{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,7]],"date-time":"2026-07-07T10:25:33Z","timestamp":1783419933236,"version":"3.54.6"},"reference-count":57,"publisher":"Springer Science and Business Media LLC","issue":"3","license":[{"start":{"date-parts":[[2023,5,30]],"date-time":"2023-05-30T00:00:00Z","timestamp":1685404800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,5,30]],"date-time":"2023-05-30T00:00:00Z","timestamp":1685404800000},"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":["Soft Comput"],"published-print":{"date-parts":[[2024,2]]},"DOI":"10.1007\/s00500-023-08512-2","type":"journal-article","created":{"date-parts":[[2023,5,30]],"date-time":"2023-05-30T15:03:04Z","timestamp":1685458984000},"page":"2517-2533","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["I-LDD: an interpretable leaf disease detector"],"prefix":"10.1007","volume":"28","author":[{"given":"Rashmi","family":"Mishra","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"family":"Kavita","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0033-4380","authenticated-orcid":false,"given":"Ankit","family":"Rajpal","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Varnika","family":"Bhatia","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sheetal","family":"Rajpal","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Manoj","family":"Agarwal","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Naveen","family":"Kumar","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2023,5,30]]},"reference":[{"key":"8512_CR1","doi-asserted-by":"crossref","unstructured":"Ahmad S (1994) A usable real-time 3D hand tracker. In: Proceedings of 1994 28th Asilomar conference on signals, systems and computers, vol\u00a02. IEEE, pp 1257\u20131261","DOI":"10.1109\/ACSSC.1994.471660"},{"issue":"1","key":"8512_CR2","first-page":"49","volume":"2","author":"P Alagumariappan","year":"2020","unstructured":"Alagumariappan P, Dewan NJ, Muthukrishnan GN, Raju BKB, Bilal RAA, Sankaran V (2020) Intelligent plant disease identification system using Machine Learning. Eng Proc 2(1):49","journal-title":"Eng Proc"},{"issue":"21","key":"8512_CR3","doi-asserted-by":"publisher","first-page":"13229","DOI":"10.1007\/s00500-021-06176-4","volume":"25","author":"R Alguliyev","year":"2021","unstructured":"Alguliyev R, Imamverdiyev Y, Sukhostat L, Bayramov R (2021) Plant disease detection based on a deep model. Soft Comput 25(21):13229\u201313242","journal-title":"Soft Comput"},{"key":"8512_CR4","doi-asserted-by":"publisher","first-page":"3649","DOI":"10.1007\/s11042-017-5537-5","volume":"78","author":"MA Alsmirat","year":"2019","unstructured":"Alsmirat MA, Al-Alem F, Al-Ayyoub M, Jararweh Y, Gupta B (2019) Impact of digital fingerprint image quality on the fingerprint recognition accuracy. Multimed Tools Appl 78:3649\u20133688","journal-title":"Multimed Tools Appl"},{"issue":"3","key":"8512_CR5","first-page":"1","volume":"25","author":"D Aqel","year":"2021","unstructured":"Aqel D, Al-Zubi S, Mughaid A, Jararweh Y (2021) Extreme learning machine for plant diseases classification: a sustainable approach for smart agriculture. Clust Comput 25(3):1\u201314","journal-title":"Clust Comput"},{"key":"8512_CR6","doi-asserted-by":"publisher","DOI":"10.1016\/j.cmpb.2020.105607","volume":"196","author":"DK Atal","year":"2020","unstructured":"Atal DK, Singh M (2020) Arrhythmia classification with ECG signals based on the optimization-enabled deep convolutional neural network. Comput Methods Programs Biomed 196:105607","journal-title":"Comput Methods Programs Biomed"},{"issue":"6","key":"8512_CR7","first-page":"1059","volume":"23","author":"A