Hazelnuts classification by hyperspectral imaging coupled with variable selection methods

Sensing for Agriculture and Food Quality and Safety XIII ◽

10.1117/12.2588287 ◽

2021 ◽

Author(s):

Giuseppe Bonifazi ◽

Giuseppe Capobianco ◽

Riccardo Gasbarrone ◽

Silvia Serranti

Keyword(s):

Variable Selection ◽

Hyperspectral Imaging ◽

Selection Methods

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Chemical spoilage extent traceability of two kinds of processed pork meats using one multispectral system developed by hyperspectral imaging combined with effective variable selection methods

Food Chemistry ◽

10.1016/j.foodchem.2016.11.093 ◽

2017 ◽

Vol 221 ◽

pp. 1989-1996 ◽

Author(s):

Weiwei Cheng ◽

Da-Wen Sun ◽

Hongbin Pu ◽

Qingyi Wei

Keyword(s):

Variable Selection ◽

Hyperspectral Imaging ◽

Selection Methods

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Application of Near-Infrared Hyperspectral Imaging with Variable Selection Methods to Determine and Visualize Caffeine Content of Coffee Beans

Food and Bioprocess Technology ◽

10.1007/s11947-016-1809-8 ◽

2016 ◽

Vol 10 (1) ◽

pp. 213-221 ◽

Author(s):

Chu Zhang ◽

Hao Jiang ◽

Fei Liu ◽

Yong He

Keyword(s):

Variable Selection ◽

Hyperspectral Imaging ◽

Near Infrared ◽

Selection Methods ◽

Caffeine Content ◽

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Comparison of models and visualization of total volatile basic nitrogen content in mutton using hyperspectral imaging and variable selection methods

Spectroscopy Letters ◽

10.1080/00387010.2018.1452268 ◽

2018 ◽

Vol 51 (5) ◽

pp. 226-235 ◽

Author(s):

Yuanyuan Qiu ◽

Rongguang Zhu ◽

Zhongjian Fan ◽

Xuedong Yao ◽

Elfed Lewis

Keyword(s):

Variable Selection ◽

Nitrogen Content ◽

Hyperspectral Imaging ◽

Selection Methods ◽

Total Volatile ◽

Total Volatile Basic Nitrogen ◽

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Variable Reduction and Variable Selection Methods Using Small, Medium and Large Datasets: A Forecast Comparison for the PEEIs

SSRN Electronic Journal ◽

10.2139/ssrn.2444421 ◽

2014 ◽

Author(s):

George Kapetanios ◽

Massimiliano Giuseppe Marcellino ◽

Fotis Papailias

Keyword(s):

Variable Selection ◽

Large Datasets ◽

Selection Methods ◽

Variable Reduction ◽

Forecast Comparison

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Variable selection methods for identifying predictor interactions in data with repeatedly measured binary outcomes

Journal of Clinical and Translational Science ◽

10.1017/cts.2020.556 ◽

2020 ◽

pp. 1-31

Author(s):

Bethany J. Wolf ◽

Yunyun Jiang ◽

Silvia H. Wilson ◽

Jim C. Oates

Keyword(s):

Variable Selection ◽

Binary Outcomes ◽

Selection Methods

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Mahalanobis Distance Based Similarity Regression Learning of NIRS for Quality Assurance of Tobacco Product with Different Variable Selection Methods

Spectrochimica Acta Part A Molecular and Biomolecular Spectroscopy ◽

10.1016/j.saa.2020.119364 ◽

2020 ◽

pp. 119364

Author(s):

Juan Huo ◽

Yuping Ma ◽

Huaiqi Li ◽

Changtong Lu ◽

Chenggang Li ◽

...

Keyword(s):

Quality Assurance ◽

Variable Selection ◽

Mahalanobis Distance ◽

Tobacco Product ◽

Selection Methods ◽

Regression Learning

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Combined performance of screening and variable selection methods in ultra-high dimensional data in predicting time-to-event outcomes

Diagnostic and Prognostic Research ◽

10.1186/s41512-018-0043-4 ◽

2018 ◽

Vol 2 (1) ◽

Author(s):

Lira Pi ◽

Susan Halabi

Keyword(s):

Variable Selection ◽

High Dimensional Data ◽

High Dimensional ◽

Selection Methods ◽

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An overview of variable selection methods in multivariate analysis of near-infrared spectra

TrAC Trends in Analytical Chemistry ◽

10.1016/j.trac.2019.01.018 ◽

2019 ◽

Vol 113 ◽

pp. 102-115 ◽

Author(s):

Yong-Huan Yun ◽

Hong-Dong Li ◽

Bai-Chuan Deng ◽

Dong-Sheng Cao

Keyword(s):

Multivariate Analysis ◽

Variable Selection ◽

Infrared Spectra ◽

Near Infrared ◽

Selection Methods ◽

Near Infrared Spectra

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Variable Selection Methods in QSAR: An Overview

Current Topics in Medicinal Chemistry ◽

10.2174/156802608786786552 ◽

2008 ◽

Vol 8 (18) ◽

pp. 1606-1627 ◽

Author(s):

Maykel Gonzalez ◽

Carmen Teran ◽

Liane Saiz-Urra ◽

Marta Teijeira

Keyword(s):

Variable Selection ◽

Selection Methods

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Variable selection methods for water demand forecasting in Ethiopia: Case study Gondar town

Cogent Environmental Science ◽

10.1080/23311843.2018.1537067 ◽

2018 ◽

Vol 4 (1) ◽

pp. 1537067 ◽

Author(s):

Mohammed Gedefaw ◽

Wang Hao ◽

Yan Denghua ◽

Abel Girma ◽

Mustafa Ibrahim Khamis

Keyword(s):

Variable Selection ◽

Water Demand ◽

Demand Forecasting ◽

Selection Methods ◽

Water Demand Forecasting ◽

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