Feasibility analysis of NIR for detecting sweet corn seeds vigor. (May 2020)

• Record Type: Journal Article
• Authors: Wang, Yali; Peng, Yankun; Zhuang, Qibin; Zhao, Xinlong
• Is Part Of: Journal of cereal science. Volume 93(2020)
• Publication Date: 2020-05
• Subjects: Corn seed -- Near infrared spectroscopy -- Vigor detection -- Discrimination; NIRS near infrared spectroscopy -- PLS partial least squares -- PLS-DA partial least squares discriminant analysis; Grain -- Periodicals; Cereal products -- Periodicals; Céréales -- Périodiques; Produits céréaliers -- Périodiques; Cereal products; Grain; Periodicals; 664.705
• DOI: 10.1016/j.jcs.2020.102977
• Languages: English
• ISSNs: 0733-5210

Abstract

Abstract: This paper explores the feasibility of particle-based detection and grading of seed vigor based on a self-built seed single-granulation device using near infrared spectroscopy (NIRS). Sweet corn with uniform kernel size was used for this study. The seed samples were divided into three types, they were normal seeds, artificially aged seeds and heat-damaged seeds. A 2-part spectral acquisition of each seed were performed, one for the collection of seeds that fall into the detection zone within the separation pipe, another was on the static platform, whose collection was performed on 5 faces of each seed. Partial least squares discriminant analysis (PLS-DA) was used to classify the original data of the seeds. In the 2 parts, the discriminant results of the unprocessed normal seeds and the artificial accelerated aging seeds, the untreated normal seeds and the heat-damaged seeds showed that classification accuracy was higher than 98%. The research indicates that the spectral data of different positions of seeds can reflect their activity information, and it is feasible to detect and classify seeds in real time in the detection area of the separation pipeline. Graphical abstract: The reflectivity of different detection positions of same seeds were similar, and there is a clear difference in reflectance intensity of vigor seeds and non-viable seeds. Image 1 Highlights: The discriminant accuracy of heat-damaged seeds models can reach 100%. The discriminant accuracy of artificially aged seeds models can reach 100%. The discriminant accuracy of comprehensive models were above 95%. It is feasible to perform the vigor detection of corn seeds granularly.