Nondestructive Detection of Keemun Black Tea Grade Based on Hyperspectral Imaging Technique

FAN Tingting; LU Jiangming; KANG Zhilong; NIU Xinhuan; MU Qingshuang

doi:10.13386/j.issn1002-0306.2020090211

Volume 42 Issue 16

Aug. 2021

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Science and Technology of Food Industry > 2021 > 42(16): 243-248. > DOI: 10.13386/j.issn1002-0306.2020090211

FAN Tingting, LU Jiangming, KANG Zhilong, et al. Nondestructive Detection of Keemun Black Tea Grade Based on Hyperspectral Imaging Technique [J]. Science and Technology of Food Industry, 2021, 42(16): 243−248. (in Chinese with English abstract). doi: 10.13386/j.issn1002-0306.2020090211.

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Nondestructive Detection of Keemun Black Tea Grade Based on Hyperspectral Imaging Technique

College of Electronical and Information Engineering, Hebei University of Technology, Tianjin 300401, China

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Received Date: September 20, 2020
Available Online: June 17, 2021

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Abstract

In order to evaluate the grade of tea products quickly and nondestructive, the near infrared (900~1700 nm) hyperspectral imaging technology was applied to classify six grades of black tea. Firstly, the hyperspectral data were visualized by linear and nonlinear dimension reduction methods. The visualization algorithms included the linear method of principal component analysis (PCA), multi-dimensional scaling (MDS), the nonlinear method of T-distributed stochastic embedding (T-SNE) and Sammon nonlinear mapping. Secondly, the classification model was established by using support vector machine (SVM) and extreme learning machine (ELM) to identify the different grades of Keemun black tea. Finally, SVM and ELM classification model were used to identify each pixel of hyperspectral image, and the prediction map was obtained. The results showed that T-SNE could divide the six grades of Keemun black tea into six different clusters, and the accuracy of SVM and ELM prediction set was 100% and 96.35%, respectively. The T-SNE visualization effect was the best, and the detection model of SVM could effectively identify the six grades of Keemun black tea. This paper provides an effective method for rapid and nondestructive testing of tea product grade, which had great significance for quality control, authenticity and adulteration detection of tea products.
- tea,
- near infrared hypers pectral imaging,
- dimension reduction,
- support vector machine,
- extreme learning machine,
- nondestructive testing

