A Rapid and Nondestructive Quality Detection Approach for Yongchuan Xiuya Tea Based on NIRS and siPLS-ANN Method
The efficient and non-destructive evaluation of Yongchuan Xiuya tea quality represents a key advancement in the tea industry. Near-infrared spectroscopy (NIRS), a non-invasive analytical technology, allows for the acquisition of spectral data while preserving sample integrity. Through preprocessing...
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2025-01-01
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| author | Ying Zhang Jie Wang Xiuhong Wu Rui Chang Hongyu Luo Juan Yang Quan Wu Ze Xu Yingfu Zhong |
| author_facet | Ying Zhang Jie Wang Xiuhong Wu Rui Chang Hongyu Luo Juan Yang Quan Wu Ze Xu Yingfu Zhong |
| author_sort | Ying Zhang |
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| description | The efficient and non-destructive evaluation of Yongchuan Xiuya tea quality represents a key advancement in the tea industry. Near-infrared spectroscopy (NIRS), a non-invasive analytical technology, allows for the acquisition of spectral data while preserving sample integrity. Through preprocessing the spectral data and employing the synergy interval partial least squares (siPLS) method to identify characteristic spectral regions, principal component analysis (PCA) is applied, followed by the development of a Jordan–Elman artificial neural network prediction model (ANN) for tea quality assessment. The optimal spectral preprocessing approach identified in this study is a combination of multiplicative scatter correction and second derivative processing. Key spectral intervals include 4377.6 cm<sup>−1</sup>–4751.7 cm<sup>−1</sup>, 4755.6 cm<sup>−1</sup>–5129.7 cm<sup>−1</sup>, 6262.7 cm<sup>−1</sup>–6633.9 cm<sup>−1</sup>, and 7386 cm<sup>−1</sup>–7756.3 cm<sup>−1</sup>, with the first three principal components achieving a cumulative contribution rate of 99.05%. Utilizing a tanh activation function, the model exhibited strong predictive performance: an Rp<sup>2</sup> of 0.980 and RMSEP of 0.341 for prediction set samples, and an Rp<sup>2</sup> of 0.978 with RMSEP of 0.373 for unknown samples. These findings demonstrate the potential of integrating NIRS with Jordan–Elman neural networks for rapid and accurate Yongchuan Xiuya tea quality evaluation, establishing a solid technological foundation for the application of NIRS in tea quality assessment. |
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| language | English |
| publishDate | 2025-01-01 |
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| series | Applied Sciences |
| spelling | doaj-art-e93112e75d474a848bf9b3e2d8dd9a8d2025-01-24T13:19:52ZengMDPI AGApplied Sciences2076-34172025-01-0115257010.3390/app15020570A Rapid and Nondestructive Quality Detection Approach for Yongchuan Xiuya Tea Based on NIRS and siPLS-ANN MethodYing Zhang0Jie Wang1Xiuhong Wu2Rui Chang3Hongyu Luo4Juan Yang5Quan Wu6Ze Xu7Yingfu Zhong8Chongqing Academy of Agricultural Sciences, Chongqing 402160, ChinaChongqing Academy of Agricultural Sciences, Chongqing 402160, ChinaChongqing Academy of Agricultural Sciences, Chongqing 402160, ChinaChongqing Academy of Agricultural Sciences, Chongqing 402160, ChinaChongqing Academy of Agricultural Sciences, Chongqing 402160, ChinaChongqing Academy of Agricultural Sciences, Chongqing 402160, ChinaChongqing Academy of Agricultural Sciences, Chongqing 402160, ChinaChongqing Academy of Agricultural Sciences, Chongqing 402160, ChinaChongqing Academy of Agricultural Sciences, Chongqing 402160, ChinaThe efficient and non-destructive evaluation of Yongchuan Xiuya tea quality represents a key advancement in the tea industry. Near-infrared spectroscopy (NIRS), a non-invasive analytical technology, allows for the acquisition of spectral data while preserving sample integrity. Through preprocessing the spectral data and employing the synergy interval partial least squares (siPLS) method to identify characteristic spectral regions, principal component analysis (PCA) is applied, followed by the development of a Jordan–Elman artificial neural network prediction model (ANN) for tea quality assessment. The optimal spectral preprocessing approach identified in this study is a combination of multiplicative scatter correction and second derivative processing. Key spectral intervals include 4377.6 cm<sup>−1</sup>–4751.7 cm<sup>−1</sup>, 4755.6 cm<sup>−1</sup>–5129.7 cm<sup>−1</sup>, 6262.7 cm<sup>−1</sup>–6633.9 cm<sup>−1</sup>, and 7386 cm<sup>−1</sup>–7756.3 cm<sup>−1</sup>, with the first three principal components achieving a cumulative contribution rate of 99.05%. Utilizing a tanh activation function, the model exhibited strong predictive performance: an Rp<sup>2</sup> of 0.980 and RMSEP of 0.341 for prediction set samples, and an Rp<sup>2</sup> of 0.978 with RMSEP of 0.373 for unknown samples. These findings demonstrate the potential of integrating NIRS with Jordan–Elman neural networks for rapid and accurate Yongchuan Xiuya tea quality evaluation, establishing a solid technological foundation for the application of NIRS in tea quality assessment.https://www.mdpi.com/2076-3417/15/2/570Yongchuan Xiuya teaqualitynear-infrared spectroscopysynergy interval partial least squaresartificial neural network |
| spellingShingle | Ying Zhang Jie Wang Xiuhong Wu Rui Chang Hongyu Luo Juan Yang Quan Wu Ze Xu Yingfu Zhong A Rapid and Nondestructive Quality Detection Approach for Yongchuan Xiuya Tea Based on NIRS and siPLS-ANN Method Applied Sciences Yongchuan Xiuya tea quality near-infrared spectroscopy synergy interval partial least squares artificial neural network |
| title | A Rapid and Nondestructive Quality Detection Approach for Yongchuan Xiuya Tea Based on NIRS and siPLS-ANN Method |
| title_full | A Rapid and Nondestructive Quality Detection Approach for Yongchuan Xiuya Tea Based on NIRS and siPLS-ANN Method |
| title_fullStr | A Rapid and Nondestructive Quality Detection Approach for Yongchuan Xiuya Tea Based on NIRS and siPLS-ANN Method |
| title_full_unstemmed | A Rapid and Nondestructive Quality Detection Approach for Yongchuan Xiuya Tea Based on NIRS and siPLS-ANN Method |
| title_short | A Rapid and Nondestructive Quality Detection Approach for Yongchuan Xiuya Tea Based on NIRS and siPLS-ANN Method |
| title_sort | rapid and nondestructive quality detection approach for yongchuan xiuya tea based on nirs and sipls ann method |
| topic | Yongchuan Xiuya tea quality near-infrared spectroscopy synergy interval partial least squares artificial neural network |
| url | https://www.mdpi.com/2076-3417/15/2/570 |
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