{"id":"W2045909750","doi":"10.1007/s11746-000-0005-9","title":"Discrimination of edible oil products and quantitative determination of their iodine value by Fourier transform near‐infrared spectroscopy","year":2000,"lang":"en","type":"article","venue":"Journal of the American Oil Chemists Society","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":51,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Partial least squares regression; Calibration; Iodine value; Linear discriminant analysis; Near-infrared spectroscopy; Analytical Chemistry (journal); Fourier transform infrared spectroscopy; Chemometrics; Chromatography; Mathematics; Spectroscopy; Chemistry; Artificial intelligence; Statistics; Computer science; Optics; Physics; Food science","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":[0.0009327466,0.0005962677,0.0002474199,0.0008089006,0.0001463415,0.0005848843,0.0003036394,0.0003404788,0.0006905309],"category_scores_gemma":[0.001971761,0.000204229,0.0003322797,0.0002643485,0.0004055616,0.000476267,0.0002817812,0.0005285519,0.0003142995],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002694984,"about_ca_system_score_gemma":0.0002315462,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001379589,"about_ca_topic_score_gemma":0.001399011,"domain_scores_codex":[0.9996005,0.00009672625,0.00002165049,0.000098634,0.0001523331,0.00003021793],"domain_scores_gemma":[0.9994154,0.0003239687,0.00007321898,0.00003035188,0.0001329972,0.00002401854],"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":[0.000224678,0.00004673602,0.0034052,0.00005442038,0.00001572526,0.00003563527,0.00004914348,0.001203634,0.9765068,0.0002149972,0.00004473442,0.01819833],"study_design_scores_gemma":[0.00002041105,0.0003259965,0.009757238,0.00001468184,0.0000406864,0.0001295218,0.00007264058,0.02991334,0.9579474,0.0003998217,0.001351153,0.00002708435],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8349281,0.0006858096,0.1618586,0.00007294166,0.00002866692,0.00005281783,0.0002662765,0.0002695782,0.001837214],"genre_scores_gemma":[0.8966441,0.0005261814,0.100922,0.0000360546,0.00001042532,0.00003219476,0.000257557,0.00006270391,0.001508742],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001379589,"threshold_uncertainty_score":0.00493294,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01025324810840916,"score_gpt":0.2684690156118608,"score_spread":0.2582157675034516,"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."}}