Use of Visible and Short-Wave Near-Infrared Hyperspectral Imaging

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Use of visible and short wave near-infrared hyperspectral imaging to fingerprinting of anthocyanins in intact grape berries MARIA PAZ DIAGO SANTAMARIA, Juan Fernandez-Novales, Armando M. Fernandes, Pedro Melo-Pinto, and Javier Tardaguila J. Agric. Food Chem., Just Accepted Manuscript • DOI: 10.1021/acs.jafc.6b01999 • Publication Date (Web): 22 Sep 2016 Downloaded from http://pubs.acs.org on September 22, 2016

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Journal of Agricultural and Food Chemistry

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Use of visible and short wave near-infrared hyperspectral imaging to

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fingerprinting of anthocyanins in intact grape berries

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Maria P. Diago1*, Juan Fernández-Novales1, Armando M. Fernandes2, Pedro Melo-

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Pinto3,4, Javier Tardaguila1.

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La Rioja). Finca La Grajera, Ctra. Burgos Km. 6, 26007, Logroño, Spain.

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INOV – INESC Inovação, Rua Alves Redol, 9, 1000-029 Lisboa, Portugal.

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CITAB-Centre for the Research and Technology of Agro-Environmental and

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Biological Sciences. Universidade de Trás-os-Montes e Alto Douro, Quinta de Prados,

Instituto de Ciencias de la Vid y del Vino (University of La Rioja, CSIC, Gobierno de

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5000-911 Vila Real, Portugal.

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de Prados, 5000-911 Vila Real, Portugal.

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*Corresponding author: Maria Paz Diago; [email protected]; Tel: +34

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941894980 (ext.410065); Fax: +34 941899728.

Departamento de Engenharias, Universidade de Trás-os-Montes e Alto Douro, Quinta

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ABSTRACT

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In red grape berries, anthocyanins account for about the 50% of the skin phenols and are

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responsible for the final wine colour. Individual anthocyanin levels and compositional

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profiles vary with cultivar, maturity, season, region and yield and have been proposed

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as chemical markers to differentiate wines and to provide valuable information

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regarding the adulteration of musts and wines. A fast, easy, solvent-free, non-

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destructive method based on visible, short wave and near infrared hyperspectral imaging

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(HSI) in intact grape berries to fingerprinting the color pigments in eight different grape

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varieties was developed and tested against HPLC. Predictive models based on modified

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partial least squares (MPLS) were built for 14 individual anthocyanins with coefficients

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of determination of cross validation (R2cv) ranging from 0.70 to 0.93. For the grouping

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of total and non-acylated anthocyanins, external validation was conducted with

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coefficient of determination of prediction (R2P) of 0.86. HSI could potentially become

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an alternative to HPLC with reduced analysis time and labour costs, while providing

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reliable and robust information of the anthocyanin composition of grape berries.

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KEYWORDS: grape color, anthocyanin profile, non-destructive method, contactless

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spectroscopy, chemometrics

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INTRODUCTION

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Anthocyanins are key secondary metabolites in grape berries which determine grape

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and wine color1.They are included in the group of flavonoids, a type of phenolic

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compounds whose base structure (2-phenyl-benzopyrilium) consists of two aromatic

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rings joined via a pyran ring. Although fifteen anthocyanidins are known to occur in

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nature2, only five are generally present in red grape berries, primarily in the first cellular

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layers of the hypodermis3. These anthocyanidins include delphinidin, peonidin,

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cyanidin, petunidin and malvidin, which is the most abundant in Vitis vinifera L.

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varieties. These pigments exist primarily as the corresponding monoglucosides in red

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grape berries, with only small quantities of acylated forms involving phenolic acids. In

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these cases, the sugar molecule is esterified with caffeic, acetic or coumaric acid4, the

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latter two being the most common.

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In red varieties, anthocyanins account for about the 50% of the skin phenols.

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However, individual anthocyanin levels and compositional profiles vary greatly with

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cultivar, maturity, season, region and yield5. Qualitative and quantitative anthocyanin

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patterns in grape skins have been widely studied by chromatographic techniques,

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initially by paper chromatography6 and later by HPLC7,8. Anthocyanins have been

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postulated as chemical markers to differentiate wines made from different varieties,

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providing valuable information regarding the adulteration of juices and wines9. HPLC

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methods require berry processing and solvent extraction of the anthocyanin compounds

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prior to analysis, which are time consuming tasks that require skilled operators. In this

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context, it may be valuable for the wine industry to have fast, easy, solvent-free, non-

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destructive methods to assess the anthocyanin changes and accumulation that occur in

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the berries during ripening as well as to fingerprinting the color pigments in different

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grapevine varieties. 3 ACS Paragon Plus Environment


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Hyperspectral imaging is a non-destructive spectroscopic technique that

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measures hundreds of narrow wavelength bands and spatial positions. At each spatial

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location, the interaction of the electromagnetic radiation with the matter at many

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different wavelengths is recorded. In recent years, hyperspectral imaging in the visible

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and near infrared (400-1700 nm) has been adopted for fruit and vegetable quality

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assessment10-12, food safety control13-15 and classification tasks16,17. In the field of

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viticulture, varietal18,19 and clone discrimination within a given grapevine variety20 was

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achieved by visible (VIS) hyperspectral imaging. Likewise, total anthocyanin

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concentration21-24, technological maturity and total phenolics25, 26 in grape berries and

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several phenolic substances in grape seeds and pomace27,28 were determined using VIS-

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NIR hyperspectral imaging.

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Using a small number of grape berries might be interesting for some wineries

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aiming to produce high quality wines by selecting, within each cluster, the berries with

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highest quality29-30. Automating the berry selection procedure might already be possible

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with existing destemmers that extract berries one-by-one from clusters31 and afterwards

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place them on a conveyor belt that passes under a hyperspectral camera. However,

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quantification of individual anthocyanin compounds and profiling of the anthocyanin

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pattern in intact berries has rarely been attempted.

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Therefore, the aim of the present work was to evaluate the capability of VIS

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short wave NIR hyperspectral imaging in intact berries to anthocyanin fingerprinting in

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several grapevine varieties. This could become an alternative method to HPLC methods,

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which could be very useful for the wine industry.

