scholarly journals Inversion Evaluation of Rare Earth Elements in Soil by Visible-Shortwave Infrared Spectroscopy

2021 ◽  
Vol 13 (23) ◽  
pp. 4886
Author(s):  
Zhaoqiang Huang ◽  
Wenxuan Huang ◽  
Sheng Li ◽  
Bin Ni ◽  
Yalong Zhang ◽  
...  
Keyword(s):  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Effective Technique ◽  
First Order ◽  
Standard Normal Variate ◽  
Scattering Correction ◽  
Standard Normal ◽  
Predicted Values ◽  
Shortwave Infrared ◽  
Active Substances

According to historical information, more than 300 metal smelting enterprises have been in the southwest of Xiongan for 300 years; however, these polluting enterprises have been gradually closed with the increased intensity of environmental protection. In the paper, 264 soil samples were collected and analyzed in the range of 400 nm–2500 nm by the spectra vista corporation (SVC), and the spectral noise was smoothed by the Savitzky–Golay filter. In order to enhance the spectral differences and curve shapes, mathematical transformations, such as the standard normal variate (SNV), first-order differential (FD), second-order differential (SD), multiple scattering correction (MSC), and continuum removal (CR), were performed on the data, and the correlation between spectral transformation and contents of REEs was analyzed. Moreover, three machine learning models—partial least-squares (PLS), random forest (RF), back propagation neural network (BPNN)—were used to predict the contents of REEs. Experimental results prove that REEs are combined with spectral active substances, such as organic compounds, clay minerals, and iron oxide, and it is possible to determine the contents of REEs using the reflection spectrum. The R2 between the predicted values and measured contents reached 0.986 by using BPNN after FD transformation. More importantly, the predicted values basically agree with the actual situation for CASI/SASI airborne hyperspectral images, and this is an effective technique to obtain the contents of REEs in soil at the study area.

Classification of Floral Origins of Honey by NIR and Chemometrics

2012 ◽  
Vol 605-607 ◽  
pp. 905-909 ◽  
Author(s):  
Xiu Ying Liang ◽  
Xiao Yu Li ◽  
Wen Jun Wu
Keyword(s):  
Spectral Data ◽  
Spectral Region ◽  
Near Infrared ◽  
Back Propagation ◽  
Nir Spectroscopy ◽  
Classification Rate ◽  
Standard Normal Variate ◽  
Mean Centering ◽  
Scattering Correction ◽  
Standard Normal

Near-infrared (NIR) spectroscopy combined with chemometrics methods has been investigated to discriminate type of honey. 147 NIR spectra of six floral origins of honey samples were collected within 4000~10000cm-1 spectral region. Spectral data were compressed using partial least squares (PLS). Back propagation neural networks (BPNN) models were constructed to distinguish the type of honey. Six spectral data pretreatments including first derivative, first derivatives followed by mean centering(MC), second derivatives, Savitzky-Golay smoothing, standard normal variate transformation (SNV) and multiplicative scattering correction (MSC) were compared to establish the optimal models for honey discrimination. Savitzky-Golay smoothing proved more effective than the other data pretreatments. BPNN models were developed within the full spectral region, 5303～6591cm-1 and 7012～10001cm-1, respectively. Results have shown that the highest(100%) classification rate was achieved within 5303～6591cm-1 wave range. Our results indicated that NIR spectroscopy with chemometrics techniques can be applied to classify rapidly honeys of different floral origin.

scholarly journals Using a DEA–AutoML Approach to Track SDG Achievements

2020 ◽  
Vol 12 (23) ◽  
pp. 10124
Author(s):  
Bodin Singpai ◽  
Desheng Wu
Keyword(s):  
Machine Learning ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Correlated Data ◽  
Integrative Approach ◽  
Classification Prediction ◽  
Predicted Values ◽  
Highly Correlated ◽  
Prediction Problems ◽  
Data Result

