{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,1]],"date-time":"2026-04-01T18:15:04Z","timestamp":1775067304861,"version":"3.50.1"},"reference-count":50,"publisher":"Springer Science and Business Media LLC","issue":"4","license":[{"start":{"date-parts":[[2023,6,28]],"date-time":"2023-06-28T00:00:00Z","timestamp":1687910400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,6,28]],"date-time":"2023-06-28T00:00:00Z","timestamp":1687910400000},"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":["Multimed Tools Appl"],"published-print":{"date-parts":[[2024,1]]},"DOI":"10.1007\/s11042-023-16058-6","type":"journal-article","created":{"date-parts":[[2023,6,28]],"date-time":"2023-06-28T05:02:07Z","timestamp":1687928527000},"page":"11433-11460","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":26,"title":["FruitQ: a new dataset of multiple fruit images for freshness evaluation"],"prefix":"10.1007","volume":"83","author":[{"given":"Olusola O.","family":"Abayomi-Alli","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9990-1084","authenticated-orcid":false,"given":"Robertas","family":"Dama\u0161evi\u010dius","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sanjay","family":"Misra","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Adebayo","family":"Abayomi-Alli","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,6,28]]},"reference":[{"issue":"7","key":"16058_CR1","doi-asserted-by":"publisher","first-page":"10.1111\/exsy.12","DOI":"10.1111\/exsy.12746","volume":"38","author":"OO Abayomi-Alli","year":"2021","unstructured":"Abayomi-Alli OO, Dama\u0161evi\u010dius R, Misra S, Maskeli\u016bnas R (2021) Cassava disease recognition from low-quality images using enhanced data augmentation model and deep learning. Expert Syst 38(7):10.1111\/exsy.12746","journal-title":"Expert Syst"},{"issue":"11","key":"16058_CR2","doi-asserted-by":"publisher","first-page":"10.3390\/s211138","DOI":"10.3390\/s21113830","volume":"21","author":"A Almadhor","year":"2021","unstructured":"Almadhor A, Rauf HT, Lali MIU, Dama\u0161evi\u010dius R, Alouffi B, Alharbi A (2021) Ai-driven framework for recognition of guava plant diseases through machine learning from dslr camera sensor based high resolution imagery. Sensors 21(11):10.3390\/s21113830","journal-title":"Sensors"},{"key":"16058_CR3","doi-asserted-by":"publisher","unstructured":"Anowar F, Sadaoui S, Selim B (2021). Conceptual and empirical comparison of dimensionality reduction algorithms (PCA, KPCA, LDA, MDS, SVD, LLE, ISOMAP, LE, ICA, t-SNE). Computer Science Review, 40 https:\/\/doi.org\/10.1016\/j.cosrev.2021.100378","DOI":"10.1016\/j.cosrev.2021.100378"},{"issue":"3","key":"16058_CR4","doi-asserted-by":"publisher","first-page":"243","DOI":"10.1016\/j.jksuci.2018.06.002","volume":"33","author":"A Bhargava","year":"2021","unstructured":"Bhargava A, Bansal A (2021) Fruits and vegetables quality evaluation using computer vision: A review. J King Saud Univ - Comput Inform Sci 33(3):243\u2013257. https:\/\/doi.org\/10.1016\/j.jksuci.2018.06.002","journal-title":"J King Saud Univ - Comput Inform Sci"},{"key":"16058_CR5","doi-asserted-by":"publisher","DOI":"10.1016\/j.scienta.2021.110684","volume":"293","author":"JJ Bird","year":"2022","unstructured":"Bird JJ, Barnes CM, Manso LJ, Ek\u00e1rt A, Faria DR (2022) Fruit quality and defect image classification with conditional GAN data augmentation. Sci Hortic 293:110684","journal-title":"Sci Hortic"},{"key":"16058_CR6","doi-asserted-by":"publisher","first-page":"46","DOI":"10.1016\/j.biosystemseng.2017.04.009","volume":"159","author":"S C\u00e1rdenas-P\u00e9rez","year":"2017","unstructured":"C\u00e1rdenas-P\u00e9rez S, Chanona-P\u00e9rez J, M\u00e9ndez-M\u00e9ndez JV, Calder\u00f3n-Dom\u00ednguez G, L\u00f3pez-Santiago R, Perea-Flores MJ, Arzate-V\u00e1zquez I (2017) Evaluation of the ripening stages of apple (Golden Delicious) by means of computer vision system. Biosyst Eng 159:46\u201358. https:\/\/doi.org\/10.1016\/j.biosystemseng.2017.04.009","journal-title":"Biosyst