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MATERIALS AND METHODS


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Berry Samples

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Grape clusters of eight red Vitis vinifera L. varieties (three clusters per variety),

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Cabernet Franc, Cabernet Sauvignon, Grenache, Merlot, Petit Verdot, Pignolo, Rebo

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and Sangiovese were handpicked at commercial harvest (October 2011) in two different

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sites located in the DOCa Rioja (Spain). Site 1 was a commercial vineyard in Tudelilla

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(Spain) (42º 18' 49.1'' N, -2º 8' 9.2'' W, 579 m above sea level) and vineyard 2 was an

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experimental vineyard located in Mendavia (Spain) (42º 27' 53.7'' N, -2º 17' 28.2'' W,

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343 m above sea level). After harvesting, clusters were immediately transported in

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portable refrigerators to the University of La Rioja and kept in a cool room at 5 °C until

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imaging. Ten berries per cultivar (80 berries in total) were randomly detached from the

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clusters and allowed to warm for 15 minutes at room temperature before hyperspectral

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imaging.

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Hyperspectral Image Acquisition

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Hyperspectral images of the individual grape berries were acquired using a

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hyperspectral camera under controlled illumination conditions in the laboratory. The

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hyperspectral camera was comprised of the Specim Imspector V10E (Specim, Oulu,

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Finland) spectrograph that decomposes light into its different wavelengths, and a JAI

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Pulnix (JAI, Yokohama, Japan) black and white camera. This imaging system covered

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the spectral range of 380 – 1028 nm with the spectral channels having a 0.6 nm spacing.

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The hyperspectral camera acquired 1392 pixels in the spatial dimension and 1040 pixels

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(channels) in the wavelength dimension. The length of the imaged line over the sample

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was 110 mm, and the distance between the hyperspectral camera and the sample to be

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imaged was 400 mm. Image acquisition was done using Coyote software (Version

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2.2.0, JAI, Japan) at a frequency of eight images per second. Spectral calibration was



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conducted following the manufacturer procedure (EHE 2006), and spatial calibration

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using a black and white target. The lighting set up was comprised of four 20 Watts, 12

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Volts halogen lamps and two 40 Watts, 220 Volts reflector lamps (Spotline, Philips,

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Eindhoven, Nederlands) powered by continuous current supply to avoid light flickering.

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The blue spot lamps were necessary for improving the illumination quality at

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wavelengths between 800 and 1000 nm, a region where halogen lamp illumination was

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insufficient. Each berry was placed on a fruit holder and positioned at the center of the

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field of view of the camera, with the pedicel perpendicular to the camera lens to avoid

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any discrepancy between the berry surface and the pedicel. Each berry was

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photographed on four sides (Figure 1A), by rotating the berry 90º between each image

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acquisition. For each side of the berry, 32 images were taken. In total, for each berry

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128 images were acquired. All imaged berries were individually weighed, stored in a

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plastic bag and frozen at -20 ºC prior to chemical analysis of their anthocyanin

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composition.

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Spectral data collected from the CCD device provided digital numbers

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corresponding to the signal intensity and not actual reflectance values. Therefore, the

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raw data were transformed into reflectance (R) units in a first step and subsequently into

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absorbance (A) units (A=Log 1/R). Image correction was carried out by acquiring white

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and dark reference images. The dark current reference image was obtained by

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completely closing the lens of the camera with its opaque cap, while the white reference

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image was acquired from a uniform, stable and a high white reflectance reference

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(99.9% diffuse reflectance), called Spectralon® (Specim, Oulu, Finland). Reflectance

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for a certain position ṝ and wavelength was calculated according to equation 1:

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(ṝ, ) =

(ṝ,) (ṝ,) (ṝ,) (ṝ,)

(1)

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Where G is the intensity of the light reflected by the berry, W is the intensity of the light

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coming from the white reference, and D is the dark current. Ambient light was

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eliminated by performing the experiments in a dark room. The reference point for ṝ was

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located outside the berry that was being imaged and corresponded to the position where

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the hyperspectral image started for image segmentation. To facilitate segmentation the

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berries were imaged using the Spectralon target as a background. The segmentation was

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done automatically by analysing the variation of reflectance over the spatial dimension

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at the wavelength of approximately 630 nm where there is a sharp difference between

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the Spectralon and the berry reflectance.

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Anthocyanin Extraction and Chromatographic Analysis

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Extraction and analysis of the anthocyanins for the identification of the anthocyanin

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profile and their quantification was conducted separately for each individual berry. The

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extraction protocol was adapted for individual berries from the procedure described

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elsewhere32. The first step involved the dissection of each berry. While still frozen, the

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skin of each berry was manually detached using a scalpel, and subsequently blotted dry

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with a kimwipe. The skin was then placed in a 15 mL Falcon centrifuge tube, added

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some milliliters of liquid nitrogen and milled for 10 seconds with a glass stirring bar

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until a powder was obtained. For the anthocyanin extraction, 10 mL of methanol

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(Methanol LC-MS grade, HiperSolv, VWR, Radnor, PE, USA) acidified at 0.1% (v/v)

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with HCl 12M were added to the centrifuge tube. This was then vortexed and placed in

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the refrigerator (at 5ºC) covered with aluminium foil for 24 hours. After this time, the

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supernatant was moved to a 50 mL Falcon tube and 4 mL of the acidified methanol 7 ACS Paragon Plus Environment


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were added to the 15 mL Falcon tube containing the berry skin. This procedure was

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repeated during eight consecutive days (all methanolic phases were pooled into the 50

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mL Falcon tube), until the absorbance value of the supernatant determined at 520 nm

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using a UV-VIS spectrophotometer (DR 5000, Hach-Lange, Loveland, CO, USA) was

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less or equal to 0.002 AU. The 50 mL Falcon tube containing the anthocyanin extract

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was kept inside the refrigerator at 5ºC covered with aluminium foil at all times until

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their content was transferred to an evaporation flask and concentrated under vacuum at

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30ºC using a rotary evaporator (Buchi Heating Bath B-490, Buchi Rotvapor R-200,

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Flawil, Switzerland) until methanol was removed. The extract was then transferred to a

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2 mL volumetric flask with at Pasteur pipet and rinsed with several fractions of 100µL

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of Milli-Q™ water (Millipore, Bedford, MA, USA), that were also transferred to the

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volumetric flask until a final extract volume of 2 mL. The 2 mL extracts of the 80

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berries were filtered through 0.45 µm pore-size PTFE filters to 2 mL Eppendorf vials

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and stored at -20ºC until chromatographic analysis.

