{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,23]],"date-time":"2026-09-23T19:14:33Z","timestamp":1790190873947,"version":"4.1.0"},"reference-count":39,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"funder":[{"DOI":"10.13039\/501100018559","name":"Shenyang Young and Middle-aged Science and Technology Innovation Talent Support Program","doi-asserted-by":"publisher","award":["RC210401"],"award-info":[{"award-number":["RC210401"]}],"id":[{"id":"10.13039\/501100018559","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012131","name":"Department of Science and Technology of Liaoning Province","doi-asserted-by":"publisher","award":["2023JH2\/101300204"],"award-info":[{"award-number":["2023JH2\/101300204"]}],"id":[{"id":"10.13039\/501100012131","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012131","name":"Department of Science and Technology of Liaoning Province","doi-asserted-by":"publisher","award":["LJ212410143091"],"award-info":[{"award-number":["LJ212410143091"]}],"id":[{"id":"10.13039\/501100012131","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62171295"],"award-info":[{"award-number":["62171295"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Computers and Electronics in Agriculture"],"published-print":{"date-parts":[[2026,9]]},"DOI":"10.1016\/j.compag.2026.112059","type":"journal-article","created":{"date-parts":[[2026,6,20]],"date-time":"2026-06-20T13:29:50Z","timestamp":1781962190000},"page":"112059","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":1,"special_numbering":"C","title":["Supervised contrastive regression learning for predicting soluble solids content of blueberries using hyperspectral imaging"],"prefix":"10.1016","volume":"251","author":[{"given":"Jinen","family":"Zhang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5331-7664","authenticated-orcid":false,"given":"Zhaokui","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wei","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hongli","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chuanyun","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhuoqun","family":"Fang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"issue":"2","key":"10.1016\/j.compag.2026.112059_b1","doi-asserted-by":"crossref","first-page":"137","DOI":"10.1080\/01140671.2001.9514171","article-title":"Effect of fruit maturation on sugar and organic acid composition in two blueberries (vaccinium arctostaphylos and v. myrtillus) native to Turkey","volume":"29","author":"Ayaz","year":"2001","journal-title":"N. Z. J. Crop. Hortic. Sci."},{"issue":"3","key":"10.1016\/j.compag.2026.112059_b2","doi-asserted-by":"crossref","first-page":"273","DOI":"10.1023\/A:1022627411411","article-title":"Support-vector networks","volume":"20","author":"Cortes","year":"1995","journal-title":"Mach. Learn."},{"key":"10.1016\/j.compag.2026.112059_b3","doi-asserted-by":"crossref","DOI":"10.1016\/j.tifs.2024.104344","article-title":"Deep learning in food authenticity: Recent advances and future trends","volume":"144","author":"Deng","year":"2024","journal-title":"Trends Food Sci. Technol."},{"issue":"1","key":"10.1016\/j.compag.2026.112059_b4","doi-asserted-by":"crossref","first-page":"98","DOI":"10.1016\/j.jfoodeng.2006.10.016","article-title":"Hyperspectral imaging for nondestructive determination of some quality attributes for strawberry","volume":"81","author":"ElMasry","year":"2007","journal-title":"J. Food Eng."},{"key":"10.1016\/j.compag.2026.112059_b5","doi-asserted-by":"crossref","DOI":"10.1007\/s12161-014-0079-1","article-title":"Prediction of soluble solids content and firmness of pears using hyperspectral reflectance imaging","volume":"8","author":"Fan","year":"2015","journal-title":"Food Anal. Methods"},{"key":"10.1016\/j.compag.2026.112059_b6","doi-asserted-by":"crossref","first-page":"79","DOI":"10.1016\/j.postharvbio.2019.02.001","article-title":"Long-term evaluation of soluble solids content of apples with biological variability by using near-infrared spectroscopy and calibration transfer method","volume":"151","author":"Fan","year":"2019","journal-title":"Postharvest Biology Technol."},{"key":"10.1016\/j.compag.2026.112059_b7","series-title":"Understanding the difficulty of training deep feedforward neural networks","first-page":"249","author":"Glorot","year":"2010"},{"key":"10.1016\/j.compag.2026.112059_b8","series-title":"Delving deep into rectifiers: Surpassing human-level performance on ImageNet classification","author":"He","year":"2015"},{"key":"10.1016\/j.compag.2026.112059_b9","series-title":"Deep residual learning for image recognition","first-page":"770","author":"He","year":"2016"},{"key":"10.1016\/j.compag.2026.112059_b10","doi-asserted-by":"crossref","first-page":"91","DOI":"10.1163\/22238980-bja10073","article-title":"Characterization of fruit quality attributes and nutritional composition of ten blueberry cultivars","volume":"70","author":"Hirzel","year":"2023","journal-title":"Isr. J. Plant Sci."