African Journal of
Agricultural Research

  • Abbreviation: Afr. J. Agric. Res.
  • Language: English
  • ISSN: 1991-637X
  • DOI: 10.5897/AJAR
  • Start Year: 2006
  • Published Articles: 6574

Full Length Research Paper

Near infrared spectroscopy (NIRS) technology applied in millet feature extraction and variety identification

Wu Cui-qing
  • Wu Cui-qing
  • College of Engineering, Shanxi Agricultural University, Taigu, Shanxi, China.
  • Google Scholar
Kong Li-juan
  • Kong Li-juan
  • College of Biological and Agricultural Engineering, Jilin University, Changchun, Jilin, China.
  • Google Scholar
Wang Sheng
  • Wang Sheng
  • College of Engineering, Shanxi Agricultural University, Taigu, Shanxi, China.
  • Google Scholar
Guo Yu-ming
  • Guo Yu-ming
  • College of Engineering, Shanxi Agricultural University, Taigu, Shanxi, China.
  • Google Scholar


  •  Received: 02 May 2017
  •  Accepted: 08 June 2017
  •  Published: 29 June 2017

Abstract

Near infrared spectroscopy (NIRS) technology is widely used on agricultural products for quality detection, classification and variety identification due to its rapid speed and high-efficiency. NIRS experiments were conducted to identify varieties of DUN millet, JIN 21 millet and 5 other types of millet. The NIRS characteristic curves and data of millet samples were collected. The spectroscopic data on different types of millet were analyzed by discriminant analysis, principal component analysis and neural network technology. The calibration set correct classification was 98.9%. A BP neural network prediction model for millet was also built. It was found that the forecast results of original wave spectrum prediction model were best, with its correlation coefficient of validation (Rv) at 0.9999, the standard error of prediction (SEP) was 0.0191 and the root mean square error of prediction (RMSEP) was 0.0189. Moreover, the Rv of first derivative spectra was 0.9976, the SEP and RMSEP were 0.1043 and 0.1437, respectively, and the Rv, SEP and RMSEP of second derivative spectra were 0.9835, 0.28735 and 0.2720 respectively. This study laid the foundation for identification of millet varieties by NIRS.

Key words: Millet, near infrared spectroscopy (NIRS), principal component analysis, neural network prediction, variety identification.