Next Article in Journal
Antioxidant Determination with the Use of Carbon-Based Electrodes
Previous Article in Journal
Electrospun Fibres of Chitosan/PVP for the Effective Chemotherapeutic Drug Delivery of 5-Fluorouracil
Article

Hyperspectral Imaging to Characterize Table Grapes

1
USC 1422 GRAPPE, INRAE, Ecole Supérieure d’Agricultures, SFR 4207 QUASAV, 55 Rue Rabelais, BP 30748, 49007 Angers CEDEX 01, France
2
Dipartimento di Scienze e Tecnologie Alimentari per una filiera agro-alimentare Sostenibile, Università Cattolica del Sacro Cuore, Via Emilia Parmense 84, 29122 Piacenza, Italy
*
Author to whom correspondence should be addressed.
Academic Editor: José Manuel Amigo
Chemosensors 2021, 9(4), 71; https://doi.org/10.3390/chemosensors9040071
Received: 28 January 2021 / Revised: 24 March 2021 / Accepted: 29 March 2021 / Published: 1 April 2021
(This article belongs to the Section Analytical Methods, Instrumentation and Miniaturization)
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 pre-treatments (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 (β-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 β-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. View Full-Text
Keywords: hyperspectral imaging; phenolics; anthocyanin; table grapes; total soluble solids; PLS; MLR; prediction; model hyperspectral imaging; phenolics; anthocyanin; table grapes; total soluble solids; PLS; MLR; prediction; model
Show Figures

Graphical abstract

MDPI and ACS Style

Gabrielli, M.; Lançon-Verdier, V.; Picouet, P.; Maury, C. Hyperspectral Imaging to Characterize Table Grapes. Chemosensors 2021, 9, 71. https://doi.org/10.3390/chemosensors9040071

AMA Style

Gabrielli M, Lançon-Verdier V, Picouet P, Maury C. Hyperspectral Imaging to Characterize Table Grapes. Chemosensors. 2021; 9(4):71. https://doi.org/10.3390/chemosensors9040071

Chicago/Turabian Style

Gabrielli, Mario, Vanessa Lançon-Verdier, Pierre Picouet, and Chantal Maury. 2021. "Hyperspectral Imaging to Characterize Table Grapes" Chemosensors 9, no. 4: 71. https://doi.org/10.3390/chemosensors9040071

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop