{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,9]],"date-time":"2026-05-09T15:57:16Z","timestamp":1778342236495,"version":"3.51.4"},"publisher-location":"Singapore","reference-count":26,"publisher":"Springer Nature Singapore","isbn-type":[{"value":"9789819665846","type":"print"},{"value":"9789819665853","type":"electronic"}],"license":[{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"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":[],"published-print":{"date-parts":[[2025]]},"DOI":"10.1007\/978-981-96-6585-3_3","type":"book-chapter","created":{"date-parts":[[2025,6,23]],"date-time":"2025-06-23T14:41:48Z","timestamp":1750689708000},"page":"29-43","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Machine Learning for\u00a0Raman Spectroscopy-Based Cyber-Marine Fish Biochemical Composition Analysis"],"prefix":"10.1007","author":[{"given":"Yun","family":"Zhou","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Gang","family":"Chen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Bing","family":"Xue","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mengjie","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jeremy S.","family":"Rooney","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kirill","family":"Lagutin","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Andrew","family":"MacKenzie","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Keith C.","family":"Gordon","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Daniel P.","family":"Killeen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,6,24]]},"reference":[{"key":"3_CR1","unstructured":"Species - Seafood NZ. https:\/\/www.seafood.co.nz\/species"},{"key":"3_CR2","doi-asserted-by":"publisher","unstructured":"Bjerrum, E.J., Glahder, M., Skov, T.: Data augmentation of spectral data for convolutional neural network (CNN) based deep chemometrics (2017). https:\/\/doi.org\/10.48550\/arXiv.1710.01927","DOI":"10.48550\/arXiv.1710.01927"},{"key":"3_CR3","doi-asserted-by":"publisher","first-page":"123475","DOI":"10.1016\/j.saa.2023.123475","volume":"305","author":"M Chang","year":"2024","unstructured":"Chang, M., He, C., Du, Y., Qiu, Y., Wang, L., Chen, H.: RaT: Raman Transformer for highly accurate melanoma detection with critical features visualization. Spectrochim. Acta Part A Mol. Biomol. Spectrosc. 305, 123475 (2024). https:\/\/doi.org\/10.1016\/j.saa.2023.123475","journal-title":"Spectrochim. Acta Part A Mol. Biomol. Spectrosc."},{"issue":"1","key":"3_CR4","doi-asserted-by":"publisher","first-page":"18","DOI":"10.1016\/j.tifs.2013.08.005","volume":"34","author":"JH Cheng","year":"2013","unstructured":"Cheng, J.H., Dai, Q., Sun, D.W., Zeng, X.A., Liu, D., Pu, H.B.: Applications of non-destructive spectroscopic techniques for fish quality and safety evaluation and inspection. Trends Food Sci. Technol. 34(1), 18\u201331 (2013). https:\/\/doi.org\/10.1016\/j.tifs.2013.08.005","journal-title":"Trends Food Sci. Technol."},{"key":"3_CR5","doi-asserted-by":"publisher","unstructured":"Chicco, D., Warrens, M.J., Jurman, G.: The coefficient of determination R-squared is more informative than SMAPE, MAE, MAPE, MSE and RMSE in regression analysis evaluation. PeerJ Comput. Sci. 7, e623 (2021). https:\/\/doi.org\/10.7717\/peerj-cs.623","DOI":"10.7717\/peerj-cs.623"},{"key":"3_CR6","doi-asserted-by":"publisher","first-page":"403","DOI":"10.1007\/978-3-642-36605-5_12","volume-title":"Handbook of Food Chemistry","author":"Z Coppes Petricorena","year":"2015","unstructured":"Coppes Petricorena, Z.: Chemical composition of fish and fishery products. In: Cheung, P., Mehta, B.M. (eds.) Handbook of Food Chemistry, pp. 403\u2013435. Springer, Heidelberg (2015). https:\/\/doi.org\/10.1007\/978-3-642-36605-5_12"},{"key":"3_CR7","doi-asserted-by":"publisher","unstructured":"Cui, C., Fearn, T.: Modern practical convolutional neural networks for multivariate regression: applications to NIR calibration. Chemometr. Intell. Lab. Syst. 182, 9\u201320 (2018). https:\/\/doi.org\/10.1016\/j.chemolab.2018.07.008","DOI":"10.1016\/j.chemolab.2018.07.008"},{"key":"3_CR8","doi-asserted-by":"publisher","unstructured":"Debus, B., Parastar, H., Harrington, P., Kirsanov, D.: Deep learning in analytical chemistry. TrAC Trends Anal. Chem. 145, 116459 (2021). https:\/\/doi.org\/10.1016\/j.trac.2021.116459","DOI":"10.1016\/j.trac.2021.116459"},{"key":"3_CR9","doi-asserted-by":"publisher","first-page":"96","DOI":"10.1016\/j.trac.2013.04.015","volume":"50","author":"J Engel","year":"2013","unstructured":"Engel, J., et al.: Breaking with trends in pre-processing? TrAC, Trends Anal. Chem. 50, 96\u2013106 (2013). https:\/\/doi.org\/10.1016\/j.trac.2013.04.015","journal-title":"TrAC, Trends Anal. Chem."},{"key":"3_CR10","doi-asserted-by":"publisher","first-page":"126812","DOI":"10.1016\/j.biortech.2022.126812","volume":"348","author":"W Gao","year":"2022","unstructured":"Gao, W., Zhou, L., Liu, S., Guan, Y., Gao, H., Hui, B.: Machine learning prediction of lignin content in poplar with Raman spectroscopy. Biores. Technol. 348, 126812 (2022). https:\/\/doi.org\/10.1016\/j.biortech.2022.126812","journal-title":"Biores. Technol."},{"key":"3_CR11","doi-asserted-by":"publisher","unstructured":"Hassoun, A., Karoui, R.: Quality evaluation of fish and other seafood by traditional and nondestructive instrumental methods: advantages and limitations. Crit. Rev. Food Sci. Nutr. 57(9), 1976\u20131998 (2017). https:\/\/doi.org\/10.1080\/10408398.2015.1047926","DOI":"10.1080\/10408398.2015.1047926"},{"issue":"2","key":"3_CR12","doi-asserted-by":"publisher","first-page":"241","DOI":"10.1177\/0003702819881762","volume":"74","author":"W He","year":"2020","unstructured":"He, W., Li, B., Yang, S.: High-frequency Raman analysis in biological tissues using dual-wavelength excitation Raman spectroscopy. Appl. Spectrosc. 74(2), 241\u2013244 (2020). https:\/\/doi.org\/10.1177\/0003702819881762","journal-title":"Appl. Spectrosc."},{"issue":"4","key":"3_CR13","doi-asserted-by":"publisher","first-page":"3647","DOI":"10.1111\/1541-4337.12968","volume":"21","author":"Y He","year":"2022","unstructured":"He, Y., Xu, W., Qu, M., Zhang, C., Wang, W., Cheng, F.: Recent advances in the application of Raman spectroscopy for fish quality and safety analysis. Compr. Rev. Food Sci. Food Saf. 21(4), 3647\u20133672 (2022). https:\/\/doi.org\/10.1111\/1541-4337.12968","journal-title":"Compr. Rev. Food Sci. Food Saf."},{"key":"3_CR14","doi-asserted-by":"publisher","unstructured":"Jernelv, I.L., Hjelme, D.R., Matsuura, Y., Aksnes, A.: Convolutional neural networks for classification and regression analysis of one-dimensional spectral data (2020). https:\/\/doi.org\/10.48550\/arXiv.2005.07530","DOI":"10.48550\/arXiv.2005.07530"},{"key":"3_CR15","doi-asserted-by":"publisher","unstructured":"Liu, B., Liu, K., Qi, X., Zhang, W., Li, B.: Classification of deep-sea cold seep bacteria by transformer combined with Raman spectroscopy. Sci. Rep. 13, 3240 (2023). https:\/\/doi.org\/10.1038\/s41598-023-28730-w","DOI":"10.1038\/s41598-023-28730-w"},{"key":"3_CR16","doi-asserted-by":"publisher","unstructured":"Mishra, P., Passos, D.: Multi-output 1-dimensional convolutional neural networks for simultaneous prediction of different traits of fruit based on near-infrared spectroscopy. Postharvest Biol. Technol. 183, 111741 (2022). https:\/\/doi.org\/10.1016\/j.postharvbio.2021.111741","DOI":"10.1016\/j.postharvbio.2021.111741"},{"key":"3_CR17","doi-asserted-by":"publisher","first-page":"104520","DOI":"10.1016\/j.chemolab.2022.104520","volume":"223","author":"D Passos","year":"2022","unstructured":"Passos, D., Mishra, P.: A tutorial on automatic hyperparameter tuning of deep spectral modelling for regression and classification tasks. Chemom. Intell. Lab. Syst. 223, 104520 (2022). https:\/\/doi.org\/10.1016\/j.chemolab.2022.104520","journal-title":"Chemom. Intell. Lab. Syst."},{"issue":"9","key":"3_CR18","doi-asserted-by":"publisher","first-page":"1580","DOI":"10.1002\/jrs.6402","volume":"53","author":"M Poth","year":"2022","unstructured":"Poth, M., Magill, G., Filgertshofer, A., Popp, O., Gro\u00dfkopf, T.: Extensive evaluation of machine learning models and data preprocessings for Raman modeling in bioprocessing. J. Raman Spectrosc. 53(9), 1580\u20131591 (2022). https:\/\/doi.org\/10.1002\/jrs.6402","journal-title":"J. Raman Spectrosc."