Rapid discrimination of main red meat species based on near-infrared hyperspectral imaging technology

2016 ◽  
Author(s):  
Lu Qiao ◽  
Yankun Peng ◽  
Kuanglin Chao ◽  
Jianwei Qin
Keyword(s):  
Hyperspectral Imaging ◽  
Near Infrared ◽  
Red Meat ◽  
Imaging Technology ◽  
Meat Species ◽  
Rapid Discrimination

Classification of hybrid seeds using near-infrared hyperspectral imaging technology combined with deep learning

2019 ◽  
Vol 296 ◽  
pp. 126630 ◽  
Author(s):  
Pengcheng Nie ◽  
Jinnuo Zhang ◽  
Xuping Feng ◽  
Chenliang Yu ◽  
Yong He
Keyword(s):  
Deep Learning ◽  
Hyperspectral Imaging ◽  
Near Infrared ◽  
Imaging Technology ◽  
Hybrid Seeds

Rapid identification of coffee bean variety by near infrared hyperspectral imaging technology

2015 ◽  
Vol 23 (2) ◽  
pp. 349-355 ◽  
Author(s):  
鲍一丹 BAO Yi-dan ◽  
陈纳 CHEN Na ◽  
何勇 HE Yong ◽  
刘飞 LIU Fei ◽  
张初 ZHANG Chu ◽  
...  
Keyword(s):  
Hyperspectral Imaging ◽  
Near Infrared ◽  
Rapid Identification ◽  
Coffee Bean ◽  
Imaging Technology ◽  
Bean Variety

scholarly journals Detection of Adulteration in Red Meat Species Using Hyperspectral Imaging

2018 ◽  
pp. 182-196
Author(s):  
Mahmoud Al-Sarayreh ◽  
Marlon M. Reis ◽  
Wei Qi Yan ◽  
Reinhard Klette
Keyword(s):  
Hyperspectral Imaging ◽  
Red Meat ◽  
Meat Species

scholarly journals Identification of Hybrid Okra Seeds Based on Near-Infrared Hyperspectral Imaging Technology

2018 ◽  
Vol 8 (10) ◽  
pp. 1793 ◽  
Author(s):  
Jinnuo Zhang ◽  
Xuping Feng ◽  
Xiaodan Liu ◽  
Yong He
Keyword(s):  
Hyperspectral Imaging ◽  
Near Infrared ◽  
Spectral Characteristics ◽  
Projection Algorithm ◽  
Rapid Screening ◽  
Support Vector ◽  
Calibration Model ◽  
Imaging Technology ◽  
Characteristic Wavelength ◽  
Full Band

Near-infrared (874–1734 nm) hyperspectral imaging technology combined with chemometrics was used to identify parental and hybrid okra seeds. A total of 1740 okra seeds of three different varieties, which contained the male parent xiaolusi, the female parent xianzhi, and the hybrid seed penzai, were collected, and all of the samples were randomly divided into the calibration set and the prediction set in a ratio of 2:1. Principal component analysis (PCA) was applied to explore the separability of different seeds based on the spectral characteristics of okra seeds. Fourteen and 86 characteristic wavelengths were extracted by using the successive projection algorithm (SPA) and competitive adaptive reweighted sampling (CARS), respectively. Another 14 characteristic wavelengths were extracted by using CARS combined with SPA. Partial least squares discriminant analysis (PLS-DA) and support vector machine (SVM) were developed based on the characteristic wavelength and full-band spectroscopy. The experimental results showed that the SVM discriminant model worked well and that the correct recognition rate was over 93.62% based on full-band spectroscopy. As for the discriminative model that was based on characteristic wavelength, the SVM model based on the CARS algorithm was better than the other two models. Combining the CARS+SVM calibration model and image processing technology, a pseudo-color map of sample prediction was generated, which could intuitively identify the species of okra seeds. The whole process provided a new idea for agricultural breeding in the rapid screening and identification of hybrid okra seeds.

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