BP) neural network and PLS were combined into the BP-PLS algorithm based on the residual learning algorithm. The model of BP-PLS (RP 2 =0.947, RMSEP=0.414) was successfully developed to improve the prediction accuracy of SSC for PE-packaged blueberries. The results suggested a promising way of using the BP-PLS method in tandem with NIR for the rapid detection of the SSC of PE-packaged blueberries. PRACTICAL APPLICATION: Most of the NIR-based research used unpacked blueberries as samples, while the use of packaged blueberries would provide researchers with a better understanding of the crucial factors at different phases of the blueberry supply chain (from harvest to sale). To meet market demands and minimize losses, NIR spectroscopy has been proven to be a rapid and nondestructive method for the determination of the SSC of PE-packaged blueberries. This study provides an effective method for monitoring the properties of blueberries in the entire supply chain.

Research on Defect Detection in Kubo Peach Based on Hyperspectral Imaging Technology Combined with CARS-MIV-GA-SVM Method.

Due to the dark red surface of ripe fresh peaches, their internal injury defects cannot be detected using the naked eye and conventional images. The rapid and accurate detection of fresh peach defects can improve the efficiency of fresh peach classification. The goal of this paper was to develop a nondestructive approach to simultaneously detecting internal injury defects and external injuries in fresh peaches. First, we collected spectral data from 347 Kubo peach samples using hyperspectral imaging technology (900-1700 nm) and carried out pretreatment. Four methods (the competitive adaptive reweighting algorithm (CARS), the combination of CARS and the average influence value algorithm (CARS-MIV), the combination of CARS and the successive projections algorithm (CARS-SPA), and the combination of CARS and uninformative variable elimination (CARS-UVE)) were used to extract the characteristic wavelength. Based on the characteristic wavelength extracted using the above methods, a genetic algorithm optimization support vector machine (GA-SVM) model and a least-squares support vector machine (LS-SVM) model were used to establish classification models. The results show that the combination of CARS and other feature wavelength extraction methods can effectively improve the prediction accuracy of the model when the number of wavelengths is small. Among them, the discriminant accuracy of the CARS-MIV-GA-SVM model reaches 93.15%. In summary, hyperspectral imaging technology can accomplish the accurate detection of Kubo peaches defects, and provides feasible ideas for the automatic classification of Kubo peaches.

Nondestructive detection of mango soluble solid content in hyperspectral imaging based on multi-combinatorial feature wavelength selection

AbstractThis paper explores the prediction of the soluble solid content (SSC) in the visible and near-infrared (400–1,000 nm) regions of Baise mango. Hyperspectral images of Baise mangoes with wavelengths of 400–1,000 nm were obtained using a hyperspectral imaging system. Multiple scatter correction (MSC) was chosen to remove the effect of noise on the accuracy of the partial least squares (PLS) regression model. On this basis, the characteristic wavelengths of mango SSC were selected using the competitive adaptive reweighted sampling (CARS), genetic algorithm (GA), uninformative variable elimination (UVE), and combined CARS + GA-SPA, CARS + UVE-SPA, and GA + UVE-SPA characteristic wavelength methods. The results show that the combined MSC-CARS + GA-SPA-PLS algorithm can reduce redundant information and improve the computational efficiency, so it is an effective method to predict the SSC of mangoes.

Quantification of polycyclic aromatic hydrocarbons in roasted Tan lamb using fluorescence hyperspectral imaging technology

Polycyclic aromatic hydrocarbons (PAHs) are one of the main harmful substances produced during the roasting of lamb. In this study, fluorescence hyperspectral imaging (FHSI) at different excitation wavelengths was used for the rapid determination of total PAHs (T-PAHs) content in roasted Tan lamb. Heterogeneous two-dimensional correlation spectroscopy (H2D-COS) was performed to resolve the fluorescence hyperspectral peaks. Competitive adaptive reweighed sampling (CARS), uninformative variable elimination (UVE), variable combination population analysis (VCPA), iterative retained information variable (IRIV) and interval variable iterative spatial shrinkage analysis (iVISSA) methods were employed to extract effective wavelengths to reduce data dimensionality. Partial least squares regression (PLSR), least squares support vector machine (LS-SVM) and convolutional neural network (CNN) algorithms were used to develop the T-PAHs content prediction model. For the FHSI at 357 nm excitation wavelength, the Smoothing-CARS-PLSR quantitative prediction model worked the best, with RC2 and RP2 reaching 0.9011 and 0.9169, and RMSEC and RMSEP reaching 0.0179 mg/kg and 0.0181 mg/kg, respectively. For the FHSI at 452 nm excitation wavelength, Smoothing-VCPA-CNN showed the best results, with RC2 and RP2 of 0.9199 and 0.9189, and RMSEC and RMSEP reaching 0.0175 mg/kg and 0.0171 mg/kg, respectively. This study provided a reference for the quantitative detection of PAHs.