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Chromatographic analyses were carried out on a Shimadzu Nexera (LC-30AD),

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equipped with auto-injector (SIL-30AC), quaternary HPLC pump, column heater (CTO-

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20-AC), diode array (DAD; SPD-M20A) and mass-spectrometer (MS) (AB-Sciex 3200

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Q-Trap) detectors. A LiChrosphere 100 RP-18 reverse phase column (5 µm packing,

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250 x 4 mm i.d.) protected with a guard column of the same material (Scharlab,

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Barcelona, Spain) thermostated at 35 ºC was used. The solvents used were: (A) Milli-Q

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water at 2% formic acid (w/w), (B) acetonitrile/solvent A (80:20, v/v) establishing the

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following gradient: isocratic 100% A in 2 min, from 100 to 92% A in 3 min, from 92 to

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86% A in 12 min, from 86 to 82% A in 5 min, from 82 to 79% A 7.5 min, from 79 to 67

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% A 25.5 min, from 67 to 50 % A in 15.5 min, 50 to 20 % A in 2.5 min, isocratic 20%


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A during 5 min, from 20 to 100% A in 3 min, and isocratic 100 % A in 9 min at a flow

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rate of 1 mL min–1. Quantification was accomplished by HPLC-DAD at 520 nm using

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an external calibration curve of Malvidin-3-O-glucoside chloride (> 95% HPLC,

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Extrasynthèse, Genay, France). Fifteen individual anthocyanins were identified

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according to their order of elution and MS transitions. These were: Malvidin 3-O-

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glucoside (MvGl); Malvidin 3-O-(6-acetyl)glucoside (MvGlAc); Malvidin 3-O-(6-p-

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coumaroyl)glucoside (MvGlCm); Petunidin 3-O-glucoside (PtGl); Petunidin 3-3-O-(6-

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acetyl)glucoside (PtGlAc); Petunidin 3- O-(6-p-coumaroyl)glucoside (PtGlCm);

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Delphinidin 3-O- glucoside (DpGl); Delphinidin 3-O-(6-acetyl)glucoside (DpGlAc);

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Delphinidin 3-O-(6-p-coumaroyl)glucoside (DpGlCm); Peonidin 3-O-glucoside (PnGl);

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Peonidin 3-O-(6-acetyl)glucoside (PnGlAc); Peonidin 3-O-(6-p-coumaroyl)glucoside

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(PnGlCm);

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(CyGlAc); Cyanidin 3-O-(6-p-coumaroyl)glucoside (CyGlCm). Total anthocyanins

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(Total Anth) were calculated as the sum of the fifteen individual compounds, while

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total-3-glucoside (Total Gl Anth), total 3-O-(6-acetyl)glucoside anthocyanins (Total

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GlAc Anth) and total 3- O-(6-p-coumaroyl)glucoside anthocyanins (Total GlCm Anth)

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contents involved the addition of the 3-glucosidated, 3-O-(6-acetyl)glucoside

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anthocyanins, and 3-O-(6-p-coumaroyl)glucoside anthocyanins, respectively.

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Data Analysis

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The averaged spectrum for each grape was used as input for a principal component

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analysis (PCA), an unsupervised pattern recognition technique, in order to provide

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information about the latent structure of spectral matrix, to find spectral differences

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among all spectral samples and also to visualize the presence of outliers

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outlier detection was performed based on Global Mahalanobis (GH) distance analysis,

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i.e. Mahalanobis distance between the center of the population and each sample in the

Cyanidin

3-O-glucoside

(CyGl);

Cyanidin


3-O-(6-acetyl)glucoside

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. Spectral


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space defined by a PCA based on the spectra information. Samples with GH values

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larger than 3 were considered outliers

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Multiplicative Scatter Correction (MSC), Standard Normal Variate (SNV) and

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Detrending to remove the effects of scattering

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treatments were tested in the development of the calibrations and denoted as a four-

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figure-code (a, b, c, d), where a was the number of the derivative; b was the gap over

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which the derivative is calculated; c was the number of data points in a running average

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or smoothing, and d corresponded to the second smoothing 38.

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. The spectral data were pre-treated with

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. Moreover, several mathematical

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Modified Partial Least Squares (MPLS)39 regression was tested for the

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prediction of the 15 individual pigments and the four groups of anthocyanins using the

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average spectra from four different positions of the grape berries in the 380 - 1028 nm

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range. In order to prevent overfitting, 6-fold cross-validation was conducted. For this,

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the calibration set was divided into six groups and each group was then validated using

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the calibration developed with the other samples. In this process another type of outliers

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were analyzed using Student’s T statistic, which indicates the difference between the

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reference and the predicted value. A critical limit of T > 2.5 was used to identify

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samples as chemical outliers38. Different pre-processing combinations based on

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derivatives, window-wise filtering and scatter correction methods were evaluated for

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building the calibration models.

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The following statistics were used to select the most adequate models: Standard

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Error of Calibration (SEC), Standard Error of Cross-Validation (SECV), and

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Determination Coefficient of Cross-Validation (R2cv). Additionally, the Residual

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Predictive Deviation (RPDCV), calculated as the ratio between the standard deviation of


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the reference data for the training set and the Standard Error of Cross-Validation

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(SECV) was also computed. According to Williams and Sobering40 a model is suitable

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for screening purposes – i.e. greater precision yields – if the RPD value is greater than

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3.

