Grading and Sorting of Grape Berries Using Visible-Near Infrared Spectroscopy on the Basis of Multiple Inner Quality Parameters

The potential of visible-near infrared (vis/NIR) spectroscopy (400 nm to 1100 nm) for classification of grape berries on the basis of multi inner quality parameters was investigated. Stored L. cv. Manicure Finger and . cv. Ugni Blanc grape berries were separated into three classes based on the distribution of total soluble solid content ( ) and total phenolic compounds ( ). Partial least squares regression (PLS) was applied to predict the quality parameters, including color space CIELAB, , and . The prediction results showed that the vis/NIR spectrum correlated with the and present in the intact grape berries with determination coefficient of prediction ( ) in the range of 0.735 to 0.823. Next, the vis/NIR spectrum was used to distinguish between berries with different and concentrations using partial least squares discrimination analysis (PLS-DA) with >77% accuracy. This study provides a method to identify stored grape quality classes based on the spectroscopy and distributions of multiple inner quality parameters.