},{"key":"10.1016\/j.compag.2026.112059_b11","first-page":"18661","article-title":"Supervised contrastive learning","volume":"33","author":"Khosla","year":"2020","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"10.1016\/j.compag.2026.112059_b12","doi-asserted-by":"crossref","DOI":"10.1016\/j.jfoodeng.2022.111254","article-title":"Application of ensemble neural-network method to integrated sugar content prediction model for citrus fruit using vis\/NIR spectroscopy","volume":"338","author":"Kim","year":"2023","journal-title":"J. Food Eng."},{"issue":"1","key":"10.1016\/j.compag.2026.112059_b13","doi-asserted-by":"crossref","first-page":"227","DOI":"10.1016\/j.aca.2011.05.048","article-title":"Characterisation of heavy oils using near-infrared spectroscopy: Optimisation of pre-processing methods and variable selection","volume":"705","author":"Laxalde","year":"2011","journal-title":"Anal. Chim. Acta"},{"key":"10.1016\/j.compag.2026.112059_b14","series-title":"A quantitative spectra analysis framework combining mixup and band attention for predicting soluble solid content of blueberries","first-page":"366","author":"Li","year":"2023"},{"issue":"86","key":"10.1016\/j.compag.2026.112059_b15","first-page":"2579","article-title":"Visualizing data using t-SNE","volume":"9","author":"van der Maaten","year":"2008","journal-title":"J. Mach. Learn. Res."},{"key":"10.1016\/j.compag.2026.112059_b16","doi-asserted-by":"crossref","DOI":"10.1016\/j.compag.2022.106945","article-title":"SpectraNet-53: A deep residual learning architecture for predicting soluble solids content with VIS-NIR spectroscopy","volume":"197","author":"Martins","year":"2022","journal-title":"Comput. Electron. Agric."},{"key":"10.1016\/j.compag.2026.112059_b17","doi-asserted-by":"crossref","DOI":"10.1016\/j.postharvbio.2023.112281","article-title":"Estimation of soluble solids content and fruit temperature in \u2019rocha\u2019 pear using vis-NIR spectroscopy and the SpectraNet\u201332 deep learning architecture","volume":"199","author":"Martins","year":"2023","journal-title":"Postharvest Biology Technol."},{"key":"10.1016\/j.compag.2026.112059_b18","first-page":"2825","article-title":"Scikit-learn: Machine learning in python","volume":"12","author":"Pedregosa","year":"2011","journal-title":"J. Mach. Learn. Res."},{"key":"10.1016\/j.compag.2026.112059_b19","doi-asserted-by":"crossref","DOI":"10.1016\/j.jfca.2023.105585","article-title":"Rapid and non-destructive determination of soluble solid content of crown pear by visible\/near-infrared spectroscopy with deep learning regression","volume":"123","author":"Qi","year":"2023","journal-title":"J. Food Comp. Anal."},{"key":"10.1016\/j.compag.2026.112059_b20","series-title":"Finding a \u201dkneedle\u201d in a haystack: Detecting knee points in system behavior","first-page":"166","author":"Satopa","year":"2011"},{"key":"10.1016\/j.compag.2026.112059_b21","doi-asserted-by":"crossref","first-page":"1547","DOI":"10.1080\/00032719.2020.1812622","article-title":"Assessment of strawberry ripeness using hyperspectral imaging","volume":"54","author":"Shao","year":"2020","journal-title":"Anal. Lett."},{"key":"10.1016\/j.compag.2026.112059_b22","doi-asserted-by":"crossref","first-page":"293","DOI":"10.1023\/A:1018628609742","article-title":"Least squares support vector machine classifiers","volume":"9","author":"Suykens","year":"1999","journal-title":"Neural Process. Lett."},{"key":"10.1016\/j.compag.2026.112059_b23","series-title":"Inception-v4, inception-ResNet and the impact of residual connections on learning","first-page":"4278","author":"Szegedy","year":"2017"},{"key":"10.1016\/j.compag.2026.112059_b24","doi-asserted-by":"crossref","first-page":"110","DOI":"10.1016\/j.biosystemseng.2019.04.012","article-title":"A multi-region combined model for non-destructive prediction of soluble solids content in apple, based on brightness grade segmentation of hyperspectral imaging","volume":"183","author":"Tian","year":"2019","journal-title":"Biosyst. Eng."},{"key":"10.1016\/j.compag.2026.112059_b25","doi-asserted-by":"crossref","DOI":"10.1016\/j.infrared.2019.103099","article-title":"Modeling for SSC and firmness detection of persimmon based on nir hyperspectral imaging by sample partitioning and variables selection","volume":"105","author":"Wei","year":"2020","journal-title":"Infrared Phys. Technol."},{"key":"10.1016\/j.compag.2026.112059_b26","doi-asserted-by":"crossref","first-page":"156","DOI":"10.1016\/j.biosystemseng.2022.07.013","article-title":"Application of hyperspectral imaging systems and artificial intelligence for quality assessment of fruit, vegetables and mushrooms: A review","volume":"222","author":"Wieme","year":"2022","journal-title":"Biosyst. Eng."},{"issue":"2","key":"10.1016\/j.compag.2026.112059_b27","doi-asserted-by":"crossref","first-page":"109","DOI":"10.1016\/S0169-7439(01)00155-1","article-title":"PLS-regression: a basic tool of chemometrics","volume":"58","author":"Wold","year":"2001","journal-title":"Chemometr. Intell. Lab. Syst."