{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,24]],"date-time":"2025-03-24T09:16:44Z","timestamp":1742807804407,"version":"3.33.0"},"reference-count":27,"publisher":"Springer Science and Business Media LLC","issue":"8","license":[{"start":{"date-parts":[[2024,9,9]],"date-time":"2024-09-09T00:00:00Z","timestamp":1725840000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,9,9]],"date-time":"2024-09-09T00:00:00Z","timestamp":1725840000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Int. J. Fuzzy Syst."],"published-print":{"date-parts":[[2024,11]]},"DOI":"10.1007\/s40815-024-01726-y","type":"journal-article","created":{"date-parts":[[2024,9,9]],"date-time":"2024-09-09T10:05:33Z","timestamp":1725876333000},"page":"2767-2782","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["A Multiscale Interactive Attention Network for Recognizing Camellia Seed Oil with Fuzzy Features"],"prefix":"10.1007","volume":"26","author":[{"given":"Ziming","family":"Li","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yuxin","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Peirui","family":"Zhao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hongai","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ninghua","family":"Yu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jiarong","family":"She","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wenhua","family":"Zhou","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,9,9]]},"reference":[{"issue":"7","key":"1726_CR1","doi-asserted-by":"publisher","first-page":"1746","DOI":"10.3390\/molecules23071746","volume":"23","author":"M Maszewska","year":"2018","unstructured":"Maszewska, M., Florowska, A., D\u0142u\u017cewska, E., Wroniak, M., Marciniak-Lukasiak, K., \u017bbikowska, A.: Oxidative stability of selected edible oils. Molecules. 23(7), 1746 (2018). https:\/\/doi.org\/10.3390\/molecules23071746","journal-title":"Molecules."},{"issue":"1","key":"1726_CR2","doi-asserted-by":"publisher","first-page":"199","DOI":"10.3390\/foods12010199","volume":"12","author":"M Momtaz","year":"2023","unstructured":"Momtaz, M., Bubli, S.Y., Khan, M.S.: Mechanisms and health aspects of food adulteration: a comprehensive review. Foods 12(1), 199 (2023)","journal-title":"Foods"},{"issue":"9\u201310","key":"1726_CR3","doi-asserted-by":"publisher","first-page":"628","DOI":"10.1002\/1438-9312(200210)104:9\/10<628::AID-EJLT628>3.0.CO;2-1","volume":"104","author":"A Kiritsakis","year":"2002","unstructured":"Kiritsakis, A., Kanavouras, A., Kiritsakis, K.: Chemical analysis, quality control and packaging issues of olive oil. Eur. J. Lipid Sci. Technol. 104(9\u201310), 628\u2013638 (2002). https:\/\/doi.org\/10.1002\/1438-9312(200210)104:9\/10<628::AID-EJLT628>3.0.CO;2-1","journal-title":"Eur. J. Lipid Sci. Technol."},{"issue":"7","key":"1726_CR4","doi-asserted-by":"publisher","first-page":"873","DOI":"10.1080\/10408398.2021.1956424","volume":"63","author":"A Sudhakar","year":"2023","unstructured":"Sudhakar, A., Chakraborty, S.K., Mahanti, N.K., Varghese, C.: Advanced techniques in edible oil authentication: a systematic review and critical analysis. Crit. Rev. Food Sci. Nutr. 63(7), 873\u2013901 (2023). https:\/\/doi.org\/10.1080\/10408398.2021.1956424","journal-title":"Crit. Rev. Food Sci. Nutr."},{"key":"1726_CR5","doi-asserted-by":"publisher","first-page":"61","DOI":"10.1016\/j.fuproc.2015.08.011","volume":"139","author":"V J\u00f3zsa","year":"2015","unstructured":"J\u00f3zsa, V., Kun-Balog, A.: Spectroscopic analysis of crude rapeseed oil flame. Fuel Process. Technol. 