Estimating soil heavy metals concentration at large scale using visible and near-infrared reflectance spectroscopy.

This study was aimed (i) to examine using diffuse reflectance spectra within VNIR region to estimate soil heavy metals concentrations at large scale, (ii) to compare the influence of different pre-processing models on predictive model accuracy, and (iii) to explore the best predictive models. A number of 325 topsoil samples were collected and their spectral data, pH, clay content, organic matter, Ni, and Cu concentrations were determined. To improve spectral data, various pre-processing methods including Savitzky-Golay smoothing filter, Savitzky-Golay smoothing filter with first and second derivatives, and standard normal variant (SNV) were used. Partial least squares regression (PLSR), principal component regression (PCR), and support vector machine regression (SVMR) models were employed to build calibration models for estimating soil heavy metals concentration followed by evaluation of provided predictive models. Results indicated that Cu had stronger correlation coefficients with spectral bands compared to Ni. Cu and Ni demonstrated strongest correlations at wavelengths 1925 and 1393 nm, respectively. Based on RMSE, R2, and RPD statistics, the PLSR model with Savitzky-Golay filter pretreatment provided the most accurate predictions for both Cu and Ni (R2 = 0.905, RMSE = 0.00123, RPD = 2.80 for Ni; R2 = 0.825, RMSE = 0.00467, RPD = 2.04 for Cu) where such prediction was much better for Ni than for Cu. Reasonable results with lower accuracy and stability were obtained for PCR (R2 = 0.742, RMSE = 0.00181, RPD = 1.91 for Ni; R2 = 0.731, RMSE = 0.00578, RPD = 1.65 for Cu) and SVMR (R2 = 0.643, RMSE = 0.00091, RPD = 3.80 for Ni; R2 = 0.505, RMSE = 0.00296, RPD = 3.22 for Cu). We concluded that reflectance spectroscopy technique could be applied as a reliable tool for detection and prediction of soil heavy metals.

Contribution to Characterizing the Meat Quality of Protected Designation of Origin Serrana and Preta de Montesinho Kids Using the Near-Infrared Reflectance Methodology.

The aims of this study were to describe and compare the meat quality characteristics of male and female kids from the "Serrana" and "Preta de Montesinho" breeds certified as "Cabrito Transmontano" and reinforce the performance of near-infrared reflectance (NIR) spectra in predicting these quality characteristics and discriminating among breeds. Samples of Longissimus thoracis (n = 32; sixteen per breed; eight males and eight females) were used. Breed significantly affected meat quality characteristics, with only color and fatty acid (FA) (C12:0) being influenced by sex. The meat of the "Serrana" breed proved to be more tender than that of the "Preta de Montesinho". However, the meat from the "Preta de Montesinho" breed showed higher intramuscular fat content and was lighter than that from the "Serrana" breed, which favors its quality of color and juiciness. The use of NIR with the linear support vector machine regression (SVMR) classification model demonstrated its capability to quantify meat quality characteristics such as pH, CIELab color, protein, moisture, ash, fat, texture, water-holding capacity, and lipid profile. Discriminant analysis was performed by dividing the sample spectra into calibration sets (75 percent) and prediction sets (25 percent) and applying the Kennard-Stone algorithm to the spectra. This resulted in 100% correct classifications with the training data and 96.7% accuracy with the test data. The test data showed acceptable estimation models with R2 > 0.99.

Synthesis of water, sanitation, and hygiene (WaSH) spatial pattern in rural India: an integrated interpretation of WaSH practices.

