Special-grade green tea is a premium tea product with the best rank and high value. Special-grade green tea is normally classified by panel sensory evaluation which is time and sample costly. Near-infrared spectroscopy is considered as a promising rapid and non-destructive analytical technique for food quality evaluation and grading. This study established a discrimination method of special-grade flat green tea using Near-infrared spectroscopy. Full spectrum was used for partial least squares (PLS) modelling to predict the sensory scores of green tea, while specific spectral regions were used for synergy interval-partial least squares (siPLS) modelling. The best performance was achieved by the siPLS model of MSC + Mean Centering pretreatments and subintervals from 15 intervals. The optimal model was used to discriminate special-grade flat green tea with the prediction accuracy of 97% and 93% in the cross-validation and external validation respectively. The chemical compositions of green tea samples were also analyzed, including polyphenols (total polyphenols, catechins and flavonol glycosides), alkaloids and amino acids. Principal components analysis result showed that there is potential correlation between specific spectral regions and the presence of polyphenols and alkaloids. Thus, NIR technique is a practical method for rapid and non-destructive discrimination of special-grade flat green tea with chemical support.
Keywords: Green tea; Near-infrared spectroscopy; Sensory quality; Sensory-related chemical compounds; Specific spectral region.
Copyright © 2018 Elsevier B.V. All rights reserved.