{"id":"W3108122377","doi":"10.1002/jms.4690","title":"Chemical profiling and separation of bioactive secondary metabolites in Maca (<scp><i>Lepidium peruvianum</i></scp>) by normal and reverse phase thin layer chromatography coupled to desorption electrospray ionization‐mass spectrometry","year":2021,"lang":"en","type":"article","venue":"Journal of Mass Spectrometry","topic":"Genomics, phytochemicals, and oxidative stress","field":"Biochemistry, Genetics and Molecular Biology","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"York University","funders":"Natural Sciences and Engineering Research Council of Canada; Coordenação de Aperfeiçoamento de Pessoal de Nível Superior","keywords":"Chemistry; Brassicaceae; Glucosinolate; Chromatography; Nutraceutical; Electrospray ionization; Mass spectrometry; High-performance liquid chromatography; Thin-layer chromatography; Botany; Food science; Brassica","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":[0.00007690951,0.0003991345,0.0001451292,0.0005431579,0.0001698802,0.0002457851,0.0001212379,0.0001915754,0.0005012538],"category_scores_gemma":[0.0001580486,0.0001323834,0.0002022708,0.0002354032,0.0001347721,0.0001484248,0.0001523481,0.0002303191,0.0002017072],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008934729,"about_ca_system_score_gemma":0.0001149788,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009912518,"about_ca_topic_score_gemma":0.001516193,"domain_scores_codex":[0.9999473,0.000008062107,0.000002837936,0.00001923558,0.00001533245,0.000007139729],"domain_scores_gemma":[0.9998792,0.00002207177,0.00003641099,0.000008817167,0.00002909212,0.00002446703],"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":[0.00002299723,0.000003481303,0.0003262775,0.00001337947,0.000004269932,0.0000271869,0.000008077877,0.000007158105,0.9989516,0.000004817191,0.000005755298,0.0006250064],"study_design_scores_gemma":[0.00003145973,0.0005949237,0.185582,0.00001900222,0.00006737206,0.001417359,0.0001771409,0.002037249,0.8058247,0.00006890597,0.004150835,0.00002910592],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9943117,0.001106191,0.002929195,0.00004151696,0.000005136984,0.00003950183,0.000698394,0.0001056778,0.000762572],"genre_scores_gemma":[0.9864749,0.0006339959,0.009387808,0.00009747969,0.000008046579,0.00006056203,0.001854019,0.00005680808,0.001426487],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0009912518,"threshold_uncertainty_score":0.001970887,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006080918965309935,"score_gpt":0.254701440394017,"score_spread":0.2486205214287071,"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."}}