Each country needs to monitor progress on their Sustainable Development Goals (SDGs) to develop strategies that meet the expectations of the United Nations. Data envelope analysis (DEA) can help identify best practices for SDGs by setting goals to compete against. Automated machine learning (AutoML) simplifies machine learning for researchers who need less time and manpower to predict future situations. This work introduces an integrative method that integrates DEA and AutoML to assess and predict performance in SDGs. There are two experiments with different data properties in their interval and correlation to demonstrate the approach. Three prediction targets are set to measure performance in the regression, classification, and multi-target regression algorithms. The back-propagation neural network (BPNN) is used to validate the outputs of the AutoML. As a result, AutoML can outperform BPNN for regression and classification prediction problems. Low standard deviation (SD) data result in poor prediction performance for the BPNN, but does not have a significant impact on AutoML. Highly correlated data result in a higher accuracy, but does not significantly affect the R-squared values between the actual and predicted values. This integrative approach can accurately predict the projected outputs, which can be used as national goals to transform an inefficient country into an efficient country.

scholarly journals Estimating Soil Properties and Nutrients by Visible and Infrared Diffuse Reflectance Spectroscopy to Characterize Vineyards

Agronomy ◽  
2021 ◽  
Vol 11 (10) ◽  
pp. 1895
Author(s):  
José Ramón Rodríguez-Pérez ◽  
Víctor Marcelo ◽  
Dimas Pereira-Obaya ◽  
Marta García-Fernández ◽  
Enoc Sanz-Ablanedo
Keyword(s):  
Soil Properties ◽  
Diffuse Reflectance Spectroscopy ◽  
Full Range ◽  
Cost Effective ◽  
Reflectance Spectroscopy ◽  
Least Squares Regression ◽  
Standard Normal Variate ◽  
Standard Normal ◽  
Western Spain ◽  
Shortwave Infrared

Visible, near, and shortwave infrared (VIS-NIR-SWIR) reflectance spectroscopy, a cost-effective and rapid means of characterizing soils, was used to predict soil sample properties for four vineyards (central and north-western Spain). Sieved and air-dried samples were measured using a portable spectroradiometer (350–2500 nm) and compared for pistol grip (PG) versus contact probe (CP) setups. Raw data processed using standard normal variate (SVN) and detrending transformation (DT) were grouped into four subsets (VIS: 350–700 nm; NIR: 701–1000 nm; SWIR: 1001–2500 nm; and full range: 350–2500 nm) in order to identify the most suitable range for determining soil characteristics. The performance of partial least squares regression (PLSR) models in predicting soil properties from reflectance spectra was evaluated by cross-validation. The four spectral subsets and transformed reflectances for each setup were used as PLSR predictor variables. The best performing PLSR models were obtained for pH, electrical conductivity, and phosphorous (R2 values above 0.92), while models for sand, nitrogen, and potassium showed moderately good performances (R2 values between 0.69 and 0.77). The SWIR subset and SVN + DT processing yielded the best PLSR models for both the PG and CP setups. VIS-NIR-SWIR reflectance spectroscopy shows promise as a technique for characterizing vineyard soils for precision viticulture purposes. Further studies will be carried out to corroborate our findings.

Identification of geographical origin of TPFD based on handheld NIR spectroscopy and PLSDA

NIR news ◽  
2020 ◽  
Vol 31 (5-6) ◽  
pp. 25-29
Author(s):  
Rita-Cindy Aye-Ayire Sedjoah ◽  
Bangxing Han ◽  
Hui Yan
Keyword(s):  
Quality Control ◽  
Near Infrared ◽  
Geographical Origin ◽  
Nir Spectroscopy ◽  
Analysis Model ◽  
First Order ◽  
Standard Normal Variate ◽  
Standard Normal ◽  
Infrared Spectrometer ◽  
Near Infrared Spectrometer

The present study is focused on the identification of geographical origin (Zhejiang, Yunnan and Anhui, China) of Dendrobium officinale’s dried stem called Tiepi fengdou by mean of the handheld near-infrared spectrometer. Raw data were preprocessed to reduce unwanted spectral variations by the first-order derivative followed by standard normal variate transformation, and partial least squares discriminant analysis model was developed for calibration. The results showed that more than 90% of the origins were identified. Therefore, it is possible to classify the geographical origin of Tiepi fengdou by the use of the handheld near-infrared spectrometer for effective quality control.

