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| Validity and Redundancy of Spectral Data in the Detection Algorithm of Sucrose-Doped Content in Tea |
| LIU Meng-xuan1, 2, 3, 4, WU Qiong5, WANG Xu-quan1, 2, 4, CHEN Qi5, ZHANG Yong-gang1, 2, HUANG Song-lei1, 2*, FANG Jia-xiong1, 2* |
1. State Key Laboratories of Transducer Technology, Shanghai Institute of Technical Physics,
Chinese Academy of Sciences, Shanghai 200083, China
2. Key Laboratory of Infrared Imaging Materials and Detectors, Shanghai Institute of Technical PhysicsChinese Academy of Sciences, Shanghai 200083, China
3. ShanghaiTech University, Shanghai 201210, China
4. University of Chinese Academy of Sciences, Beijing 100049, China
5. Technology Center of Hefei Customs District, Hefei 245000, China |
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Abstract Near-infrared spectroscopy (NIRS) technology integrated with Genetic Algorithm-Back Propagation (GA-BP) neural network was used to spectral sucrose-doped content in 162 tea samples in the NIR wavelength range of 1~2.5 μm. The parameters of the GA and BP neural network were optimized by the sample set to analyze the validity and redundancy of spectral bands. The raw data in the range of 1~2.5 μm was divided into 1~1.7, 1~1.3, 1.3~1.7, 1.7~2.5 and 2~2.2 μm sets. The established quantitative detection model was used to conduct model training on different wavelength bands at the same resolution. The prediction results show that, for the target content, data redundancy appears in both 1~1.7 and 1~2.5 μm bands. The model could be effectively extracted using only 1.3~1.7 or 1.7~2.5 μm band. The prediction model was also conducted using different spectral resolutions from 2 to 20 nm in the same band. In the wavelength range of 1~2.5 μm, the R was between 0.9 and 0.95 when the RMSEP ranged from 1.7 to 2.1. While in the wavelength range of 1~1.7 μm, the R was in the range of 0.9 to 0.93 when the RMSEP was between 1.95 and 2.25. The results indicate that, for the target content, redundancy exists in the 1~2.5 and 1~1.7 μm bands on both wavelength range and spectral resolution. Through the analysis of spectral features and modeling of the algorithm, the effectiveness of spectral data acquirement could be improved dramatically; for the detection of sucrose-doped content in tea, a much narrower wavelength range and lower spectral resolution could be adopted.
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Received: 2021-10-26
Accepted: 2022-02-23
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Corresponding Authors:
HUANG Song-lei, FANG Jia-xiong
E-mail: huangsl@mail.sitp.ac.cn; jxfang@mail.sitp.ac.cn
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