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• W2944228068 endingPage "102923" @default.
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• W2944228068 abstract "The quality of green tea is conventionally tested by human sensory panel, who assign quality scores to different teas. In order to overcome the deficiency of sensory evaluation methods, near infrared (NIR) reflectance spectroscopy combined with nonlinear methods was used to evaluate the sensory quality of green tea in this study. Nonlinear dimension reduction methods such as Kernel Principal Component Analysis (KPCA), Sparse Principal Component Analysis (SPCA), and Local Tangent Space Alignment (LTSA) were used to reduce the dimension of the spectral data, and compared with linear dimension reduction methods such as Principal Component Analysis (PCA), Multidimensional Scaling (MDS). Relevance vector machine (RVM) as a nonlinear quantitative analysis method was performed comparatively to develop calibration models between the sensory scores of the sensory panels and objective determination (NIR spectra measurements), and compared with back-propagation artificial network and linear PLS model. Experiment results showed that the non-linear reduction algorithm LTSA could obtain better effect on spatial reduction dimensional for NIR spectral data of tea. And when the number K of the neighborhoods in LTSA was 15, the RMSECV of RVM model was the minimum. The performance of LTSA-RVM model was superior to the others when 8 intrinsic variables were used. In the prediction set, Rp, RMSEP and RPD were 0.963, 1.461 and 3.788, respectively. The absolute value of prediction error of all prediction samples in LTSA-RVM are below 3.0, which indicated that the sensory scores predicted by NIR were consistent with the scores by tea assessors. The results of recovery study were 98.42%–102.81% at 5 real tea samples and the relative error lower than 3%. The research demonstrates that NIR technology combined with nonlinear analysis methods can effectively estimate sensory quality of tea and also present a potential way for fast and effective determination of the sensory quality of green tea." @default.
• W2944228068 created "2019-05-16" @default.
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• W2944228068 date "2019-07-01" @default.
• W2944228068 modified "2023-10-16" @default.
• W2944228068 title "Local tangent space alignment and relevance vector machine as nonlinear methods for estimating sensory quality of tea using NIR spectroscopy" @default.
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• W2944228068 doi "https://doi.org/10.1016/j.vibspec.2019.05.005" @default.
• W2944228068 hasPublicationYear "2019" @default.
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