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.
Highlights
A large amount of millets are grown in Northern China (Lu et al, 2005)
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
The spectroscopic data on different types of millet were analyzed by discriminant analysis, principal component analysis and neural network technology
Summary
A large amount of millets are grown in Northern China (Lu et al, 2005). They are very popular miscellaneous grain crops and are commonly used as a food source (Liu et al, 2012). Millet is an important food because of the variety of rare nutrients it provides, and is widely respected as a healthy food (Yang et al, 2012). It has practical significance to identify the quality of millet varieties. Applying NIRS on agricultural materials is beneficial because this approach is nondestructive and it has been widely used in many agricultural application areas such as classifying agricultural products (Qiu et al, 2009; Jia et al, 2014), conducting quality inspection
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