},{"key":"3_CR19","doi-asserted-by":"publisher","unstructured":"Ren, P., Zhou, R.g., Li, Y., Xiong, S.: One-dimensional multi-head attention mechanism neural network for species blood and semen identification from Raman spectroscopy. Preprint, SSRN (2023). https:\/\/doi.org\/10.2139\/ssrn.4333620","DOI":"10.2139\/ssrn.4333620"},{"key":"3_CR20","doi-asserted-by":"publisher","unstructured":"Ren, P., Zhou, R.G., Li, Y., Xiong, S., Han, B.: Raman ConvMSANet: a high-accuracy neural network for Raman spectroscopy blood and semen identification. ACS Omega 8(33), 30421\u201330431 (2023). https:\/\/doi.org\/10.1021\/acsomega.3c03572","DOI":"10.1021\/acsomega.3c03572"},{"key":"3_CR21","doi-asserted-by":"publisher","unstructured":"Robinson, D., et al.: Genetic algorithm for feature and latent variable selection for nutrient assessment in horticultural products. In: 2021 IEEE Congress on Evolutionary Computation (CEC), pp. 272\u2013279 (2021). https:\/\/doi.org\/10.1109\/CEC45853.2021.9504794","DOI":"10.1109\/CEC45853.2021.9504794"},{"key":"3_CR22","doi-asserted-by":"publisher","unstructured":"Rohman, A., Putri, A.R., Irnawati, Windarsih, A., Nisa, K., Lestari, L.A.: The employment of analytical techniques and chemometrics for authentication of fish oils: a review. Food Control 124, 107864 (2021). https:\/\/doi.org\/10.1016\/j.foodcont.2021.107864","DOI":"10.1016\/j.foodcont.2021.107864"},{"issue":"4","key":"3_CR23","doi-asserted-by":"publisher","first-page":"466","DOI":"10.1111\/1541-4337.12138","volume":"14","author":"JL Xu","year":"2015","unstructured":"Xu, J.L., Riccioli, C., Sun, D.W.: An overview on nondestructive spectroscopic techniques for lipid and lipid oxidation analysis in fish and fish products. Compr. Rev. Food Sc. Food Saf. 14(4), 466\u2013477 (2015). https:\/\/doi.org\/10.1111\/1541-4337.12138","journal-title":"Compr. Rev. Food Sc. Food Saf."},{"key":"3_CR24","doi-asserted-by":"publisher","unstructured":"Xuesong, H., Pu, C., Jingyan, L., Yupeng, X., Dan, L., Xiaoli, C.: Commentary on the review articles of spectroscopy technology combined with chemometrics in the last three years. Appl. Spectrosc. Rev. 59(4), 423\u2013482 (2024). https:\/\/doi.org\/10.1080\/05704928.2023.2204946","DOI":"10.1080\/05704928.2023.2204946"},{"key":"3_CR25","doi-asserted-by":"publisher","first-page":"6","DOI":"10.1016\/j.aca.2019.06.012","volume":"1081","author":"J Yang","year":"2019","unstructured":"Yang, J., Xu, J., Zhang, X., Wu, C., Lin, T., Ying, Y.: Deep learning for vibrational spectral analysis: recent progress and a practical guide. Anal. Chim. Acta 1081, 6\u201317 (2019). https:\/\/doi.org\/10.1016\/j.aca.2019.06.012","journal-title":"Anal. Chim. Acta"},{"key":"3_CR26","unstructured":"Zhou, Y.: Yun-K\/Supplementary-Material-Machine-Learning-for-Raman-Spectroscopy (2024). https:\/\/github.com\/Yun-K\/Supplementary-Material-Machine-Learning-for-Raman-Spectroscopy"}],"container-title":["Lecture Notes in Computer Science","Neural Information Processing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-96-6585-3_3","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,23]],"date-time":"2025-06-23T14:41:51Z","timestamp":1750689711000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-96-6585-3_3"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025]]},"ISBN":["9789819665846","9789819665853"],"references-count":26,"URL":"https:\/\/doi.org\/10.1007\/978-981-96-6585-3_3","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025]]},"assertion":[{"value":"24 June 2025","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICONIP","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Neural Information Processing","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Auckland","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"New Zealand","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2024","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2 December 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"6 December 2024","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"31","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"iconip2024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/iconip2024.org","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}