Detection of moisture content and size of pumpkin seeds based on hyperspectral reflection and transmission imaging techniques

Moisture content and size can affect the vigor and weight of pumpkin seeds, where vigor is the key to evaluating seed quality, and weight is an important characteristic of the seeds. Therefore, detecting the moisture content and size of pumpkin seeds contributes to improving seed quality. In this study, hyperspectral reflection and transmission imaging techniques were used to detect the moisture and size of single pumpkin seeds. Linear PLSR and nonlinear LSSVM models were established to predict the moisture content of single pumpkin seeds using reflection and transmission spectral data. Five variable selection and data fusion methods, namely randomization test, variable selection based on C-value, uninformative variable elimination, Monte Carlo-uninformative variable elimination, and competitive adaptive reweighted sampling (CARS), were adopted to optimize the models. Compared with the models based on reflection and transmission spectra, low-level data fusion improved the model performance. The mid-level data fusion with models based on the CARS algorithm achieved the optimal performance, corresponding to R2P and RMSEP of 0.9219, 0.0279%, and 0.9231, 0.0278%, for the PLSR and LSSVM models, respectively. Finally, it was demonstrated that using hyperspectral reflection images to determine the size of pumpkin seeds is feasible, and the length and width of all samples were determined. The results show that hyperspectral imaging techniques and data fusion can effectively detect the moisture content of single pumpkin seeds, and measuring the size of pumpkin seeds is feasible using image processing algorithms.

Clinical spectrum and prognostic impact of cancer in critically ill patients with HIV: a multicentre cohort study

BackgroundBoth AIDS-defining and non-AIDS-defining cancers (ADC/NADC) predispose people living with HIV (PLHIV) to critical illnesses. The objective of this multicentre study was to investigate the prognostic impact of ADC and NADC in PLHIV admitted to the intensive care unit (ICU).MethodsAll PLHIV admitted over the 2015–2020 period in 12 university-affiliated ICUs in France were included in the study cohort. The effect of ADC and NADC on in-hospital mortality (primary study endpoint) was measured through logistic regression with augmented backward elimination of potential independent variables. The association between ADC/NADC and treatment limitation decision (TLD) during the ICU stay (secondary study endpoint) was analysed. One-year mortality in patients discharged alive from the index hospital admission (exploratory study endpoint) was compared between those with ADC, NADC or no cancer.ResultsAmongst the 939 included PLHIV (median age, 52 [43–59] years; combination antiretroviral therapy, 74.4%), 97 (10.3%) and 106 (11.3%) presented with an active NADC (mostly lung and intestinal neoplasms) and an active ADC (predominantly AIDS-defining non-Hodgkin lymphoma), respectively. Inaugural admissions were common. Bacterial sepsis and non-infectious neoplasm-related complications accounted for most of admissions in these subgroups. Hospital mortality was 12.4% in patients without cancer, 30.2% in ADC patients and 45.4% in NADC patients (P < 0.0001). NADC (adjusted odds ratio [aOR], 7.00; 95% confidence interval [CI], 4.07–12.05) and ADC (aOR, 3.11; 95% CI 1.76–5.51) were independently associated with in-hospital death after adjustment on severity and frailty markers. The prevalence of TLD was 8.0% in patients without cancer, 17.9% in ADC patients and 33.0% in NADC patients (P < 0.0001)—organ failures and non-neoplastic comorbidities were less often considered in patients with cancer. One-year mortality in survivors of the index hospital admission was 7.8% in patients without cancer, 17.0% in ADC patients and 33.3% in NADC patients (P < 0.0001).ConclusionsNADC and ADC are equally prevalent, stand as a leading argument for TLD, and strongly predict in-hospital death in the current population of PLHIV requiring ICU admission.