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WinISI II software package version 1.50 (Infrasoft International, Port Matilda,

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PA, USA) and the Unscrambler X software package version 10.3 (CAMO ASA, Oslo,

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Norway) were used for chemometric analysis. Samples for the calibration and external

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validation sets were selected using the CENTRE algorithm included in the WinISI II

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software package. This algorithm performs a principal component analysis, reducing the

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original spectral information (log 1/R values) to a small number of linearly independent

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variables, thus facilitating the calculation of spectral distances. These new variables

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were used to calculate the center of the spectral population and the distance (expressed

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as the Mahalanobis distance) of each sample in the calibration set from that centre.

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Having ordered the samples from the two vineyard sites (N = 80) by spectral distances

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(from smallest to greatest distance to the centre), the 20 samples forming the external

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validation set were selected by taking one sample out of every four in the global set,

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leaving a calibration set comprising 60 samples. No anomalous spectra or outliers were

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identified38. The best-fitting equations, obtained from the four anthocyanin groups for

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the calibration set, were subsequently evaluated by external validation using samples

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not involved in the calibration procedure.

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RESULTS AND DISCUSSION

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Anthocyanin Composition of Individual Berries 11 ACS Paragon Plus Environment


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Fifteen individual anthocyanins were identified and the sums of the groups with

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different glycosylated moieties were computed (Table 1). Total Anth ranged from

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505.82 mg L-1 in Petit Verdot berries, to 2463.18 mg L-1 in Rebo berries. On average,

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Total Gl Anth represented 91.2% of Total Anth, while Total GlAc Anth 1.0% and Total

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GlCm Anth a 7.8%. Of the 3-glucosidated anthocyanins, MvGl was the majority

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pigment and CyGl the minority one, across all studied varieties, while differential

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profiles for PtGl, DpGl and PnGl were found among varieties (Table 1). In absolute

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terms, DpGlAc was the anthocyanin with the smallest concentration of the 15 identified

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pigments, with values below the quantification limit in Cabernet Franc, Cabernet

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Sauvignon, Grenache, Petit Verdot and Pignolo.

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The correlation coefficients between the concentrations of all individual and

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groups of anthocyanins were also computed and are shown as supporting information in

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the Supplementary Table 1 (Table S1).

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Spectral Features

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The spectra recorded from the imaged four positions of the berries are displayed in

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Figure 1B. The absorbance values of the spectra corresponding to two of the four grape

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berry rotating positions were substantially different than those of the other two positions

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(Figure 1B). Within a cluster, given the spherical shape of the berries, their whole

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surface is not equally exposed to the solar radiation. Therefore, spectra with higher

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absorbance values may correspond to the part of the berry exposed, while those spectra

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with lower absorbance would potentially correspond to the part of the berry facing the

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inner section of the cluster. For this reason the spectra from all pixels in the 128 images

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per berry were averaged to a unique mean spectrum per berry, as this is meant to more


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accurately represent the average anthocyanin content and distribution of the whole

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berry.

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Typical absorbance spectra collected of the whole set of berries in the zone

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between 380 and 1034 nm are shown in Figure 2A. The effect of derivative was mostly

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apparent for the first derivative of the spectrum, which enabled separating the

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overlapping absorption bands, and revealed certain characteristic absorbance peaks

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around 550 nm, 696 nm, 720nm and 990 nm wavelengths and an inflection point around

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600 nm (Figure 2A, B). The spectral regions between 400 and 550 nm substantially

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contributed to the loadings of the model and are characteristic of chemical compounds

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whose structure involves the presence of aromatic ring compounds, such as the

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anthocyanins, which exhibit their maximum peak of absorbance around 520 nm,

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although small shifts may occur for each specific anthocyanin compound41. Also in this

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region the main absorbance bands of chlorophyll a (maximum at 430 nm) and b

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(maximum at 453 nm), as well as those of carotenoids can be found 42, 43. Additionally,

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the high absorbance values between 600 and 700 nm were also indicative of an

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abundance of chlorophylls, as both chlorophyll a and b are described to exhibit a local

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maximum at 662 and 642 nm, respectively41. The absorbance band observed between

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960 and 990 nm could also be related to the water content in the berries45, which

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constitutes 70 to 80 % of the grape berry mass.

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Development of Calibration, Cross Validation and Prediction Models

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Table 2 summarizes the range, mean value and standard deviation of each individual

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anthocyanin concentration and those of the groups, for the global set of samples of all

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varieties. All individual anthocyanins were well represented with a high variability,



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having a similar number of samples (between 74 and 80), with the exception of

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MvGlAc, DpGlAc and CyGlAc, with cardinality values equal or less than 62. Special

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attention deserves the DpGlAc (Table 2), whose range was inferior to 1mg L-1. For this

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anthocyanin, only 15 values could be recorded, as in the remaining 65 berry samples

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DpGlAc concentration was below the quantification threshold.

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Table 3 shows the calibration, cross-validation and external prediction statistics

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of the best models for the prediction of 15 individual anthocyanins and four anthocyanin

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groups using the combination of signal pretreatments that yielded the best results in

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each case. In this table, N, minimum and maximum refer to the number of samples,

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minimum and maximum values of each dataset used in the model after removal of

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chemical outliers, which accounted for 3-16% of initial data. The model of DpGlAc

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could not be built due to its reduced number of samples. Additionally, the loadings for

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all calibration models for individual and groups of anthocyanins are plotted in Figure S1

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of the Supporting Information.

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While the calibration models for all anthocyanins and groups showed

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determination coefficients (R2c) above 0.90, with the exception of PnGlAc and CyGlAc,

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the best prediction models exhibited a determination coefficient of cross validation

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(R2cv) between the 0.77 and 0.93 marks (Table 3). The SECV values must be analyzed

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in association with the SD and range of the parameters studied. The ratio of

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performance to deviation (RPD) was used to indicate the prediction capacity of the

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models. The RPD values ranged from 2.33 to 6.51, indicating that the performance of

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the calibration models for most of the individual pigments and groups of anthocyanins

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was very satisfactory, and fitted to the recommended values (close or larger than three).