Intrusion Detection Using Back Propagation Neural Network and Quick Reduct Algorithms

2018 ◽  
pp. 317-325
Author(s):  
S. Vijaya Rani ◽  
G. N. K Suresh Babu
Keyword(s):  
Feature Selection ◽  
Intrusion Detection ◽  
Intrusion Detection System ◽  
Detection System ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Data Sets ◽  
Effective Technique ◽  
Data Set ◽  
Quick Reduct

It is a big challenge to safeguard a network and data due to various network threats and attacks in a network system. Intrusion detection system is an effective technique to negotiate the issues of network security by utilizing various network classifiers. It detects malicious attacks. The data sets available in the study of intrusion detection system were DARPA, KDD 1999 cup, NSL_KDD, DEFCON, ISCX-UNB, KDD 1999 cup data set is the best and old data set for research purpose on intrusion detection. The data is preprocessed, normalized and trained by BPN algorithm. Further the normalized data is discretized using Entropy discretization and feature selection carried out by quick reduct methods. After feature selection, the concerned feature from normalized data is processed through BPN for better accuracy and efficiency of the system.

scholarly journals Hyperspectral Imaging for the Nondestructive Quality Assessment of the Firmness of Nanguo Pears Under Different Freezing/Thawing Conditions

Sensors ◽  
2019 ◽  
Vol 19 (14) ◽  
pp. 3124 ◽  
Author(s):  
Zhang ◽  
Shang ◽  
Wang ◽  
Zhang ◽  
Yu ◽  
...  
Keyword(s):  
Root Mean Square Error ◽  
Mean Square Error ◽  
Root Mean Square ◽  
Hyperspectral Imaging ◽  
Correlation Coefficients ◽  
Mean Square ◽  
Critical Temperatures ◽  
Standard Normal Variate ◽  
Scattering Correction ◽  
Standard Normal

Firmness changes in Nanguo pears under different freezing/thawing conditions have been characterized by hyperspectral imaging (HSI). Four different freezing/thawing conditions (the critical temperatures, numbers of cycles, holding time and cooling rates) were set in this experiment. Four different pretreatment methods were used: multivariate scattering correction (MSC), standard normal variate (SNV), Savitzky-Golay standard normal variate (S-G-SNV) and Savitzky-Golay multiplicative scattering correction (S-G-MSC). Combined with competitive adaptive reweighted sampling (CARS) to identify characteristic wavelengths, firmness prediction models of Nanguo pears under different freezing/thawing conditions were established by partial least squares (PLS) regression. The performance of the firmness model was analyzed quantitatively by the correlation coefficient (R), the root mean square error of calibration (RMSEC), the root mean square error of prediction (RMSEP) and the root mean square error of cross validation (RMSECV). The results showed that the MSC-PLS model has the highest accuracy at different cooling rates and holding times; the correlation coefficients of the calibration set (Rc) were 0.899 and 0.927, respectively, and the correlation coefficients of the validation set (Rp) were 0.911 and 0.948, respectively. The accuracy of the SNV-PLS model was the highest at different numbers of cycles, and the Rc and the Rp were 0.861 and 0.848, respectively. The RMSEC was 65.189, and the RMSEP was 65.404. The accuracy of the S-G-SNV-PLS model was the highest at different critical temperatures, with Rc and Rp values of 0.854 and 0.819, respectively, and RMSEC and RMSEP values of 74.567 and 79.158, respectively.