Bhatia","year":"2020","unstructured":"Bhatia A, Chug A, Prakash Singh A (2020) Application of extreme learning machine in plant disease prediction for highly imbalanced dataset. J Stat Manag Syst 23(6):1059\u20131068","journal-title":"J Stat Manag Syst"},{"issue":"1","key":"8512_CR8","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s42483-020-00049-8","volume":"2","author":"CH Bock","year":"2020","unstructured":"Bock CH, Barbedo JG, Del Ponte EM, Bohnenkamp D, Mahlein A-K (2020) From visual estimates to fully automated sensor-based measurements of plant disease severity: status and challenges for improving accuracy. Phytopathol Res 2(1):1\u201330","journal-title":"Phytopathol Res"},{"key":"8512_CR9","doi-asserted-by":"crossref","unstructured":"Chug A, Bhatia A, Singh AP, Singh D (2022) A novel framework for image-based plant disease detection using hybrid deep learning approach. Soft Comput 4:1\u201326","DOI":"10.3233\/IDA-216011"},{"key":"8512_CR10","first-page":"1","volume":"7","author":"J Dem\u0161ar","year":"2006","unstructured":"Dem\u0161ar J (2006) Statistical comparisons of classifiers over multiple data sets. J Mach Learn Res 7:1\u201330","journal-title":"J Mach Learn Res"},{"issue":"3","key":"8512_CR11","doi-asserted-by":"publisher","first-page":"1457","DOI":"10.1007\/s00500-022-07446-5","volume":"27","author":"A Diana Andrushia","year":"2023","unstructured":"Diana Andrushia A, Mary Neebha T, Trephena Patricia A, Umadevi S, Anand N, Varshney A (2023) Image-based disease classification in grape leaves using convolutional capsule network. Soft Comput 27(3):1457\u20131470","journal-title":"Soft Comput"},{"key":"8512_CR12","unstructured":"FAO (2020) 2020 is International Year of Plant Health, howpublished. https:\/\/www.unep.org\/news-and-stories\/story\/2020-international-year-plant-health. Accessed 05 Jun 2022"},{"key":"8512_CR13","unstructured":"Geneva (2021) International Day of Plant Health\u2014Geneva Environment Network. https:\/\/www.genevaenvironmentnetwork.org\/resources\/updates\/international-day-of-plant-health\/. Accessed 08 Jan 2022"},{"key":"8512_CR14","doi-asserted-by":"publisher","first-page":"610","DOI":"10.1109\/TSMC.1973.4309314","volume":"6","author":"RM Haralick","year":"1973","unstructured":"Haralick RM, Shanmugam K, Dinstein IH (1973) Textural features for image classification. IEEE Trans Syst Man Cybern 6:610\u2013621","journal-title":"IEEE Trans Syst Man Cybern"},{"key":"8512_CR15","first-page":"13","volume":"62","author":"BK Hatuwal","year":"2020","unstructured":"Hatuwal BK, Shakya A, Joshi B (2020) Plant leaf disease recognition using random forest, KNN, SVM and CNN. Polibits 62:13\u201319","journal-title":"Polibits"},{"issue":"2","key":"8512_CR16","doi-asserted-by":"publisher","first-page":"179","DOI":"10.1109\/TIT.1962.1057692","volume":"8","author":"M-K Hu","year":"1962","unstructured":"Hu M-K (1962) Visual pattern recognition by moment invariants. IRE Trans Inf Theory 8(2):179\u2013187","journal-title":"IRE Trans Inf Theory"},{"key":"8512_CR17","unstructured":"Huang G-B, Zhu Q-Y, Siew C-K (2004) Extreme learning machine: a new learning scheme of feedforward neural networks. In: 2004 IEEE international joint conference on neural networks (IEEE Cat. No. 04CH37541), vol\u00a02. IEEE, pp 985\u2013990"},{"issue":"1\u20133","key":"8512_CR18","doi-asserted-by":"publisher","first-page":"489","DOI":"10.1016\/j.neucom.2005.12.126","volume":"70","author":"G-B Huang","year":"2006","unstructured":"Huang G-B, Zhu Q-Y, Siew C-K (2006) Extreme learning machine: theory and applications. Neurocomputing 