# Dataset containing spectral data from hyperspectral imaging and sugar content measurements of grapes berries in various maturity stage

Maxime Ryckewaert, Daphné Heran, Carole Feilhes, Fanny Prezman, Eric Serrano, Aldrig Courand, Sílvia Mas Garcia, Maxime Metz, Ryad Bendoula

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## To cite this version:

Maxime Ryckewaert, Daphné Heran, Carole Feilhes, Fanny Prezman, Eric Serrano, et al., Dataset containing spectral data from hyperspectral imaging and sugar content measurements of grapes berries in various maturity stage. Data in Brief, 2023, 46, pp.108822. 10.1016/j.dib.2022.108822. hal-03940279

HAL Id: hal-03940279

https://hal.inrae.fr/hal-03940279

Submitted on 16 Jan 2023

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Distributed under a Creative Commons Attribution 4.0 International License
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Data Article

Dataset containing spectral data from
hyperspectral imaging and sugar content
measurements of grapes berries in various
maturity stage

Maxime Ryckewaert*, Daphné Héran, Carole Feilhes,
Fanny Prezman, Eric Serrano, Aldrig Courand, Silvia Mas-Garcia,
Maxime Metz, Ryad Bendoula

ITAP, Univ Montpellier, INRAE, Institut Agro, Montpellier, IFV, 1920 Route de Lisle-sur-Tarn, 81310 Peyrole, France

ARTICLE INFO

Article history:

Received 14 March 2022
Revised 6 December 2022
Accepted 7 December 2022
Available online 12 December 2022

Keywords:

Hyperspectral imaging
Digital agriculture
Chemometrics
Grape berries
Sugar content
Spectroscopy

A B S T R A C T

In the dataset presented in this article, two hundred and sev-
enty four trays containing one hundred berries were mea-
sured by a hyperspectral camera in the visible/near-infrared
spectral domain. This dataset was formed to study the use
of hyperspectral imaging for maturity monitoring of grape
berries [2]. This dataset contains reflectance spectra from hy-
perspectral camera of grape berries of three different vari-
eties and chemical composition (sugar content).

© 2022 The Authors. Published by Elsevier Inc.

This is an open access article under the CC BY license
(http://creativecommons.org/licenses/by/4.0/)

Specifications Table

<table>
   <tr>
    <td>
     Subject
    </td>
    <td>
     Analytical Chemistry: Spectroscopy
    </td>
   </tr>
   <tr>
    <td>
     Specific subject area
    </td>
    <td>
     Maturity monitoring of grape berries with sugar content and spectral measurements
    </td>
   </tr>
   <tr>
    <td>
     Type of data
    </td>
    <td>
     Table
    </td>
   </tr>
  </table>

(continued on next page)

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<table>
   <tr>
    <td>
     Subject
    </td>
    <td>
     Analytical Chemistry: Spectroscopy
    </td>
   </tr>
   <tr>
    <td>
     Specific subject area
    </td>
    <td>
     Maturity monitoring of grape berries with sugar content and spectral measurements
    </td>
   </tr>
   <tr>
    <td>
     Type of data
    </td>
    <td>
     Table
    </td>
   </tr>
  </table>

