The choice of the proper moment for harvesting is a crucial aspect in winemaking process, since the chemical attributes of grape berries strongly influence red wine quality. In particular, phenolic composition of red grapes plays a significant role in many sensory properties of wine related to color and taste. Anthocyanins are the most important phenolic compounds for red grapes: they accumulate in the grape skin during ripening, and they are responsible for the purple color of ripe berries. Routine analysis for the determination of grapes phenolic maturity includes chromatographic and spectroscopic techniques, that are time-consuming and expensive. In this work, we propose an innovative device conceived for the determination of grape phenolic maturity based on RGB images of grape berries acquired with a smartphone. The device has been designed to be used directly in the vineyard thanks to its small size and to the possibility of acquiring geolocated images of the berries under controlled lighting conditions. In this study, grape samples of three different varieties (Ancellotta, Lambrusco Salamino and Sangiovese) were collected at different harvest times from veraison to maturity and imaged by means of a common smartphone using the device. The RGB images were then converted into one-dimensional signals, named colourgrams, which codify the color properties of the images. The dataset of colourgrams was then used to calculate calibration models using Partial Least Squares (PLS) regression in order to relate color information with chemical parameters generally employed to evaluate grape phenolic maturity, such as total anthocyanins content and extractable anthocyanins content. The calibration models were implemented in a software interface that allows to acquire geolocated images of the grape samples, visualize the outcomes of the analysis, visualize maps and plots related to phenolic maturity, store data and share relevant information.
Design and application of a smartphone-based device for in vineyard determination of anthocyanins content in red grapes / Menozzi, C; Calvini, R; Nigro, G; Tessarin, P; Bossio, D; Calderisi, M; Ferrari, V; Foca, G; Ulrici, A. - In: MICROCHEMICAL JOURNAL. - ISSN 0026-265X. - 191:(2023), pp. .-108811. [10.1016/j.microc.2023.108811]
Design and application of a smartphone-based device for in vineyard determination of anthocyanins content in red grapes
Menozzi, C;Calvini, R;Ferrari, V;Foca, G;Ulrici, A
2023
Abstract
The choice of the proper moment for harvesting is a crucial aspect in winemaking process, since the chemical attributes of grape berries strongly influence red wine quality. In particular, phenolic composition of red grapes plays a significant role in many sensory properties of wine related to color and taste. Anthocyanins are the most important phenolic compounds for red grapes: they accumulate in the grape skin during ripening, and they are responsible for the purple color of ripe berries. Routine analysis for the determination of grapes phenolic maturity includes chromatographic and spectroscopic techniques, that are time-consuming and expensive. In this work, we propose an innovative device conceived for the determination of grape phenolic maturity based on RGB images of grape berries acquired with a smartphone. The device has been designed to be used directly in the vineyard thanks to its small size and to the possibility of acquiring geolocated images of the berries under controlled lighting conditions. In this study, grape samples of three different varieties (Ancellotta, Lambrusco Salamino and Sangiovese) were collected at different harvest times from veraison to maturity and imaged by means of a common smartphone using the device. The RGB images were then converted into one-dimensional signals, named colourgrams, which codify the color properties of the images. The dataset of colourgrams was then used to calculate calibration models using Partial Least Squares (PLS) regression in order to relate color information with chemical parameters generally employed to evaluate grape phenolic maturity, such as total anthocyanins content and extractable anthocyanins content. The calibration models were implemented in a software interface that allows to acquire geolocated images of the grape samples, visualize the outcomes of the analysis, visualize maps and plots related to phenolic maturity, store data and share relevant information.
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