139, 61\u201366 (2015). https:\/\/doi.org\/10.1016\/j.fuproc.2015.08.011","journal-title":"Fuel Process. Technol."},{"key":"1726_CR6","doi-asserted-by":"publisher","DOI":"10.1007\/s13197-021-05057-w","author":"M Roy","year":"2022","unstructured":"Roy, M., Yadav, B.K.: Electronic nose for detection of food adulteration: a review. J. Food Sci. Technol. (2022). https:\/\/doi.org\/10.1007\/s13197-021-05057-w","journal-title":"J. Food Sci. Technol."},{"key":"1726_CR7","doi-asserted-by":"publisher","first-page":"1984","DOI":"10.1016\/j.foodchem.2021.131984","volume":"377","author":"R Ni","year":"2022","unstructured":"Ni, R., Yan, H., Tian, H., Zhan, P., Zhang, Y.: Characterization of key odorants in fried red and green huajiao (Zanthoxylum bungeanum maxim. and Zanthoxylum schinifolium sieb. et Zucc.) oils. Food Chem. 377, 1984 (2022). https:\/\/doi.org\/10.1016\/j.foodchem.2021.131984","journal-title":"Food Chem."},{"issue":"20","key":"1726_CR8","doi-asserted-by":"publisher","first-page":"5638","DOI":"10.3390\/su11205638","volume":"11","author":"A Sagan","year":"2019","unstructured":"Sagan, A., Blicharz-Kania, A., Szmigielski, M., Andrejko, D., Sobczak, P., Zawi\u015blak, K., Starek, A.: Assessment of the properties of rapeseed oil enriched with oils characterized by high content of \u03b1-linolenic acid. Sustainability 11(20), 5638 (2019). https:\/\/doi.org\/10.3390\/su11205638","journal-title":"Sustainability"},{"key":"1726_CR9","doi-asserted-by":"publisher","DOI":"10.1155\/2019\/3163204","author":"T Chen","year":"2019","unstructured":"Chen, T., Qi, X., Chen, M., Chen, B.: Gas chromatography-ion mobility spectrometry detection of odor fingerprint as markers of rapeseed oil refined grade. J. Anal. Methods Chem. (2019). https:\/\/doi.org\/10.1155\/2019\/3163204","journal-title":"J. Anal. Methods Chem."},{"issue":"6","key":"1726_CR10","doi-asserted-by":"publisher","first-page":"706","DOI":"10.1002\/jrs.4891","volume":"47","author":"H Ali","year":"2016","unstructured":"Ali, H., Nawaz, H., Saleem, M., Nurjis, F., Ahmed, M.: Qualitative analysis of desi ghee, edible oils, and spreads using Raman spectroscopy. J. Raman Spectrosc. 47(6), 706\u2013711 (2016). https:\/\/doi.org\/10.1002\/jrs.4891","journal-title":"J. Raman Spectrosc."},{"key":"1726_CR11","doi-asserted-by":"publisher","first-page":"106464","DOI":"10.1016\/j.cmpb.2021.106464","volume":"212","author":"Y Jiao","year":"2021","unstructured":"Jiao, Y., Yuan, J., Qiang, Y., Fei, S.: Deep embeddings and logistic regression for rapid active learning in histopathological images. Comput. Methods Programs Biomed. 212, 106464 (2021). https:\/\/doi.org\/10.1016\/j.cmpb.2021.106464","journal-title":"Comput. Methods Programs Biomed."},{"issue":"8","key":"1726_CR12","doi-asserted-by":"publisher","first-page":"1295","DOI":"10.3390\/electronics9081295","volume":"9","author":"M Ahmed","year":"2020","unstructured":"Ahmed, M., Seraj, R., Islam, S.M.S.: The k-means algorithm: a comprehensive survey and performance evaluation. Electronics 9(8), 1295 (2020). https:\/\/doi.org\/10.3390\/electronics9081295","journal-title":"Electronics"},{"key":"1726_CR13","doi-asserted-by":"publisher","first-page":"119136","DOI":"10.1016\/j.ins.2023.119136","volume":"642","author":"H Wang","year":"2023","unstructured":"Wang, H., Li, G., Wang, Z.: Fast SVM classifier for large-scale classification problems. Inf. Sci. 642, 119136 (2023). https:\/\/doi.org\/10.1016\/j.ins.2023.119136","journal-title":"Inf. Sci."},{"key":"1726_CR14","doi-asserted-by":"publisher","first-page":"110761","DOI":"10.1016\/j.knosys.2023.110761","volume":"277","author":"Q Li","year":"2023","unstructured":"Li, Q., Zhao, S., Zhao, S., Wen, J.: Logistic regression matching pursuit algorithm for text classification. Knowl.