Literature DB >> 28858390

Non-destructive technique for determining the viability of soybean (Glycine max) seeds using FT-NIR spectroscopy.

Dewi Kusumaningrum1, Hoonsoo Lee2, Santosh Lohumi1, Changyeun Mo3, Moon S Kim2, Byoung-Kwan Cho1.   

Abstract

BACKGROUND: The viability of seeds is important for determining their quality. A high-quality seed is one that has a high capability of germination that is necessary to ensure high productivity. Hence, developing technology for the detection of seed viability is a high priority in agriculture. Fourier transform near-infrared (FT-NIR) spectroscopy is one of the most popular devices among other vibrational spectroscopies. This study aims to use FT-NIR spectroscopy to determine the viability of soybean seeds.
RESULTS: Viable and artificial ageing seeds as non-viable soybeans were used in this research. The FT-NIR spectra of soybean seeds were collected and analysed using a partial least-squares discriminant analysis (PLS-DA) to classify viable and non-viable soybean seeds. Moreover, the variable importance in projection (VIP) method for variable selection combined with the PLS-DA was employed. The most effective wavelengths were selected by the VIP method, which selected 146 optimal variables from the full set of 1557 variables.
CONCLUSIONS: The results demonstrated that the FT-NIR spectral analysis with the PLS-DA method that uses all variables or the selected variables showed good performance based on the high value of prediction accuracy for soybean viability with an accuracy close to 100%. Hence, FT-NIR techniques with a chemometric analysis have the potential for rapidly measuring soybean seed viability.
© 2017 Society of Chemical Industry. © 2017 Society of Chemical Industry.

Entities:  

Keywords:  near infrared spectroscopy; non-destructive measurement; soybean; viability

Mesh:

Year:  2017        PMID: 28858390     DOI: 10.1002/jsfa.8646

Source DB:  PubMed          Journal:  J Sci Food Agric        ISSN: 0022-5142            Impact factor:   3.638


  4 in total

1.  Single-Kernel FT-NIR Spectroscopy for Detecting Supersweet Corn (Zea mays L. Saccharata Sturt) Seed Viability with Multivariate Data Analysis.

Authors:  Guangjun Qiu; Enli Lü; Huazhong Lu; Sai Xu; Fanguo Zeng; Qin Shui
Journal:  Sensors (Basel)       Date:  2018-03-28       Impact factor: 3.576

2.  Dehiscence method: a seed-saving, quick and simple viability assessment in rice.

Authors:  Ling-Xiang Xu; Yi-Xin Lin; Li-Hong Wang; Yuan-Chang Zhou
Journal:  Plant Methods       Date:  2018-08-10       Impact factor: 4.993

3.  Rapidly and exactly determining postharvest dry soybean seed quality based on machine vision technology.

Authors:  Ping Lin; Li Xiaoli; Du Li; Shanchao Jiang; Zhiyong Zou; Qun Lu; Yongming Chen
Journal:  Sci Rep       Date:  2019-11-20       Impact factor: 4.379

4.  Corn Seed Defect Detection Based on Watershed Algorithm and Two-Pathway Convolutional Neural Networks.

Authors:  Linbai Wang; Jingyan Liu; Jun Zhang; Jing Wang; Xiaofei Fan
Journal:  Front Plant Sci       Date:  2022-02-23       Impact factor: 5.753

  4 in total

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