{"id":"W4413788790","doi":"10.3390/analytica6030029","title":"Rapid Spectroscopic Analysis for Food and Feed Quality Control: Prediction of Protein and Nutrient Content in Barley Forage Using LIBS and Chemometrics","year":2025,"lang":"en","type":"article","venue":"Analytica—A Journal of Analytical Chemistry and Chemical Analysis","topic":"Laser-induced spectroscopy and plasma","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"National Research Council Canada; Institut National de la Recherche Scientifique","funders":"National Research Council Canada","keywords":"Chemometrics; Partial least squares regression; Laser-induced breakdown spectroscopy; Forage; Nutrient; Extreme learning machine; Food science; Biological system; Chemistry; Environmental science; Mathematics; Agronomy; Computer science; Machine learning; Biology; Spectroscopy; Chromatography; Statistics","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0007566932,0.0003029946,0.001474938,0.0006248595,0.00006172973,0.00008371285,0.000127393,0.0002690573,0.00003254552],"category_scores_gemma":[0.0005183779,0.0002686347,0.0004177972,0.002491009,0.0002378455,0.0001383147,0.00005342961,0.000405081,3.86174e-8],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001123884,"about_ca_system_score_gemma":0.00004385952,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001917072,"about_ca_topic_score_gemma":0.00001101553,"domain_scores_codex":[0.99772,0.00004310457,0.001221258,0.0003808309,0.0002940411,0.0003407666],"domain_scores_gemma":[0.9984704,0.0005188426,0.0002792838,0.000201435,0.0002263475,0.0003037521],"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":[0.001075742,0.0005354621,0.1952305,0.00233275,0.02877554,0.00002017175,0.0001462227,0.001785815,0.7680624,0.001294315,0.00003495813,0.0007061583],"study_design_scores_gemma":[0.004043756,0.0002409648,0.02360312,0.0001966977,0.01968073,0.00001449333,0.0003191126,0.4399303,0.509648,0.001862855,0.00006368896,0.0003962546],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.98057,0.001709068,0.0170186,0.0002475931,0.00001222497,0.0001537526,0.0001060213,0.00001254892,0.0001701791],"genre_scores_gemma":[0.9983232,0.0005754823,0.0009459376,0.00002834753,0.00004443206,0.000004815029,0.00001631259,0.00001090576,0.00005055922],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4381444,"threshold_uncertainty_score":0.9999766,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02224416231383267,"score_gpt":0.2521403568587878,"score_spread":0.2298961945449551,"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."}}