Open Access. Powered by Scholars. Published by Universities.®

Food Science Commons

Open Access. Powered by Scholars. Published by Universities.®

Articles 1 - 3 of 3

Full-Text Articles in Food Science

Arkansas Soybean Performance Tests 2022, J. F. Carlin, R. B. Mulloy, R. D. Bond Jun 2023

Arkansas Soybean Performance Tests 2022, J. F. Carlin, R. B. Mulloy, R. D. Bond

Arkansas Agricultural Experiment Station Research Series

Soybean variety and strain performance tests are conducted each year in Arkansas by the University of Arkansas System Division of Agriculture’s Arkansas Crop Variety Improvement Program. The tests provide information to companies developing varieties and/ or marketing seed within the state, and aid the Arkansas Cooperative Extension Service in formulating variety recommendations for soybean producers.


Marginal Agricultural Land Identification In The Lower Mississippi Alluvial Valley, Prakash Tiwari May 2023

Marginal Agricultural Land Identification In The Lower Mississippi Alluvial Valley, Prakash Tiwari

Theses and Dissertations

This study identified marginal agricultural lands in the Lower Mississippi Alluvial Valley using crop yield predicting models. The Random Forest Regression (RFR) and Multiple Linear Regression (MLR) models were trained and validated using county-level crop yield data, climate data, soil properties, and Normalized Difference Vegetation Index (NDVI). The RFR model outperformed MLR model in estimating soybean and corn yields, with an index of agreement (d) of 0.98 and 0.96, Nash-Sutcliffe model efficiency (NSE) of 0.88 and 0.93, and root mean square error (RMSE) of 9.34% and 5.84%, respectively. Marginal agricultural lands were estimated to 26,366 hectares using cost and sales …


Reproducibility Evaluation Of Soybean Oligopeptides Based On Peptidomic Analyses, Yu Xiao-Yi, Xu Ju-Cai, Wu Ying, Liu Wan-Shun, Guo Dan, Li Xiao-Min Apr 2023

Reproducibility Evaluation Of Soybean Oligopeptides Based On Peptidomic Analyses, Yu Xiao-Yi, Xu Ju-Cai, Wu Ying, Liu Wan-Shun, Guo Dan, Li Xiao-Min

Food and Machinery

Objective: This study aimed to analyze the peptide composition and evaluate the reproducibility of the soybean oligopeptides. Methods: High performance liquid chromatography tandem high resolution mass spectrometry was employed for peptidomic analysis of the soybean oligopeptides prepared at the same condition at different batches. Generality and individuality of the samples were also discussed based on the heat map analysis and principal component analysis (PCA). Results: About 1 000 peptides were identified from each of the materials and most of them were short peptides. From the set analysis, 101 peptides were found of detectability from all samples, but there were also …