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Moreover, the relative low number of PLS factors (between four and seven) for most of

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the compounds and the fact that the models were built using grape berries of eight

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different varieties contributed to the robustness of the models. These results are very

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remarkable, as they indicate that from a unique spectrum between 380 and 1028 nm,

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obtained from hyperspectral imaging of a berry, the whole anthocyanin profile and

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composition of that berry can be reliably obtained without the need of wet chemistry

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extraction using solvents and further HPLC analysis. Other works have also reported

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very good linear models to predict the total anthocyanin content in intact grape berries,

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with R2CV above 0.95, using spectral measurements in the VIS (400 to 800nm) and NIR

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range (1100 to 2000nm)32 or the total phenolic content, with R2CV = 0.89, using spectral

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measurements in the NIR range of 900 to 1700 nm26, 46. Non-linear models were also

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successful in predicting the total anthocyanin content in grape berries of Cabernet

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Sauvignon using hyperspectral imaging in the range of 400 to 1000 nm21. Additionally,

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in the current study both the spectroscopic and HPLC measurements were carried out on

357

a single berry, instead of multiple-berry samples21,23,32. This is relevant because the

358

smaller the berry sample size, the harder is to obtain accurate enological results. The

359

approach of analyzing berries individually would mimic a potential scenario of a sorting

360

table where berry selection (after destemming) could be driven from the total

361

anthocyanin content provided by a contactless, hyperspectral camera above the

362

conveyor belt. This approach can be considered to have a potentially large impact on

363

grape composition monitoring and control of continuously operating sorting belts which

364

deliver the berries to one fermentation tank or another depending on the type of wine

365

aimed.

366



367

Nevertheless, modelling and profiling of individual anthocyanin compounds was

368

seldom reported. The closest approach, but focused on the phenolic constituents other

369

than anthocyanins was attempted using NIR hyperspectral imaging (950 to 1650nm) in

370

samples of Vitis vinifera L. Zalema grape skins, stems and seeds, separated from

371

winemaking pomace28. In this study, R2cv values above 0.90 were obtained for

372

epicatechin, individual hydroxicinnamic acids, such as caffeic, caftaric and trans-

373

coutaric acids, and kaempferol derivatives, but previous freeze-drying of the samples

374

was necessary to obtain reliable results.

375

In order to evaluate the best models an external validation was developed to

376

predict the two main global anthocyanin groups. The robustness of the selected models

377

was tested using external validation set samples, which did not belong to the internal

378

validation set (Table 3). For the internal validation (data not shown) N = 60 samples

379

while for the external validation sets N = 20 samples. A relatively wide range was

380

present in both data sets. Structured selection with the CENTRE algorithm - only using

381

spectral information treatment algorithms - proved adequate, since the internal and

382

external validation sets displayed similar values for mean, range and standard deviation

383

for all the parameters studied. Regarding the external validation, the determination

384

coefficients of prediction (R2P) were above 0.80 for eight individual anthocyanins as

385

well as for Total Anth and Total Gl Anth (Table 3). These results are in agreement with

386

previous works22-24 who examined the potential of NIR (900-1700 nm) hyperspectral

387

imaging for the screening of total and non-acylated anthocyanins in intact berries of

388

several cultivars. The best predictive models for the two major anthocyanin groups,

389

Total Anth and Total Gl Anth for both internal and external validation sets along with

390

the prediction lines at 95% of confidence are displayed in Figure 3. A high data

391

dispersion together with a good fit can be seen along the correlation lines. 16 ACS Paragon Plus Environment

Page 16 of 35

Page 17 of 35


392 393

The potential of VIS-NIR hyperspectral imaging for the screening and profiling

394

of acylated and non-acylated individual anthocyanins and their sums, as well as total

395

anthocyanins in intact grape berries was investigated and confirmed. The developed

396

models were robust and applicable to berries of eight different varieties, but further

397

examination of the influence of more grapevine cultivars, seasons and origins should be

398

conducted with the aim of developing even more robust, global predictive models. This

399

non-destructive, spectral technology could potentially be considered as an alternative to

400

widely adopted HPLC analysis. This could be beneficial in terms of reducing the

401

analysis time and labour costs and avoid the use of solvents, while providing reliable

402

and robust information of the anthocyanin composition of grape berries.

403 404

ABBREVIATIONS

405

MvGl: Malvidin 3-O-glucoside

406

MvGlAc: Malvidin 3-O-(6-acetyl)glucoside

407

MvGlCm: Malvidin 3-O-(6-p-coumaroyl)glucoside

408

PtGl: Petunidin 3-O-glucoside

409

PtGlAc: Petunidin 3-O-(6-acetyl)glucoside

410

PtGlCm: Petunidin 3-O-(6-p-coumaroyl)glucoside

411

DpGl: Delphinidin 3-O-glucoside

412

DpGlAc: Delphinidin 3-O-(6-acetyl)glucoside

413

DpGlCm: Delphinidin 3-O-(6-p-coumaroyl)glucoside

414

PnGl: Peonidin 3-O-glucoside

415

PnGlAc: Peonidin 3-O-(6-acetyl)glucoside

416

PnGlCm: Peonidin 3-O-(6-p-coumaroyl)glucoside



417

CyGl: Cyanidin 3-O-glucoside

418

CyGlAc: Cyanidin 3-O-(6-acetyl)glucoside

419

CyGlCm: Cyanidin 3-O-(6-p-coumaroyl)glucoside

420

Total Anth: Total anthocyanins

421

Total Gl Anth: Total 3-O-glucoside anthocyanins

422

Total GlAc Anth: Total 3-O-(6-acetyl)glucoside anthocyanins

423

Total GlCm Anth: Total 3-O-(6-p-coumaroyl)glucoside anthocyanins

424

DAD: Diode array detector

425

HPLC: High performance liquid chromatography

426

HSI: Hyperspectral imaging

427

MPLS: Modified partial least squares

428

MSC: Multiplicative scatter correction

429

NIR: Near infrared radiation

430

PCA: Principal component analysis

431

PTFE: Polytetrafluoroethylene

432

R: Reflectance

433

R2CV: Determination coefficient of cross validation

434

R2P: Determination coefficient of prediction

435

RPD: Residual predictive deviation

436

SEC: Standard error of calibration

437

SECV: Standard error of cross validation

438

SNV: Standard normal variate

439

UV: Ultra violet radiation

440

VIS: Visible radiation

441


Page 18 of 35

Page 19 of 35


442

Acknowledgments

443

Special thanks to Borja Millan and Salvador Gutiérrez for their assistance during image

444

and acquisition and image processing, and to Victor Sicilia and Teresa Díaz for their

445

help in the extraction of anthocyanins and sample preparation.