scholarly journals HPLC Determination and FT-MIR Prediction of Sugars from Juices of Different Apple Cultivars during Fruits Development

2015 ◽  
Vol 43 (1) ◽  
pp. 222-228 ◽  
Author(s):  
Andruţa E. MUREŞAN ◽  
Sevastiţa MUSTE ◽  
Romina Alina VLAIC ◽  
Otilia BOBIŞ ◽  
Crina MUREŞAN ◽  
...  
Keyword(s):  
High Performance ◽  
Cross Validation ◽  
Full Bloom ◽  
Mid Infrared ◽  
Standard Normal Variate ◽  
Satisfactory Performance ◽  
Calibration Models ◽  
Standard Normal ◽  
Apple Cultivars ◽  
Predicted Values

Individual sugars were analyzed by high performance liquid chromatography (HPLC) in samples of apple juices obtained from the fruits of ‘Jonathan’, ‘Starkrimson’ and ‘Golden Delicious’ cultivars. Samples were harvested from the inside and the periphery of the crown, at different periods during fruits growth, from 7 to 144 days after full bloom (DAFB). Values from 0.42 to 14.33%, 0.29 to 4.06% and 0 to 4.28% were determined for fructose, glucose and sucrose, respectively. The values of fructose and glucose have increased significantly (p<0.05), starting with the seventh DAFB, regardless of the studied cultivar, while sucrose increased slowly at the beginning and then faster starting 65 DAFB. Fourier transform mid-infrared (FT-MIR) analysis confirmed the differences between juice samples, the region 900-1500 cm-1 being the most specific to sugars signals. FT-MIR coupled to partial least squares (PLS) calibration models for predicting individual sugars of apple juices were developed. The optimal regions and pre-treatments of the spectra were 900-1500 cm-1 and Savitzky Golay first derivative (d1) for fructose, 900-1200 cm-1 and d2 for glucose and 900-1200 cm-1 and standard normal variate for sucrose. In cross-validation, the PLS calibration models showed very good performance for fructose (Rcval 2=0.95; standard error of cross-validation (SECV) =0.907) and acceptable for glucose (Rcval 2=0.85; SECV=0.424), while for sucrose showed only satisfactory performance (Rcval 2=0.75; SECV=0.561). For practical relevance, the FT-MIR predicted values were compared against the HPLC determined reference values in external validations tests. The best results were achieved for fructose (Rp 2=0.94; RPD=4.9), while glucose (Rp 2=0.84; RPD=2.61) and sucrose (Rp 2=0.7; RPD=2.08) models reached satisfactory values.

scholarly journals Investigation on Texture Changes and Classification between Cold-Fresh and Freeze-Thawed Tan Mutton

2019 ◽  
Vol 2019 ◽  
pp. 1-10 ◽  
Author(s):  
Dongdong Li ◽  
Yaling Peng ◽  
Haihong Zhang
Keyword(s):  
Near Infrared ◽  
Successive Projections Algorithm ◽  
Classification Rate ◽  
Full Spectrum ◽  
Original Spectrum ◽  
Standard Normal Variate ◽  
Scattering Correction ◽  
Standard Normal ◽  
Validation Set