Eng"},{"issue":"2018","key":"16058_CR7","doi-asserted-by":"publisher","first-page":"558","DOI":"10.1016\/j.compag.2018.12.019","volume":"156","author":"D Cavallo","year":"2019","unstructured":"Cavallo D, Pietro Cefola M, Pace B, Logrieco AF, Attolico G (2019) Non-destructive and contactless quality evaluation of table grapes by a computer vision system. Comput Electron Agric 156(2018):558\u2013564. https:\/\/doi.org\/10.1016\/j.compag.2018.12.019","journal-title":"Comput Electron Agric"},{"key":"16058_CR8","doi-asserted-by":"publisher","unstructured":"Chauhan C, Dhir A, Akram MU, Salo J (2021). Food loss and waste in food supply chains. A systematic literature review and framework development approach. J Clean Prod, 295, 126438. https:\/\/doi.org\/10.1016\/j.jclepro.2021.126438","DOI":"10.1016\/j.jclepro.2021.126438"},{"key":"16058_CR9","doi-asserted-by":"publisher","first-page":"4712","DOI":"10.3390\/rs13224712","volume":"13","author":"L Chen","year":"2021","unstructured":"Chen L, Li S, Bai Q, Yang J, Jiang S, Miao Y (2021) Review of Image Classification Algorithms Based on Convolutional Neural Networks. Remote Sens 13:4712. https:\/\/doi.org\/10.3390\/rs13224712","journal-title":"Remote Sens"},{"issue":"3","key":"16058_CR10","doi-asserted-by":"publisher","first-page":"4003","DOI":"10.32604\/cmc.2021.018758","volume":"69","author":"WH Cho","year":"2021","unstructured":"Cho WH, Kim SK, Na MH, Na IS (2021) Fruit ripeness prediction based on DNN feature induction from sparse dataset. Comput, Mater Continua 69(3):4003\u20134024. https:\/\/doi.org\/10.32604\/cmc.2021.018758","journal-title":"Comput, Mater Continua"},{"issue":"4","key":"16058_CR11","doi-asserted-by":"publisher","first-page":"598","DOI":"10.1016\/j.physbeh.2012.04.015","volume":"107","author":"GV Civille","year":"2012","unstructured":"Civille GV, Oftedal KN (2012) Sensory evaluation techniques\u2014Make \u201cgood for you\u201d taste \u201cgood\u201d. Physiol Behav 107(4):598\u2013605","journal-title":"Physiol Behav"},{"key":"16058_CR12","doi-asserted-by":"publisher","first-page":"674","DOI":"10.1016\/j.patcog.2018.03.008","volume":"81","author":"S Das","year":"2018","unstructured":"Das S, Datta S, Chaudhuri BB (2018) Handling data irregularities in classification: Foundations, trends, and future challenges. Pattern Recogn 81:674\u2013693. https:\/\/doi.org\/10.1016\/j.patcog.2018.03.008","journal-title":"Pattern Recogn"},{"key":"16058_CR13","doi-asserted-by":"publisher","DOI":"10.4018\/978-1-6684-5141-0.ch003","volume-title":"Artificial Intelligence Applications in Agriculture and Food Quality Improvement (pp. 29\u201354)","author":"Z Elbir","year":"2022","unstructured":"Elbir Z, Caferoglu BA, Cihan O (2022) Freshness Grading of Agricultural Products Using Artificial Intelligence. In: Khan M, Khan R, Praveen P (eds) Artificial Intelligence Applications in Agriculture and Food Quality Improvement (pp. 29\u201354). IGI Global. https:\/\/doi.org\/10.4018\/978-1-6684-5141-0.ch003"},{"issue":"2","key":"16058_CR14","doi-asserted-by":"publisher","first-page":"5083","DOI":"10.32604\/cmc.2022.023357","volume":"71","author":"LG Fahad","year":"2022","unstructured":"Fahad LG, Tahir SF, Rasheed U, Saqib H, Hassan M, Alquhayz H (2022) Fruits and vegetables freshness categorization using deep learning. Comput, Mater Continua 71(2):5083\u20135098. https:\/\/doi.org\/10.32604\/cmc.2022.023357","journal-title":"Comput, Mater Continua"},{"issue":"11","key":"16058_CR15","doi-asserted-by":"publisher","first-page":"10.3390\/agronom","DOI":"10.3390\/agronomy11112107","volume":"11","author":"G Fenu","year":"2021","unstructured":"Fenu G, Malloci FM (2021) DiaMOS plant: A dataset for diagnosis and monitoring plant disease. Agronomy 11(11):10.3390\/agronomy11112107","journal-title":"Agronomy"},{"issue":"4","key":"16058_CR16","first-page":"10.1007\/s42979-","volume":"3","author":"Y Fu","year":"2022","unstructured":"Fu Y, Nguyen M, Yan WQ (2022) Grading methods for fruit freshness based on deep learning. SN Computer. Science 3(4):10.1007\/s42979-022-01152-7","journal-title":"Science"},{"key":"16058_CR17","doi-asserted-by":"publisher","first-page":"323","DOI":"10.1016\/j.compeleceng.2019.04.011","volume":"76","author":"G Geetharamani","year":"2019","unstructured":"Geetharamani G, Pandian A (2019) Identification of plant leaf diseases using a nine-layer deep convolutional neural network. Comput Electr Eng 76:323\u2013338","journal-title":"Comput Electr Eng"},{"key":"16058_CR18","doi-asserted-by":"crossref","unstructured":"He K, Zhang X, Ren S, Sun J (2016) Deep Residual Learning for Image Recognition. In: Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA, 27\u201330, pp. 770\u2013778","DOI":"10.1109\/CVPR.2016.90"},{"key":"16058_CR19","unstructured":"Hughes D, Salath\u00e9 M (2015). An open access repository of images on plant health to enable the development of mobile disease diagnostics. arXiv preprint arXiv:1511.08060."},{"key":"16058_CR20","unstructured":"Iandola FN, Han S, Moskewicz MW, Ashraf K, Dally WJ, Keutzer K (2016) SqueezeNet: Alex-Net-level Accuracy with 50x Fewer Parameters and <0.5 MB Model Size. arXiv 2019, arXiv:1602.07360"},{"issue":"1","key":"16058_CR21","doi-asserted-by":"publisher","first-page":"24","DOI":"10.1016\/j.inpa.2021.01.005","volume":"9","author":"N Ismail","year":"2022","unstructured":"Ismail N, Malik OA (2022) Real-time visual inspection system for grading fruits using computer vision and deep learning techniques. Inform Proc Agricult 9(1):24\u201337. https:\/\/doi.org\/10.1016\/j.inpa.2021.01.005","journal-title":"Inform Proc Agricult"},{"key":"16058_CR22","doi-asserted-by":"publisher","unstructured":"Javaid M, Haleem A, Rab S, Pratap Singh R, Suman R (2021). Sensors for daily life: A review. In Sensors International (Vol. 2, p. 100121). Elsevier BV. https:\/\/doi.org\/10.1016\/j.sintl.2021.100121","DOI":"10.1016\/j.sintl.2021.100121"},{"issue":"16","key":"16058_CR23","doi-asserted-by":"publisher","first-page":"22355","DOI":"10.1007\/s11042-021-11282-4","volume":"81","author":"J Kang","year":"2022","unstructured":"Kang J, Gwak J (2022) Ensemble of multi-task deep convolutional neural networks using transfer learning for fruit freshness classification. Multimed Tools Appl 81(16):22355\u201322377. https:\/\/doi.org\/10.1007\/s11042-021-11282-4","journal-title":"Multimed Tools Appl"},{"key":"16058_CR24","doi-asserted-by":"crossref","unstructured":"Kaur P, Harnal S, Gautam V, Singh MP, Singh SP (2022). An approach for characterization of infected area in tomato leaf disease based on deep learning and object detection technique. Eng Appl Artif Intell, 115, 105210.","DOI":"10.1016\/j.engappai.2022.105210"},{"issue":"6","key":"16058_CR25","doi-asserted-by":"publisher","first-page":"7611","DOI":"10.1007\/s11042-022-12150-5","volume":"81","author":"A Kazi","year":"2022","unstructured":"Kazi A, Panda SP (2022) Determining the freshness of fruits in the food industry by image classification using transfer learning. Multimed Tools Appl 81(6):7611\u20137624. https:\/\/doi.org\/10.1007\/s11042-022-12150-5","journal-title":"Multimed Tools Appl"},{"key":"16058_CR26","volume-title":"Secondary Analysis of Electronic Health Records","author":"M Komorowski","year":"2016","unstructured":"Komorowski M, Marshall DC, Salciccioli JD, Crutain Y (2016) Exploratory Data Analysis. In: Secondary Analysis of Electronic Health Records. Springer, Cham (CH)"},{"key":"16058_CR27","doi-asserted-by":"publisher","unstructured":"Ma J, Sun D-W, Qu J-H, Liu D, Pu H, Gao W-H, Zeng X-A (2014). Applications of Computer Vision for Assessing Quality of Agri-food Products: A Review of Recent Research Advances. In Crit Rev Food Sci Nutr (Vol. 56, Issue 1, pp. 113\u2013127). https:\/\/doi.org\/10.1080\/10408398.2013.873885","DOI":"10.1080\/10408398.2013.873885"},{"issue":"1","key":"16058_CR28","doi-asserted-by":"publisher","first-page":"134","DOI":"10.1007\/s12393-021-09290-z","volume":"14","author":"NR