E-viri
Recenzirano
-
Wang, Zhentao; Chen, Guoqing; Jiang, Rui; Zhao, Minyi; Lin, Tenghui; Wang, Ruidong; Wang, Jinfeng
Journal of food composition and analysis, September 2024, 2024-09-00, Letnik: 133Journal Article
Industrial paraffin contamination addition to rice is considered a highly observed food safety issue with adverse effects on human health. Therefore, the ability to identify the level of industrial paraffin contamination in rice has become critical. In this study, the industrial paraffin contamination levels (IPCL) in rice were identified by using hyperspectral imaging (HSI) alongside deep learning classification models. We established a deep learning network, SC-HybridSN, for rapid discrimination of rice IPCL using spatial and spectral features. An ablation experiment was conducted to investigate the roles of each module, and comparisons were made with conventional models (PLS-DA, SVM) under various training conditions. The extracted results clearly showed that the SC-HybridSN model exhibited superior classification performance under conditions involving the single data set, multi-source data set, and small sample training. These results suggest that the proposed model has the capability to swiftly and non-destructively identify rice IPCL. Display omitted ●Demonstration of the feasibility of utilizing HSI and deep learning for the identification of IPCL in rice.●SC-HybridSN was constructed to develop IPCL classification model.●The proposed method has achieved an accuracy rate of 97.38–100 % in various modeling scenarios.
Avtor
![loading ... loading ...](themes/default/img/ajax-loading.gif)
Vnos na polico
Trajna povezava
- URL:
Faktor vpliva
Dostop do baze podatkov JCR je dovoljen samo uporabnikom iz Slovenije. Vaš trenutni IP-naslov ni na seznamu dovoljenih za dostop, zato je potrebna avtentikacija z ustreznim računom AAI.
Leto | Faktor vpliva | Izdaja | Kategorija | Razvrstitev | ||||
---|---|---|---|---|---|---|---|---|
JCR | SNIP | JCR | SNIP | JCR | SNIP | JCR | SNIP |
Baze podatkov, v katerih je revija indeksirana
Ime baze podatkov | Področje | Leto |
---|
Povezave do osebnih bibliografij avtorjev | Povezave do podatkov o raziskovalcih v sistemu SICRIS |
---|
Vir: Osebne bibliografije
in: SICRIS
To gradivo vam je dostopno v celotnem besedilu. Če kljub temu želite naročiti gradivo, kliknite gumb Nadaljuj.