{"id":"W4281477499","doi":"10.1016/j.indcrop.2022.115107","title":"Production of antioxidative protein hydrolysates from corn distillers solubles: Process optimization, antioxidant activity evaluation, and peptide analysis","year":2022,"lang":"en","type":"article","venue":"Industrial Crops and Products","topic":"Protein Hydrolysis and Bioactive Peptides","field":"Biochemistry, Genetics and Molecular Biology","cited_by":18,"is_retracted":false,"has_abstract":false,"ca_institutions":"Ministry of Agriculture, Food and Rural Affairs; University of Guelph","funders":"Ontario Ministry of Agriculture, Food and Rural Affairs; BioFuelNet Canada","keywords":"Antioxidant; Chemistry; DPPH; Hydrolysate; Hydrolysis; Food science; Response surface methodology; Yeast; Enzymatic hydrolysis; Fermentation; Hydroxyl radical; Peptide; Superoxide; Biochemistry; Chromatography; Enzyme","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005203045,0.0006261948,0.0003689888,0.0004275587,0.0001886688,0.0003668918,0.0001936471,0.0002274487,0.0003140539],"category_scores_gemma":[0.0003733133,0.0001546949,0.0003461275,0.0006039427,0.000193489,0.0003088185,0.0002162853,0.0006167767,0.0001054259],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003262379,"about_ca_system_score_gemma":0.0004003282,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001567554,"about_ca_topic_score_gemma":0.002608473,"domain_scores_codex":[0.9997595,0.00003914494,0.00002665556,0.00004062933,0.00009393563,0.0000400826],"domain_scores_gemma":[0.9998217,0.00002824535,0.00005552334,0.00001341257,0.00004448548,0.00003666329],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00008489737,0.00004612239,0.0001981902,0.0000310836,0.000008302477,0.00002059144,0.00001091617,0.0002006953,0.9978631,0.00002299192,0.000009220362,0.001503888],"study_design_scores_gemma":[0.000006514068,0.0001116888,0.001546526,0.000002279789,0.00001369743,0.00003125229,0.00001141604,0.0004106154,0.9975759,0.00001337351,0.0002740132,0.000002646555],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9920282,0.0008070401,0.006160391,0.00007286836,0.0000153701,0.00005876293,0.0003453377,0.00003486932,0.000477154],"genre_scores_gemma":[0.9853132,0.0007334808,0.01166928,0.00002973398,0.000007976146,0.00003519953,0.0009984837,0.00002675003,0.00118581],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001567554,"threshold_uncertainty_score":0.003116846,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02537673363932922,"score_gpt":0.2637683629933269,"score_spread":0.2383916293539977,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}