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The tensile strength (TS) of compacted ribbon is a critical quality attribute in the roller compaction process that impacts the quality of the finished product. This study investigated the use of Near Infrared Hyperspectral Imaging Spectroscopy (NIR-HIS) technology for predicting TS of compacted ribbons, considering the effects of surface curvature, different spectral preprocessing methods, and variable selection methods on a predictive model based on Partial Least Squares regression (PLSr). The spectral preprocessing methods evaluated were Mean Centering (MC) and Standard Normal Variate (SNV). The variable selection methods were Filter by Regression Coefficient (REG), Variable Importance in Projection (VIP), Competitive Adaptive Reweighted Sampling (CARS), and Genetic Algorithm (GA). The results indicated that curved surfaces had no significant impact on the predictive performance of the model (p-value of 0.39 for RMSEP). The PLSr-CARS method, combined with MC spectral preprocessing, was successful in selecting and reducing the number of wavelengths from 182 to 5, as indicated by high values of R2pred and RPD, and a low RMSEP value (0.97, 5.75, and 7.60 %, respectively). An MLR model using the 5 wavelengths was also developed, showing similar performance to the PLSr model. Both the MLR and PLSr models demonstrated high predictive accuracy and reliability. These models can perform well even when developed using only a few wavelengths, leading to significant reductions in processing time and measurement costs, making them valuable tools for quality control in the pharmaceutical industry.

The increasing number of patients with hypertension is a problem worldwide. Daily blood pressure monitoring is important for the prevention of this disease, and a non‐contact measurement method is desirable. A previous study applied independent components analysis to facial skin temperatures measured remotely using a thermography device for extracting features related to resting blood pressure. Then, a blood pressure estimation model was constructed and obtained an accuracy of 7.73 mmHg root mean square error. However, this previous study did not consider the variables selection methods and models to be constructed. Considering these factors is expected to improve the accuracy of estimation. The objective of this study is to optimize the resting blood pressure estimation model. Two variable selection methods and three modeling methods were applied to independent components of facial thermal images, and the accuracy was compared. The result showed an accuracy of 6.80 mmHg root mean square error with a pattern of support vector regression model using independent components selected by Boruta. In addition, eight explanatory variables were selected by Boruta, and suggesting that the lip, orbital, and lateral nasal regions were related to resting blood pressure. © 2024 Institute of Electrical Engineer of Japan and Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]

Peanuts, owing to their composition of complex carbohydrates, plant protein, unsaturated fatty acids, and essential minerals (magnesium, iron, zinc, and potassium), hold significant potential as a vital component of the human diet. Additionally, their low water requirements and nitrogen fixation capacity make them an appropriate choice for cultivation in adverse environmental conditions. The germination ability of seeds profoundly impacts the final yield of the crop; assessing seed viability is of extreme importance. Conventional methods for assessing seed viability and germination are both time-consuming and costly. To address these challenges, this study investigated Visible–Near-Infrared Spectroscopy (Vis/NIR) in the wavelength range of 500–1030 nm as a nondestructive and rapid method to determine the viability of two varieties of peanut seeds: North Carolina-2 (NC-2) and Spanish flower (Florispan). The study subjected the seeds to three levels of artificial aging through heat treatment, involving incubation in a controlled environment at a relative humidity of 85% and a temperature of 50 °C over 24 h intervals. The absorbance spectra noise was significantly mitigated and corrected to a large extent by combining the Savitzky–Golay (SG) and multiplicative scatter correction (MSC) methods. To identify the optimal wavelengths for seed viability assessment, a range of metaheuristic algorithms were employed, including world competitive contest (WCC), league championship algorithm (LCA), genetics (GA), particle swarm optimization (PSO), ant colony optimization (ACO), imperialist competitive algorithm (ICA), learning automata (LA), heat transfer optimization (HTS), forest optimization (FOA), discrete symbiotic organisms search (DSOS), and cuckoo optimization (CUK). These algorithms offer powerful optimization capabilities for effectively extracting relevant wavelength information from spectral data. Results revealed that all the algorithms demonstrated remarkable accuracy in predicting the allometric coefficient of seeds, achieving correlation coefficients exceeding 0.985 and errors below 0.0036, respectively. In terms of execution time, the ICA (2.3635 s) and LCA (44.9389 s) algorithms exhibited the most and least efficient performance, respectively. Conversely, the FOA and the LCA algorithms excelled in identifying the least number of optimal wavelengths (10 wavelengths). Subsequently, the seeds were classified based on the wavelengths selected via the FOA (10 wavelengths) and (DSOS (16 wavelengths) methods, in conjunction with logistic regression (LR), decision tree (DT), multilayer perceptron (MP), support vector machine (SVM), k-nearest neighbor (K-NN), and naive Bayes (NB) classifiers. The DSOS–DT and FOA–MP methods demonstrated the highest accuracy, yielding values of 0.993 and 0.983, respectively. Conversely, the DSOS–LR and DSOS–KNN methods obtained the lowest accuracy, with values of 0.958 and 0.961, respectively. Overall, our findings demonstrated that Vis/NIR spectroscopy, coupled with variable selection algorithms and learning methods, presents a suitable and nondestructive approach for detecting seed viability. [ABSTRACT FROM AUTHOR]

