Supervised Statistical Learning Prediction of Soybean Varieties and Cultivation Sites Using Rapid UPLC-MS Separation, Method Validation, and Targeted Metabolomic Analysis of 31 Phenolic Compounds in the Leaves

Soybean (Glycine max; SB) leaf (SL) is an abundant non-conventional edible resource that possesses value-adding bioactive compounds. We predicted the attributes of SB based on the metabolomes of an SL using targeted metabolomics. The SB was planted in two cities, and SLs were regularly obtained from the SB plant. Nine flavonol glycosides were purified from SLs, and a validated simultaneous quantification method was used to establish rapid separation by ultrahigh-performance liquid chromatography-mass detection. Changes in 31 targeted compounds were monitored, and the compounds were discriminated by various supervised machine learning (ML) models. Isoflavones, quercetin derivatives, and flavonol derivatives were discriminators for cultivation days, varieties, and cultivation sites, respectively, using the combined criteria of supervised ML models. The neural model exhibited higher prediction power of the factors with high fitness and low misclassification rates while other models showed lower. We propose that a set of phytochemicals of SL is a useful predictor for discriminating characteristics of edible plants.

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SHADOWDETECTION FROM VHR AERIAL IMAGES IN URBAN AREA BY USING 3D CITY MODELS AND A DECISION FUSION APPROACH

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-archives-xlii-2-w7-579-2017 ◽

2017 ◽

Vol XLII-2/W7 ◽

pp. 579-586 ◽

Cited By ~ 3

Author(s):

K. L. Zhou ◽

B. G. H. Gorte

Keyword(s):

Decision Fusion ◽

Shadow Detection ◽

Aerial Images ◽

Supervised Machine Learning ◽

3D City Model ◽

Training Samples ◽

Machine Learning Approach ◽

Two Cities ◽

City Model ◽

Fusion Approach

In VHR(very high resolution) aerial images, shadows indicating height information are valuable for validating or detecting changes on an existing 3D city model. In the paper, we propose a novel and full automatic approach for shadow detection from VHR images. Instead of automatic thresholding, the supervised machine learning approach is expected with better performance on shadow detection, but it requires to obtain training samples manually. The shadow image reconstructed from an existing 3D city model can provide free training samples with large variety. However, as the 3D model is often not accuracy, incomplete and outdated, a small portion of training samples are mislabeled. The erosion morphology is provided to remove boundary pixels which have high mislabeling possibility from the reconstructed image. Moreover, the quadratic discriminant analysis (QDA) which is resistant to the mislabeling is chosen. Further, two feature domains, RGB and ratio of the hue over the intensity, are analyzed to have complementary effects on better detecting different objects. Finally, a decision fusion approach is proposed to combine the results wisely from preliminary classifications from two feature domains. The fuzzy membership is a confidence measurement and determines the way of making decision, in the meanwhile the memberships are weighted by an entropy measurements to indicate their certainties. The experimental results on two cities in the Netherlands demonstrate that the proposed approach outperforms the two separate classifiers and two stacked-vector fusion approaches.

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Detecting Impoliteness and Incivility in Online Discussions : Classification Approaches for German User Comments

Computational Communication Research ◽

10.5117/ccr2020.1.005.kath ◽

2020 ◽

Vol 2 (1) ◽

pp. 109-134 ◽

Cited By ~ 1

Author(s):

Anke Stoll ◽

Marc Ziegele ◽

Oliver Quiring

Keyword(s):

Online Discussions ◽

Supervised Machine Learning ◽

Lexical Resources ◽

Coding Scheme ◽

Word Level ◽

User Comments ◽

Machine Learning Approach ◽

Misclassification Rates ◽

N Gram ◽

Offensive Language

Abstract Impoliteness and incivility in online discussions have recently been discussed as relevant issues in communication science. However, automatically detecting these concepts with computational methods is challenging. In our study, we build and compare supervised classification models to predict impoliteness and incivility in online discussions on German media outlets on Facebook. Using a sample of 10,000 hand-coded user comments and a theory-grounded coding scheme, we develop classifiers on different feature sets including unigram and n-gram distributions as well as various dictionary-based features. Our findings show that impoliteness and incivility can be measured to a certain extent on the word level of a comment, but the models suffer from high misclassification rates, even if lexical resources are included. This is mainly because the classifiers cannot reveal subtle forms of incivility and because comment authors often use predictive words of incivility or impoliteness in non-offensive ways or in different contexts. Still, when applying the classifiers to a comparable set of comments, we find that the machine-coded categories and the hand-coded categories reveal similar patterns regarding the distribution of and the user reactions to uncivil/impolite comments. The findings of our study therefore provide new insights into the supervised machine learning approach to the detection of different forms of offensive language.

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Rapid separation and characterization of comprehensive ingredients in Yangxinshi tablet and rat plasma by ultrahigh-performance liquid chromatography–quadrupole time-of-flight mass spectrometry

Journal of Liquid Chromatography &amp Related Technologies ◽

10.1080/10826076.2017.1335213 ◽

2017 ◽

Vol 40 (12) ◽

pp. 616-634 ◽

Cited By ~ 1

Author(s):

Yang Li ◽

Chengjian Li ◽

Jing Yu ◽

Yue Gao ◽

Yahong Zhao ◽

...