Requirement Change Prediction Model for Small Software Systems

The software industry plays a vital role in driving technological advancements. Software projects are complex and consist of many components, so change is unavoidable in these projects. The change in software requirements must be predicted early to preserve resources, since it can lead to project failures. This work focuses on small-scale software systems in which requirements are changed gradually. The work provides a probabilistic prediction model, which predicts the probability of changes in software requirement specifications. The first part of the work considers analyzing the changes in software requirements due to certain variables with the help of stakeholders, developers, and experts by the questionnaire method. Then, the proposed model incorporates their knowledge in the Bayesian network as conditional probabilities of independent and dependent variables. The proposed approach utilizes the variable elimination method to obtain the posterior probability of the revisions in the software requirement document. The model was evaluated by sensitivity analysis and comparison methods. For a given dataset, the proposed model computed the low state revisions probability to 0.42, and the high state revisions probability to 0.45. Thus, the results proved that the proposed approach can predict the change in the requirements document accurately by outperforming existing models.

MaxSAT resolution for regular propositional logic

Proof systems for SAT are unsound for MaxSAT because they preserve satisfiability but fail to preserve the minimum number of unsatisfied clauses. Consequently, there has been a need to define cost-preserving resolution-style proof systems for MaxSAT. In this paper, we present the first MaxSAT resolution proof system specifically defined for regular propositional clausal forms and prove its soundness and completeness. The defined proof system provides an exact approach to solving Regular MaxSAT and Weighted Regular MaxSAT with variable elimination algorithms.

Mixed-integer programming models for irregular strip packing based on vertical slices and feasibility cuts

The irregular strip-packing problem, also known as nesting or marker making, is defined as the automatic computation of a non-overlapping placement of a set of non-convex polygons onto a rectangular strip of fixed width and unbounded length, such that the strip length is minimized. Nesting methods based on heuristics are a mature technology, and currently, the only practical solution to this problem. However, recent performance gains of the Mixed-Integer Programming (MIP) solvers, together with the known limitations of the heuristics methods, have encouraged the exploration of exact optimization models for nesting during the last decade. Despite the research effort, there is room to improve the efficiency of the current family of exact MIP models for nesting. In order to bridge this gap, this work introduces a new family of continuous MIP models based on a novel formulation of the NoFit-Polygon Covering Model (NFP-CM), called NFP-CM based on Vertical Slices (NFP-CM-VS). Our new family of MIP models is based on a new convex decomposition of the feasible space of relative placements between pieces into vertical slices, together with a new family of valid inequalities, symmetry breakings, and variable eliminations derived from the former convex decomposition. Our experiments show that our new NFP-CM-VS models outperform the current state-of-the-art MIP models. Ten instances are solved up to optimality within one hour for the first time, including one with 27 pieces. Finally, we provide a detailed reproducibility protocol and dataset as supplementary material to allow the exact replication of our models, experiments, and results.

An MINLP formulation to identify thermodynamically-efficient distillation configurations

Designing configurations for separation of multicomponent mixtures by distillation is challenging because of (i) combinatorial explosion of the choice set, and (ii) nonconvex nature of the governing equations. This work proposes a novel Mixed Integer Nonlinear Program (MINLP) that is formulated to identify configurations that minimize the total exergy loss/maximize thermodynamic efficiency of the separation process. The formulation in its default form has several nonlinear nonconvex equations. We propose a model reformulation via a simple variable elimination technique to reduce the system of nonlinear equations to a single equation, which we refer to as the exergy constraint. We describe the properties of exergy constraints and exploit them in deriving additional valid cuts for the problem. Finally, we use the model for a case study concerning the recovery of Natural Gas Liquids (NGLs) from shale gas.

Certified SAT solving with GPU accelerated inprocessing

Since 2013, the leading SAT solvers in SAT competitions all use inprocessing, which, unlike preprocessing, interleaves search with simplifications. However, inprocessing is typically a performance bottleneck, in particular for hard or large formulas. In this work, we introduce the first attempt to parallelize inprocessing on GPU architectures. As one of the main challenges in GPU programming is memory locality, we present new compact data structures and devise a data-parallel garbage collector. It runs in parallel on the GPU to reduce memory consumption and improve memory locality. Our new parallel variable elimination algorithm is roughly twice as fast as previous work. Moreover, we augment the variable elimination with the first parallel algorithm for functional dependency extraction in an attempt to find more logical gates to eliminate that cannot be found with syntactic approaches. We present a novel algorithm to generate clausal proofs in parallel to validate all simplifications running on the GPU besides the CDCL search, giving high credibility to our solver and its use in critical applications such as model checkers. In experiments, our new solver ParaFROST solves numerous benchmarks faster on the GPU than its sequential counterparts. With functional dependency extraction, inprocessing in ParaFROST was more effective in reducing the solving time. Last but not least, all proofs generated by ParaFROST were successfully verified.