The crude protein content is critical to quality assessment when screening for nutrients, taste quality and commercial value. Thus, this study performed a non-destructive and rapid prediction of protein content in paddy based on line-scanning near-infrared hyperspectral imaging (1001–2300 nm) technology. Partial least squares regression (PLSR), principal component regression (PCR) and multiple linear regression (MLR) predictive models were established to evaluate protein content (5.5037–8.2543 g 100 g−1) in 100 intact paddy, and the models achieved high performance. Spectral pre-processing with De-trending to a certain extent could enhance the smoothness of the spectrum and reduce spectral noise effectively. Successive projection algorithm (SPA) was used to extract characteristic wavelengths to simplify the models. A set of 18 feature variables were selected from the original wavelength, and the SPA-PLSR model has the best performance to predict protein content in paddy. In addition, the simplified performed model with a higher value of coefficient of determination (R2) of R2C and R2P was 0.9078 and 0.8836, and the lower root mean square error (RMSE) of RMSEC and RMSEP was 0.0912 and 0.1675, respectively. The distribution maps of each sample protein contents in each pixel were obtained using the prediction model. Therefore, experimental results indicated the feasibility and possibility of a rapid and non-destructive hyperspectral imaging technique to detect the chemical component in paddy. [ABSTRACT FROM AUTHOR]

In the second half of the 20th century, the behaviour of the world's population has undergone drastic changes: it has grown very rapidly, become longer-lived and moved preferably to urban areas. Migration of populations to cities has a special focus because it not only generates socio-economic development, but also causes different economic, social, and environmental issues. How to assess the sustainable development of cities and identify the most representative aspects of sustainability dimensions has become an essential challenge for effective planning and government policies. However, the selection of indicators has scarcely been addressed when Data Envelopment Analysis (DEA) has been used for sustainability assessment. This paper aims to identify the key indicators to determine the eco-efficient performance towards a sustainable development of a set of cities. For this purpose, the joint use of a variable selection method and a DEA model is proposed and applied to a set of 31 Spanish cities. The results obtained identified five social (population percentage at risk of poverty, number of registered gender violence cases, population density), five economic (average household income, average rental price per m2, gross domestic product per capita, public/private vehicle ratio, indebtedness), and three environmental (ozone, NO2, PM10) indicators for the assessment. Moreover, 14 Spanish cities were considered non-eco-efficient, and targets to achieve eco-efficient levels were proposed. Policy makers could focus primarily on the social and economic aspects of non-eco-efficient cities, paying attention, for example, to the risk of poverty of the population, indebtedness of local governments, average rental price and NO2 emissions.

