Determination of Freshness of Mackerel (Scomber japonicus) Using Shortwave Infrared Hyperspectral Imaging
DC Field | Value | Language |
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dc.contributor.author | Cho, Jeong-Seok | - |
dc.contributor.author | Choi, Byungho | - |
dc.contributor.author | Lim, Jeong-Ho | - |
dc.contributor.author | Choi, Jeong Hee | - |
dc.contributor.author | Yun, Dae-Yong | - |
dc.contributor.author | Park, Seul-Ki | - |
dc.contributor.author | Lee, Gyuseok | - |
dc.contributor.author | Park, Kee-Jai | - |
dc.contributor.author | Lee, Jihyun | - |
dc.date.accessioned | 2023-08-25T05:40:43Z | - |
dc.date.available | 2023-08-25T05:40:43Z | - |
dc.date.issued | 2023-06 | - |
dc.identifier.issn | 2304-8158 | - |
dc.identifier.issn | 2304-8158 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/cau/handle/2019.sw.cau/67412 | - |
dc.description.abstract | Shortwave infrared (SWIR) hyperspectral imaging was applied to classify the freshness of mackerels. Total volatile basic nitrogen (TVB-N) and acid values, as chemical compounds related to the freshness of mackerels, were also analyzed to develop a prediction model of freshness by combining them with hyperspectral data. Fresh mackerels were divided into three groups according to storage periods (0, 24, and 48 h), and hyperspectral data were collected from the eyes and whole body, separately. The optimized classification accuracies were 81.68% using raw data from eyes and 90.14% using body data by multiple scatter correction (MSC) pretreatment. The prediction accuracy of TVB-N was 90.76%, and the acid value was 83.76%. These results indicate that hyperspectral imaging, as a nondestructive method, can be used to verify the freshness of mackerels and predict the chemical compounds related to the freshness. | - |
dc.language | 영어 | - |
dc.language.iso | ENG | - |
dc.publisher | MDPI | - |
dc.title | Determination of Freshness of Mackerel (Scomber japonicus) Using Shortwave Infrared Hyperspectral Imaging | - |
dc.type | Article | - |
dc.identifier.doi | 10.3390/foods12122305 | - |
dc.identifier.bibliographicCitation | FOODS, v.12, no.12 | - |
dc.description.isOpenAccess | Y | - |
dc.identifier.wosid | 001015297400001 | - |
dc.identifier.scopusid | 2-s2.0-85163857737 | - |
dc.citation.number | 12 | - |
dc.citation.title | FOODS | - |
dc.citation.volume | 12 | - |
dc.type.docType | Article | - |
dc.publisher.location | 스위스 | - |
dc.subject.keywordAuthor | mackerel | - |
dc.subject.keywordAuthor | freshness | - |
dc.subject.keywordAuthor | TVB-N | - |
dc.subject.keywordAuthor | hyperspectral imaging | - |
dc.subject.keywordAuthor | chemometrics | - |
dc.subject.keywordPlus | ACID REACTIVE SUBSTANCES | - |
dc.subject.keywordPlus | RAPID PREDICTION | - |
dc.subject.keywordPlus | MOISTURE-CONTENT | - |
dc.subject.keywordPlus | TVB-N | - |
dc.subject.keywordPlus | QUALITY | - |
dc.subject.keywordPlus | FISH | - |
dc.subject.keywordPlus | FILLETS | - |
dc.subject.keywordPlus | CLASSIFICATION | - |
dc.subject.keywordPlus | SAFETY | - |
dc.subject.keywordPlus | SPECTROSCOPY | - |
dc.relation.journalResearchArea | Food Science & Technology | - |
dc.relation.journalWebOfScienceCategory | Food Science & Technology | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
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