-Based Syst. 277, 110761 (2023). https:\/\/doi.org\/10.1016\/j.knosys.2023.110761","journal-title":"Knowl.-Based Syst."},{"key":"1726_CR15","doi-asserted-by":"publisher","first-page":"110181","DOI":"10.1016\/j.asoc.2023.110181","volume":"138","author":"G Singh","year":"2023","unstructured":"Singh, G., Pal, Y., Dahiya, A.K.: Classification of power quality disturbances using linear discriminant analysis. Appl. Soft Comput. 138, 110181 (2023). https:\/\/doi.org\/10.1016\/j.asoc.2023.110181","journal-title":"Appl. Soft Comput."},{"key":"1726_CR16","doi-asserted-by":"publisher","unstructured":"Kanna, R, R., Ulagamuthalvi, V.: A Novel Detection on Wheat Disease through CL and RGB Filters by LDA and QDA[C]. 2023 2nd International Conference on Smart Technologies and Systems for Next Generation Computing (ICSTSN). 1\u20135(2023). https:\/\/doi.org\/10.1109\/ICSTSN57873.2023.10151536.","DOI":"10.1109\/ICSTSN57873.2023.10151536"},{"key":"1726_CR17","doi-asserted-by":"publisher","first-page":"106749","DOI":"10.1016\/j.compag.2022.106749","volume":"194","author":"W Zhang","year":"2022","unstructured":"Zhang, W., Zhou, G., Chen, A., Hu, Y.: Deep multi-scale dual-channel convolutional neural network for Internet of Things apple disease detection. Comput. Electron. Agric. 194, 106749 (2022). https:\/\/doi.org\/10.1016\/j.compag.2022.106749","journal-title":"Comput. Electron. Agric."},{"key":"1726_CR18","doi-asserted-by":"publisher","unstructured":"Fang, S., Wang, Y., Zhou, G., Chen, A., Cai, W., Wang, Q., Hu, Y., Li, L.: Multi-channel feature fusion networks with hard coordinate attention mechanism for maize disease identification under complex backgrounds. Computers and Electronics in Agriculture. 203, 107486, ISSN 0168-1699. https:\/\/doi.org\/10.1016\/j.compag.2022.107486.","DOI":"10.1016\/j.compag.2022.107486"},{"issue":"10","key":"1726_CR19","doi-asserted-by":"publisher","first-page":"1636","DOI":"10.1016\/j.radphyschem.2007.01.005","volume":"76","author":"E Nuray","year":"2022","unstructured":"Nuray, E., \u00d6zkan, \u00d6.: The changes of fatty acid and amino acid compositions in sea bream (Sparus aurata) during irradiation process. Radiat. Phys. Chem. 76(10), 1636\u20131641 (2022). https:\/\/doi.org\/10.1016\/j.radphyschem.2007.01.005","journal-title":"Radiat. Phys. Chem."},{"key":"1726_CR20","doi-asserted-by":"publisher","first-page":"112565","DOI":"10.1016\/j.foodres.2023.112565","volume":"165","author":"C Wang","year":"2023","unstructured":"Wang, C., Li, Z., Wu, W.: Understanding fatty acid composition and lipid profile of rapeseed oil in response to nitrogen management strategies. Food Res. Int. 165, 112565 (2023). https:\/\/doi.org\/10.1016\/j.foodres.2023.112565","journal-title":"Food Res. Int."},{"key":"1726_CR21","doi-asserted-by":"publisher","first-page":"117233","DOI":"10.1016\/j.eswa.2022.117233","volume":"202","author":"J Mushava","year":"2022","unstructured":"Mushava, J., Murray, M.: A novel XGBoost extension for credit scoring class-imbalanced data combining a generalized extreme value link and a modified focal loss function. Expert Syst. Appl. 202, 117233 (2022). https:\/\/doi.org\/10.1016\/j.eswa.2022.117233","journal-title":"Expert Syst. Appl."},{"key":"1726_CR22","doi-asserted-by":"publisher","unstructured":"Durmus, H., Kirci, M., Gunes, E.O.: Disease detection on the leaves of the tomato plants by using deep learning. In: 2017 6th International Conference on Agro-Geoinformatics. 