{"id":"W3015573605","doi":"10.1016/j.lwt.2020.109368","title":"Detection of durum wheat pasta adulteration with common wheat by infrared spectroscopy and chemometrics: A case study","year":2020,"lang":"en","type":"article","venue":"LWT","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":31,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"Canadian Physiotherapy Association; Consejo Nacional de Investigaciones Científicas y Técnicas","keywords":"Chemometrics; Linear discriminant analysis; Partial least squares regression; Common wheat; Mathematics; Near-infrared spectroscopy; Spectroscopy; Winter wheat; Analytical Chemistry (journal); Food science; Chemistry; Statistics; Chromatography; Agronomy; Biology; Physics","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005587249,0.0007113202,0.0004463285,0.001216529,0.0007903139,0.0009150983,0.0007094328,0.002147198,0.0006622703],"category_scores_gemma":[0.001349157,0.0003058623,0.000660507,0.0008267825,0.0008244421,0.0004687879,0.0004271686,0.0004980201,0.0003470406],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005288366,"about_ca_system_score_gemma":0.0002001464,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004133371,"about_ca_topic_score_gemma":0.005888793,"domain_scores_codex":[0.9994107,0.0001071193,0.00002935469,0.0001397969,0.00024599,0.00006696077],"domain_scores_gemma":[0.9992778,0.0003125139,0.0001458765,0.00006484452,0.0001334826,0.00006537174],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.00219323,0.001641849,0.2490005,0.0005166932,0.0002269317,0.1912579,0.003092097,0.005303042,0.4389232,0.001100256,0.001266395,0.1054779],"study_design_scores_gemma":[0.00007753702,0.004225255,0.1564176,0.00009108368,0.0003630944,0.23757,0.004344949,0.05165075,0.5321618,0.001277127,0.0116731,0.0001476784],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9919641,0.0005472665,0.005460143,0.0002047843,0.00001361044,0.00003026514,0.00007963945,0.00005938332,0.001640691],"genre_scores_gemma":[0.9897119,0.0004964308,0.007782247,0.00007067278,0.00001749029,0.00000585401,0.00004669444,0.00002709153,0.001841537],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.004133371,"threshold_uncertainty_score":0.008218646,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01175395674388446,"score_gpt":0.2530128578899395,"score_spread":0.2412589011460551,"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."}}