chemosensors Article Hyperspectral Imaging to Characterize Table Grapes Mario Gabrielli 1,2 , Vanessa Lançon-Verdier 1 , Pierre Picouet 1 and Chantal Maury 1, *   Citation: 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 Academic Editor: José Manuel Amigo Received: 28 January 2021 Accepted: 29 March 2021 Published: 1 April 2021 Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affil- iations. Copyright: © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/). 1 USC 1422 GRAPPE, INRAE, Ecole Supérieure d’Agricultures, SFR 4207 QUASAV, 55 Rue Rabelais, BP 30748, 49007 Angers CEDEX 01, France; mario.gabrielli@unicatt.it (M.G.); v.lancon-verdier@groupe-esa.com (V.L.-V.); p.picouet@groupe-esa.com (P.P.) 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 * Correspondence: c.maury@groupe-esa.com; Tel.: +33-241235547 Abstract: 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. Keywords: hyperspectral imaging; phenolics; anthocyanin; table grapes; total soluble solids; PLS; MLR; prediction; model 1. Introduction Grapes are one of the most consumed fruits in the word, as fresh fruit, grape juice, raisins, and wine. About 36% of grape production concerned the fresh fruit consumption (International Organization of Vine and Wine statistics). The European production of table grapes (~1.9 million tons) is mainly located in the Mediterranean area, with the domination of Italy (61%), Greece (16%), Spain (15%), and France (1.5%) [1]. The French production of table grapes is mostly in Vaucluse and Tarn-et-Garonne. About 80% of the production concern only three varieties: Alphonse Lavallée, Chasselas, and Muscat de Hambourg. French table grape production (~30,000 tons) represents approximately 40% of the national consumption, while the 60% remaining is mainly imported from Spain and Italy. The right commercial harvest of table grapes is usually determined by different param- eters like skin color, texture softening, titratable acidity, total soluble solids, and sometimes with flavonoid content, and aromatic compounds [2,3]. Visual attributes of table grapes, such as intensity and uniformity of color, large size of berries, and brightness are the main characteristics that influence consumer choice [4,5]. Color is of high importance to assess quality in the food industry [6]. Furthermore, some studies have found clear evidences that a greater consumption of fresh grapes decreases the risk of cardiovascular diseases and can- cer [7,8]. This beneficial effect is mainly related to the presence of minerals, fibers, vitamins, and phytochemical compounds including flavonoids and anthocyanins [9,10]. However, the concentration of these quality attributes changes during postharvest storage and thus influence sensory perception and nutritional value of table grapes. Chemosensors 2021, 9, 71. https://doi.org/10.3390/chemosensors9040071 https://www.mdpi.com/journal/chemosensors