Mavani","year":"2022","unstructured":"Mavani NR, Ali JM, Othman S, Hussain MA, Hashim H, Rahman NA (2022) Application of Artificial Intelligence in Food Industry\u2014a Guideline. Food Eng Rev 14(1):134\u2013175. https:\/\/doi.org\/10.1007\/s12393-021-09290-z","journal-title":"Food Eng Rev"},{"key":"16058_CR29","doi-asserted-by":"publisher","unstructured":"Medhi E, Deb N (2022). PSFD-musa: A dataset of banana plant, stem, fruit, leaf, and disease. Data in Brief, 43 https:\/\/doi.org\/10.1016\/j.dib.2022.108427","DOI":"10.1016\/j.dib.2022.108427"},{"key":"16058_CR30","doi-asserted-by":"publisher","first-page":"996","DOI":"10.3390\/rs14040996","volume":"14","author":"P Melki","year":"2022","unstructured":"Melki P, Bombrun L, Millet E, Diallo B, ElChaoui ElGhor H, Da Costa J-P (2022) Exploratory Analysis on Pixelwise Image Segmentation Metrics with an Application in Proximal Sensing. Remote Sens 14:996","journal-title":"Remote Sens"},{"key":"16058_CR31","doi-asserted-by":"publisher","unstructured":"Meshram V, Patil K (2022). FruitNet: Indian fruits image dataset with quality for machine learning applications. Data in Brief, 40 https:\/\/doi.org\/10.1016\/j.dib.2021.107686","DOI":"10.1016\/j.dib.2021.107686"},{"key":"16058_CR32","unstructured":"Meshram V, Thanomliang K, Ruangkan S, Chumchu P, Patil K (2020), \"FruitsGB: Top Indian Fruits with quality\", IEE."},{"key":"16058_CR33","unstructured":"Nemade SB, Sonavane SP (2020). Co-occurrence patterns-based fruit quality detection for hierarchical fruit image annotation. Journal of King Saud University-Computer and Information Sciences"},{"key":"16058_CR34","doi-asserted-by":"publisher","unstructured":"Ni J, Gao J, Deng, L, Han Z (2020). Monitoring the change process of banana freshness by GoogLeNet. IEEE Access, https:\/\/doi.org\/10.1109\/ACCESS.2020.3045394","DOI":"10.1109\/ACCESS.2020.3045394"},{"key":"16058_CR35","doi-asserted-by":"publisher","unstructured":"Rajbongshi A, Sazzad S, Shakil R, Akter B, Sara U (2022). A comprehensive guava leaves and fruits dataset for guava disease recognition. Data in Brief, 42 https:\/\/doi.org\/10.1016\/j.dib.2022.108174","DOI":"10.1016\/j.dib.2022.108174"},{"key":"16058_CR36","doi-asserted-by":"publisher","unstructured":"Rauf HT, Saleem BA, Lali MIU, Khan MA, Sharif M, Bukhari SAC (2019). A citrus fruits and leaves dataset for detection and classification of citrus diseases through machine learning. Data in Brief, 26 https:\/\/doi.org\/10.1016\/j.dib.2019.104340","DOI":"10.1016\/j.dib.2019.104340"},{"key":"16058_CR37","doi-asserted-by":"crossref","unstructured":"Sandler M, Howard A, Zhu M, Zhmoginov A, Chen LC (2018) MobileNet V2: Inverted Residuals and Linear Bottlenecks. In Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA, pp. 18\u201322","DOI":"10.1109\/CVPR.2018.00474"},{"key":"16058_CR38","doi-asserted-by":"publisher","first-page":"4843","DOI":"10.1109\/ACCESS.2020.3048415","volume":"9","author":"A Sharma","year":"2021","unstructured":"Sharma A, Jain A, Gupta P, Chowdary V (2021) Machine Learning Applications for Precision Agriculture: A Comprehensive Review. IEEE Access 9:4843\u20134873","journal-title":"IEEE Access"},{"key":"16058_CR39","doi-asserted-by":"publisher","DOI":"10.1016\/j.compag.2022.107214","volume":"200","author":"A Sherafati","year":"2022","unstructured":"Sherafati A, Mollazade K, Saba MK, Vesali F (2022) TomatoScan: An Android-based application for quality evaluation and ripening determination of tomato fruit. Comput Electron Agric 200:107214","journal-title":"Comput Electron Agric"},{"key":"16058_CR40","doi-asserted-by":"publisher","unstructured":"Sonwani E, Bansal U, Alroobaea R, Baqasah AM; Hedabou M (2022). An Artificial Intelligence Approach Toward Food Spoilage Detection and Analysis. Frontiers in Public Health (Vol. 9). https:\/\/doi.org\/10.3389\/fpubh.2021.816226","DOI":"10.3389\/fpubh.2021.816226"},{"key":"16058_CR41","doi-asserted-by":"crossref","unstructured":"Strong DM, Lee YW, Wang RY, (1997) Data quality in context. Commun ACM , 40, 103\u2013110.","DOI":"10.1145\/253769.253804"},{"key":"16058_CR42","doi-asserted-by":"crossref","unstructured":"Suryawanshi Y, Patil K, Chumchu P (2022). VegNet: Dataset of vegetable quality images for machine learning applications. Data in Brief, 108657.","DOI":"10.1016\/j.dib.2022.108657"},{"key":"16058_CR43","unstructured":"Tan M, Le QV (2020) EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks. arXiv 2020, arXiv:1905.11946."