446 447

This work is partially supported by: European Investment Funds by

448

FEDER/COMPETE/POCI– Operacional Competitiveness and Internacionalization

449

Programme, under Project POCI-01-0145-FEDER-006958 and National Funds by FCT

450

-

451

UID/AGR/04033/2013 and by I&D project NORTE-01-0145-FEDER-000017, Interact

452

- Integrative Research in Environment, Agro-Chain and Technology, co-funded by

453

Fundo Europeu de Desenvolvimento Regional (FEDER) through NORTE 2020

454

(Programa Operacional Regional do Norte 2014/2020).

Portuguese

Foundation

for

Science

and

Technology,

under the

project

455 456

Supporting Information Available: The correlation coefficients between the

457

concentrations of all individual and groups of anthocyanins are shown as supporting

458

information in Table S1. The loadings of all calibration models for individual and

459

groups of anthocyanins are plotted in Figure S1.

460 461 462 463 464 465 466

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and characterisation by UV-VIS. Current Protocols in Food Analytical

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45. Williams, P.C. Implementation of near-infrared technology. Near Infrared

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Echávarri, J.F.; Heredia, F.J. Simplified method for the screening of

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technological maturity of red grape and total pehnolic compouns of red grape

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skin: Application of the characteristic vector method to near-infrared spectra. J.

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Agric. Food Chem. 2015, 63, 4284-4290.

608 609 610 611

Figure captions

612

Figure 1. (A) Scheme of the 4-step grape berry rotation (90º each rotation step) for the

613

acquisition of hyperspectral images of four sides of the berry; (B) Average absorbance

614

spectra corresponding to the four sides of an imaged Cabernet Sauvignon grape berry.

615



616

Figure 2. (A) Absorbance (Log (1/R)) (A) and (B) first derivative of the absorbance

617

(D1log (1/R)) spectra of the whole set of grape berries of eight different varieties (80

618

samples) in the VIS-NIR region studied.

619 620

Figure 3. Correlation of the values obtained of HPLC with respect to those predicted by

621

the hyperspectral imaging models and external validation. (A) Total Anthocyanins

622

(Total Anths); (B) Total 3-glucoside anthocyanins (Total Gl Anth). Correlation lines:

623

solid line and predictions intervals: dashed line for cross-validation models,

624

respectively. Dotted line represents the 1:1 line.


Page 26 of 35

Page 27 of 35

625 626 627


Table 1. Overview of the mean and standard deviation anthocyanin concentration in grape skin extracts of each grapevine variety. Results are expressed in mg L-1 malvidin-3-O-glucoside. Grapevine variety

Compound Cabernet Franc

Cabernet Sauvignon

Grenache

Merlot

Petit Verdot

Pignolo

Rebo

Sangiovese

Total Anth.

919.49 ± 223.52

877.86 ± 172.80

595.01 ± 149.11

1358.35 ± 299.72

505.82 ±152.68

997.13 ± 265.44

2463.18 ± 379.25

1736.62 ± 378.87

Total Gl Anth.

801.45 ± 186.56

827.33 ± 157.30

544.71 ± 135.48

1197.84 ± 238.06

419.09 ± 128.50

941.13 ± 255.45

2262.66 ± 324.61

1723.88 ± 375.84

4.14 ± 1.35

6.22 ± 3.30

3.55 ± 1.43

24.72 ± 9.37

8.97 ± 3.90

5.74 ± 3.77

46.88 ± 22.64

2.72 ± 1.36

Total GlCm Anth.

