{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/124601"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/124601","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Prediction of moisture and protein in corn kernels from multiple origins based on NIR-PLSR with gradient boosting machines for feature selection","abstract":"Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2026-05-01","abstract_html":"Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2026-05-01","abstract_has_math":false,"creators":["Zheng, Runyu"],"institution":"University of Illinois at Urbana-Champaign","degree_name":"M.S.","degree_level":"Thesis","degree_discipline":"Agricultural & Biological Engr","degree_department":null,"school":null,"contributors":["Kamruzzaman, Mohammed","Allen, Cody M.","Rausch, Kent D.","Singh, Vijay"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2024,"date_issued":"2024-05","date_published":"2024-05","updated_at":"2026-07-22T22:25:02Z","subjects":["Gradient Boosting Machine (gbm)","Feature Selection","Shapley Additive Explanations (shap)","Partial Least Squares Regression (plsr)","Corn Kernels","Near-infrared (nir) Spectroscopy","Component Prediction."],"languages":["en","eng"],"rights":["Copyright 2024 Runyu Zheng"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://hdl.handle.net/2142/124601","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Kamruzzaman, Mohammed","Allen, Cody M.","Rausch, Kent D.","Singh, Vijay"]},{"key":"dc:creator","label":"Author","values":["Zheng, Runyu"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2024-05","2024-05-02"]},{"key":"dc:type","label":"Dc Type","values":["text"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Agricultural & Biological Engr"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Thesis"]},{"key":"thesis:degree_name","label":"Degree Name","values":["M.S."]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["University of Illinois at Urbana-Champaign"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Gradient Boosting Machine (gbm)","Feature Selection","Shapley Additive Explanations (shap)","Partial Least Squares Regression (plsr)","Corn Kernels","Near-infrared (nir) Spectroscopy","Component Prediction."]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["en","eng"]},{"key":"dc:rights","label":"Dc Rights","values":["Copyright 2024 Runyu Zheng"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://hdl.handle.net/2142/124601"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2026-05-01","The student, Runyu Zheng, accepted the attached license on 2024-04-30 at 14:36.","The student, Runyu Zheng, submitted this Thesis for approval on 2024-04-30 at 15:06.","This Thesis was approved for publication on 2024-05-02 at 15:57.","DSpace SAF Submission Ingestion Package generated from Vireo submission #20720 on 2024-09-16 at 00:44:54","Differences in moisture levels and protein content impact both nutritional value and processing efficiency of corn kernels. Near-infrared (NIR) spectroscopy can be used to estimate kernel composition, but models to do so are typically trained on samples collected from only a few environments which can lead to underestimation of both the error rates and bias of models. In this study, corn samples grown across an internationally diverse set of environments were assembled. NIR spectroscopy with chemometrics and partial least squares regression (PLSR) was used to determine moisture and protein of this international panel of corn grain samples. The potential of five feature selection methods to improve prediction accuracy by extracting sensitive wavelengths for moisture and protein in corn kernels was assessed. SHapley Additive exPlanations (SHAP) values were used to measure the impact of each feature/wavelength on the model prediction. Gradient boosting machines (GBMs), specifically CatBoost and LightGBM, were effective in selecting crucial wavelengths for moisture (1409, 1900, 1908, 1932, 1953, and 2174 nm) and protein (887, 1212, 1705, 1891, 2097, and 2456 nm), producing PLSR models with coefficients of determination of validation (R2V) of 0.97 and 0.82, root mean square errors of validation (RMSEV) of 0.45% and 0.51%, and ratios of performance to deviation of validation (RPDV) of 6.20 and 2.41, for kernel protein and kernel moisture content, respectively. SHAP plots revealed the significant contribution of 2174 nm to moisture prediction and 1891 nm to protein prediction as well as their respective influence tendencies. These results illustrate the effectiveness of GBMs in NIR spectroscopy in feature engineering for predicting chemical components in the agriculture and food sectors, including developing a multi-country global calibration model for moisture and protein in corn kernels."]},{"key":"dc:format","label":"Dc Format","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["Prediction of moisture and protein in corn kernels from multiple origins based on NIR-PLSR with gradient boosting machines for feature selection"]}]}],"canonical_facts":{"dc:contributor":["Kamruzzaman, Mohammed","Allen, Cody M.","Rausch, Kent D.","Singh, Vijay"],"dc:creator":["Zheng, Runyu"],"dc:date":["2024-05","2024-05-02"],"dc:description":["Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2026-05-01","The student, Runyu Zheng, accepted the attached license on 2024-04-30 at 14:36.","The student, Runyu Zheng, submitted this Thesis for approval on 2024-04-30 at 15:06.","This Thesis was approved for publication on 2024-05-02 at 15:57.","DSpace SAF Submission Ingestion Package generated from Vireo submission #20720 on 2024-09-16 at 00:44:54","Differences in moisture levels and protein content impact both nutritional value and processing efficiency of corn kernels. Near-infrared (NIR) spectroscopy can be used to estimate kernel composition, but models to do so are typically trained on samples collected from only a few environments which can lead to underestimation of both the error rates and bias of models. In this study, corn samples grown across an internationally diverse set of environments were assembled. NIR spectroscopy with chemometrics and partial least squares regression (PLSR) was used to determine moisture and protein of this international panel of corn grain samples. The potential of five feature selection methods to improve prediction accuracy by extracting sensitive wavelengths for moisture and protein in corn kernels was assessed. SHapley Additive exPlanations (SHAP) values were used to measure the impact of each feature/wavelength on the model prediction. Gradient boosting machines (GBMs), specifically CatBoost and LightGBM, were effective in selecting crucial wavelengths for moisture (1409, 1900, 1908, 1932, 1953, and 2174 nm) and protein (887, 1212, 1705, 1891, 2097, and 2456 nm), producing PLSR models with coefficients of determination of validation (R2V) of 0.97 and 0.82, root mean square errors of validation (RMSEV) of 0.45% and 0.51%, and ratios of performance to deviation of validation (RPDV) of 6.20 and 2.41, for kernel protein and kernel moisture content, respectively. SHAP plots revealed the significant contribution of 2174 nm to moisture prediction and 1891 nm to protein prediction as well as their respective influence tendencies. These results illustrate the effectiveness of GBMs in NIR spectroscopy in feature engineering for predicting chemical components in the agriculture and food sectors, including developing a multi-country global calibration model for moisture and protein in corn kernels."],"dc:format":["application/pdf"],"dc:identifier":["https://hdl.handle.net/2142/124601"],"dc:language":["en","eng"],"dc:rights":["Copyright 2024 Runyu Zheng"],"dc:subject":["Gradient Boosting Machine (gbm)","Feature Selection","Shapley Additive Explanations (shap)","Partial Least Squares Regression (plsr)","Corn Kernels","Near-infrared (nir) Spectroscopy","Component Prediction."],"dc:title":["Prediction of moisture and protein in corn kernels from multiple origins based on NIR-PLSR with gradient boosting machines for feature selection"],"dc:type":["text"],"thesis:degree_discipline":["Agricultural & Biological Engr"],"thesis:degree_level":["Thesis"],"thesis:degree_name":["M.S."],"thesis:institution_name":["University of Illinois at Urbana-Champaign"]},"updated_at":"2026-07-22T22:25:02Z"}