Matches in SemOpenAlex for { <https://semopenalex.org/work/W3186375094> ?p ?o ?g. }
Showing items 1 to 90 of
90
with 100 items per page.
- W3186375094 endingPage "10565" @default.
- W3186375094 startingPage "10558" @default.
- W3186375094 abstract "<h2>ABSTRACT</h2> Total bacterial count (TBC) is a widely accepted index for assessing microbial quality of milk, and cultivation-based methods are commonly used as standard methods for its measurement. However, these methods are laborious and time-consuming. This study proposes a method combining E-nose technology and artificial neural network for rapid prediction of TBC in milk. The qualitative model generated an accuracy rate of 100% when identifying milk samples with high, medium, or low levels of TBC, on both the testing and validating subsets. Predicted TBC values generated by the quantitative model demonstrated strong coefficient of multiple determination (R<sup>2</sup> > 0.99) with reference values. Mean relative difference between predicted and reference values (mean ± standard deviation) of TBC were 1.1 ± 1.7% and 0.4 ± 0.8% on the testing and validating subsets involving 24 and 28 tested samples, respectively. Paired <i>t</i>-test implied that the difference between predicted and reference values of TBC was insignificant for both the testing and validating subsets. As low as ~1 log cfu/mL of TBC present in tested samples were precisely predicted. Results of this study indicated that combination of E-nose technology and artificial neural network generated reliable predictions of TBC in milk. The method proposed in this study was reliable, rapid, and cost efficient for assessing microbial quality milk, and thus would potentially have realistic application in dairy section." @default.
- W3186375094 created "2021-08-02" @default.
- W3186375094 creator A5043007901 @default.
- W3186375094 creator A5056716164 @default.
- W3186375094 date "2021-10-01" @default.
- W3186375094 modified "2023-10-17" @default.
- W3186375094 title "Application of E-nose technology combined with artificial neural network to predict total bacterial count in milk" @default.
- W3186375094 cites W1976043670 @default.
- W3186375094 cites W1984480813 @default.
- W3186375094 cites W1985644123 @default.
- W3186375094 cites W2019087583 @default.
- W3186375094 cites W2019156389 @default.
- W3186375094 cites W2033862893 @default.
- W3186375094 cites W2036408670 @default.
- W3186375094 cites W2045917834 @default.
- W3186375094 cites W2055847982 @default.
- W3186375094 cites W2058342658 @default.
- W3186375094 cites W2060315146 @default.
- W3186375094 cites W2064818300 @default.
- W3186375094 cites W2072867725 @default.
- W3186375094 cites W2073516505 @default.
- W3186375094 cites W2083724479 @default.
- W3186375094 cites W2083844448 @default.
- W3186375094 cites W2098993791 @default.
- W3186375094 cites W2151996078 @default.
- W3186375094 cites W2172065961 @default.
- W3186375094 cites W2508881151 @default.
- W3186375094 cites W2765357561 @default.
- W3186375094 cites W2767825412 @default.
- W3186375094 cites W2769057403 @default.
- W3186375094 cites W2774750366 @default.
- W3186375094 cites W2778342873 @default.
- W3186375094 cites W2781843646 @default.
- W3186375094 cites W2890666771 @default.
- W3186375094 cites W2904863800 @default.
- W3186375094 cites W2942299955 @default.
- W3186375094 cites W2943991194 @default.
- W3186375094 cites W2780240035 @default.
- W3186375094 doi "https://doi.org/10.3168/jds.2020-19987" @default.
- W3186375094 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/34304876" @default.
- W3186375094 hasPublicationYear "2021" @default.
- W3186375094 type Work @default.
- W3186375094 sameAs 3186375094 @default.
- W3186375094 citedByCount "4" @default.
- W3186375094 countsByYear W31863750942023 @default.
- W3186375094 crossrefType "journal-article" @default.
- W3186375094 hasAuthorship W3186375094A5043007901 @default.
- W3186375094 hasAuthorship W3186375094A5056716164 @default.
- W3186375094 hasBestOaLocation W31863750941 @default.
- W3186375094 hasConcept C105795698 @default.
- W3186375094 hasConcept C119128265 @default.
- W3186375094 hasConcept C153180895 @default.
- W3186375094 hasConcept C154945302 @default.
- W3186375094 hasConcept C22679943 @default.
- W3186375094 hasConcept C23895516 @default.
- W3186375094 hasConcept C33923547 @default.
- W3186375094 hasConcept C41008148 @default.
- W3186375094 hasConcept C50644808 @default.
- W3186375094 hasConcept C51989270 @default.
- W3186375094 hasConceptScore W3186375094C105795698 @default.
- W3186375094 hasConceptScore W3186375094C119128265 @default.
- W3186375094 hasConceptScore W3186375094C153180895 @default.
- W3186375094 hasConceptScore W3186375094C154945302 @default.
- W3186375094 hasConceptScore W3186375094C22679943 @default.
- W3186375094 hasConceptScore W3186375094C23895516 @default.
- W3186375094 hasConceptScore W3186375094C33923547 @default.
- W3186375094 hasConceptScore W3186375094C41008148 @default.
- W3186375094 hasConceptScore W3186375094C50644808 @default.
- W3186375094 hasConceptScore W3186375094C51989270 @default.
- W3186375094 hasIssue "10" @default.
- W3186375094 hasLocation W31863750941 @default.
- W3186375094 hasOpenAccess W3186375094 @default.
- W3186375094 hasPrimaryLocation W31863750941 @default.
- W3186375094 hasRelatedWork W1974988838 @default.
- W3186375094 hasRelatedWork W1979381110 @default.
- W3186375094 hasRelatedWork W2062105804 @default.
- W3186375094 hasRelatedWork W2155680698 @default.
- W3186375094 hasRelatedWork W2418244472 @default.
- W3186375094 hasRelatedWork W3100654574 @default.
- W3186375094 hasRelatedWork W3101214755 @default.
- W3186375094 hasRelatedWork W39355511 @default.
- W3186375094 hasRelatedWork W4311044804 @default.
- W3186375094 hasRelatedWork W790224052 @default.
- W3186375094 hasVolume "104" @default.
- W3186375094 isParatext "false" @default.
- W3186375094 isRetracted "false" @default.
- W3186375094 magId "3186375094" @default.
- W3186375094 workType "article" @default.