113.90 ± 47.53

44.31 ± 22.93

46.74 ± 14.68

135.80 ± 61.57

77.76 ± 26.34

50.26 ± 17.33

153.63 ± 52.00

10.02 3.65

MvGl

554.94 ± 143.38

472.75 ± 100.70

433.10 ± 100.08

605.40 ± 99.56

339.44 ± 91.39

390.56 ± 117.40

948.86 ± 120.03

602.44 ± 157.13

MvGlAc

0.00 ± 0.00

1.87 ± 3.04

2.03 ± 1.24

21.42 ± 8.99

3.49 ± 2.87

1.29 ± 2.82

38.57 ± 22.17

0.62 ± 0.81

MvGlCm

77.14 ± 35.40

23.58 ± 8.59

33.82 ± 10.76

66.00 ± 28.15

61.07 ± 19.93

24.93 ± 9.59

60.34 ± 20.88

2.61 ± 1.08

PtGl

85.40 ± 23.52

104.82 ± 23.17

36.46 ± 12.66

173.98 ± 35.28

36.14 ± 17.43

140.98 ± 46.72

381.52 ± 56.14

265.59 ± 60.62

PtGlAc

3.30 ± 1.38

3.83 ± 0.88

1.42 ± 0.45

1.98 ± 0.73

5.36 ± 2.69

3.77 ± 0.95

4.45 ± 1.24

0.58 ± 0.39

PtGlCm

9.19 ± 3.01

3.63 ± 1.44

2.43 ± 0.87

15.53 ± 6.82

5.51 ± 2.72

6.03 ± 2.00

23.47 ± 7.67

1.00 ± 0.40

80.23 ± 30.54

157.67 ± 40.98

25.20 ± 10.00

202.90 ± 44.04

24.52 ± 14.01

168.59 ± 57.17

611.56 ± 100.52

203.50 47.09

DpGlAc

nd

nd

nd

0.25 ± 0.29

nd

nd

0.01 ± 0.05

0.16 ± 0.30

DpGlCm

8.50 ± 2.67

5.25 ± 1.63

1.80 ± 0.77

20.67 ± 9.91

4.72 ± 2.51

8.00 ± 2.38

43.78 ± 14.55

1.10 ± 0.51

67.48 ± 23.76

66.68 ± 20.30

45.41 ± 29.00

164.52 ± 62.50

17.51 ± 5.96

151.25 ± 39.39

167.94 ± 32.35

338.14 ± 77.89

PnGlAc

0.62 ± 0.41

0.37 ± 0.12

0.09 ± 0.11

0.71 ± 0.51

0.12 ± 0.11

0.47 ± 0.30

2.46 ± 0.43

0.90 ± 0.68

PnGlCm

12.04 ± 7.01

7.64 ± 12.44

5.28 ± 2.30

22.50 ± 12.89

3.81 ± 1.30

7.58 ± 3.21

9.77 ± 3.67

1.93 ± 0.85

CyGl

13.40 ± 4.96

25.41 ± 14.10

4.55 ± 3.26

51.04 ± 18.41

1.47 ± 0.88

89.75 ± 25.58

152.79 ± 38.17

314.21 ± 91.29

CyGlAc

0.22 ± 0.09

0.15 ± 0.13

0.01 ± 0.03

0.35 ± 0.33

0.01 ± 0.01

0.21 ± 0.34

1.39 ± 1.01

0.47 ± 0.41

CyGlCm

7.03 ± 3.60

4.21 ± 1.04

3.42 ± 1.32

11.10 ± 5.32

2.64 ± 0.53

3.71 ± 0.97

16.26 ± 6.16

3.37 ± 1.34

Total GlAc Anth

DpGI

PnGl

1

ACS Paragon Plus Environment


Page 28 of 35

628 629 630 631 632 633

Total Anthocyanins (Total Anth.); Total 3-O-glucoside anthocyanins (Total Gl Anth.); Total 3-O-(6-acetyl)glucoside anthocyanins (Total GlAc Anth.); Total 3-O-(6-p-coumaroyl)glucoside anthocyanins (Total GlCm Anth.); Malvidin 3-O-glucoside (MvGl); Malvidin 3-O-(6-acetyl)glucoside (MvGlAc); Malvidin 3-O-(6-p-coumaroyl)glucoside (MvGlCm); Petunidin 3-O-glucoside (PtGl); Petunidin 3-O-(6-acetyl)glucoside (PtGlAc); Petunidin 3-O-(6-p-coumaroyl)glucoside (PtGlCm); Delphinidin 3-O-glucoside (DpGl); Delphinidin 3-O-(6-acetyl)glucoside (DpGlAc); Delphinidin 3-O-(6-p-coumaroyl)glucoside (DpGlCm); Peonidin 3-Oglucoside (PnGl); Peonidin 3-O-(6-acetyl)glucoside (PnGlAc); Peonidin 3-O-(6-p-coumaroyl)glucoside (PnGlCm); Cyanidin 3-O-glucoside (CyGl); Cyanidin 3-O-(6-acetyl)glucoside (CyGlAc); Cyanidin 3-O-(6-pcoumaroyl)glucoside (CyGlCm). nd: not detected.

634 635

2


Page 29 of 35


636 637

638 639 640 641 642 643 644 645 646

Table 2. Statistics overview of the anthocyanin concentration of grape skin extracts of all grapevine varieties, expressed in mg L-1 of malvidin-3-O-glucoside. Compound

N

Minimum

Maximum

Mean

SD

Total Anth.

80

315.87

3250.33

1181.68

664.96

Total Gl Anth.

80

280.73

2942.01

1089.76

629.16

Total GlAc Anth.

80

1.16

75.88

12.87

16.89

Total GlCm Anth.

80

4.02

232.45

79.05

58.78

MvGl

80

226.56

1153.86

543.44

211.88

MvGlAc

52

nd

65.69

8.66

15.54

MvGlCm

80

0.73

137.64

43.69

30.98

PtGl

80

16.25

480.79

153.11

118.13

PtGlAc

78

nd

9.42

3.09

1.96

PtGlCm

80

0.28

34.65

8.35

8.13

DpGI

80

9.67

807.71

184.27

182.90

DpGlAc

15

nd

0.98

0.05

0.17

DpGlCm

80

0.50

65.69

11.73

14.81

PnGl

80

10.06

461.56

127.37

104.98

PnGlAc

74

nd

3.25

0.72

0.80

PnGlCm

80

0.34

42.85

8.82

9.06

CyGl

80

0.58

476.94

81.58

106.89

CyGlAc

62

nd

4.13

0.35

0.58

CyGlCm

80

0.74

28.41

6.47

5.52

N: Number of samples; SD: standard deviation. Total Anthocyanins (Total Anth.); Total 3-O-glucoside anthocyanins (Total Gl Anth.); Total 3-O-(6-acetyl)glucoside anthocyanins (Total GlAc Anth.); Total 3-O-(6-p-coumaroyl)glucoside anthocyanins (Total GlCm Anth.); Malvidin 3-O-glucoside (MvGl); Malvidin 3-O-(6-acetyl)glucoside (MvGlAc); Malvidin 3-O-(6-pcoumaroyl)glucoside (MvGlCm); Petunidin 3-O-glucoside (PtGl); Petunidin 3-O-(6-acetyl)glucoside (PtGlAc); Petunidin 3-O-(6-pcoumaroyl)glucoside (PtGlCm); Delphinidin 3-O-glucoside (DpGl); Delphinidin 3-O-(6-acetyl)glucoside (DpGlAc); Delphinidin 3O-(6-p-coumaroyl)glucoside (DpGlCm); Peonidin 3-O-glucoside (PnGl); Peonidin 3-O-(6-acetyl)glucoside (PnGlAc); Peonidin 3O-(6-p-coumaroyl)glucoside (PnGlCm); Cyanidin 3-O-glucoside (CyGl); Cyanidin 3-O-(6-acetyl)glucoside (CyGlAc); Cyanidin 3O-(6-p-coumaroyl)glucoside (CyGlCm). nd: not detected.

