{"id":"W4380200828","doi":"10.1016/j.heliyon.2023.e17115","title":"Detection of dextran, maltodextrin and soluble starch in the adulterated Lycium barbarum polysaccharides (LBPs) using Fourier-transform infrared spectroscopy (FTIR) and machine learning models","year":2023,"lang":"en","type":"article","venue":"Heliyon","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":31,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Key Research and Development Program of Ningxia; Science and Technology Department of Ningxia","keywords":"Maltodextrin; Fourier transform infrared spectroscopy; Starch; Chemistry; Partial least squares regression; Artificial intelligence; Polysaccharide; Principal component analysis; Adulterant; Support vector machine; Mathematics; Chromatography; Food science; Computer science; Machine learning; Spray drying; Biochemistry; Chemical engineering","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005835637,0.0002141216,0.0003204669,0.0005074543,0.0002729661,0.00008969282,0.0001602797,0.0001612766,0.00008142043],"category_scores_gemma":[0.00008380506,0.0001817171,0.00004982538,0.001559892,0.0001207715,0.0002528211,0.00005879591,0.0005860974,0.000002244486],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007529818,"about_ca_system_score_gemma":0.00004222525,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005295955,"about_ca_topic_score_gemma":0.0002338605,"domain_scores_codex":[0.9983478,0.00007551161,0.0003836474,0.0003705813,0.0003458996,0.0004765643],"domain_scores_gemma":[0.9993091,0.000232678,0.00009917295,0.0002163597,0.00007350832,0.00006922981],"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":[0.0002330682,0.00004817977,0.005759584,0.0005994019,0.00004563273,0.00001188903,0.00158631,0.000576175,0.9902852,0.00006763313,0.000004375193,0.0007825276],"study_design_scores_gemma":[0.0009013609,0.0001635975,0.001027981,0.0001642688,0.0001118082,0.00002573104,0.00205574,0.09906943,0.8947587,0.001394021,0.00009279299,0.0002345526],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9917058,0.004816944,0.001898005,0.00009477876,0.00003217962,0.0001306801,0.00001531995,0.00008411649,0.00122213],"genre_scores_gemma":[0.9950391,0.003930138,0.0004886949,0.00002289786,0.0000548401,0.00001778905,0.00001849518,0.00003137703,0.0003966876],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.09849326,"threshold_uncertainty_score":0.7410207,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03907744254722068,"score_gpt":0.2767035886306504,"score_spread":0.2376261460834297,"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."}}