Rural areas largely lack access to improved drinking water, sanitation, and hygiene (WaSH) facilities in India. This requires documentation of WaSH practices at the local level for better understanding and sustainable development. In this paper, a global positioning system (GPS)-based household survey was carried out in 67 villages of Phagi tehsil using individual questionnaires to evaluate the existing WaSH conditions spatially at the panchayat level. Three sub-indices were used for WaSH risk areas mapping and prediction with the integration of machine learning algorithms. Survey results indicate the improvement in the availability of toilet facilities; however, a gap was found between toilet ownership and its usage by villagers. Data show that only six panchayats have almost zero open defecation practices among the 32 panchayats of Phagi tehsil. The findings highlight that presence of toilets in house, water supply in toilets, and high literacy rate lead to an increase in toilet usage by the population. WaSH index scores indicate that panchayats like Mandawari, Mendwas, Chandma Kalan, and Rotwara have worst conditions and fall in the high-risk category. Moreover, support vector machine regression (SVMR) results reveal that WaSH scores are mainly affected by open defecation (r = 0.94), water supply in toilets (r = 0.92), and female members' participation in sanitation facilities decision-making (r = 0.53), followed by literacy rate (r = 0.33). Findings demonstrate the association between gender inequalities and WaSH conditions, and the potential of the WaSH index as a monitoring tool by local policymakers to shrink the WaSH gaps.

Rapid determination and classification of crude oils by ATR-FTIR spectroscopy and chemometric methods.

Classification based on °API gravity is very important to estimate the parameters related to the extraction, purification, toxicity, and pricing of crude oils. Spectroscopy methods show some advantages over ASTM and API methods for crude oil analysis. The attenuated total reflection Fourier-transform infrared (ATR-FTIR) spectroscopy coupled with chemometric methods has been applied as a quick and non-destructive method for crude oil analysis. In this work, a new analytical method using ATR-FTIR spectroscopy associated with chemometric methods were proposed for adressing regression and classification tasks for crude oils analysis based on °API gravity values. The designed methods are rapid, economic, and nondestructive ways in production process of oil industry. The spectral data were used for estimation of °API gravity using two approaches according to PLS-R and SVM-R algorithm, separately. The ATR-FTIR spectral data were also analyzed by classification method using the partial least squares-discriminant analysis (PLS-DA) for crude oil classification. The samples were classified into three classes based on their °API gravity values. The SVM-R model showed better results than PLS-R for °API gravity values using the F-test at 95% of confidence. The result of classification, showed about 100% accuracy and a zero classification error for calibration and prediction samples in PLS-DA algorithm.

ATR-FTIR spectroscopy and chemometric techniques for determination of polymer solution viscosity in the presence of SiO2 nanoparticle and salinity.

An analytical method was proposed for quantitative determination of rheological properties of polyacrylamide (PAM) solution in the presence of SiO2 nanoparticle and NaCl. The viscosity of PAM-SiO2 nanohybrid solution was predicted using attenuated total reflection Fourier-transform infrared (ATR-FTIR) spectroscopy in the wavenumber range of 800-3000 cm-1 and chemometrics methods. Support vector machine regression (SVM-R) as a non-linear multivariate calibration procedure and partial least squares regression (PLS-R) as a linear procedure were applied for calibration. Preprocessing methods such as baseline correction and standard normal variate (SNV) were also utilized. Root mean square error of prediction (RMSEP) in SNV-SVM and SNV-PLS methods were 3.231 and 6.302, respectively. Considering the complexity of the samples, the SVM-R model was found to be reliable. The proposed method is rapid and simple without any sample preparation step for measurement of the viscosity of polymer solutions in chemical enhanced oil recovery (CEOR).

Effect of incremental feature enrichment on healthcare text classification system: A machine learning paradigm.

BACKGROUND AND OBJECTIVE: Healthcare tweets are particularly challenging due to its sparse layout and its limited character size. Compared to previous method based on "bag of words" (BOW) model, this study uniquely identifies the enrichment protocol and learns how semantically different aspects of feature selection such as BOW (feature F0), term frequency inverse document frequency (TF-IDF, feature F1), and latent semantic indexing (LSI, feature F2) when applied sequentially with classifier improves the overall performance. METHODS: To study this enrichment concept, our ML model is tested on two kinds of diverse data sets: (i) D1: Disease data with conjunctivitis, diarrhea, stomach ache, cough and nausea related tweets, and (ii) D2: WebKB4 dataset, while adapting three kind of classifiers (a) C1: support vector machine with radial basis function (SVMR), (b) C2: Multi-layer perceptron (MLP) and (c) C3: Random Forest (RF). Partition protocol (K10) was adapted with different performance metrics to evaluate machine learning (ML)-system. RESULTS: Using the combination of F1, C1, D1, K10, ML accuracy was: 94%, while with F2, C1, D1, K10, ML accuracy was 97%. Using the incremental feature enrichment from F0 to F2, K10 protocol gave F1 improvement over F0 by 4.98% on Disease dataset, while F2 improvement over F0 was by 11.78% on WebKB4 dataset. We demonstrated the generalization over memorization process in our ML-design. The system was tested for stability and reliability. CONCLUSIONS: We conclude that semantically different aspects of feature selection, when adapted sequentially, leads to improvement in ML-accuracy for healthcare data sets. We validated the system by taking non-healthcare data sets.

