为了通过利用葡萄栽培新技术来提高葡萄酒的生产过程,这项研究提出了一种使用非破坏性快速评估酿酒葡萄成熟度水平的新方法,原位红外光谱和人工智能技术。在我们先前的工作的基础上,重点是从可见和近红外(VNIR)和短波红外(SWIR)区域估算糖含量(○白利糖度),这项研究将其范围扩大到包括pH和可滴定酸度,决定葡萄成熟度的关键参数,反过来,葡萄酒质量,提供更具代表性的估计途径。数据来自四个葡萄品种-霞多丽,Malagouzia,长相思,和Syrah-在KtimaGerovassiliou葡萄园的2023年收获和收获前物候阶段,希腊北部开发了一个全面的光谱库,覆盖VNIR-SWIR光谱(350-2500nm),在原位进行测量。pH值的地面实况数据,可滴定酸度,和糖含量是使用常规实验室方法获得的:总可溶性固体(TSS)(○白利糖度),通过滴定可滴定的酸度(以每升必须的酒石酸毫克数表示)和pH计的pH值,在必须样品的不同成熟阶段进行分析。通过采用机器学习算法,从点高光谱数据中预测成熟度指标,包括偏最小二乘回归(PLS),随机森林回归(RF),支持向量回归(SVR)和卷积神经网络(CNN),结合各种预处理技术。还考虑了多输出模型来同时预测所有三个指标以利用它们的相互关系。提出了一种新的多输入多输出CNN模型,结合了多头注意力机制,并能够识别其关注的光谱区域,因此具有更高的可解释性程度。我们的结果表明,在估计糖含量方面具有很高的准确性,pH值,和可滴定的酸度,最佳模型的平均R2值分别为0.84、0.76和0.79,跨所有属性。与最佳的单输出模型相比,多输出模型的预测结果没有提高,提出的CNN模型与下一个最佳模型相当。可解释性分析强调,CNN模型侧重于与糖的存在相关的光谱区域(即,葡萄糖和果糖)和羧酸基团。这项研究强调了便携式光谱技术在实时、酿酒葡萄成熟度的无损评估,从而为葡萄酒生产行业的明智决策提供有价值的工具。通过将pH和可滴定酸度整合到分析中,我们的方法提供了葡萄质量的整体观点,促进更全面和有效的葡萄栽培做法。
In the pursuit of enhancing the wine production process through the utilization of new technologies in viticulture, this study presents a novel approach for the rapid assessment of wine grape maturity levels using non-destructive, in situ infrared spectroscopy and artificial intelligence techniques. Building upon our previous work focused on estimating sugar content (∘Brix) from the visible and near-infrared (VNIR) and short-wave infrared (SWIR) regions, this research expands its scope to encompass pH and titratable acidity, critical parameters determining the grape maturity degree, and in turn, wine quality, offering a more representative estimation pathway. Data were collected from four grape varieties-Chardonnay, Malagouzia, Sauvignon Blanc, and Syrah-during the 2023 harvest and pre-harvest phenological stages in the vineyards of Ktima Gerovassiliou, northern Greece. A comprehensive spectral library was developed, covering the VNIR-SWIR spectrum (350-2500 nm), with measurements performed in situ. Ground truth data for pH, titratable acidity, and sugar content were obtained using conventional laboratory methods: total soluble solids (
TSS) (∘Brix) by refractometry, titratable acidity by titration (expressed as mg tartaric acid per liter of must) and pH by a pH meter, analyzed at different maturation stages in the must samples. The maturity indicators were predicted from the point hyperspectral data by employing machine learning algorithms, including Partial Least Squares regression (PLS), Random Forest regression (RF), Support Vector Regression (SVR), and Convolutional Neural Networks (CNN), in conjunction with various pre-processing techniques. Multi-output models were also considered to simultaneously predict all three indicators to exploit their intercorrelations. A novel multi-input-multi-output CNN model was also proposed, incorporating a multi-head attention mechanism and enabling the identification of the spectral regions it focuses on, and thus having a higher interpretability degree. Our results indicate high accuracy in the estimation of sugar content, pH, and titratable acidity, with the best models yielding mean R2 values of 0.84, 0.76, and 0.79, respectively, across all properties. The multi-output models did not improve the prediction results compared to the best single-output models, and the proposed CNN model was on par with the next best model. The interpretability analysis highlighted that the CNN model focused on spectral regions associated with the presence of sugars (i.e., glucose and fructose) and of the carboxylic acid group. This study underscores the potential of portable spectrometry for real-time, non-destructive assessments of wine grape maturity, thereby providing valuable tools for informed decision making in the wine production industry. By integrating pH and titratable acidity into the analysis, our approach offers a holistic view of grape quality, facilitating more comprehensive and efficient viticultural practices.