70(1\u20133):489\u2013501","journal-title":"Neurocomputing"},{"key":"8512_CR19","unstructured":"Hughes D, Salath\u00e9 M, et\u00a0al (2015) An open access repository of images on plant health to enable the development of mobile disease diagnostics. arXiv preprint arXiv:1511.08060"},{"issue":"6","key":"8512_CR20","doi-asserted-by":"publisher","first-page":"1038","DOI":"10.1049\/iet-ipr.2017.0822","volume":"12","author":"S Kaur","year":"2018","unstructured":"Kaur S, Pandey S, Goel S (2018) Semi-automatic leaf disease detection and classification system for soybean culture. IET Image Proc 12(6):1038\u20131048","journal-title":"IET Image Proc"},{"issue":"1","key":"8512_CR21","doi-asserted-by":"publisher","first-page":"012002","DOI":"10.1088\/1755-1315\/951\/1\/012002","volume":"951","author":"A Khakimov","year":"2022","unstructured":"Khakimov A, Salakhutdinov I, Omolikov A, Utaganov S (2022) Traditional and current-prospective methods of agricultural plant diseases detection: a review. IOP Confer Ser: Earth Environ Sci 951(1):012002","journal-title":"IOP Confer Ser: Earth Environ Sci"},{"issue":"3","key":"8512_CR22","doi-asserted-by":"publisher","first-page":"231","DOI":"10.1109\/42.764896","volume":"18","author":"JK Kim","year":"1999","unstructured":"Kim JK, Park HW (1999) Statistical textural features for detection of microcalcifications in digitized mammograms. IEEE Trans Med Imaging 18(3):231\u2013238","journal-title":"IEEE Trans Med Imaging"},{"key":"8512_CR23","doi-asserted-by":"crossref","unstructured":"Klassen W, Vreysen M (2021) Area-wide integrated pest management and the sterile insect technique. In: Sterile insect technique. CRC Press, pp 75\u2013112","DOI":"10.1201\/9781003035572-3"},{"issue":"2","key":"8512_CR24","doi-asserted-by":"publisher","first-page":"342","DOI":"10.3390\/s20020342","volume":"20","author":"Y Kortli","year":"2020","unstructured":"Kortli Y, Jridi M, Al Falou A, Atri M (2020) Face recognition systems: a survey. Sensors 20(2):342","journal-title":"Sensors"},{"issue":"01","key":"8512_CR25","first-page":"213","volume":"10","author":"VG Krishnan","year":"2022","unstructured":"Krishnan VG, Deepa J, Rao PV, Divya V, Kaviarasan S (2022) An automated segmentation and classification model for banana leaf disease detection. J Appl Biol Biotechnol 10(01):213\u2013220","journal-title":"J Appl Biol Biotechnol"},{"issue":"6","key":"8512_CR26","doi-asserted-by":"publisher","first-page":"8155","DOI":"10.1007\/s11042-022-11910-7","volume":"81","author":"Y Kurmi","year":"2022","unstructured":"Kurmi Y, Gangwar S, Chaurasia V, Goel A (2022) Leaf images classification for the crops diseases detection. Multimed Tools Appl 81(6):8155\u20138178","journal-title":"Multimed Tools Appl"},{"key":"8512_CR27","doi-asserted-by":"publisher","first-page":"249","DOI":"10.1016\/j.patrec.2020.04.036","volume":"135","author":"T Lacombe","year":"2020","unstructured":"Lacombe T, Favreliere H, Pillet M (2020) Modal features for image texture classification. Pattern Recogn Lett 135:249\u2013255","journal-title":"Pattern Recogn Lett"},{"issue":"3","key":"8512_CR28","doi-asserted-by":"publisher","first-page":"3513","DOI":"10.3233\/JIFS-179155","volume":"37","author":"Z Libo","year":"2019","unstructured":"Libo Z, Tian H, Chunyun G, Elhoseny M (2019) Real-time detection of cole diseases and insect pests in wireless sensor networks. J Intell Fuzzy Syst 37(3):3513\u20133524","journal-title":"J Intell Fuzzy Syst"},{"issue":"3","key":"8512_CR29","doi-asserted-by":"publisher","first-page":"469","DOI":"10.3390\/biology11030469","volume":"11","author":"AA Lima","year":"2022","unstructured":"Lima AA, Mridha MF, Das SC, Kabir MM, Islam MR, Watanobe Y (2022) A comprehensive survey on the detection, classification, and challenges of neurological disorders. Biology 11(3):469","journal-title":"Biology"},{"key":"8512_CR30","unstructured":"Lucas JA (2020) Plant pathology and plant pathogens. Wiley"},{"key":"8512_CR31","unstructured":"MacQueen J (1967) Classification and analysis of multivariate observations. In: 5th Berkeley Symp. Math. Statist. Probability, pp 281\u2013297"},{"issue":"2","key":"8512_CR32","first-page":"180","volume":"1","author":"M Manida","year":"2022","unstructured":"Manida M (2022) The future of food and agriculture trends and challenges. Agric Food E-Newslett 1(2):180","journal-title":"Agric Food E-Newslett"},{"issue":"7","key":"8512_CR33","doi-asserted-by":"publisher","first-page":"1047","DOI":"10.3390\/agronomy10071047","volume":"10","author":"A Merot","year":"2020","unstructured":"Merot A, Fermaud M, Gosme M, Smits N (2020) Effect of conversion to organic farming on pest and disease control in French vineyards. Agronomy 10(7):1047","journal-title":"Agronomy"},{"issue":"5","key":"8512_CR34","doi-asserted-by":"publisher","first-page":"1335","DOI":"10.1094\/PDIS-04-19-0741-RE","volume":"104","author":"M Moumni","year":"2020","unstructured":"Moumni M, Allagui MB, Mancini V, Murolo S, Tarchoun N, Romanazzi G (2020) Morphological and molecular identification of seedborne fungi in squash (Cucurbita maxima, Cucurbita moschata). Plant Dis 104(5):1335\u20131350","journal-title":"Plant Dis"},{"key":"8512_CR35","unstructured":"NASEM (2019) Science breakthroughs to advance food and agricultural research by 2030. The National Academies Press"},{"key":"8512_CR36","unstructured":"Oh S-H, Park S-W, Kim B-J (2002) DWT (discrete wavelet transform) based watermark system. In: 2002 Digest of technical papers. International conference on consumer electronics (IEEE Cat. No. 02CH37300). IEEE, pp 192\u2013193"},{"key":"8512_CR37","doi-asserted-by":"crossref","unstructured":"Ojala T, Pietik\u00e4inen M, M\u00e4enp\u00e4\u00e4 T (2001) A generalized local binary pattern operator for multiresolution gray scale and rotation invariant texture classification. In: International conference on advances in pattern recognition. Springer, pp 399\u2013408","DOI":"10.1007\/3-540-44732-6_41"},{"key":"8512_CR38","first-page":"2277","volume":"51","author":"H Pallathadka","year":"2022","unstructured":"Pallathadka H, Ravipati P, Sajja GS, Phasinam K, Kassanuk T, Sanchez DT, Prabhu P (2022) Application of machine learning techniques in rice leaf disease detection. Mater Today: Proc 51:2277\u20132280","journal-title":"Mater Today: Proc"},{"key":"8512_CR39","doi-asserted-by":"crossref","unstructured":"Panchal P, Raman VC, Mantri S (2019) Plant diseases detection and classification using machine learning models. In: 2019 4th international conference on computational systems and information technology for sustainable solution (CSITSS), vol\u00a04. IEEE, pp 1\u20136","DOI":"10.1109\/CSITSS47250.2019.9031029"},{"key":"8512_CR40","doi-asserted-by":"publisher","DOI":"10.1016\/j.displa.2021.102053","volume":"69","author":"S Qi","year":"2021","unstructured":"Qi S, Ning X, Yang G, Zhang L, Long P, Cai W, Li W (2021) Review of multi-view 3D object recognition methods based on deep learning. Displays 69:102053","journal-title":"Displays"},{"issue":"1","key":"8512_CR41","first-page":"193","volume":"16","author":"S Rajpal","year":"2022","unstructured":"Rajpal S, Agarwal M, Rajpal A, Lakhyani N, Saggar A, Kumar N (2022) COV-ELM classifier: an extreme learning machine based identification of COVID-19 using Chest X-Ray Images. Intell Dec Technol 16(1):193\u2013203","journal-title":"Intell Dec Technol"},{"key":"8512_CR42","doi-asserted-by":"crossref","unstructured":"Ribeiro MT, Singh S, Guestrin C (2016) \u201c Why should I trust you?