{"id":"W4385232660","doi":"10.1016/j.fbp.2023.07.006","title":"Rapid and non-destructive detection of hard to cook chickpeas using NIR hyperspectral imaging and machine learning","year":2023,"lang":"en","type":"article","venue":"Food and Bioproducts Processing","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":17,"is_retracted":false,"has_abstract":false,"ca_institutions":"Lethbridge College; University of Guelph","funders":"","keywords":"Hyperspectral imaging; Convolutional neural network; Pattern recognition (psychology); Artificial intelligence; Partial least squares regression; Mean squared error; Mathematics; Artificial neural network; Root mean square; Biological system; Computer science; Biology; Engineering; Statistics","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001373636,0.0001745597,0.0002489104,0.0002578716,0.0003075074,0.00009339024,0.00005173418,0.00005335753,0.000009632207],"category_scores_gemma":[0.000131098,0.0001612838,0.00002203655,0.0008300944,0.0001288838,0.0001907132,0.00008077471,0.0001882407,5.710496e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002709822,"about_ca_system_score_gemma":0.00003044178,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005370101,"about_ca_topic_score_gemma":0.000003425063,"domain_scores_codex":[0.99897,0.000008254706,0.0001768011,0.0004613951,0.0001301473,0.0002533433],"domain_scores_gemma":[0.9996076,0.00001904992,0.0001141906,0.00009387115,0.00007624072,0.00008903807],"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":[0.00004364295,0.00001175059,0.01006665,0.0003666873,0.00003629419,0.000002350583,0.000797932,0.000005939864,0.9624811,0.000003314768,0.00000141823,0.02618292],"study_design_scores_gemma":[0.0003281528,0.00009513378,0.004790045,0.0001025573,0.0001174403,0.00008609789,0.00235686,0.002347982,0.9893603,0.0001427502,0.0000580618,0.0002146753],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9925461,0.00682325,0.00009841896,0.0001529286,0.00002711668,0.00005344011,0.000006986547,0.00008295323,0.0002087629],"genre_scores_gemma":[0.9983695,0.0002254815,0.001071087,0.00001518217,0.0001680188,0.000002383982,0.000005521784,0.00002261299,0.0001201921],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02687914,"threshold_uncertainty_score":0.6576961,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0215896960155388,"score_gpt":0.2535402163090572,"score_spread":0.2319505202935184,"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."}}