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Non-destructive quantification of key quality characteristics in individual grapevine berries using near-infrared spectroscopy

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Title: Non-destructive quantification of key quality characteristics in individual grapevine berries using near-infrared spectroscopy
Authors: Lucie Cornehl, Pascal Gauweiler, Xiaorong Zheng, Julius Krause, Florian Schwander, Reinhard Töpfer, Robin Gruna, Anna Kicherer
Source: Frontiers in Plant Science, Vol 15 (2024)
Publisher Information: Frontiers Media S.A., 2024.
Publication Year: 2024
Collection: LCC:Plant culture
Subject Terms: maturity, NIRS, precision viticulture, quality, handheld, field phenotyping, Plant culture, SB1-1110
More Details: It is crucial for winegrowers to make informed decisions about the optimum time to harvest the grapes to ensure the production of premium wines. Global warming contributes to decreasing acidity and increasing sugar levels in grapes, resulting in bland wines with high contents of alcohol. Predicting quality in viticulture is thus pivotal. To assess the average ripeness, typically a sample of one hundred berries representative for the entire vineyard is collected. However, this process, along with the subsequent detailed must analysis, is time consuming and expensive. This study focusses on predicting essential quality parameters like sugar and acid content in Vitis vinifera (L.) varieties ‘Chardonnay’, ‘Riesling’, ‘Dornfelder’, and ‘Pinot Noir’. A small near-infrared spectrometer was used measuring non-destructively in the wavelength range from 1 100 nm to 1 350 nm while the reference contents were measured using high-performance liquid chromatography. Chemometric models were developed employing partial least squares regression and using spectra of all four grapevine varieties, spectra gained from berries of the same colour, or from the individual varieties. The models exhibited high accuracy in predicting main quality-determining parameters in independent test sets. On average, the model regression coefficients exceeded 93% for the sugars fructose and glucose, 86% for malic acid, and 73% for tartaric acid. Using these models, prediction accuracies revealed the ability to forecast individual sugar contents within an range of ± 6.97 g/L to ± 10.08 g/L, and malic acid within ± 2.01 g/L to ± 3.69 g/L. This approach indicates the potential to develop robust models by incorporating spectra from diverse grape varieties and berries of different colours. Such insight is crucial for the potential widespread adoption of a handheld near-infrared sensor, possibly integrated into devices used in everyday life, like smartphones. A server-side and cloud-based solution for pre-processing and modelling could thus avoid pitfalls of using near-infrared sensors on unknown varieties and in diverse wine-producing regions.
Document Type: article
File Description: electronic resource
Language: English
ISSN: 1664-462X
Relation: https://www.frontiersin.org/articles/10.3389/fpls.2024.1386951/full; https://doaj.org/toc/1664-462X
DOI: 10.3389/fpls.2024.1386951
Access URL: https://doaj.org/article/b6fdf1fa5b524de3887c2bbd751d6c92
Accession Number: edsdoj.b6fdf1fa5b524de3887c2bbd751d6c92
Database: Directory of Open Access Journals
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  Data: It is crucial for winegrowers to make informed decisions about the optimum time to harvest the grapes to ensure the production of premium wines. Global warming contributes to decreasing acidity and increasing sugar levels in grapes, resulting in bland wines with high contents of alcohol. Predicting quality in viticulture is thus pivotal. To assess the average ripeness, typically a sample of one hundred berries representative for the entire vineyard is collected. However, this process, along with the subsequent detailed must analysis, is time consuming and expensive. This study focusses on predicting essential quality parameters like sugar and acid content in Vitis vinifera (L.) varieties ‘Chardonnay’, ‘Riesling’, ‘Dornfelder’, and ‘Pinot Noir’. A small near-infrared spectrometer was used measuring non-destructively in the wavelength range from 1 100 nm to 1 350 nm while the reference contents were measured using high-performance liquid chromatography. Chemometric models were developed employing partial least squares regression and using spectra of all four grapevine varieties, spectra gained from berries of the same colour, or from the individual varieties. The models exhibited high accuracy in predicting main quality-determining parameters in independent test sets. On average, the model regression coefficients exceeded 93% for the sugars fructose and glucose, 86% for malic acid, and 73% for tartaric acid. Using these models, prediction accuracies revealed the ability to forecast individual sugar contents within an range of ± 6.97 g/L to ± 10.08 g/L, and malic acid within ± 2.01 g/L to ± 3.69 g/L. This approach indicates the potential to develop robust models by incorporating spectra from diverse grape varieties and berries of different colours. Such insight is crucial for the potential widespread adoption of a handheld near-infrared sensor, possibly integrated into devices used in everyday life, like smartphones. A server-side and cloud-based solution for pre-processing and modelling could thus avoid pitfalls of using near-infrared sensors on unknown varieties and in diverse wine-producing regions.
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