University Library
  • Login
A gateway to Melbourne's research publications
Minerva Access is the University's Institutional Repository. It aims to collect, preserve, and showcase the intellectual output of staff and students of the University of Melbourne for a global audience.
View Item 
  • Minerva Access
  • Veterinary and Agricultural Sciences
  • Agriculture and Food Systems
  • Agriculture and Food Systems - Research Publications
  • View Item
  • Minerva Access
  • Veterinary and Agricultural Sciences
  • Agriculture and Food Systems
  • Agriculture and Food Systems - Research Publications
  • View Item
JavaScript is disabled for your browser. Some features of this site may not work without it.

    Classification of Smoke Contaminated Cabernet Sauvignon Berries and Leaves Based on Chemical Fingerprinting and Machine Learning Algorithms

    Thumbnail
    Download
    published version (2.634Mb)

    Citations
    Altmetric
    Author
    Summerson, V; Gonzalez Viejo, C; Szeto, C; Wilkinson, KL; Torrico, DD; Pang, A; De Bei, R; Fuentes, S
    Date
    2020-09-01
    Source Title
    Sensors
    Publisher
    MDPI
    University of Melbourne Author/s
    Fuentes Jara, Sigfredo Augusto; Pang, Alexis; Gonzalez Viejo Duran, Claudia; Torrico, Damir; Gonzalez Viejo Duran, Claudia
    Affiliation
    Agriculture and Food Systems
    Metadata
    Show full item record
    Document Type
    Journal Article
    Citations
    Summerson, V., Gonzalez Viejo, C., Szeto, C., Wilkinson, K. L., Torrico, D. D., Pang, A., De Bei, R. & Fuentes, S. (2020). Classification of Smoke Contaminated Cabernet Sauvignon Berries and Leaves Based on Chemical Fingerprinting and Machine Learning Algorithms. SENSORS, 20 (18), https://doi.org/10.3390/s20185099.
    Access Status
    Open Access
    URI
    http://hdl.handle.net/11343/251554
    DOI
    10.3390/s20185099
    Abstract
    Wildfires are an increasing problem worldwide, with their number and intensity predicted to rise due to climate change. When fires occur close to vineyards, this can result in grapevine smoke contamination and, subsequently, the development of smoke taint in wine. Currently, there are no in-field detection systems that growers can use to assess whether their grapevines have been contaminated by smoke. This study evaluated the use of near-infrared (NIR) spectroscopy as a chemical fingerprinting tool, coupled with machine learning, to create a rapid, non-destructive in-field detection system for assessing grapevine smoke contamination. Two artificial neural network models were developed using grapevine leaf spectra (Model 1) and grape spectra (Model 2) as inputs, and smoke treatments as targets. Both models displayed high overall accuracies in classifying the spectral readings according to the smoking treatments (Model 1: 98.00%; Model 2: 97.40%). Ultraviolet to visible spectroscopy was also used to assess the physiological performance and senescence of leaves, and the degree of ripening and anthocyanin content of grapes. The results showed that chemical fingerprinting and machine learning might offer a rapid, in-field detection system for grapevine smoke contamination that will enable growers to make timely decisions following a bushfire event, e.g., avoiding harvest of heavily contaminated grapes for winemaking or assisting with a sample collection of grapes for chemical analysis of smoke taint markers.

    Export Reference in RIS Format     

    Endnote

    • Click on "Export Reference in RIS Format" and choose "open with... Endnote".

    Refworks

    • Click on "Export Reference in RIS Format". Login to Refworks, go to References => Import References


    Collections
    • Minerva Elements Records [52443]
    • Agriculture and Food Systems - Research Publications [655]
    Minerva AccessDepositing Your Work (for University of Melbourne Staff and Students)NewsFAQs

    BrowseCommunities & CollectionsBy Issue DateAuthorsTitlesSubjectsThis CollectionBy Issue DateAuthorsTitlesSubjects
    My AccountLoginRegister
    StatisticsMost Popular ItemsStatistics by CountryMost Popular Authors