[Application of Support Vector Machine Regression in Ozone Forecasting].

Support vector machine regression (SVMr) was proposed to forecast hourly ozone (O3) concentrations, daily maximum O3 concentrations, and maximum 8 h moving average O3 concentrations (O3 8 h) by employing the observations of meteorological variables and O3 and its precursors during the high O3 periods from May 20 to August 15, 2016 at an industrial area in Nanjing. The squared correlation coefficient (R2) of the hourly O3 concentrations forecast was 0.84. The mean absolute error (MAE) and mean absolute percentage error (MAPE) were 3.44×10-9 and 24.48, respectively. The key factors for the hourly O3 forecast were the O3 pre-concentrations, amount of ultraviolet radiation B (UVB), and the NO2 concentration. The main factors for the O3 daily maximum forecast were the NOx concentrations at 07:00 and the UVB level. Temperature and UVB played an important role in predicting O3 8 h. In general, taking precursors into account could increase the accuracy of O3 prediction by 10%-28%. For O3 concentration forecasting, SVMr gave significantly better predictions than multiple linear regression methods.

Determination of Magnesium Oxide Content in Mineral Medicine Talcum Using Near-Infrared Spectroscopy Integrated with Support Vector Machine.

In this research paper, a fast, quantitative, analytical model for magnesium oxide (MgO) content in medicinal mineral talcum was explored based on near-infrared (NIR) spectroscopy. MgO content in each sample was determined by ethylenediaminetetraacetic acid (EDTA) titration and taken as reference value of NIR spectroscopy, and then a variety of processing methods of spectra data were compared to establish a good NIR spectroscopy model. To start, 50 batches of talcum samples were categorized into training set and test set using the Kennard-Stone (K-S) algorithm. In a partial least squares regression (PLSR) model, both leave-one-out cross-validation (LOOCV) and training set validation (TSV) were used to screen spectrum preprocessing methods from multiplicative scatter correction (MSC), and finally the standard normal variate transformation (SNV) was chosen as the optimal pretreatment method. The modeling spectrum bands and ranks were optimized using PLSR method, and the characteristic spectrum ranges were determined as 11995-10664, 7991-6661, and 4326-3999 cm-1, with four optimal ranks. In the support vector machine (SVM) model, the radical basis function (RBF) kernel function was used. Moreover, the full spectrum data of samples pretreated with SNV, the characteristic spectrum data screened using synergy interval partial least squares (SiPLS), and the scoring data of the first four ranks obtained by a partial least squares (PLS) dimension reduction of characteristic spectrum were taken as input variables of SVM, and the MgO content reference values of various sample were taken as output values. In addition, the SVM model internal parameters were optimized using the grid optimization method (GRID), particle swarm optimization (PSO), and genetic algorithm (GA) so that the optimal C and g-values were determined and the validation model was established. By comprehensively comparing the validation effects of different models, it can be concluded that the scoring data of the first four ranks obtained by PLS dimension reduction of characteristic spectrum were taken as input variables of SVM, and the PLS-SVM regression model established using GRID was the optimal NIR spectroscopy quantitative model of talc. This PLS-SVM regression model (rank = 4) measured that the MgO content of talcum was in the range of 17.42-33.22%, with root mean square error of cross validation (RMSECV) of 2.2127%, root mean square error of calibration (RMSEC) of 0.6057%, and root mean square error of prediction (RMSEP) of 1.2901%. This model showed high accuracy and strong prediction capacity, which can be used for rapid prediction of MgO content in talcum.