Conference paper Open Access
Nikiforos Samarinas; Nikolaos Tsakiridis; Nikolaos Tziolas; Eleni Kalopesa; Konstantinos Karyotis; Dionysis Bochtis; George Zalidis; Zoe Doulgeri
Spectroscopy is a widespread technique used in many scientific fields such as in the food production. The use of hyperspectral data and specifically in the visible and near infrared (VNIR) and in the short-wave infrared (SWIR) regions in grape production is of great interest. Due to its fine spectral resolution, hyperspectral analysis can contribute to both fruit monitoring and quality control at all stages of maturity with a simple and inexpensive way. This work presents an application of a contact probe spectrometer that covers the VNIR–SWIR spectrum (350–2500 nm) for the quantitative estimation of the wine grapes’ ripeness. A total of 110 samples of grape vine Syrah (Vitis vinifera Syrah) variety were collected over the 2020 harvest and pre-harvest seasons from Ktima Gerovassiliou located in Northern Greece. Their total soluble solids content (oBrix) was measured in-situ using a refractometer. Two different machine learning algorithms, namely partial least square regression (PLS) and random forest (RF) were applied along with several spectral pre-processing methods in order to predict the oBrix content from the VNIR–SWIR hyperspectral data. Additionally, the most important features of the spectrum were identified, as indicated by the most accurate models. The performance of the different models was examined in terms of the following metrics: coefficient of the determination (R2), root mean square error (RMSE) and ratio of performance to interquartile distance (RPIQ). The values of R2 = 0.90, RMSE =1.51 and RPIQ = 4.41 for PLS and 0.92, 1.34, 4.96 for RF respectively, indicate that by using a portable VNIR–SWIR spectrometer it is possible to estimate the wine grape maturity in-situ.