},{"key":"10.1016\/j.compag.2026.112059_b28","doi-asserted-by":"crossref","DOI":"10.1016\/j.fbio.2023.103050","article-title":"Known and potential health benefits and mechanisms of blueberry anthocyanins: A review","volume":"55","author":"Wu","year":"2023","journal-title":"Food Biosci."},{"key":"10.1016\/j.compag.2026.112059_b29","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.ifset.2013.04.014","article-title":"Advanced applications of hyperspectral imaging technology for food quality and safety analysis and assessment: A review \u2014 Part I: Fundamentals","volume":"19","author":"Wu","year":"2013","journal-title":"Innov. Food Sci. Emerg. Technol."},{"key":"10.1016\/j.compag.2026.112059_b30","doi-asserted-by":"crossref","DOI":"10.1016\/j.infrared.2021.104003","article-title":"Developing deep learning based regression approaches for prediction of firmness and pH in kyoho grape using vis\/NIR hyperspectral imaging","volume":"120","author":"Xu","year":"2022","journal-title":"Infrared Phys. Technol."},{"key":"10.1016\/j.compag.2026.112059_b31","series-title":"C-mixup: Improving generalization in regression","author":"Yao","year":"2022"},{"key":"10.1016\/j.compag.2026.112059_b32","series-title":"Supervised contrastive regression","author":"Zha","year":"2022"},{"key":"10.1016\/j.compag.2026.112059_b33","series-title":"Mixup: Beyond empirical risk minimization","author":"Zhang","year":"2017"},{"key":"10.1016\/j.compag.2026.112059_b34","doi-asserted-by":"crossref","first-page":"48","DOI":"10.1016\/j.aca.2019.01.002","article-title":"DeepSpectra: An end-to-end deep learning approach for quantitative spectral analysis","volume":"1058","author":"Zhang","year":"2019","journal-title":"Anal. Chim. Acta"},{"issue":"9","key":"10.1016\/j.compag.2026.112059_b35","doi-asserted-by":"crossref","first-page":"2352","DOI":"10.1016\/S2095-3119(20)63236-1","article-title":"Evaluation of sugar and organic acid composition and their levels in highbush blueberries from two regions of China","volume":"19","author":"ZHANG","year":"2020","journal-title":"J. Integr. Agric."},{"issue":"8","key":"10.1016\/j.compag.2026.112059_b36","doi-asserted-by":"crossref","first-page":"9460","DOI":"10.3390\/ijms13089460","article-title":"Expression patterns, activities and carbohydrate-metabolizing regulation of sucrose phosphate synthase, sucrose synthase and neutral invertase in pineapple fruit during development and ripening","volume":"13","author":"Zhang","year":"2012","journal-title":"Int. J. Mol. Sci."},{"key":"10.1016\/j.compag.2026.112059_b37","doi-asserted-by":"crossref","DOI":"10.1016\/j.foodchem.2020.126536","article-title":"Developing deep learning based regression approaches for determination of chemical compositions in dry black goji berries (lycium ruthenicum murr.) using near-infrared hyperspectral imaging","volume":"319","author":"Zhang","year":"2020","journal-title":"Food Chem."},{"key":"10.1016\/j.compag.2026.112059_b38","doi-asserted-by":"crossref","DOI":"10.1016\/j.postharvbio.2020.111286","article-title":"Self-adaptive models for predicting soluble solid content of blueberries with biological variability by using near-infrared spectroscopy and chemometrics","volume":"169","author":"Zheng","year":"2020","journal-title":"Postharvest Biology Technol."},{"key":"10.1016\/j.compag.2026.112059_b39","doi-asserted-by":"crossref","DOI":"10.1016\/j.postharvbio.2020.111286","article-title":"Self-adaptive models for predicting soluble solid content of blueberries with biological variability by using near-infrared spectroscopy and chemometrics","volume":"169","author":"Zheng","year":"2020","journal-title":"Postharvest Biology Technol."}],"container-title":["Computers and Electronics in Agriculture"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S016816992600654X?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S016816992600654X?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,8,4]],"date-time":"2026-08-04T20:08:31Z","timestamp":1785874111000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S016816992600654X"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,9]]},"references-count":39,"alternative-id":["S016816992600654X"],"URL":"https:\/\/doi.org\/10.1016\/j.compag.2026.112059","relation":{},"ISSN":["0168-1699"],"issn-type":[{"value":"0168-1699","type":"print"}],"subject":[],"published":{"date-parts":[[2026,9]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Supervised contrastive regression learning for predicting soluble solids content of blueberries using hyperspectral imaging","name":"articletitle","label":"Article Title"},{"value":"Computers and Electronics in Agriculture","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.compag.2026.112059","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 Elsevier B.V. All rights are reserved, including those for text and data mining, AI training, and similar technologies.","name":"copyright","label":"Copyright"}],"article-number":"112059"}}