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

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.

From the Laboratory to The Vineyard—Evolution of The Measurement of Grape Composition using NIR Spectroscopy towards High-Throughput Analysis

“…Pan and collaborators evaluated the potential of VIS-NIR spectroscopy to measure TSS and TPhenol in Manicure Finger and Ugni Blanc cultivars [25]. CIELAB, TSS, and TPhenol calibrations were developed using PLS regression.…”

“…The authors reported that the developed models exhibited accuracies in the range of 77% to 94%, and concluded that the technique showed significant promise to the modern fruit and vegetable industry, particularly considering that efficient sorting and labour saving such non-destructive technologies would provide. In addition, classification models were developed to monitor time of storage (classification rates >0.77) [25].…”

Rapid Measurement of Cellulose, Hemicellulose, and Lignin Content in Sargassum horneri by Near-Infrared Spectroscopy and Characteristic Variables Selection Methods

“…With the rapid development of chemometrics, the main disadvantages such as low absorptance, serious overlap, and weak characteristics in the application of near-infrared (NIR) spectroscopy have been overcome. The NIR spectroscopy analytical technique has been widely used for the prediction of quality parameters in the fields of food, agriculture, forestry, and energy due to the advantages of rapid analysis, concise operation, and accurate and nondestructive testing [23][24][25][26][27][28]. NIR spectroscopy records the spectral bands that correspond to C-H, O-H, N-H vibrations, which are abundant in lignocellulose; this is the theoretical basis for using NIR spectroscopy to determine the lignocellulose content in biomass.…”

The Impact of Optical Berry Sorting on Red Wine Composition and Sensory Properties

“…Even though this study used outdated equipment compared to today’s standards, it shows that white grapes can be sorted into different ripeness levels and this can affect the quality of the wine produced. A recent study used visible near-infrared spectroscopy to classify table grapes into different groups based on soluble solid and phenolic content [ 3 ]. The researchers were able to differentiate berries of different classes with accuracy ranging from 77% to 94%.…”