Determination of Chlorpyrifos in Pears by Raman Spectroscopy with Random Forest Regression Analysis

2019 ◽  
Vol 53 (6) ◽  
pp. 821-833
Author(s):  
Xiaofan Du ◽  
Ping Wang ◽  
Lei Fu ◽  
Huifang Liu ◽  
Zhenxi Zhang ◽  
...  
Keyword(s):  
Raman Spectroscopy ◽  
Regression Analysis ◽  
Random Forest ◽  
Random Forest Regression

scholarly journals RANDOM FOREST REGRESSION FOR THE ESTIMATION OF LEAF AREA INDEX OF OKRA CROP USING GROUND BASED BISTATIC SCATTEROMETER

2018 ◽  
Vol XLII-5 ◽  
pp. 719-725
Author(s):  
S. A. Yadav ◽  
R. Prasad ◽  
A. K. Vishwakarma ◽  
V. P. Yadav
Keyword(s):  
Regression Analysis ◽  
Random Forest ◽  
Leaf Area Index ◽  
Leaf Area ◽  
Growth Stages ◽  
Data Sets ◽  
Angle Of Incidence ◽  
Random Forest Regression ◽  
Area Index ◽  
Scattering Coefficients

<p><strong>Abstract.</strong> The specular bistatic scattering mechanism of Okra's crop was analyzed using dual polarized ground based bistatic scatterometer system at X, C, and L bands in the specular direction with the azimuthal angle(&amp;theta;<span class="thinspace"></span>=<span class="thinspace"></span>0&amp;deg;). An outdoor Okra crop bed of area 10<span class="thinspace"></span>&amp;times;<span class="thinspace"></span>10<span class="thinspace"></span>m<sup>2</sup> was specially prepared for the estimation of leaf area index (LAI) at HH and VV polarizations over the angular range of incidence angle 20&amp;deg; to 60&amp;deg; at steps of 10&amp;deg;. The regression analysis was done between bistatic specular scattering coefficients and crop biophysical parameter at X, C, and L bands for HH and VV polarization at different angle of incidence to determine the optimum parameters of bistatic scatterometer system. The linear regression analysis showed the high correlation at 40&amp;deg; angle of incidence for all bands and polarizations for the Okra crop. The computed scattering coefficients and measured LAI of Okra crop for the seven growth stages at 40&amp;deg; angle of incidence were interpolated into 61 data sets. The data sets were divided into input, validation and testing for the training and testing of the developed random forest regression (RF) model for the estimation of LAI for Okra crop. The estimated values of LAI of Okra crop, by the developed RF regression model, were found more closer to the observed values at X band for VV polarization with coefficient of determination (R<sup>2</sup><span class="thinspace"></span>=<span class="thinspace"></span>0.928) and low root mean square error (RMSE<span class="thinspace"></span>=<span class="thinspace"></span>0.260<span class="thinspace"></span>m<sup>2</sup>/m<sup>2</sup>) in comparison to C and L bands.</p>

scholarly journals UHPLC–MS/MS-Based Nontargeted Metabolomics Analysis Reveals Biomarkers Related to the Freshness of Chilled Chicken

Foods ◽  
2020 ◽  
Vol 9 (9) ◽  
pp. 1326
Author(s):  
Tao Zhang ◽  
Shanshan Zhang ◽  
Lan Chen ◽  
Hao Ding ◽  
Pengfei Wu ◽  
...  
Keyword(s):  
Regression Analysis ◽  
Random Forest ◽  
Linear Regression ◽  
Multiple Linear Regression ◽  
Linear Regression Analysis ◽  
Detection Methods ◽  
Metabolic Profiles ◽  
Stepwise Multiple Linear Regression ◽  
Random Forest Regression ◽  
Metabolic Biomarkers

To identify metabolic biomarkers related to the freshness of chilled chicken, ultra-high-performance liquid chromatography–mass spectrometry (UHPLC–MS/MS) was used to obtain profiles of the metabolites present in chilled chicken stored for different lengths of time. Random forest regression analysis and stepwise multiple linear regression were used to identify key metabolic biomarkers related to the freshness of chilled chicken. A total of 265 differential metabolites were identified during storage of chilled chicken. Of these various metabolites, 37 were selected as potential biomarkers by random forest regression analysis. Receiver operating characteristic (ROC) curve analysis indicated that the biomarkers identified using random forest regression analysis showed a strong correlation with the freshness of chilled chicken. Subsequently, stepwise multiple linear regression analysis based on the biomarkers identified by using random forest regression analysis identified indole-3-carboxaldehyde, uridine monophosphate, s-phenylmercapturic acid, gluconic acid, tyramine, and Serylphenylalanine as key metabolic biomarkers. In conclusion, our study characterized the metabolic profiles of chilled chicken stored for different lengths of time and identified six key metabolic biomarkers related to the freshness of chilled chicken. These findings can contribute to a better understanding of the changes in the metabolic profiles of chilled chicken during storage and provide a basis for the further development of novel detection methods for the freshness of chilled chicken.

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