<footnote>* Corresponding author.</footnote>

<footnote>E-mail address: maxime.ryckewaert@inrae.fr (M. Ryckewaert).</footnote>
<table><tr><td>How the data were acquired</td><td>Reflectance spectra were acquired with a hyperspectral camera (Specim IQ, Specim, Finland) having a spectral range from 400 nm to 1000 nm and a spectral resolution equal to 7 nm. Sugar content measurements were performed on berry musts with a refractometer (HI-96816, Hanna Instruments).</td></tr><tr><td>Data format</td><td>Raw</td></tr><tr><td>Description of data collection</td><td>Three different grape varieties were collected in Gaillac (France), in summer 2020. Grape berries were then sorted using NaCl densimetric baths and were placed on a tray for hyperspectral acquisition. Reflectance spectra were obtained with pixel selection corresponding to berries. Sugar content were measured on berry musts with a refractometer (HI-96816, Hanna Instruments).</td></tr><tr><td>Data source location</td><td><ul><li>Institution: Institut Français de la Vigne et du vin (IFV)</li><li>City/Town/Region: Peyrole, Tarn, Occitanie</li><li>Country: France</li></ul></td></tr><tr><td>Data accessibility</td><td>Repository name: Mendeley Data<br>Data identification number: gjwx64sgkp.1<br>Direct URL to data: https://doi.org/10.17632/gjwx64sgkp.1</td></tr><tr><td>Related research article</td><td>For a published article: [2]<br>Aldrig Courand, Maxime Metz, Daphné Héran, Carole Feilhes, Fanny Prezman, Eric Serrano, Ryad Bendoula, and Maxime Ryckewaert. Evaluation of a robust regression method (RoBoost-PLSR) to predict biochemical variables for agronomic applications: Case study of grape berry maturity monitoring. Chemometrics and Intelligent Laboratory Systems, 221:104485, February 2022. ISSN 01697439. doi: https://doi.org/10.1016/j.chemolab.2021.104485</td></tr></table>

## Value of the Data

* These spectra were acquired under controlled conditions over a range of sugar values representative of all stages of maturity.

* This dataset is useful for testing and comparing prediction methods.

* This dataset can be used to build models to predict the sugar content of grapes.

* This dataset is intended for scientists to test new methods (variable selection, data exploration, regression methods) or to have reference data to guide their future experiments.

## 1. Data Description

Chemical and NIR spectra measurements were made on 274 samples (one sample is composed by one hundred berries) from three different grape varieties (Table 1). The table containing the dataset (DATASET.csv) is represented so that the rows correspond to the samples and the columns correspond to the variables. The first column corresponds to the tray key, the second column to the variety, the third column to the sugar content and the following columns correspond to reflectance spectra values obtained on the indicated spectral bands. For the three varieties, sugar content values are similar and comprised between 100 and 300 (g/L), representative of all stages of maturity.

Grape berries were sorted using NaCl densimetric baths and were placed on a tray for hyperspectral acquisition.

From these one hundred berries, an average spectrum was calculated from each image. At this end, 274 reflectance spectra were obtained on three different grape varieties with two red grape varieties (Syrah and Fer Servadou) and one white grape variety (Mauzac).

<caption>**Table 1**</caption>

Number of observations (hyperspectral images and sugar contents) constituting the whole data set for the three grape varieties, Syrah, Fer and Mauzac.

<table><thead><tr><th></th><th>Syrah</th><th>Fer</th><th>Mauzac</th></tr></thead><tbody><tr><td>Number of observations</td><td>126</td><td>63</td><td>85</td></tr></tbody></table>
<caption>**Table 2**</caption>

Cross-validation (cv) results of PLSR prediction models for total sugar content (latent variable number (LV), root mean square error (RMSE<sub>cv</sub>) and determination coefficient (R<sup>2</sup><sub>cv</sub>))

<table><thead><tr><td>Variety</td><td>LV</td><td>RMSE<sub>cv</sub> (g/L)</td><td>R<sup>2</sup><sub>cv</sub></td></tr></thead><tbody><tr><td>Syrah</td><td>6</td><td>9.31</td><td>0.937</td></tr><tr><td>Fer Servadou</td><td>7</td><td>19.45</td><td>0.623</td></tr><tr><td>Mauzac</td><td>5</td><td>28.78</td><td>0.298</td></tr></tbody></table>

To illustrate, PLSR results are shown for total sugar content for each grape variety (cf. Table 2).

## 2. Experimental Design, Materials and Methods

### 2.1. Samples and analyses

The sampling of grape berries started one or two weeks after veraison and before harvest in summer 2020 on plots of the experimental vineyard Domaine Expérimental Viticole Tarnais located in Gaillac (France). The berries belonged to three grape varieties, two red grape varieties (Syrah and Fer Servadou) and one white grape variety (Mauzac). For each variety, thirty bunches were collected approximately once a week.

