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Recognition of different Longjing fresh tea varieties using hyperspectral imaging technology and chemometrics
Author(s) -
Yan Lei,
Pang Lei,
Wang Hua,
Xiao Jiang
Publication year - 2020
Publication title -
journal of food process engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.507
H-Index - 45
eISSN - 1745-4530
pISSN - 0145-8876
DOI - 10.1111/jfpe.13378
Subject(s) - hyperspectral imaging , pattern recognition (psychology) , artificial intelligence , artificial neural network , computer science , principal component analysis , support vector machine , feature (linguistics) , mathematics , philosophy , linguistics
Hyperspectral imaging technology was applied to detect and recognize six different varieties of Longjing fresh tea. The data contained image and spectral information at 370–1042 nm; color and texture features were the foci of the image research. Spectral pre‐processing was performed by multiplicative scatter correction (MSC) and standard normal variate (SNV), and then, we selected the corresponding position variable and vegetation indexes as spectral features. Representative features including the most information were chose by principal component analysis (PCA). A novel back propagation (BP) neural network, with a self‐generated number of hidden layer neurons, was proposed. Using spectral features, image features, and spectral image fusion features as input, three fresh tea recognition models were established: the improved BP neural network, traditional BP neural network, and support vector machine (SVM). Results suggested that the improved BP neural network could promote performance of the model, especially for the spectral pre‐processed data. Mixed‐feature models did better than individual feature models, with 100% accuracy of the predictive set. This study shows that hyperspectral imaging technology can be a potential rapid and nondestructive approach to identify different varieties of Longjing fresh teas. Practical applications This article introduced application of hyperspectral imaging technology to identify Longjing fresh tea of different origins and varieties. Samples were analyzed using spectral and image characteristics. We provided a basis for full utilization of tea characteristics. At the same time, an improved BP neural network, with less calculation complexity and workload than the traditional BP neural network, was proposed. In summary, we outlined a convenient and reliable method for differentiation of Longjing fresh teas. Furthermore, we established a theoretical foundation for development of portable instruments to be used in similar studies.