Peanuts, owing to their composition of complex carbohydrates, plant protein, unsaturated fatty acids, and essential minerals (magnesium, iron, zinc, and potassium), hold significant potential as a vital component of the human diet. Additionally, their low water requirements and nitrogen fixation capacity make them an appropriate choice for cultivation in adverse environmental conditions. The germination ability of seeds profoundly impacts the final yield of the crop; assessing seed viability is of extreme importance. Conventional methods for assessing seed viability and germination are both time-consuming and costly. To address these challenges, this study investigated Visible–Near-Infrared Spectroscopy (Vis/NIR) in the wavelength range of 500–1030 nm as a nondestructive and rapid method to determine the viability of two varieties of peanut seeds: North Carolina-2 (NC-2) and Spanish flower (Florispan). The study subjected the seeds to three levels of artificial aging through heat treatment, involving incubation in a controlled environment at a relative humidity of 85% and a temperature of 50 °C over 24 h intervals. The absorbance spectra noise was significantly mitigated and corrected to a large extent by combining the Savitzky–Golay (SG) and multiplicative scatter correction (MSC) methods. To identify the optimal wavelengths for seed viability assessment, a range of metaheuristic algorithms were employed, including world competitive contest (WCC), league championship algorithm (LCA), genetics (GA), particle swarm optimization (PSO), ant colony optimization (ACO), imperialist competitive algorithm (ICA), learning automata (LA), heat transfer optimization (HTS), forest optimization (FOA), discrete symbiotic organisms search (DSOS), and cuckoo optimization (CUK). These algorithms offer powerful optimization capabilities for effectively extracting relevant wavelength information from spectral data. Results revealed that all the algorithms demonstrated remarkable accuracy in predicting the allometric coefficient of seeds, achieving correlation coefficients exceeding 0.985 and errors below 0.0036, respectively. In terms of execution time, the ICA (2.3635 s) and LCA (44.9389 s) algorithms exhibited the most and least efficient performance, respectively. Conversely, the FOA and the LCA algorithms excelled in identifying the least number of optimal wavelengths (10 wavelengths). Subsequently, the seeds were classified based on the wavelengths selected via the FOA (10 wavelengths) and (DSOS (16 wavelengths) methods, in conjunction with logistic regression (LR), decision tree (DT), multilayer perceptron (MP), support vector machine (SVM), k-nearest neighbor (K-NN), and naive Bayes (NB) classifiers. The DSOS–DT and FOA–MP methods demonstrated the highest accuracy, yielding values of 0.993 and 0.983, respectively. Conversely, the DSOS–LR and DSOS–KNN methods obtained the lowest accuracy, with values of 0.958 and 0.961, respectively. Overall, our findings demonstrated that Vis/NIR spectroscopy, coupled with variable selection algorithms and learning methods, presents a suitable and nondestructive approach for detecting seed viability.

The use of biomass is increasing because it is a form of renewable energy that provides high heating value. Rapid measurements could be used to check the quality of biomass pellets during production. This research aims to apply a near-infrared (NIR) hyperspectral imaging system for the evaluation of the true density of individual biomass pellets during the production process. Real-time measurement of the true density could be beneficial for the operation settings, such as the ratio of the binding agent to the raw material, the temperature of operation, the production rate, and the mixing ratio. The true density could also be used for rough measurement of the bulk density, which is a necessary parameter in commercial production. Therefore, knowledge of the true density is required during production in order to maintain the pellet quality as well as operation conditions. A prediction model was developed using partial least squares (PLS) regression across different wavelengths selected using different spectral pre-treatment methods and variable selection methods. After model development, the performance of the models was compared. The best model for predicting the true density of individual pellets was developed with first-derivative spectra (D1) and variables selected by the genetic algorithm (GA) method, and the number of variables was reduced from 256 to 53 wavelengths. The model gave R2cal, R2val, SEC, SEP, and RPD values of 0.88, 0.89, 0.08 g/cm3, 0.07 g/cm3, and 3.04, respectively. The optimal prediction model was applied to construct distribution maps of the true density of individual biomass pellets, with the level of the predicted values displayed in colour bars. This imaging technique could be used to check visually the true density of biomass pellets during the production process for warnings to quality control equipment.