Fusing spectral and image information for characterization of black tea grade based on hyperspectral technology

Tea grade evaluation plays an important role in detecting the quality of tea. In this work, we proposed a method for black tea grade discrimination, which is based on the hyperspectral (HSI) technique, chemometric algorithms and data fusion strategies. Standard Normal Variable (SNV) and Multiple Scattering Correction (MSC) were used for the pretreatment of HSI-NIR data. Moreover, the spectral features of catechins, tea polyphenols and soluble sugars were extracted separately by the Uninformative Variable Elimination (UVE). To improve the accuracy of the model, the spectral data were secondarily downscaled by the Successive Projection Algorithm (SPA) and Kernel Principal Component Analysis (KPCA), respectively. Image features were extracted by Principal Component Analysis (PCA) and Gray Level Co-generation Matrix (GLCM). Simultaneously, the image features had been fused with the spectral features in order to realize data fusion. Partial Least Squares Discriminant Analysis (PLS-DA), Support Vector Machines (SVM) and Probabilistic Neural Networks (PNN) were utilized to build the discriminant models. The results show that hyperspectral techniques combined with data fusion strategies could improve the model accuracy. The UVE-SPA-GLCM-SVM was chosen as the optimal model with 98.33% accuracy of the validation set. The method provides a theoretical basis for online black tea grade differentiation.

A Novel Method for Quantifying Plant Morphological Characteristics Using Normal Vectors and Local Curvature Data via 3D Modelling-A Case Study in Leaf Lettuce.

Three-dimensional measurement is a high-throughput method that can record a large amount of information. Three-dimensional modelling of plants has the possibility to not only automate dimensional measurement, but to also enable visual assessment to be quantified, eliminating ambiguity in human judgment. In this study, we have developed new methods that could be used for the morphological analysis of plants from the information contained in 3D data. Specifically, we investigated characteristics that can be measured by scale (dimension) and/or visual assessment by humans. The latter is particularly novel in this paper. The characteristics that can be measured on a scale-related dimension were tested based on the bounding box, convex hull, column solid, and voxel. Furthermore, for characteristics that can be evaluated by visual assessment, we propose a new method using normal vectors and local curvature (LC) data. For these examinations, we used our highly accurate all-around 3D plant modelling system. The coefficient of determination between manual measurements and the scale-related methods were all above 0.9. Furthermore, the differences in LC calculated from the normal vector data allowed us to visualise and quantify the concavity and convexity of leaves. This technique revealed that there were differences in the time point at which leaf blistering began to develop among the varieties. The precise 3D model made it possible to perform quantitative measurements of lettuce size and morphological characteristics. In addition, the newly proposed LC-based analysis method made it possible to quantify the characteristics that rely on visual assessment. This research paper was able to demonstrate the following possibilities as outcomes: (1) the automation of conventional manual measurements, and (2) the elimination of variability caused by human subjectivity, thereby rendering evaluations by skilled experts unnecessary.

A Novel Variable Selection Method Based on Binning-Normalized Mutual Information for Multivariate Calibration.

Variable (wavelength) selection is essential in the multivariate analysis of near-infrared spectra to improve model performance and provide a more straightforward interpretation. This paper proposed a new variable selection method named binning-normalized mutual information (B-NMI) based on information entropy theory. "Data binning" was applied to reduce the effects of minor measurement errors and increase the features of near-infrared spectra. "Normalized mutual information" was employed to calculate the correlation between each wavelength and the reference values. The performance of B-NMI was evaluated by two experimental datasets (ideal ternary solvent mixture dataset, fluidized bed granulation dataset) and two public datasets (gasoline octane dataset, corn protein dataset). Compared with classic methods of backward and interval PLS (BIPLS), variable importance projection (VIP), correlation coefficient (CC), uninformative variables elimination (UVE), and competitive adaptive reweighted sampling (CARS), B-NMI not only selected the most featured wavelengths from the spectra of complex real-world samples but also improved the stability and robustness of variable selection results.

