Hyperspectral imaging to characterize table grapes

Mario Gabrielli, Vanessa Lançon-Verdier, Pierre Picouet, Chantal Maury

Research output: Contribution to journalArticle


Table grape quality is of importance for consumers and thus for producers. Its objective quality is usually determined by destructive methods mainly based on sugar content. This study proposed to evaluate the possibility of hyperspectral imaging to characterize table grapes quality through its sugar (TSS), total flavonoid (TF), and total anthocyanin (TA) contents. Different data pretreatments (WD, SNV, and 1st and 2nd derivative) and different methods were tested to get the best prediction models: PLS with full spectra and then Multiple Linear Regression (MLR) were realized after selecting the optimal wavelengths thanks to the regression coefficients (coefficients) and the Variable Importance in Projection (VIP) scores. All models were good at showing that hyperspectral imaging is a relevant method to predict sugar, total flavonoid, and total anthocyanin contents. The best predictions were obtained from optimal wavelength selection based on coefficients for TSS and from VIPs optimal wavelength windows using SNV pre-treatment for total flavonoid and total anthocyanin content. Thus, good prediction models were proposed in order to characterize grapes while reducing the data sets and limit the data storage to enable an industrial use.
Original languageEnglish
Pages (from-to)71-92
Number of pages22
Publication statusPublished - 2021


  • Anthocyanin
  • Hyperspectral imaging
  • MLR
  • Model
  • PLS
  • Phenolics
  • Prediction
  • Table grapes
  • Total soluble solids


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