When people who adhere to tax morality act in a situation where there is no sense of risk, no acceptance of the government, or no environment conducive to tax compliance, it is easier to see how they are motivated to do so. Tax morality is also known as the ethics of compliance. It is the independent cause that motivates a positive tax behaviour. Employees' religious beliefs may impact their ideas and actions in organizational life, just as individuals' attitudes, values, emotions, abilities, and behaviours influence their thoughts and actions at work. Religion can positively influence a worker's loyalty, morale, and communication. In this context, the research seeks to determine whether religiosity and religion may have an effect on tax morale, examining whether an individual's religiosity reduces tax evasion and increases the degree of tax morale. Using machine learning variable selection techniques appropriate for categorical variables, we have used the dataset of the Joint EVS/WVS 2017-2020 (European Value Survey/World Value Survey), allowing for comparisons of tax morality in more than 79 nations globally (chi-squared and mutual information). The empirical findings showed that the most important aspects of religiosity, such as religious denomination, belief in God, and the significance of God, along with the degree of trust placed in other religions and churches, have a considerable positive impact on the level of tax morale. Another significant conclusion relates to how much people feel the government is responsible, how much they care about their nation, and how satisfied they are with the political system—findings that have been shown to boost employee morale. The following are a person's primary traits that indicate their financial morale: an adult above the age of 25, a full-time worker or retired person, married, and living alone. Therefore, employees that are morally upright, trustworthy, diligent, and committed to the workplace values of justice and decency raise morale generally and improve an organisation's success. A business may enhance its reputation and help to secure its long-term success by establishing behavioural policies. [ABSTRACT FROM AUTHOR]

The tensile strength (TS) of compacted ribbon is a critical quality attribute in the roller compaction process that impacts the quality of the finished product. This study investigated the use of Near Infrared Hyperspectral Imaging Spectroscopy (NIR-HIS) technology for predicting TS of compacted ribbons, considering the effects of surface curvature, different spectral preprocessing methods, and variable selection methods on a predictive model based on Partial Least Squares regression (PLSr). The spectral preprocessing methods evaluated were Mean Centering (MC) and Standard Normal Variate (SNV). The variable selection methods were Filter by Regression Coefficient (REG), Variable Importance in Projection (VIP), Competitive Adaptive Reweighted Sampling (CARS), and Genetic Algorithm (GA). The results indicated that curved surfaces had no significant impact on the predictive performance of the model (p-value of 0.39 for RMSEP). The PLSr-CARS method, combined with MC spectral preprocessing, was successful in selecting and reducing the number of wavelengths from 182 to 5, as indicated by high values of R 2 pred and RPD, and a low RMSEP value (0.97, 5.75, and 7.60 %, respectively). An MLR model using the 5 wavelengths was also developed, showing similar performance to the PLSr model. Both the MLR and PLSr models demonstrated high predictive accuracy and reliability. These models can perform well even when developed using only a few wavelengths, leading to significant reductions in processing time and measurement costs, making them valuable tools for quality control in the pharmaceutical industry., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2024 The Authors.)

Although the video surveillance system plays an important role in intelligent transportation, the limited camera views make it difficult to observe many traffic events. In this paper, we collect and combine the traffic flow variables from the multi-source sensors, and propose a PITED method based on Random Forest (RF) and Permutation importance (PI) for traffic event detection. This model selects the suitable traffic flow variables by means of permutation arrangement of importance, and establishes the whole process of acquisition, preprocessing, quantization, modeling and evaluation. Moreover, the real traffic data are collected and tested in this paper for evaluating the experiment performance, including the miss/false rate of traffic event, and average detection time. The experimental results show that the detection rate is more than 85% and the false alarm rate is less than 3%. It means the model is effective and efficient in the practical application regardless of both workdays and holidays. [ABSTRACT FROM AUTHOR]