},{"key":"16058_CR44","unstructured":"Van der Maaten L, Hinton G (2008) Visualizing data using t-SNE. J Mach Learn Res 9(11)"},{"key":"16058_CR45","doi-asserted-by":"publisher","first-page":"156","DOI":"10.1016\/j.biosystemseng.2022.07.013","volume":"222","author":"J Wieme","year":"2022","unstructured":"Wieme J, Mollazade K, Malounas I, Zude-Sasse M, Zhao M, Gowen A, \u2026 J. (2022) Application of hyperspectral imaging systems and artificial intelligence for quality assessment of fruit, vegetables and mushrooms: A review. Biosyst Eng 222:156\u2013176","journal-title":"Biosyst Eng"},{"key":"16058_CR46","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1155\/2016\/8048246","volume":"2016","author":"H Xu","year":"2016","unstructured":"Xu H, Mao R, Liao H, Zhang H, Lu M, Chen G (2016) Index Based Hidden Outlier Detection in Metric Space. Sci Program 2016:1\u201314. https:\/\/doi.org\/10.1155\/2016\/8048246","journal-title":"Sci Program"},{"key":"16058_CR47","doi-asserted-by":"crossref","unstructured":"Yang J, Luo X, Zhang X, Passos D, Xie L, Rao X, ..., Ying L, (2022). A deep learning approach to improving spectral analysis of fruit quality under interseason variation. Food Control, 109108.","DOI":"10.1016\/j.foodcont.2022.109108"},{"issue":"2","key":"16058_CR48","first-page":"282","volume":"24","author":"MH Zarnaq","year":"2022","unstructured":"Zarnaq MH, Omid M, Firouz MS, Jafarian M, Bazyar P (2022) Freshness and quality assessment of parsley using image processing and artificial intelligence techniques. CIGR J 24(2):282\u2013290","journal-title":"CIGR J"},{"key":"16058_CR49","doi-asserted-by":"crossref","unstructured":"Zhang X, Zhou X, Lin M, Sun J (2018) Shufflenet: An Extremely Efficient Convolutional Neural Network for Mobile Devices. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA, 18\u201323, pp. 6848\u20136856.","DOI":"10.1109\/CVPR.2018.00716"},{"key":"16058_CR50","doi-asserted-by":"publisher","first-page":"217","DOI":"10.1016\/j.patrec.2020.03.004","volume":"133","author":"R Zhu","year":"2020","unstructured":"Zhu R, Guo Y, Xue J-H (2020) Adjusting the imbalance ratio by the dimensionality of imbalanced data. Pattern Recogn Lett 133:217\u2013223. https:\/\/doi.org\/10.1016\/j.patrec.2020.03.004","journal-title":"Pattern Recogn Lett"}],"container-title":["Multimedia Tools and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-023-16058-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11042-023-16058-6\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-023-16058-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,1,10]],"date-time":"2024-01-10T09:46:44Z","timestamp":1704880004000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11042-023-16058-6"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,6,28]]},"references-count":50,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2024,1]]}},"alternative-id":["16058"],"URL":"https:\/\/doi.org\/10.1007\/s11042-023-16058-6","relation":{},"ISSN":["1380-7501","1573-7721"],"issn-type":[{"value":"1380-7501","type":"print"},{"value":"1573-7721","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,6,28]]},"assertion":[{"value":"9 January 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"18 March 2023","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"18 June 2023","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"28 June 2023","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 no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}