647 648 649 650



651 652

Page 30 of 35

Table 3. Calibration statistical descriptors and internal and external validation results for the VIS-NIR spectral models of anthocyanin concentration in grapes skin extracts (values are expressed as mg L-1 malvidin-3-O-glucoside) Compound

Spectral Treatment

N

SD

Minimum

Maximum

PLS Factor

Calibration

Cross Validation

SEC

R2

C

SECV

R2

cv

RPD

External1 Validation SEP R2

p

Snv-DT 1.5.5.1

76

629.46

315.87

2839.96

4

148.25

0.94

189.05

0.91

3.51

281.58

0.86

Total Gl Anth.

1.5.5.1

74

562.69

280.73

2521.24

4

125.97

0.95

155.94

0.92

4.03

272.60

0.86

Total GlAc Anth.

2.5.5.1

73

12.80

1.16

54.26

5

1.98

0.98

4.12

0.90

4.10

4.82

0.51

Total GlCm Anth.

1.5.5.1

73

56.49

4.02

221.37

6

13.42

0.94

23.09

0.83

2.55

42.72

0.40

Snv-DT 1.5.5.1

76

201.85

226.56

1100.02

4

56.014

0.92

73.93

0.87

2.87

109.10

0.83

MvGlAc

2.5.5.1

48

13.39

0.09

46.49

6

1.32

0.99

4.13

0.90

4.27

3.30

0.90

MvGlCm

Snv-DT 1.5.5.1

75

29.78

0.73

119.94

7

6.99

0.94

13.32

0.80

2.33

23.33

0.57

PtGl

Snv-DT 1.5.5.1

75

108.00

16.25

426.94

4

23.29

0.95

29.44

0.93

4.01

49.66

0.87

PtGlAc

1.5.5.1

72

1.50

0.23

6.09

3

0.73

0.76

0.98

0.57

2.00

1.18

0.35

PtGlCm

1.5.5.1

71

6.72

0.28

26.06

7

1.12

0.97

2.04

0.91

3.98

4.44

0.84

Snv-DT 1.5.5.1

78

158.65

9.67

657.71

4

36.89

0.95

48.41

0.91

3.78

71.39

0.88

DpGlAc

--

--

--

--

--

--

--

--

--

--

--

--

--

DpGlCm

Snv-DT 1.5.5.1

72

11.59

0.50

46.49

7

1.83

0.97

3.28

0.92

4.51

2.34

0.48

PnGl

Snv-DT 1.5.5.1

73

86.47

10.06

383.09

7

15.93

0.97

30.35

0.88

3.46

49.10

0.81

PnGlAc

Snv-DT 2.5.5.1

69

0.69

0.02

2.82

2

0.26

0.86

0.31

0.80

2.61

0.35

0.88

PnGlCm

Snv-DT 2.5.5.1

71

7.56

0.34

37.53

4

2.14

0.92

3.81

0.75

2.38

6.50

0.54

CyGl

1.5.5.1

67

47.50

0.58

159.63

6

8.59

0.97

16.43

0.88

6.51

48.85

0.69

CyGlAc

1.5.5.1

54

0.33

0.01

1.26

2

0.14

0.81

0.16

0.77

3.94

0.16

0.85

CyGlCm

1.5.5.1

70

4.80

1.46

19.14

5

1.21

0.94

1.78

0.86

3.10

3.82

0.63

Total Anth.

MvGl

DpGI

1


Page 31 of 35


653 654 655 656 657 658 659

Snv-DT: standard normal variate plus Detrending; N: number of samples were the ones used for calibration and cross validation models after outlier detection; SD: standard deviation; SEC: standard error of calibration; R2c: determination coefficient of calibration; SECV: standard error of cross-validation; R2cv: determination coefficient of cross-validation; RPD: residual predictive deviation. Total Anthocyanins (Total Anth.); Total 3O-glucoside anthocyanins (Total Gl Anth.); Total 3-O-(6-acetyl)glucoside anthocyanins (Total GlAc Anth.); Total 3-O-(6-p-coumaroyl)glucoside anthocyanins (Total GlCm Anth.); Malvidin 3-O-glucoside (MvGl); Malvidin 3-O-(6-acetyl)glucoside (MvGlAc); Malvidin 3-O-(6-p-coumaroyl)glucoside (MvGlCm); Petunidin 3-O-glucoside (PtGl); Petunidin 3-O-(6-acetyl)glucoside (PtGlAc); Petunidin 3-O-(6-pcoumaroyl)glucoside (PtGlCm); Delphinidin 3-O-glucoside (DpGl); Delphinidin 3-O-(6-acetyl)glucoside (DpGlAc); Delphinidin 3-O-(6-p-coumaroyl)glucoside (DpGlCm); Peonidin 3-O-glucoside (PnGl); Peonidin 3O-(6-acetyl)glucoside (PnGlAc); Peonidin 3-O-(6-p-coumaroyl)glucoside (PnGlCm); Cyanidin 3-O-glucoside (CyGl); Cyanidin 3-O-(6-acetyl)glucoside (CyGlAc); Cyanidin 3-O-(6-p-coumaroyl)glucoside (CyGlCm).

660

1

For the predictive models, internal validation was carried out with N=60 samples while external validation used N=20 samples.

661 662 663

2



Figure 1. (A) Scheme of the 4-step grape berry rotation (90º each rotation step) for the acquisition of hyperspectral images of four sides of the berry; (B) Average absorbance spectra corresponding to the four sides of an imaged Cabernet Sauvignon grape berry. 248x92mm (96 x 96 DPI)


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Page 33 of 35


Figure 2. (A) Absorbance (Log (1/R)) (A) and (B) first derivative of the absorbance (D1log (1/R)) spectra of the whole set of grape berries of eight different varieties (80 samples) in the VIS-NIR region studied. 145x179mm (96 x 96 DPI)



Figure 3. Correlation of the values obtained of HPLC with respect to those predicted by the hyperspectral imaging models and external validation. (A) Total Anthocyanins (Total Anths); (B) Total 3-glucoside anthocyanins (Total Gl Anth). Correlation lines: solid line and predictions intervals: dashed line for crossvalidation models, respectively. Dotted line represents the 1:1 line. 96x190mm (96 x 96 DPI)


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TOC graphic 44x23mm (300 x 300 DPI)


Use of Visible and Short-Wave Near-Infrared Hyperspectral Imaging

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