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[1]	Orrillo I, Cruz-Tirado J P, Cardenas A, et al. Hyperspectral imaging as a powerful tool for identification of papaya seeds in black pepper[J]. Food Control,2019,101:45−52. doi: 10.1016/j.foodcont.2019.02.036
[2]	Ren G X, Wang Y J, Ning J M, et al. Using near-infrared hyperspectral imaging with multiple decision tree methods to delineate black tea quality[J]. Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy,2020,237:118407. doi: 10.1016/j.saa.2020.118407
[3]	Ren G, Wang S, Ning J, et al. Quantitative analysis and geographical traceability of black tea using Fourier transform near-infrared spectroscopy (FT-NIRS)[J]. Food Research International,2013,53(2):822−826. doi: 10.1016/j.foodres.2012.10.032
[4]	李清光, 李晓钟, 钟芳. 基于矿质元素含量和支持向量机的茶叶鉴别分析[J], 江苏大学学报(自然科学版), 2011, 32(6): 636−641.
[5]	孙俊, 靳海涛, 武小红, 等. 基于低秩自动编码器及高光谱图像的茶叶品种鉴别[J]. 农业机械学报,2018,49(8):316−323. doi: 10.6041/j.issn.1000-1298.2018.08.037
[6]	Sharma P, Ghosh A, Tudu B, et al. Monitoring the fermentation process of black tea using QCM sensor based electronic nose[J]. Sensors & Actuators: B. Chemical,2015,219:67−74.
[7]	Yaroshenko I, Kirsanov D, Kartsova L, et al. On the application of simple matrix methods for electronic tongue data processing: Case study with black tea samples[J]. Sensors & Actuators: B. Chemical,2014,191:146−157.
[8]	Baldwin E A, Bai J, Plotto A, et al. Electronic noses and tongues: Applications for the food and pharmaceutical industries[J]. Sensors,2011,11(5):4744−4766. doi: 10.3390/s110504744
[9]	徐海卫, 胡常安, 汤江文, 等. 基于机器视觉的神经网络在茶叶鉴别中的应用[J]. 中国测试,2014,40(3):89−92. doi: 10.11857/j.issn.1674-5124.2014.03.024
[10]	陆江锋, 单春芳, 裘正军. 茶叶外形特征数字化及不同等级茶叶鉴别研究[J]. 现代农机,2015(3):49−51.
[11]	He Y, Li X, Deng X. Discrimination of varieties of tea using near infrared spectroscopy by principal component analysis and BP model[J], Journal of Food Engineering, 2006, 79(4): 845−850.
[12]	Chen Q S, Zhang J W, Hao L. Study on discrimination of Roast green tea (Camellia sinensis L.) according to geographical origin by FT-NIR spectroscopy and supervised pattern recognition[J]. Spectrochimica acta. Part A, Molecular and Biomolecular Spectroscopy,2009,72(4):1238−1242.
[13]	Chen Q C, Zhang J W, Hao F C, et al. Feasibility study on identification of green, black and Oolong teas using near-infrared reflectance spectroscopy based on support vector machine (SVM)[J]. Spectrochimica acta. Part A, Molecular and Biomolecular Spectroscopy,2007,66(3):568−574. doi: 10.1016/j.saa.2006.03.038
[14]	Wang S, Yang X, Zhang Y, et al. Identification of green, oolong and black teas in China via wavelet packet entropy and fuzzy support vector machine[J]. Entropy,2015,17(10):6663−6682.
[15]	Xi W, Huang J, Wei F, et al. Identification of green tea varieties and fast quantification of total polyphenols by near-infrared spectroscopy and ultraviolet-visible spectroscopy with chemometric algorithms[J]. Analytical Methods,2015,7:787−792. doi: 10.1039/C4AY02106A
[16]	Panigrahi N, Bhol C S, Das B S. Rapid assessment of black tea quality using diffuse reflectance spectroscopy[J]. Journal of Food Engineering,2016,190(Dec.):101−108.
[17]	黄培贤, 姚志湘, 粟晖, 等. 高光谱图像技术在食品无损检测中的研究进展[J]. 食品工业科技,2012,33(15):412−417.
[18]	Sun J, Zhou X, Hu Y, et al. Visualizing distribution of moisture content in tea leaves using optimization algorithms and NIR hyperspectral imaging[J]. Computers and Electronics in Agriculture,2019,160:161−167.
[19]	Wang Y J, Li T H, Jin G, et al. Qualitative and quantitative diagnosis of nitrogen nutrition of tea plants under field condition using hyperspectral imaging coupled with chemometrics[J]. Journal of the Science of Food and Agriculture,2020,100(1):1997−2004.
[20]	Wang Y J, Hu Xi, Jin Ge, et al. Rapid prediction of chlorophylls and carotenoids contents in tea leaves under different levels of nitrogen application based on hyperspectral imaging[J]. Journal of the Science of Food and Agriculture,2018:153−159.
[21]	Li L Q, Sheng X M, Ni J M, et al. Evaluating green tea quality based on multisensor data fusion combining hyperspectral imaging and olfactory visualization systems[J]. Journal of the Science of Food and Agriculture,2018:1787−1794.
[22]	Mishra P, Nordon A, Asaari M S M, et al. Fusing spectral and textural information in near-infrared hyperspectral imaging to improve green tea classification modelling[J]. Journal of Food Engineering,2019:40−47.
[23]	Hong Z, He Y. Rapid and Nondestructive Discrimination of Geographical Origins of longjing tea using hyperspectral imaging at two spectral ranges coupled with machine learning methods[J]. Applied Sciences,2020,10(3):1173−1184. doi: 10.3390/app10031173
[24]	Zhuang X G, W L L, Chen Q, et al. Identification of green tea origins by near-infrared(NIR) spectroscopy and different regression tools[J]. Science China(Technological Sciences),2017,60(1):84−90. doi: 10.1007/s11431-016-0464-0
[25]	Cox T F, Cox M A A. Multidimensional scaling, second edition[M]. 2000.
[26]	Hinton G E. Visualizing high-dimensional data using t-SNE[J]. Vigiliae Christianae,2008,9:2579−2605.
[27]	Vapnik V N, Vapnik V. Statistical learning theory[M], 1998.

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