FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W3111908560> ?p ?o ?g. }

• W3111908560 endingPage "108405" @default.
• W3111908560 startingPage "108405" @default.
• W3111908560 abstract "This study demonstrates a novel approach to develop global calibration models for predicting intramuscular fat (IMF) and pH across various red meat species and muscle types. A total of 8 hyperspectral imaging (HSI) datasets were used from different experiments, comprising data from three species: beef, lamb and venison across various muscle type, slaughter season and measurement conditions. Prediction models were developed using Partial Least Squares Regression (PLSR) and Deep Convolutional Neural Networks (DCNN) using a total of 1080 and 1116 samples for IMF and pH, respectively. Models for pH and IMF via both techniques yielded high Rc2 (0.86-0.93) and low SEC values. Also, reasonably accurate prediction performance was observed with high Rp2 (0.86-0.89) and low SEP values. Overall results illustrated the comprehensiveness of these global calibration models with the ability to predict IMF and pH of red meat samples irrespective of species and muscle type." @default.
• W3111908560 created "2020-12-21" @default.
• W3111908560 creator A5009327980 @default.
• W3111908560 creator A5040800716 @default.
• W3111908560 creator A5043722027 @default.
• W3111908560 creator A5048105937 @default.
• W3111908560 date "2021-11-01" @default.
• W3111908560 modified "2023-10-16" @default.
• W3111908560 title "A global calibration model for prediction of intramuscular fat and pH in red meat using hyperspectral imaging" @default.
• W3111908560 cites W1521436688 @default.
• W3111908560 cites W1966662574 @default.
• W3111908560 cites W1966693645 @default.
• W3111908560 cites W1972600584 @default.
• W3111908560 cites W1978651736 @default.
• W3111908560 cites W1983458080 @default.
• W3111908560 cites W1989511025 @default.
• W3111908560 cites W1995966489 @default.
• W3111908560 cites W2001026186 @default.
• W3111908560 cites W2015159529 @default.
• W3111908560 cites W2038550525 @default.
• W3111908560 cites W2048976066 @default.
• W3111908560 cites W2059271138 @default.
• W3111908560 cites W2060230684 @default.
• W3111908560 cites W2065084513 @default.
• W3111908560 cites W2068128918 @default.
• W3111908560 cites W2069978330 @default.
• W3111908560 cites W2090790364 @default.
• W3111908560 cites W2093833427 @default.
• W3111908560 cites W2098722265 @default.
• W3111908560 cites W2150644038 @default.
• W3111908560 cites W2158143121 @default.
• W3111908560 cites W2258094320 @default.
• W3111908560 cites W2282823150 @default.
• W3111908560 cites W2291526792 @default.
• W3111908560 cites W2340760070 @default.
• W3111908560 cites W2461968591 @default.
• W3111908560 cites W2519560813 @default.
• W3111908560 cites W2528938896 @default.
• W3111908560 cites W2536508924 @default.
• W3111908560 cites W2605819438 @default.
• W3111908560 cites W2620378345 @default.
• W3111908560 cites W2738328392 @default.
• W3111908560 cites W2753591095 @default.
• W3111908560 cites W2754359995 @default.
• W3111908560 cites W2789935862 @default.
• W3111908560 cites W2792817593 @default.
• W3111908560 cites W2802404814 @default.
• W3111908560 cites W2807115651 @default.
• W3111908560 cites W2897933472 @default.
• W3111908560 cites W2899208923 @default.
• W3111908560 cites W2911487745 @default.
• W3111908560 cites W2944248482 @default.
• W3111908560 cites W2950623765 @default.
• W3111908560 cites W2964705231 @default.
• W3111908560 cites W2974140502 @default.
• W3111908560 cites W2991469127 @default.
• W3111908560 cites W3025928057 @default.
• W3111908560 cites W3028231515 @default.
• W3111908560 cites W3047807393 @default.
• W3111908560 cites W3188586 @default.
• W3111908560 doi "https://doi.org/10.1016/j.meatsci.2020.108405" @default.
• W3111908560 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/33451871" @default.
• W3111908560 hasPublicationYear "2021" @default.
• W3111908560 type Work @default.
• W3111908560 sameAs 3111908560 @default.
• W3111908560 citedByCount "21" @default.
• W3111908560 countsByYear W31119085602021 @default.
• W3111908560 countsByYear W31119085602022 @default.
• W3111908560 countsByYear W31119085602023 @default.
• W3111908560 crossrefType "journal-article" @default.
• W3111908560 hasAuthorship W3111908560A5009327980 @default.
• W3111908560 hasAuthorship W3111908560A5040800716 @default.
• W3111908560 hasAuthorship W3111908560A5043722027 @default.
• W3111908560 hasAuthorship W3111908560A5048105937 @default.
• W3111908560 hasConcept C105795698 @default.
• W3111908560 hasConcept C154945302 @default.
• W3111908560 hasConcept C159078339 @default.
• W3111908560 hasConcept C165838908 @default.
• W3111908560 hasConcept C185592680 @default.
• W3111908560 hasConcept C22354355 @default.
• W3111908560 hasConcept C2776635132 @default.
• W3111908560 hasConcept C2778089274 @default.
• W3111908560 hasConcept C31903555 @default.
• W3111908560 hasConcept C33923547 @default.
• W3111908560 hasConcept C39432304 @default.
• W3111908560 hasConcept C41008148 @default.
• W3111908560 hasConcept C81363708 @default.
• W3111908560 hasConceptScore W3111908560C105795698 @default.
• W3111908560 hasConceptScore W3111908560C154945302 @default.
• W3111908560 hasConceptScore W3111908560C159078339 @default.
• W3111908560 hasConceptScore W3111908560C165838908 @default.
• W3111908560 hasConceptScore W3111908560C185592680 @default.
• W3111908560 hasConceptScore W3111908560C22354355 @default.
• W3111908560 hasConceptScore W3111908560C2776635132 @default.
• W3111908560 hasConceptScore W3111908560C2778089274 @default.
• W3111908560 hasConceptScore W3111908560C31903555 @default.
• W3111908560 hasConceptScore W3111908560C33923547 @default.
• W3111908560 hasConceptScore W3111908560C39432304 @default.

• next