\u201d Explaining the predictions of any classifier. In: Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp 1135\u20131144","DOI":"10.1145\/2939672.2939778"},{"issue":"23","key":"8512_CR43","doi-asserted-by":"publisher","DOI":"10.1073\/pnas.2022239118","volume":"118","author":"JB Ristaino","year":"2021","unstructured":"Ristaino JB, Anderson PK, Bebber DP, Brauman KA, Cunniffe NJ, Fedoroff NV, Finegold C, Garrett KA, Gilligan CA, Jones CM, Martin MD, MacDonald GK, Neenan P, Records A, Schmale DG, Tateosian L, Wei Q (2021) The persistent threat of emerging plant disease pandemics to global food security. Proc Natl Acad Sci, Eng, Med Others 118(23):e2022239118","journal-title":"Proc Natl Acad Sci, Eng, Med Others"},{"key":"8512_CR44","doi-asserted-by":"crossref","unstructured":"Roy K, Chaudhuri SS, Frnda J, Bandopadhyay S, Ray IJ, Banerjee S, Nedoma J (2023) Detection of tomato leaf diseases for agro-based industries using novel PCA DeepNet. IEEE Access 11:14983\u201315001","DOI":"10.1109\/ACCESS.2023.3244499"},{"issue":"11","key":"8512_CR45","doi-asserted-by":"publisher","first-page":"468","DOI":"10.3390\/plants8110468","volume":"8","author":"MH Saleem","year":"2019","unstructured":"Saleem MH, Potgieter J, Arif KM (2019) Plant disease detection and classification by deep learning. Plants 8(11):468","journal-title":"Plants"},{"issue":"5","key":"8512_CR46","volume":"10","author":"G Stiglic","year":"2020","unstructured":"Stiglic G, Kocbek P, Fijacko N, Zitnik M, Verbert K, Cilar L (2020) Interpretability of machine learning-based prediction models in healthcare. Wiley Interdiscipl Rev: Data Min Knowl Discov 10(5):e1379","journal-title":"Wiley Interdiscipl Rev: Data Min Knowl Discov"},{"issue":"1","key":"8512_CR47","doi-asserted-by":"publisher","first-page":"11","DOI":"10.1007\/BF00130487","volume":"7","author":"MJ Swain","year":"1991","unstructured":"Swain MJ, Ballard DH (1991) Color indexing. Int J Comput Vis 7(1):11\u201332","journal-title":"Int J Comput Vis"},{"issue":"1","key":"8512_CR48","doi-asserted-by":"publisher","first-page":"138","DOI":"10.1109\/TBC.2018.2871376","volume":"65","author":"Z Tang","year":"2018","unstructured":"Tang Z, Zheng Y, Gu K, Liao K, Wang W, Yu M (2018) Full-reference image quality assessment by combining features in spatial and frequency domains. IEEE Trans Broadcast 65(1):138\u2013151","journal-title":"IEEE Trans Broadcast"},{"issue":"16","key":"8512_CR49","doi-asserted-by":"publisher","first-page":"17525","DOI":"10.1109\/JSEN.2020.3032438","volume":"21","author":"V Udutalapally","year":"2020","unstructured":"Udutalapally V, Mohanty SP, Pallagani V, Khandelwal V (2020) scrop: a novel device for sustainable automatic disease prediction, crop selection, and irrigation in internet-of-agro-things for smart agriculture. IEEE Sens J 21(16):17525\u201317538","journal-title":"IEEE Sens J"},{"issue":"1","key":"8512_CR50","doi-asserted-by":"publisher","first-page":"23","DOI":"10.1007\/s40708-017-0075-5","volume":"5","author":"N Varuna Shree","year":"2018","unstructured":"Varuna Shree N, Kumar T (2018) Identification and classification of brain tumor MRI images with feature extraction using DWT and probabilistic neural network. Brain informatics 5(1):23\u201330","journal-title":"Brain informatics"},{"issue":"24","key":"8512_CR51","doi-asserted-by":"publisher","first-page":"18069","DOI":"10.1007\/s00521-019-04051-w","volume":"32","author":"A Vellido","year":"2020","unstructured":"Vellido A (2020) The importance