Estimation of Sugar Content in Wine Grapes via In Situ VNIR-SWIR Point Spectroscopy Using Explainable Artificial Intelligence Techniques.

Spectroscopy is a widely used technique that can contribute to food quality assessment in a simple and inexpensive way. Especially in grape production, the visible and near infrared (VNIR) and the short-wave infrared (SWIR) regions are of great interest, and they may be utilized for both fruit monitoring and quality control at all stages of maturity. The aim of this work was the quantitative estimation of the wine grape ripeness, for four different grape varieties, by using a highly accurate contact probe spectrometer that covers the entire VNIR-SWIR spectrum (350-2500 nm). The four varieties under examination were Chardonnay, Malagouzia, Sauvignon-Blanc, and Syrah and all the samples were collected over the 2020 and 2021 harvest and pre-harvest phenological stages (corresponding to stages 81 through 89 of the BBCH scale) from the vineyard of Ktima Gerovassiliou located in Northern Greece. All measurements were performed in situ and a refractometer was used to measure the total soluble solids content (°Brix) of the grapes, providing the ground truth data. After the development of the grape spectra library, four different machine learning algorithms, namely Partial Least Squares regression (PLS), Random Forest regression, Support Vector Regression (SVR), and Convolutional Neural Networks (CNN), coupled with several pre-treatment methods were applied for the prediction of the °Brix content from the VNIR-SWIR hyperspectral data. The performance of the different models was evaluated using a cross-validation strategy with three metrics, namely the coefficient of the determination (R2), the root mean square error (RMSE), and the ratio of performance to interquartile distance (RPIQ). High accuracy was achieved for Malagouzia, Sauvignon-Blanc, and Syrah from the best models developed using the CNN learning algorithm (R2>0.8, RPIQ≥4), while a good fit was attained for the Chardonnay variety from SVR (R2=0.63, RMSE=2.10, RPIQ=2.24), proving that by using a portable spectrometer the in situ estimation of the wine grape maturity could be provided. The proposed methodology could be a valuable tool for wine producers making real-time decisions on harvest time and with a non-destructive way.

Association between epicardial fat thickness and weight homeostasis hormones in patients with noncachectic heart failure.

The relationship between echocardiographically measured epicardial fat thickness (EFT) and plasma concentrations of leptin, ghrelin, and adiponectin has not been evaluated in patients with noncachectic heart failure (HF). Patients with noncachectic HF and age- and sex-matched controls did not differ significantly in EFT, whereas EFT values showed significant positive correlation with body mass index (BMI) in both groups and were negatively related with brain natriuretic peptide and positively with log leptin values in the HF group. In the control group, a positive correlation with high-sensitivity C-reactive protein (hsCRP) and a negative correlation with log ghrelin were found. In multivariable analysis, log leptin was a significant predictor of EFT in patients with HF, but this effect was not retained after adjusting for BMI. In contrast, log ghrelin and hsCRP were significant predictors of EFT in controls even after adjusting for BMI.