To study the texture, microstructural changes, and classification of cold-fresh (C-F), freeze-thawed once (F-T0), and freeze-thawed twice Tan mutton (F-Tt), the aforementioned three types of Tan mutton were subjected to near-infrared hyperspectrum scanning, scanning electron microscopy, and TPA testing. The original spectrum of Tan mutton was obtained at a wavelength range of 900∼1,700 nm after hyperspectrum scanning; a spectrum fragment ranging from 918 nm to 1,008 nm was intercepted, and the remaining original spectrum was used as a studied spectrum (“full spectrum” hereafter). The full spectrum was pretreated by SNV (standard normal variate), MSC (multiple scattering correction), and SNV + MSC and then extracted feature wavelengths by SPA (successive projections algorithm) and CARS (competitive adaptive reweighted sampling) algorithm, and 25 feature wavelengths were obtained. By combining these feature wavelengths with classified variables, the SNV + MSC−CARS−PLS-DA (partial least squares-discriminate analysis, PLS-DA) and SNV + MSC−SPA−PLS-DA models for classification of C-F and F-T Tan mutton were established. In contrast, SNV + MSC−CARS−PLS-DA yielded the highest classification rate of 98% and 100% for calibration set and validation set, respectively. The results indicated that the texture and surface microstructure of F-T Tan mutton deteriorated, and more worsely with F-T time. SNV+MSC-CARS-PLS-DA could be well used to classify C-F, F-T0, and F-Tt Tan mutton.

scholarly journals Forecasting of Short-Term Metro Ridership with Support Vector Machine Online Model

2018 ◽  
Vol 2018 ◽  
pp. 1-13 ◽  
Author(s):  
Xuemei Wang ◽  
Ning Zhang ◽  
Yunlong Zhang ◽  
Zhuangbin Shi
Keyword(s):  
Support Vector Machine ◽  
Prediction Models ◽  
Moving Average ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Support Vector ◽  
Time Interval ◽  
Short Term ◽  
Nonlinear Characteristics ◽  
Predicted Values

Forecasting for short-term ridership is the foundation of metro operation and management. A prediction model is necessary to seize the weekly periodicity and nonlinearity characteristics of short-term ridership in real-time. First, this research captures the inherent periodicity of ridership via seasonal autoregressive integrated moving average model (SARIMA) and proposes a support vector machine overall online model (SVMOOL) which insets the weekly periodic characteristics and trains the updated data day by day. Then, this research captures the nonlinear characteristics of the ridership via successive ridership value inputs and proposes a support vector machine partial online model (SVMPOL) which insets the nonlinear characteristics and trains the updated data of the predicted day by time interval (such as 5-min). Afterwards, to avoid the drawbacks and to take advantages of the strengths of the two individual online models, this research takes the average predicted values of two models as the final predicted values, which are called support vector machine combined online model (SVMCOL). Finally, this research uses the 5-min ridership at Zhujianglu and Sanshanjie Stations of Nanjing Metro to compare the SVMCOL model with three well-known prediction models including SARIMA, back-propagation neural network (BPNN), and SVM models. The resultant performance comparisons suggest that SARIMA is superior for the stable weekday ridership to other models. Yet the SVMCOL model is the best performer for the unstable weekend ridership and holiday ridership. It shows that for metro operation manager that gear toward timely response to real-world unstable and abnormal situations, the SVMCOL may be a better tool than the three well-known models.

Economic benefit of shale gas exploitation based on back propagation neural network

2020 ◽  
Vol 39 (6) ◽  
pp. 8823-8830
Author(s):  
Jiafeng Li ◽  
Hui Hu ◽  
Xiang Li ◽  
Qian Jin ◽  
Tianhao Huang
Keyword(s):  
Neural Network ◽  
Shale Gas ◽  
Bp Neural Network ◽  
Large Scale ◽  
Linear Prediction ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Economic Benefits ◽  
Gas Well ◽  
Well Production

Under the influence of COVID-19, the economic benefits of shale gas development are greatly affected. With the large-scale development and utilization of shale gas in China, it is increasingly important to assess the economic impact of shale gas development. Therefore, this paper proposes a method for predicting the production of shale gas reservoirs, and uses back propagation (BP) neural network to nonlinearly fit reservoir reconstruction data to obtain shale gas well production forecasting models. Experiments show that compared with the traditional BP neural network, the proposed method can effectively improve the accuracy and stability of the prediction. There is a nonlinear correlation between reservoir reconstruction data and gas well production, which does not apply to traditional linear prediction methods

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