{"id":"W4413970758","doi":"10.3390/molecules30163379","title":"Optimization of Extraction Methods for NMR and LC-MS Metabolite Fingerprint Profiling of Botanical Ingredients in Food and Natural Health Products (NHPs)","year":2025,"lang":"en","type":"article","venue":"Molecules","topic":"Metabolomics and Mass Spectrometry Studies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Canadian Light Source (Canada); University of Guelph","funders":"","keywords":"Metabolite profiling; Chromatography; Metabolite; Chemistry; Extraction (chemistry); Profiling (computer programming); Fingerprint (computing); Metabolomics; Computer science; Biochemistry","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00250278,0.001487839,0.0006841061,0.001265244,0.0006344546,0.0006902605,0.0005532209,0.0006050928,0.00142561],"category_scores_gemma":[0.002027861,0.0004077434,0.0005778521,0.0009039156,0.0005296547,0.0008416802,0.0007099785,0.0009516756,0.0009462657],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003661753,"about_ca_system_score_gemma":0.0008355734,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001103076,"about_ca_topic_score_gemma":0.002760182,"domain_scores_codex":[0.9982265,0.0003643109,0.0001916809,0.0004212589,0.0006115128,0.0001847048],"domain_scores_gemma":[0.999333,0.0001943445,0.0001178173,0.0000629369,0.0002533676,0.00003852572],"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":[0.0001517447,0.00009907893,0.0006392744,0.0003042668,0.00003024528,0.0001089326,0.00007760865,0.0004546648,0.9861892,0.0001636807,0.0001590644,0.0116222],"study_design_scores_gemma":[0.00002950278,0.0006941039,0.007464883,0.00008887445,0.0001083204,0.0003234589,0.0001579157,0.003105164,0.9776229,0.0002608046,0.01008297,0.00006122523],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"methods","genre_scores_codex":[0.6589621,0.01242293,0.309233,0.0006215048,0.0003539551,0.004270929,0.003178329,0.00142631,0.009530963],"genre_scores_gemma":[0.5423623,0.009656438,0.435917,0.000656794,0.0001358989,0.004044822,0.003199866,0.0004481817,0.003578627],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.00250278,"threshold_uncertainty_score":0.01323617,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01194294266638379,"score_gpt":0.3386337444349664,"score_spread":0.3266908017685826,"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."}}