{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,9]],"date-time":"2025-11-09T03:53:28Z","timestamp":1762660408632},"reference-count":11,"publisher":"EDP Sciences","license":[{"start":{"date-parts":[[2023,12,6]],"date-time":"2023-12-06T00:00:00Z","timestamp":1701820800000},"content-version":"vor","delay-in-days":339,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["BIO Web Conf."],"published-print":{"date-parts":[[2023]]},"abstract":"<jats:p>This study used a tomography-like analysis to reconstruct the hyperspectral data from different tissues of the grapes: skin, pulp, and seeds. The dataset included 216 grapes of Loureiro (VIVC 25085) and 205 Vinh\u00e3o (VIVC 13100) at various dates from the veraison until the harvest. A more comprehensive spectral data analysis identified how the internal tissues are related to the total grape spectra. Each tissue was reconstructed separately by decomposing the whole grapevine hyperspectral information. The results showed that the spectral reconstruction was more successful for Loureiro than Vinh\u00e3o, with a mean absolute error of 6.08% and 33.32%, respectively. Partial least squares (PLS) regression models were developed for both cultivars using the reconstructed spectral data, enabling the modelling of \u00baBrix, puncture force (N), chlorophyll (a.u.), and anthocyanin content (a.u.). These models exhibited strong performance, with <jats:italic>R<\/jats:italic><jats:sup>2<\/jats:sup> &gt; 0.8 and mean absolute percentage errors (MAPE) below 37%. This study emphasises the critical role of considering the grape\u2019s internal tissue in assessing its maturation process. The findings introduce an innovative methodology for efficiently evaluating grape maturation dynamics and inner tissue characteristics. By highlighting the importance of internal tissue analysis, this research paves the way for expedited and accurate monitoring of grape maturation, offering valuable insights into physiological-based viticultural practices and grape quality assessment.<\/jats:p>","DOI":"10.1051\/bioconf\/20236801017","type":"journal-article","created":{"date-parts":[[2023,12,6]],"date-time":"2023-12-06T09:04:21Z","timestamp":1701853461000},"page":"01017","source":"Crossref","is-referenced-by-count":1,"title":["Tomography-like for hyperspectral bi-directional grape tissue reconstruction based on machine learning: Implications for diagnosis composition and precision maturation monitoring"],"prefix":"10.1051","volume":"68","author":[{"given":"Renan","family":"Tosin","sequence":"first","affiliation":[]},{"given":"Rui","family":"Martins","sequence":"additional","affiliation":[]},{"given":"Mario","family":"Cunha","sequence":"additional","affiliation":[]}],"member":"250","published-online":{"date-parts":[[2023,12,6]]},"reference":[{"key":"R1","doi-asserted-by":"crossref","unstructured":"Fernandez-Novales J., Tardaguila J., Gutierrez S., Paz Diago M., Molecules 24(15), (2019)","DOI":"10.3390\/molecules24152795"},{"key":"R2","doi-asserted-by":"crossref","first-page":"847","DOI":"10.1016\/j.foodchem.2015.12.083","volume":"199","author":"Farhadi","year":"2016","journal-title":"Food Chem"},{"key":"R3","doi-asserted-by":"crossref","first-page":"102","DOI":"10.1016\/j.postharvbio.2012.01.002","volume":"67","author":"Bonghi","year":"2012","journal-title":"Postharvest Biology and Technology"},{"key":"R4","doi-asserted-by":"crossref","unstructured":"Gouot J.C., Smith J.P., Holzapfel B.P., Barril C., Environmental and Experimental Botany 168 (2019)","DOI":"10.1016\/j.envexpbot.2019.103866"},{"issue":"4","key":"R5","doi-asserted-by":"crossref","first-page":"315","DOI":"10.1016\/j.apjtb.2015.12.015","volume":"6","author":"Silva","year":"2016","journal-title":"Asian Pacific Journal of Tropical Biomedicine"},{"issue":"5","key":"R6","doi-asserted-by":"crossref","first-page":"1325","DOI":"10.1021\/acs.jafc.8b05768","volume":"67","author":"Rousserie","year":"2019","journal-title":"J Agric Food Chem"},{"key":"R7","doi-asserted-by":"crossref","unstructured":"Martins R.C., Barroso T.G., Jorge P., Cunha M., Santos F., Computers and Electronics in Agriculture 194 (2022)","DOI":"10.1016\/j.compag.2022.106710"},{"issue":"2","key":"R8","doi-asserted-by":"crossref","first-page":"477","DOI":"10.13031\/2013.29556","volume":"53","author":"Guidetti","year":"2010","journal-title":"Transactions of the ASABE"},{"key":"R9","doi-asserted-by":"crossref","unstructured":"Tosin R., P\u00f4\u00e7as I., Novo H., Teixeira J., Fontes N., Gra\u00e7a A., Cunha M., Scientia Horticulturae 278 (2021)","DOI":"10.1016\/j.scienta.2020.109860"},{"issue":"5","key":"R10","doi-asserted-by":"crossref","first-page":"301","DOI":"10.1002\/(SICI)1099-128X(199809\/10)12:5<301::AID-CEM515>3.0.CO;2-S","volume":"12","author":"Westerhuis","year":"1998","journal-title":"Journal of Chemometrics"},{"issue":"11","key":"R11","doi-asserted-by":"crossref","first-page":"10040","DOI":"10.3390\/s101110040","volume":"10","author":"Ben Ghozlen","year":"2010","journal-title":"Sensors (Basel)"}],"container-title":["BIO Web of Conferences"],"original-title":[],"link":[{"URL":"https:\/\/www.bio-conferences.org\/10.1051\/bioconf\/20236801017\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,12,6]],"date-time":"2023-12-06T09:06:44Z","timestamp":1701853604000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.bio-conferences.org\/10.1051\/bioconf\/20236801017"}},"subtitle":[],"editor":[{"given":"P.","family":"Roca","sequence":"first","affiliation":[]}],"short-title":[],"issued":{"date-parts":[[2023]]},"references-count":11,"alternative-id":["bioconf_oiv2023_01017"],"URL":"https:\/\/doi.org\/10.1051\/bioconf\/20236801017","relation":{},"ISSN":["2117-4458"],"issn-type":[{"value":"2117-4458","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023]]}}}