Rapid and Nondestructive Discrimination of Geographical Origins of Longjing Tea using Hyperspectral Imaging at Two Spectral Ranges Coupled with Machine Learning Methods

[1]  R. Brereton,et al.  Partial least squares discriminant analysis: taking the magic away , 2014 .

[2]  A. A. Gomes,et al.  Simultaneous Classification of Teas According to Their Varieties and Geographical Origins by Using NIR Spectroscopy and SPA-LDA , 2014, Food Analytical Methods.

[3]  Wei He,et al.  Validation of origins of tea samples using partial least squares analysis and Euclidean distance method with near-infrared spectroscopy data. , 2012, Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy.

[4]  A. K. Hazarika,et al.  Quality assessment of fresh tea leaves by estimating total polyphenols using near infrared spectroscopy , 2018, Journal of Food Science and Technology.

[5]  Chu Zhang,et al.  Rapid and non-destructive measurement of spinach pigments content during storage using hyperspectral imaging with chemometrics , 2017 .

[6]  Thomas Arnold,et al.  Hyperspectral imaging: a novel approach for plant root phenotyping , 2018, Plant Methods.

[7]  Jun-Hu Cheng,et al.  Prediction of total volatile basic nitrogen contents using wavelet features from visible/near-infrared hyperspectral images of prawn (Metapenaeus ensis). , 2016, Food chemistry.

[8]  Yangping Wen,et al.  A novel strategy of near-infrared spectroscopy dimensionality reduction for discrimination of grades, varieties and origins of green tea , 2019, Vibrational Spectroscopy.

[9]  Chu Zhang,et al.  Hyperspectral imaging analysis for ripeness evaluation of strawberry with support vector machine , 2016 .

[10]  Yong He,et al.  Identifying Freshness of Spinach Leaves Stored at Different Temperatures Using Hyperspectral Imaging , 2019, Foods.

[11]  Chu Zhang,et al.  Detection of Subtle Bruises on Winter Jujube Using Hyperspectral Imaging With Pixel-Wise Deep Learning Method , 2019, IEEE Access.

[12]  Lili Wang,et al.  Rapid Determination of Green Tea Origins by Near-Infrared Spectroscopy and Multi-Wavelength Statistical Discriminant Analysis , 2019, Journal of Applied Spectroscopy.

[13]  Jingming Ning,et al.  Classification of five Chinese tea categories with different fermentation degrees using visible and near-infrared hyperspectral imaging , 2016 .

[14]  Jifeng Shen,et al.  Nondestructive identification of green tea varieties based on hyperspectral imaging technology , 2018 .

[15]  Paul J. Williams,et al.  Classification of maize kernels using NIR hyperspectral imaging. , 2016, Food chemistry.

[16]  J. Ruan,et al.  Multi-element composition and isotopic signatures for the geographical origin discrimination of green tea in China: A case study of Xihu Longjing , 2018 .

[17]  Guolan Lu,et al.  Medical hyperspectral imaging: a review , 2014, Journal of biomedical optics.

[18]  Chu Zhang,et al.  Non-Destructive and Rapid Variety Discrimination and Visualization of Single Grape Seed Using Near-Infrared Hyperspectral Imaging Technique and Multivariate Analysis , 2018, Molecules.

[19]  Ronei J. Poppi,et al.  Quantitative analysis of piroxicam polymorphs pharmaceutical mixtures by hyperspectral imaging and chemometrics , 2011 .

[20]  J. Suykens,et al.  A tutorial on support vector machine-based methods for classification problems in chemometrics. , 2010, Analytica chimica acta.

[21]  Quansheng Chen,et al.  Evaluating green tea quality based on multisensor data fusion combining hyperspectral imaging and olfactory visualization systems. , 2018, Journal of the science of food and agriculture.

[22]  Wenxiu Pan,et al.  Simultaneous and Rapid Measurement of Main Compositions in Black Tea Infusion Using a Developed Spectroscopy System Combined with Multivariate Calibration , 2015, Food Analytical Methods.

[23]  Kunbo Wang,et al.  Analysis of chemical components in green tea in relation with perceived quality, a case study with Longjing teas , 2009 .

[24]  Yubin Lan,et al.  An Identification of the Growing Area of Longjing Tea Based on the Fisher's Discriminant Analysis with the Combination of Principal Components Analysis , 2013, Intell. Autom. Soft Comput..

[25]  Jun Sun,et al.  Classification of oolong tea varieties based on hyperspectral imaging technology and BOSS‐LightGBM model , 2019, Journal of Food Process Engineering.

[26]  Chu Zhang,et al.  Identification of coffee bean varieties using hyperspectral imaging: influence of preprocessing methods and pixel-wise spectra analysis , 2018, Scientific Reports.

[27]  Yidan Bao,et al.  Hyperspectral imaging for seed quality and safety inspection: a review , 2019, Plant Methods.

[28]  Dai Weidong,et al.  Separation of aroma components in Xihu Longjing tea using simultaneous distillation extraction with comprehensive two-dimensional gas chromatography-time-of-flight mass spectrometry , 2016 .

[29]  Yidan Bao,et al.  Near-Infrared Hyperspectral Imaging Combined with Deep Learning to Identify Cotton Seed Varieties , 2019, Molecules.

[30]  Jiewen Zhao,et al.  Automated tea quality classification by hyperspectral imaging. , 2009, Applied optics.

[31]  Juan Li,et al.  The Quality Control of Tea by Near-Infrared Reflectance (NIR) Spectroscopy and Chemometrics , 2019, Journal of Spectroscopy.

[32]  K. Wei,et al.  Geographical tracing of Xihu Longjing tea using high performance liquid chromatography. , 2014, Food chemistry.