Infrared spectroscopy investigation of fresh grapevine (Vitis vinifera) shoots, leaves, and berries using novel chemometric applications for viticultural data

Abstract

Infrared spectroscopy provides extensive spectral and chemical information for plant material. However, the raw spectral data of fresh grapevine organs are poorly understood. This study investigated the spectral properties of grapevine shoots, leaves, and berries collected throughout the growing season. Near infrared (NIR) spectral analysis was performed using a solid probe (NIR-SP) and a rotating integrating sphere (NIR-RS) on samples collected from multiple cultivars and vintages from commercial growing sites at various phenological stages. Clustering patterns of the spectral properties were investigated using principal component analysis (PCA), and unsupervised self-organising maps (SOM) as a novel approach. Separation based on grapevine organ type was seen using both PCA and unsupervised SOM. Thereafter, unsupervised SOM analysis showed organ-specific differences between phenological stages (berries and shoots) and lignification (shoots). Supervised SOM was performed for classification purposes as a novel application on viticultural data for the prediction of grapevine organ and accurate prediction of organ type at 90.3% was observed for the NIR-SP dataset collected from 2019 to 2021. The prediction of individual phenological stages proved more challenging, but when phenological stages were grouped together, a prediction of 85.6% for the NIR-RS grape berry dataset was found. The prediction of shoot lignification yielded accurate results of 74.4% and 89.9% for the NIR-SP 2019–2021 and NIR-RS 2020–2021 datasets, respectively. Furthermore, spectral variable selection with orthogonal partial least squares discriminant analysis (OPLS-DA) and S-plots improved the discrimination of shoots and leaves up to 14% for the NIR-RS 2020–2021 dataset. Lignification predictions also improved by 9.8% for the NIR-SP shoot 2019–2021 (92.3%) and 12.7% for the NIR-RS shoot 2020–2021 (95.9%) datasets. Clustering methods, especially unsupervised SOM, showed important groupings in the raw spectral data for fresh grapevine organs. Organ type, phenological stage, and lignification were highlighted for showing prominent separation over multiple vintages.

Plant phenotyping relevance

NIR分光とSOM、PCA、OPLS-DAを用いて、ブドウの器官、フェノロジー段階、シュートの木化を推定・分類する手法を中心的に開発・評価している。

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