1\u20135(2017). https:\/\/doi.org\/10.1109\/Agro-Geoinformatics.2017.8047016","DOI":"10.1109\/Agro-Geoinformatics.2017.8047016"},{"key":"1726_CR23","doi-asserted-by":"publisher","first-page":"107605","DOI":"10.1016\/j.compag.2022.107605","volume":"205","author":"Y Zhang","year":"2023","unstructured":"Zhang, Y., Huang, S., Zhou, G., Hu, Y., Li, L.: Identification of tomato leaf diseases based on multi-channel automatic orientation recurrent attention network. Comput. Electron. Agric. 205, 107605 (2023). https:\/\/doi.org\/10.1016\/j.compag.2022.107605","journal-title":"Comput. Electron. Agric."},{"key":"1726_CR24","doi-asserted-by":"publisher","DOI":"10.1111\/mice.13113","author":"L Sun","year":"2023","unstructured":"Sun, L., Yang, Y., Zhou, G., Chen, A., Zhang, Y., Cai, W., Li, L.: An integration-competition network for bridge crack segmentation under complex scenes. Comput. Aided Civil and Infrastructure Eng. (2023). https:\/\/doi.org\/10.1111\/mice.13113","journal-title":"Comput. Aided Civil and Infrastructure Eng."},{"key":"1726_CR25","doi-asserted-by":"publisher","first-page":"106341","DOI":"10.1016\/j.engappai.2023.106341","volume":"123","author":"C Cai","year":"2023","unstructured":"Cai, C., Wang, Q., Cai, W., Yang, Y., Hu, Y., Li, L., Wang, Y., Zhou, G.: Identification of grape leaf diseases based on VN-BWT and Siamese DWOAM-DRNet. Eng. Appl. Artif. Intell. 123, 106341 (2023). https:\/\/doi.org\/10.1016\/j.engappai.2023.106341","journal-title":"Eng. Appl. Artif. Intell."},{"key":"1726_CR26","doi-asserted-by":"publisher","first-page":"105835","DOI":"10.1016\/j.engappai.2023.105835","volume":"119","author":"M Yang","year":"2023","unstructured":"Yang, M., Wu, P., Feng, H.: MemSeg: A semi-supervised method for image surface defect detection using differences and commonalities. Eng. Appl. Artif. Intell. 119, 105835 (2023). https:\/\/doi.org\/10.1016\/j.engappai.2023.105835","journal-title":"Eng. Appl. Artif. Intell."},{"key":"1726_CR27","doi-asserted-by":"publisher","first-page":"137306","DOI":"10.1016\/j.foodchem.2023.137306","volume":"433","author":"X Dou","year":"2024","unstructured":"Dou, X., Wang, X., Ma, F., Yu, L., Mao, J., Jiang, J., Li, P.: Geographical origin identification of camellia oil based on fatty acid profiles combined with one-class classification. Food Chem. 433, 137306 (2024). https:\/\/doi.org\/10.1016\/j.foodchem.2023.137306","journal-title":"Food Chem."}],"container-title":["International Journal of Fuzzy Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s40815-024-01726-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s40815-024-01726-y\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s40815-024-01726-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,1,23]],"date-time":"2025-01-23T14:08:33Z","timestamp":1737641313000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s40815-024-01726-y"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,9,9]]},"references-count":27,"journal-issue":{"issue":"8","published-print":{"date-parts":[[2024,11]]}},"alternative-id":["1726"],"URL":"https:\/\/doi.org\/10.1007\/s40815-024-01726-y","relation":{},"ISSN":["1562-2479","2199-3211"],"issn-type":[{"type":"print","value":"1562-2479"},{"type":"electronic","value":"2199-3211"}],"subject":[],"published":{"date-parts":[[2024,9,9]]},"assertion":[{"value":"28 November 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"16 February 2024","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"1 March 2024","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"9 September 2024","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare that they have no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interest"}}]}}