Discrimination of cracked soybean seeds by near-infrared spectroscopy and random forest variable selection

doi:10.1016/j.infrared.2021.103731

	Discrimination of cracked soybean seeds by near-infrared spectroscopy and random forest variable selection
	Wang, Liusan 1; Huang, Ziliang ; Wang, Rujing
刊名	INFRARED PHYSICS & TECHNOLOGY
	2021-06-01
卷号	115
关键词	Soybean seeds Crack Near infrared spectroscopy Random forest Variable selection
ISSN号	1350-4495
DOI	10.1016/j.infrared.2021.103731
通讯作者	Wang, Liusan()
英文摘要	The presence of crack in soybean seeds reduces the quality of soybean seeds. Therefore, it is essential to assess the quality of the seeds before storage and sowing. The current visual inspection methods to precisely select cracked soybean seeds are subjective, inconsistent and slow, and chemical methods are destructive and time consuming. In this study, a discrimination of cracked soybean seeds method by near-infrared spectroscopy and random forest variable selection is proposed. Two hundred soybean seeds spectra were acquired using an FT-NIR spectrometer. One hundred and fifty soybean seeds (seventy-five normal and seventy-five cracked) were applied for training and validation sets, fifty soybean seeds (twenty-five normal and twenty-five nature cracked) applied for a test set. Principal component analysis (PCA) and random forest (RF) were used to assess the spectral data from the FTNIR spectrometer. Moreover, three random forest variable selection methods, namely recursive feature elimination (REF), Boruta and VarSelRF algorithms, were applied. The classification accuracy of 80% was achieved for random forest in the test set. The mainly wavenumber variables selected by the three variable selection algorithms were all around the wavenumbers 7066 and 10,522cm(-1). Among the three random forest variable selection algorithms, the performance of REF algorithm was superior, an accuracy of 84% was achieved in the test set. The selected variables combined with the results of RF models demonstrated the major contributors to classify the cracked and normal soybean seeds are moisture, amorphous cellulose and fiber content on the absorption spectrum. The results of present study demonstrated the proposed method can be used for detecting cracked soybean seeds.
资助项目	National Key Research and Development Program of China[2018YFD0101004]
WOS关键词	CLASSIFICATION ; ELIMINATION ; TRANSMITTANCE ; KERNELS ; PROTEIN ; DAMAGE
WOS研究方向	Instruments & Instrumentation ; Optics ; Physics
语种	英语
出版者	ELSEVIER
WOS记录号	WOS:000663405200011
资助机构	National Key Research and Development Program of China
内容类型	期刊论文
源URL	[http://ir.hfcas.ac.cn:8080/handle/334002/123841]
专题	中国科学院合肥物质科学研究院
通讯作者	Wang, Liusan
作者单位	1.Chinese Acad Sci, Inst Intelligent Machines, Hefei 230031, Peoples R China 2.Intelligent Agr Engn Lab Anhui Prov, Hefei 230031, Peoples R China
推荐引用方式 GB/T 7714	Wang, Liusan,Huang, Ziliang,Wang, Rujing. Discrimination of cracked soybean seeds by near-infrared spectroscopy and random forest variable selection[J]. INFRARED PHYSICS & TECHNOLOGY,2021,115.
APA	Wang, Liusan,Huang, Ziliang,&Wang, Rujing.(2021).Discrimination of cracked soybean seeds by near-infrared spectroscopy and random forest variable selection.INFRARED PHYSICS & TECHNOLOGY,115.
MLA	Wang, Liusan,et al."Discrimination of cracked soybean seeds by near-infrared spectroscopy and random forest variable selection".INFRARED PHYSICS & TECHNOLOGY 115(2021).