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
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.
Key words：Genetic algorithm; BP neural network; Near-infrared spectroscopy; Validity; Tea
基金资助: Supported by the National Natural Science Foundation of China (62175250)， Science and Technology Major Project of the Ministry of Science and Technology of Anhui Province (s202003a0620001), and Open project of State Key Laboratories of Transducer Technology(SKT1907)
作者简介: LIU Meng xuan, (1997—)， female, master， Research on Spectral Analysis Algorithms e-mail: m15800963373@163.com
引用本文:
刘梦璇，吴 琼，王绪泉，陈 琦，张永刚，黄松垒，方家熊. 茶叶掺糖含量检测算法中光谱数据有效性及冗余度研究[J]. 光谱学与光谱分析, 2022, 42(11): 3647-3652.
LIU Meng-xuan, WU Qiong, WANG Xu-quan, CHEN Qi, ZHANG Yong-gang, HUANG Song-lei, FANG Jia-xiong. Validity and Redundancy of Spectral Data in the Detection Algorithm of Sucrose-Doped Content in Tea. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(11): 3647-3652.
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