Grape berries were prepared in the laboratory. They were cut at the pedicel to preserve the whole fruit. Sorting was carried out by batches of the same degree of ripeness using sodium chloride (NaCl) baths. For this purpose, twelve NaCl baths of increasing concentrations from 70 to 190 g/L were prepared to classify the berries according to their berry density corresponding to sugar concentrations from 110 to 279 g/L. [1]. Berry musts were obtained with one hundred berries of the same degree of ripeness and sugar content measurements were made with a refractometer (HI-96816, Hanna Instruments).

### 2.2. Hyperspectral images and NIR spectra acquisition

Reflectance spectra were acquired before preparing a hundred berry must. These berries were placed on a tray to make the berries visible. These measurements were made with a hyperspectral camera (Specim IQ, Specim, Finland) with a spectral range of 400 nm to 1000 nm and a spectral resolution equal to 7 nm.

The camera was positioned at 1.5m from the stage. A halogen lamp was used for lighting (Arrilite 750 Plus ARRI, Munich, Germany). Constant angles of -50° and 50° were maintained between the axes of the halogen lamp and the axis of the hyperspectral camera. A certified reflectance standard (Labsphere, SRS-40-010) was placed next to the tray containing the hundred bays in order to know the reference reflected intensity ($I_o(\lambda)$). This procedure allows to standardise the images coming from the non-uniformities of the instrumentation (light source, lens, detector). For each image, spectra of the berries were selected by using the Spectral Angle Mapper (SAM) and were averaged.

### 2.3. Data analysis

All the calculations were run under Matlab (The Mathworks, Natick, MA, USA). PLS-R algorithm was used to perform model sugar content [3]. Model results for each varieties were evaluated on the basis of the coefficient of determination (R<sup>2</sup><sub>cv</sub>) and the root mean square standard error of cross-validation (RMSE<sub>cv</sub>).
**Declaration of Competing Interest**

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

**Data availability**

Spectral dataset of grape berries from hyperspectral imaging for maturity monitoring
(Original data) (Mendeley Data)

CRediT Author Statement

**Maxime Ryckewaert:** Conceptualization, Investigation, Methodology, Software, Supervision, Writing – original draft; **Daphné Héran:** Methodology, Conceptualization, Writing – review & editing, Investigation; **Carole Feilhes:** Methodology, Conceptualization, Data curation, Visualization, Investigation, Resources; **Fanny Prezman:** Methodology, Conceptualization, Data curation, Resources; **Eric Serrano:** Supervision, Validation, Resources; **Silvia Mas-Garcia:** Methodology, Conceptualization; **Maxime Metz:** Methodology, Software, Resources; **Ryad Bendoula:** Investigation, Supervision, Writing – original draft, Resources.

Acknowledgments

This work has benefited from a financial support from the InterregSudoe under the reference
SOE3/P2/E0911.

References

[1] A. Bigard, Varietal differences in solute accumulation and grape development, Montpellier SupAgro, phdthesis, Dec. 2018 URL https://tel.archives-ouvertes.fr/tel-02542686 .

[2] A. Courand, M. Metz, D. Héran, C. Feilhes, F. Prezman, E. Serrano, R. Bendoula, M. Ryckewaert, Evaluation of a robust regression method (RoBoost-PLSR) to predict biochemical variables for agronomic applications: case study of grape berry maturity monitoring, Chemometr. Intell. Laboratory Syst. 221 (Feb. 2022) 104485 ISSN 01697439URL https://linkinghub.elsevier.com/retrieve/pii/S0169743921002537 , doi:10.1016/j.chemolab.2021.104485.

[3] S. Wold, M. Sjöström, L. Eriksson, PLS-regression: a basic tool of chemometrics, Chemometr. Intell. Laboratory Syst. 58 (2) (2001) 109-130.