{"id":"W4386384130","doi":"10.1016/j.fbio.2023.103083","title":"Characterization of volatile components of microwave dried perilla leaves using GC–MS and E-nose","year":2023,"lang":"en","type":"article","venue":"Food Bioscience","topic":"Advanced Chemical Sensor Technologies","field":"Engineering","cited_by":42,"is_retracted":false,"has_abstract":false,"ca_institutions":"McGill University","funders":"Guangxi Key Research and Development Program","keywords":"Perilla; Aroma; Chemistry; Perilla frutescens; Electronic nose; Gas chromatography–mass spectrometry; Flavor; Gas chromatography; Food science; Chromatography; Mass spectrometry; Organic chemistry; Raw material; Biology","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":[0.0001120482,0.000308396,0.0002200578,0.0004758429,0.0002662115,0.0002372968,0.0002219107,0.0002911396,0.001434754],"category_scores_gemma":[0.0001508866,0.0001164487,0.0003842163,0.0003224058,0.0001985701,0.000331828,0.0002086331,0.0004674057,0.0002993868],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000155089,"about_ca_system_score_gemma":0.0002108652,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001008885,"about_ca_topic_score_gemma":0.001842195,"domain_scores_codex":[0.9998873,0.000006712063,0.000006414904,0.00003833103,0.00004485465,0.00001633981],"domain_scores_gemma":[0.9999386,0.00001874118,0.00001107067,0.000005509972,0.00001774691,0.000008334977],"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":[0.00007498456,0.000009927404,0.0004330643,0.00004263394,0.000007857201,0.0000344002,0.00001995963,0.00003273322,0.9970734,0.00002223526,0.00002463109,0.002224145],"study_design_scores_gemma":[0.00001152782,0.0002864293,0.04453931,0.00001401469,0.00005056159,0.0003325937,0.0001367977,0.001603011,0.9500945,0.0001191243,0.002786641,0.00002538827],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9839304,0.001973081,0.01036101,0.00008908936,0.00007448403,0.00005265187,0.001673248,0.0001222076,0.001723843],"genre_scores_gemma":[0.982929,0.001174224,0.01018973,0.0002241096,0.00003449799,0.0000954701,0.001518542,0.00005352282,0.003781036],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001434754,"threshold_uncertainty_score":0.004799724,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0267068136269963,"score_gpt":0.2278752251785057,"score_spread":0.2011684115515094,"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."}}