{"id":"W4389052687","doi":"10.1016/j.fbio.2023.103392","title":"SR-FTIR microspectroscopy: Emerging 2D structural-chemical analytical technique for food quality and safety monitoring","year":2023,"lang":"en","type":"article","venue":"Food Bioscience","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":22,"is_retracted":false,"has_abstract":false,"ca_institutions":"McGill University","funders":"McGill University","keywords":"Fourier transform infrared spectroscopy; Synchrotron; Chemistry; Food industry; Food products; Food safety; Food quality; Chemical imaging; Fourier transform; Process engineering; Biochemical engineering; Analytical Chemistry (journal); Computer science; Food science; Optics; Environmental chemistry; Physics; Engineering; Artificial intelligence","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00145306,0.001364563,0.0007108285,0.001186392,0.0003549701,0.0009635349,0.001391212,0.001544887,0.002405021],"category_scores_gemma":[0.0008888393,0.0005496384,0.0005995493,0.0008315532,0.0007148975,0.001690351,0.0009796489,0.001634829,0.001295459],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004870941,"about_ca_system_score_gemma":0.0006111885,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000572402,"about_ca_topic_score_gemma":0.001389852,"domain_scores_codex":[0.9988447,0.0001716044,0.00003062867,0.0003069738,0.0005756319,0.00007049572],"domain_scores_gemma":[0.9991682,0.0002386371,0.0002487139,0.00008813372,0.0001961202,0.00006019399],"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":[0.00003832249,0.00003566763,0.0002988827,0.0001718334,0.00001818915,0.00003336859,0.00003595982,0.0003616393,0.9794509,0.001119108,0.0006024966,0.01783366],"study_design_scores_gemma":[0.00002034266,0.0003192748,0.002405076,0.00003126204,0.00003281365,0.0005178755,0.00007925807,0.03886682,0.9413962,0.001615172,0.01464098,0.00007498487],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1507341,0.01031903,0.8225619,0.001286993,0.0004247181,0.0001484328,0.001688158,0.004410248,0.008426427],"genre_scores_gemma":[0.2737969,0.006020685,0.7059216,0.001156379,0.0003678226,0.0001840194,0.001395858,0.0005819336,0.01057481],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002405021,"threshold_uncertainty_score":0.008045614,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04799328493532124,"score_gpt":0.3656277344397424,"score_spread":0.3176344495044212,"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."}}