When people who adhere to tax morality act in a situation where there is no sense of risk, no acceptance of the government, or no environment conducive to tax compliance, it is easier to see how they are motivated to do so. Tax morality is also known as the ethics of compliance. It is the independent cause that motivates a positive tax behaviour. Employees’ religious beliefs may impact their ideas and actions in organizational life, just as individuals’ attitudes, values, emotions, abilities, and behaviours influence their thoughts and actions at work. Religion can positively influence a worker’s loyalty, morale, and communication. In this context, the research seeks to determine whether religiosity and religion may have an effect on tax morale, examining whether an individual’s religiosity reduces tax evasion and increases the degree of tax morale. Using machine learning variable selection techniques appropriate for categorical variables, we have used the dataset of the Joint EVS/WVS 2017-2020 (European Value Survey/World Value Survey), allowing for comparisons of tax morality in more than 79 nations globally (chi-squared and mutual information). The empirical findings showed that the most important aspects of religiosity, such as religious denomination, belief in God, and the significance of God, along with the degree of trust placed in other religions and churches, have a considerable positive impact on the level of tax morale. Another significant conclusion relates to how much people feel the government is responsible, how much they care about their nation, and how satisfied they are with the political system—findings that have been shown to boost employee morale. The following are a person’s primary traits that indicate their financial morale: an adult above the age of 25, a full-time worker or retired person, married, and living alone. Therefore, employees that are morally upright, trustworthy, diligent, and committed to the workplace values of justice and decency raise morale generally and improve an organisation’s success. A business may enhance its reputation and help to secure its long-term success by establishing behavioural policies.

Changes in wind speed directly affect the output of wind turbines, so in power system planning, accurate prediction of future wind speed can help adjust the stability and reliability of power networks. In order to solve the problem that unknown wind speed prediction systems are unable to select features autonomously and to alleviate the insufficient prediction accuracy of single interval type 2 fuzzy models, this paper proposes a new deep convolutional interval type 2 fuzzy system (DCIT2FS) based on adaptive feature selection. First off, type 2 fuzzy sets are employed in the construction of two stage interval type 2 fuzzy curves and surfaces to address the issue of dimensionality explosion brought on by complex system modelling. By removing inaccurate and redundant information, this strategy achieves automated screening of key input aspects of an unknown system. Second, the model's interpretability is improved by the addition of the interval type 2 fuzzy system to the deep convolutional structure, which also addresses the issue that a single interval type 2 fuzzy model cannot achieve the required level of prediction accuracy. Thirdly, the incorporation of the feature pyramid structure boosts the mining of underlying data to increase the model's capacity to predict outcomes accurately while simultaneously strengthening its resistance to interference. Finally, model prediction performance tests are completed using meteorological feature data for both time periods, and generalisability tests are conducted using the benchmark dataset. The experimental results show that the prediction accuracy of the model proposed in this paper is improved by at least 7.70% and 1.23% in two wind speed prediction experiments compared to other models. • A new deep convolutional interval type 2 fuzzy prediction model is designed. • The feature pyramid structure is added to DCIT2FS in an innovative way. • A method TSIT2FCS is designed to realise the automatic selection of input features. • The hybrid model TSIT2FCS-DCIT2FS is successfully applied in wind speed prediction. [ABSTRACT FROM AUTHOR]

When modeling the kink regression model, it is possible to have an excessive number of explanatory variables and their corresponding coefficients, thereby leading to the over-parameterization and multicollinearity problems. Motivated by these problems, five sparse estimation methods, namely LASSO, sparse Ridge, SCAD, MCP, and Bridge, are considered to perform simultaneous variable selection and parameter estimation, as alternatives to the Ordinary Least Squares (OLS), in the kink regression model. To compare the performance of these sparse estimators, both simulation and real data applications are proposed. According to the simulation results, we demonstrate the superior performance of sparse estimations in terms of selection accuracy and prediction by comparing them to the non-sparse estimations. However, it is not apparent which sparse estimations are more appropriate for estimating the kink regression. However, in an application study, the comparison result indicates that the SCAD penalty would be a preferable penalty function for the application of kink regression to the life expectancy data as the lowest EBIC and the highest Adj - R 2 are obtained. [ABSTRACT FROM AUTHOR]

Although the video surveillance system plays an important role in intelligent transportation, the limited camera views make it difficult to observe many traffic events. In this paper, we collect and combine the traffic flow variables from the multi-source sensors, and propose a PITED method based on Random Forest (RF) and Permutation importance (PI) for traffic event detection. This model selects the suitable traffic flow variables by means of permutation arrangement of importance, and establishes the whole process of acquisition, preprocessing, quantization, modeling and evaluation. Moreover, the real traffic data are collected and tested in this paper for evaluating the experiment performance, including the miss/false rate of traffic event, and average detection time. The experimental results show that the detection rate is more than 85% and the false alarm rate is less than 3%. It means the model is effective and efficient in the practical application regardless of both workdays and holidays.