Rapid authentication of variants of Gastrodia elata Blume using near-infrared spectroscopy combined with chemometric methods

The variety is one of the most important factors to generate difference of chemical compositions, which unavoidably influences the quality of natural medicine. Thus, simple and rapid authentication of different variants has great academic and practical significance. In this study, the goal was achieved with the help of near infrared spectroscopy (NIR) and chemometrics by using Gastrodia elata Blume as an example. A total of 540 samples including two classes of variants and their forms were investigated as a whole. The mean spectra of samples of each class and their 2-D synchronous correlation spectra were simultaneously applied to discover the difference of chemical characteristics. After hybrid pre-processing of the first and second derivative combined with Savitzky-Golay and Norris filtering, partial least squares discrimination analysis (PLS-DA) on the basis of latent variable projection was used to assess the feasibility for classification. The results show higher prediction accuracy in both internal test set and external prediction set. In order to further improve the robustness for modeling, three methods for wavelength selection were comprehensively compared to optimize PLS-DA models, including variable importance in the projection (VIP), random frog (RF), and Monte Carlo uninformative variable elimination (MC-UVE). The prediction accuracy of combination of the 2nd derivative, Norris, MC-UVE and PLS-DA achieved to 99.11% and 98.89% corresponding to the internal test set and external prediction set, respectively. The strategies proposed in this work perform effectiveness for rapid and accurate authentication of variants of plants with high chemical complexity.

Estimation of key potentially toxic elements in arid agricultural soils using Vis-NIR spectroscopy with variable selection and PLSR algorithms

Potentially toxic elements (PTEs) pose a significant threat to soil and the environment. Therefore, the fast quantification of PTEs is crucial for better management of contaminated sites. Versatile technique such as Visible near-infrared spectroscopy (Vis–NIRS) (350–2,500 nm) has attracted tremendous attention for assessing PTEs and has achieved promising results combined with successful multivariate analysis. This research investigated the potential of Vis–NIRS combined with partial least squares regression (PLSR) and variable selection methods to assess key PTEs (Cd, Co, Cu, Cr, Pb, and Zn) in agricultural soils under arid conditions. The soil samples (80) were collected from a polluted area around Al-Moheet drainage, Minya Governorate–upper Egypt. The samples were scanned using an ASD FieldSpec-4 spectroradiometer. Simulated annealing (SA) and uninformative variable elimination (UVE) were used to select the effective wavelengths in predicting PTEs. PLSR was used to develop the spectral models using the full range (FR-PLS) and feature-selected spectra techniques SA (SA-PLS) and UVE (UVE-PLS). The results indicated that UVE-PLS models performed better than FR-PLS and SA-PLS models in predicting the key PTEs. The obtained coefficient of determination (R2) and the ratio of performance to deviation (RPD) were 0.74 and 2.48 (Cr), 0.72 and 2.03 (Pb), 0.62 and 1.86 (Cd), 0.59 and 1.78 (Cu), 0.52 and 1.68 (Co), and 0.46 and 1.41 (Zn), respectively. The results suggested that the UVE-PLS spectral model is promising for predicting Cr, Pb, and Cd, and can be improved for predicting Cu, Co, and Zn elements in agricultural soils.

Effect of different spectral processing methods on the quantitative model of Astragalus polysaccharides detected by NIR spectroscopy

Quality control of Chinese herbal medicines requires reliable and rapid detection techniques, Many scholars have used NIR spectroscopy for research in Chinese herbal medicine industry. The aim of this study was to evaluate the feasibility of combining NIR spectroscopy and machine learning to predict the adulteration content of Astragalus polysaccharides. The astragalus polysaccharide and rice flour were mixed to form astragalus polysaccharide adulterant with different concentration. the Quantitative prediction models are built using the support vector machine (SVM). Different pre-processing methods of SG, SNV, WT, SG+SNV, SG+WT were used to process the spectra, and then the continuous projection algorithm (SPA) and uninformative variable elimination (UVE) were used to select the characteristic wavelengths. The SVM prediction model based on SG+SNV+SPA is optimal with an RPD of 2.653, indicating that the model has good predictive ability. The results showed that NIR technique could be used for quantitative analysis of Astragalus polysaccharides.