Abstract
The traditional method used to determine the moisture content of tea leaves is time consuming and destructive. To address this problem, an effective and non-destructive prediction method based on near-infrared spectroscopy (NIRS) is proposed in this paper. This new method combines discrete wavelet transforms (DWT) with the bootstrap soft shrinkage algorithm (BOSS). To eliminate uninformative or interfering variables, DWT is applied to remove the noise in the spectral data by decomposing the origin spectrum into six layers. BOSS is used to select informative variables by reducing the dimensions of the sub-layers’ reconstruction spectrum. After selecting the effective variables using DWT and BOSS, a prediction model based on partial least squares (PLS) is built. To validate effectiveness and stability of the prediction model, full-spectrum PLS, genetic algorithm PLS (GA-PLS), and interval PLS (iPLS) were compared with the proposed method. The experiment results illustrate that the proposed prediction model outperforms the other classical models considered in this study and shows promise for the prediction of the moisture content in Yinghong No. 9 tea leaves.
Highlights
Processed leaves and leaf buds of tea tree are used to produce tea, which are popular in many parts of the world [1]
Many attempts have been made to determine moisture based on near-infrared spectroscopy (NIRS)
1, we found of thethe haswere the highest accuracy, with an RRcc2 the of 0.9410, RMSECinofTable
Summary
Processed leaves and leaf buds of tea tree are used to produce tea, which are popular in many parts of the world [1]. Traditional tea making is complicated; the drying of fresh leaves is the primary and indispensable stage of this process [2] and moisture content is a key index in the drying process [3]. Improper handling may lead to inaccurate measurements when determining moisture content. An accurate and rapid detection approach would be indispensable for determining the moisture content of tea leaves during tea making [4]. Many attempts have been made to determine moisture based on near-infrared spectroscopy (NIRS). Moisture measurements are commonly recorded by detecting mass loss after heating to evaporate moisture. This procedure damages the samples and is time consuming. The disadvantages of NIRS include broad overlapping, difficultly interpreting
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