Algae can produce odor substances and toxins that make the smell and taste of water unpleasant and impair the quality of human and aquatic life. Appropriate countermeasures can be implemented in advance in water purification processes to prevent algal disorders if the occurrence of algal blooms is accurately predicted. Several models have been developed to predict algal blooms. However, a comprehensive model that can be universally applied under various conditions is lacking. In this study, automatic relevance determination, a sparse modeling algorithm, and support vector machine were combined to construct prediction models for algal blooms in four Japanese dam reservoirs to predict their occurrence over 7 days. Automatic relevance determination was applied to a dataset consisting of monthly water quality data and daily hydraulic and meteorological data to identify variables relevant to the concentrations of Microcystis spp. and Dolichospermum spp., which are bloom-forming cyanobacteria and are dominant in freshwater ecosystems. A dataset of selected variables was used to train and validate the support vector machine models. The results of variable selection by automatic relevance determination revealed that the average concentration of total nitrogen in the past year and the average maximum temperature in the past 7 days may have an association with the algal concentration. Support vector machine models resulted in 92.3% accuracy and 86.4% precision for Microcystis spp. and 71.4% accuracy and 77.5% precision for Dolichospermum spp. on average in binary classification. The competitive relationship between Microcystis spp. and Dolichospermum spp., which differs according to the nutrient level and temperature, probably affects the prediction performance of the models. Our study suggests that the combination of sparse modeling and machine learning is applicable to the construction of a prediction model for site-specific algal bloom events in dam reservoirs. [Display omitted] • Models using automatic relevance determination and support vector machine are built. • The average accuracy and precision of constructed model are 83.3% and 82.6%. • Total nitrogen and maximum water temperature mainly influence the formation of algal bloom. • Competition between Microcystis spp. and Dolichospermum spp. influences prediction. [ABSTRACT FROM AUTHOR]

The new EU Regulation urges shipping operators to set up systems for the monitoring, reporting, and verification of CO2 emissions. Indeed, new monitoring data acquisition systems installed on modern ships have brought a navigation data overload that needs to be correctly handled to make proper decisions about their operation. However, in today's market, there is no standard solution or method available that can be robustly adopted in real environments for the shipping industry. In view of the novel attempts for solving this issue proposed by statisticians, marine engineers, and practitioners, this paper presents an extensive comparison of several regression techniques that can exploit the navigation information usually available in modern ships: variable selection methods, penalized regression methods, latent variable methods and tree-based ensemble methods. The comparison is made by means of operational data collected on a Ro-Pax cruise ship owned by the Italian shipping company Grimaldi Group. The goal of this analysis is twofold: (1) to identify methodologies with more potential at analyzing the data collected from this shipping industry scenario and (2) to develop a predictive model for CO2 emissions with good characteristics of accuracy and robustness. [ABSTRACT FROM AUTHOR]

Bee algorithm (BA) is an optimization algorithm inspired by the natural foraging behaviour of honey bees to find the optimal solution which can be proposed to feature selection. In this paper, shuffling cross–validation–BA (CV–BA) was applied to select the best descriptors that could describe the retention factor (logk) in the biopartitioning micellar chromatography (BMC) of 79 heterogeneous pesticides. Six descriptors were obtained using BA and then the selected descriptors were applied for model development using multiple linear regression (MLR). The descriptor selection was also performed using stepwise, genetic algorithm and simulated annealing methods and MLR was applied to model development and then the results were compared with those obtained from shuffling CV–BA. The results showed that shuffling CV–BA can be applied as a powerful descriptor selection method. Support vector machine (SVM) was also applied for model development using six selected descriptors by BA. The obtained statistical results using SVM were better than those obtained using MLR, as the root mean square error (RMSE) and correlation coefficient (R) for whole data set (training and test), using shuffling CV–BA–MLR, were obtained as 0.1863 and 0.9426, respectively, while these amounts for the shuffling CV–BA−SVM method were obtained as 0.0704 and 0.9922, respectively. [ABSTRACT FROM AUTHOR]

