Classification of smoke contaminated cabernet sauvignon berries and leaves based on chemical fingerprinting and machine learning algorithms

Vasiliki Summerson, Claudia Gonzalez Viejo, Colleen Szeto, Kerry L. Wilkinson, Damir D. Torrico, Alexis Pang, Roberta De Bei, Sigfredo Fuentes

Research output: Contribution to journalArticlepeer-review

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.

Original languageEnglish (US)
Article number5099
Pages (from-to)1-24
Number of pages24
JournalSensors (Switzerland)
Volume20
Issue number18
DOIs
StatePublished - Sep 2 2020
Externally publishedYes

Keywords

  • Climate change
  • Near‐infrared spectroscopy
  • Remote sensing
  • Smoke taint
  • Volatile phenols

ASJC Scopus subject areas

  • Analytical Chemistry
  • Information Systems
  • Atomic and Molecular Physics, and Optics
  • Biochemistry
  • Instrumentation
  • Electrical and Electronic Engineering

Fingerprint

Dive into the research topics of 'Classification of smoke contaminated cabernet sauvignon berries and leaves based on chemical fingerprinting and machine learning algorithms'. Together they form a unique fingerprint.

Cite this