of interpretability and visualization in machine learning for applications in medicine and health care. Neural Comput Appl 32(24):18069\u201318083","journal-title":"Neural Comput Appl"},{"key":"8512_CR52","doi-asserted-by":"crossref","unstructured":"Weszka JS, Dyer CR, Rosenfeld A (1976) A comparative study of texture measures for terrain classification. IEEE Trans Syst, Man, Cybern SMC-6(4):269\u2013285","DOI":"10.1109\/TSMC.1976.5408777"},{"issue":"1","key":"8512_CR53","volume":"1962","author":"TS Xian","year":"2021","unstructured":"Xian TS, Ngadiran R (2021) Plant diseases classification using machine learning. J Phys: Confer Ser 1962(1):012024","journal-title":"J Phys: Confer Ser"},{"key":"8512_CR54","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1155\/2009\/260516","volume":"2009","author":"B Yanikoglu","year":"2009","unstructured":"Yanikoglu B, Kholmatov A (2009) Online signature verification using Fourier descriptors. EURASIP J Adv Signal Process 2009:1\u201313","journal-title":"EURASIP J Adv Signal Process"},{"issue":"15","key":"8512_CR55","doi-asserted-by":"publisher","DOI":"10.1088\/1361-6560\/aad3ab","volume":"63","author":"A Zargari","year":"2018","unstructured":"Zargari A, Du Y, Heidari M, Thai TC, Gunderson CC, Moore K, Mannel RS, Liu H, Zheng B, Qiu Y (2018) Prediction of chemotherapy response in ovarian cancer patients using a new clustered quantitative image marker. Phys Med Biol 63(15):155020","journal-title":"Phys Med Biol"},{"issue":"19","key":"8512_CR56","doi-asserted-by":"publisher","first-page":"3188","DOI":"10.3390\/rs12193188","volume":"12","author":"N Zhang","year":"2020","unstructured":"Zhang N, Yang G, Pan Y, Yang X, Chen L, Zhao C (2020) A review of advanced technologies and development for hyperspectral-based plant disease detection in the past three decades. Remote Sens 12(19):3188","journal-title":"Remote Sens"},{"issue":"3","key":"8512_CR57","doi-asserted-by":"publisher","first-page":"187","DOI":"10.2174\/1574893614666190723115832","volume":"15","author":"X Zhou","year":"2020","unstructured":"Zhou X, Li Z, Xie H, Feng T, Lu Y, Wang C, Chen R (2020) Leukocyte image segmentation based on adaptive histogram thresholding and contour detection. Curr Bioinform 15(3):187\u2013195","journal-title":"Curr Bioinform"}],"container-title":["Soft Computing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00500-023-08512-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00500-023-08512-2\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00500-023-08512-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,2,1]],"date-time":"2024-02-01T06:08:15Z","timestamp":1706767695000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00500-023-08512-2"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,5,30]]},"references-count":57,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2024,2]]}},"alternative-id":["8512"],"URL":"https:\/\/doi.org\/10.1007\/s00500-023-08512-2","relation":{},"ISSN":["1432-7643","1433-7479"],"issn-type":[{"value":"1432-7643","type":"print"},{"value":"1433-7479","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,5,30]]},"assertion":[{"value":"10 May 2023","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"30 May 2023","order":2,"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. This article does not contain any studies with human participants or animals performed by any of the authors.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"This article does not contain any studies with human participants or animals performed by any of the authors.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical approval"}}]}}