Keyword(s):

Mass Spectrometry ◽

Liquid Chromatography ◽

Time Of Flight ◽

Rat Plasma ◽

Rapid Separation ◽

Flight Mass Spectrometry ◽

Ultrahigh Performance Liquid Chromatography

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UPLC-DAD-QTOF/MS Analysis of Flavonoids from 12 Varieties of Korean Mulberry Fruit

Journal of Food Quality ◽

10.1155/2019/1528917 ◽

2019 ◽

Vol 2019 ◽

pp. 1-8 ◽

Cited By ~ 3

Author(s):

O-Chul Kwon ◽

Wan-Taek Ju ◽

Hyun-Bok Kim ◽

Gyoo-Byung Sung ◽

Yong-Soon Kim

Keyword(s):

Mass Spectrometry ◽

Antioxidant Properties ◽

Quercetin Derivatives ◽

Flight Mass Spectrometry ◽

Mulberry Fruit ◽

Ultrahigh Performance Liquid Chromatography ◽

White Mulberry ◽

Mulberry Fruits ◽

New Compounds ◽

First Time

Mulberry (Morus alba L.) has been used in East Asia (Korea, China, and Japan) as a medicine because of its various pharmacological effects including the excellent antioxidant properties of its fruit. This study analyzed extracts from 12 varieties of Korean mulberry fruit for flavonoids using ultrahigh-performance liquid chromatography coupled with diode array detection and quadrupole time-of-flight mass spectrometry (UPLC-DAD-QTOF/MS). Six quercetin derivatives were identified by mass spectrometry (MS) based on the [quercetin + H]+ ion (m/z 303), while four kaempferol derivatives were identified based on the [kaempferol + H]+ ion (m/z 287). Two new compounds (morkotin A and morkotin C, quercetin derivatives) were identified for the first time in mulberry fruit. The total flavonoid contents of the mulberry fruits ranged from 35.0 ± 2.3 mg/100 g DW in the Baek Ok Wang variety (white mulberry) to 119.9 ± 7.0 mg/100 g DW in the Dae Shim variety. This study has, for the first time, evaluated the flavonoid chromatographic profiles of 12 varieties of Korean mulberry fruits in a following quali-quantitative approach, which will contribute to improved utilization of these fruits as health foods.

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Metabolomics of Two Pecan Varieties Provides Insights into Scab Resistance

Metabolites ◽

10.3390/metabo8040056 ◽

2018 ◽

Vol 8 (4) ◽

pp. 56

Author(s):

Zhentian Lei ◽

Clayton Kranawetter ◽

Barbara Sumner ◽

David Huhman ◽

Daniel Wherritt ◽

...

Keyword(s):

Mass Spectrometry ◽

Tandem Mass Spectrometry ◽

Scab Resistance ◽

Tandem Mass ◽

Targeted Metabolomics ◽

Metabolomics Data ◽

Biochemical Basis ◽

Quercetin Derivatives ◽

Qualitative And Quantitative ◽

Pecan Scab

UHPLC-MS-based non-targeted metabolomics was used to investigate the biochemical basis of pecan scab resistance. Two contrasting pecan varieties, Kanza (scab-resistant) and Pawnee (scab-susceptible), were profiled and the metabolomics data analyzed using multivariate statistics. Significant qualitative and quantitative metabolic differences were observed between the two varieties. Both varieties were found to have some unique metabolites. Metabolites that were only present or more abundant in Kanza relative to Pawnee could potentially contribute to the scab resistance in Kanza. Some of these metabolites were putatively identified as quercetin derivatives using tandem mass spectrometry. This suggests that quercetin derivatives could be important to pecan scab resistance.

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Detecting Incivility and Impoliteness in Online Discussions. Classification Approaches for German User Comments.

10.31235/osf.io/a47ch ◽

2019 ◽

Author(s):

Anke Stoll ◽

Marc Ziegele ◽

Oliver Quiring

Keyword(s):

Online Discussions ◽

Supervised Machine Learning ◽

Lexical Resources ◽

Coding Scheme ◽

Word Level ◽

User Comments ◽

Machine Learning Approach ◽

Misclassification Rates ◽

N Gram ◽

Offensive Language

Impoliteness and incivility in online discussions have recently been discussed as relevant issues in communication science. However, automatically detecting these concepts with computational methods is challenging. In our study, we build and compare supervised classification models to predict impoliteness and incivility in online discussions on German media outlets on Facebook. Using a sample of 10,000 hand-coded user comments and a theory-grounded coding scheme, we develop classifiers on different feature sets including unigram and n-gram distributions as well as various dictionary-based features. Our findings show that impoliteness and incivility can be measured to a certain extent on the word level of a comment, but the models suffer from high misclassification rates, even if lexical resources are included. This is mainly because the classifiers cannot reveal subtle forms of incivility and because comment authors often use predictive words of incivility or impoliteness in non-offensive ways or in different contexts. Still, when applying the classifiers to a comparable set of comments, we find that the machine-coded categories and the hand-coded categories reveal similar patterns regarding the distribution of and the user reactions to uncivil/impolite comments. The findings of our study therefore provide new insights into the supervised machine learning approach to the detection of different forms of offensive language.

Download Full-text