{"id":"W2004609402","doi":"10.1016/j.foodchem.2007.06.015","title":"Preliminary study on the application of visible–near infrared spectroscopy and chemometrics to classify Riesling wines from different countries","year":2007,"lang":"en","type":"article","venue":"Food Chemistry","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":124,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"Australian Government; Alberta Water Research Institute","keywords":"Chemometrics; Partial least squares regression; Principal component analysis; Linear discriminant analysis; Wine; Mathematics; Near-infrared spectroscopy; Calibration; Analytical Chemistry (journal); Chemistry; Statistics; Food science; Chromatography; Optics; 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":[0.002880933,0.0004109913,0.0004450595,0.0008786246,0.0008684898,0.0007162464,0.0003621643,0.0004754132,0.0009362668],"category_scores_gemma":[0.002317136,0.000143262,0.0007094219,0.0008649506,0.0003948772,0.0005709605,0.0003468726,0.0005063426,0.0004134138],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003684177,"about_ca_system_score_gemma":0.0005005589,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007802851,"about_ca_topic_score_gemma":0.007808818,"domain_scores_codex":[0.9992235,0.0003486631,0.00005143178,0.000160138,0.0001546465,0.00006168648],"domain_scores_gemma":[0.9981267,0.0007793674,0.000085337,0.0001361058,0.0007311505,0.0001413913],"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":[0.003507041,0.003435769,0.1403435,0.0003193777,0.0003159441,0.0002838151,0.001559173,0.005322414,0.7022445,0.000954734,0.000861221,0.1408525],"study_design_scores_gemma":[0.0001204618,0.01567871,0.4650007,0.00003409923,0.0005137008,0.0006620958,0.002295715,0.02074062,0.4802878,0.0009281369,0.01364263,0.0000953357],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9841564,0.0004330965,0.01210395,0.0001407804,0.00001736496,0.00009953773,0.0002351688,0.00004169171,0.002772094],"genre_scores_gemma":[0.9684492,0.0003696229,0.02632655,0.00009909566,0.00002479322,0.00003542363,0.0009582193,0.00001668803,0.003720481],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.007802851,"threshold_uncertainty_score":0.01551485,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01648469175218077,"score_gpt":0.2768992809580753,"score_spread":0.2604145892058945,"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."}}