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Sensors (Basel). 2019 Jun 7;19(11). pii: E2600. doi: 10.3390/s19112600.

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

Author information

1
College of Food Science and Technology, Nanjing Agriculture University, Nanjing 210095, China. 2015108046@njau.edu.cn.
2
College of Food Science and Technology, Nanjing Agriculture University, Nanjing 210095, China. fengli@njau.edu.cn.
3
School of computer and Information Engineering, Chuzhou University, Chuzhou 239000, China. songdajie@chzu.edu.cn.
4
College of Food Science and Technology, Nanjing Agriculture University, Nanjing 210095, China. kangtu@njau.edu.cn.
5
College of Food Science and Technology, Nanjing Agriculture University, Nanjing 210095, China. jpeng@njau.edu.cn.
6
College of Food Science and Technology, Nanjing Agriculture University, Nanjing 210095, China. pan_leiqing@njau.edu.cn.

Abstract

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 Vitis vinifera L. cv. Manicure Finger and Vitis vinifera L. cv. Ugni Blanc grape berries were separated into three classes based on the distribution of total soluble solid content (SSC) and total phenolic compounds (TP). Partial least squares regression (PLS) was applied to predict the quality parameters, including color space CIELAB, SSC, and TP. The prediction results showed that the vis/NIR spectrum correlated with the SSC and TP present in the intact grape berries with determination coefficient of prediction (RP2) in the range of 0.735 to 0.823. Next, the vis/NIR spectrum was used to distinguish between berries with different SSC and TP 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.

KEYWORDS:

grading and sorting; grape; partial least squares regression; total phenolic compounds; total soluble solid content

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