With the implementation of quality by design (QbD), critical attributes of raw material (drug substance and excipients) are of significantly importance in pharmaceutical manufacturing process. It is desirable for the quality control of critical material attributes (CMAs) of excipients to ensure the quality of end product. This paper explored the feasibility of an at-line method for the quantitative analysis of hydroxypropoxy group in hydroxypropyl methylcellulose (HPMC) with near infrared spectroscopy (NIRS). Hydroxypropoxy group content can be seen as a CMA of HPMC for quality control. The partial least squares (PLS) model was built with 61 samples including 47 samples as calibration set, 14 samples as validation set by sample set partitioning based on joint x – y distances (SPXY) method. Multiplicative scattering correction (MSC) combined with Savitzkye-Golay (SG) smoothing with first derivative was used as the appropriate pretreatment method. Three variable selection methods including interval partial least-squares (iPLS), competitive adaptive reweighted Sampling (CARS), and the combination of the two methods (iPLS-CARS) were performed for optimizing the model. The results indicated that NIRS could predict rapidly and effectively the content of hydroxypropoxy group in HPMC. NIRS could be a potential method for the quality control of CMAs. [ABSTRACT FROM AUTHOR]

Highlights: [•] Visible and near-infrared reflectance spectroscopy can monitor soil heavy metals. [•] Summary on mechanisms for estimating heavy metal concentrations. [•] Discussions on three types of spectra and their roles in monitoring heavy metals. [•] Comprehensive review on methods for monitoring heavy metals. [•] Discussions on challenges in mapping soil contaminations with heavy metals. [Copyright &y& Elsevier]

This paper proposes a novel variable selection method involving the use of a newly defined metric called the partial Kullback–Leibler (KL) information measure to evaluate the contribution of each variable (dimension) in the data. In this method, the probability density functions of recorded data are estimated through a multidimensional probabilistic neural network trained on the basis of KL information theory. The partial KL information measure is then defined as the ratio of the values before and after dimension elimination in the data. The effective dimensions for classification can be selected eliminating ineffective ones based on the partial KL information in a one-by-one manner. In the experiments, the proposed method was applied to channel selection with nine subjects (including an amputee), and effective channels were selected from all channels attached to each subject’s forearm. The results showed that the number of channels was reduced by \54.3 \pm \19.1\\%, and the average classification rate for evaluation data using selected three or four channels was \96.6 \pm \2.8\% (min: \92.1\%, max: \100\%). These outcomes indicate that the proposed method can be used to select effective channels (optimal or quasi-optimal) for accurate classification. [ABSTRACT FROM PUBLISHER]

• Seven heavy metals of Sargassum fusiforme were analyzed simultaneously using LIBS data. • An effective framework consisting of TV and PLSR methods was proposed for LIBS data. • Three classic variable selection methods were adopted to create the comparison models. • TV-PLSR framework has better performance than full variables and feature variables models. • Satisfactory quantitative results were obtained using TV-PLSR framework. As a nutritious and popular seafood among consumers, Sargassum fusiforme is susceptible to the toxic heavy metals because of its strong adsorption properties. In this study, laser-induced breakdown spectroscopy (LIBS) coupled with a simple framework (only remove some noise and low-intensity variables, and then combine with PLS algorithm) was used to establish the detection models to simultaneously and quantitatively analyze the content of heavy metals arsenic (As), chromium (Cd), cadmium (Cr), copper (Cu), mercury (Hg), lead (Pb) and zinc (Zn) in Sargassum fusiforme. As comparisons, three classic variable methods of successive projections algorithm (SPA), uninformative variable elimination (UVE) and variable importance in projection (VIP) were adopted. The final results showed that six of seven heavy metal models from the TV-PLSR model were optimal. These results demonstrate that the TV-PLSR framework combined with LIBS technique is an effective framework for quantitatively analyzing the heavy metals in Sargassum fusiforme. [ABSTRACT FROM AUTHOR]