Non-destructive detection of single corn seed vigor based on visible/near-infrared spatially resolved spectroscopy combined with chemometrics

Liu, Wenxi; Luo, Bin; Kang, Kai; Xia, Yu; Zhang, Han

doi:10.1016/j.saa.2024.124089

Cited by 1 publication

(1 citation statement)

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“…The classification accuracy of the prediction set was higher than 95%. Liu et al [18] built a single-fiber spatially resolved device using visible/near-infrared spectroscopy to inspect maize seed vigor and achieved good prediction results. However, the studies stated above still involve the static assessment of maize seeds.…”

Section: Introductionmentioning

confidence: 99%

Discrimination of New and Aged Seeds Based on On-Line Near-Infrared Spectroscopy Technology Combined with Machine Learning

Zhu,

Fan,

Zuo

et al. 2024

Foods

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The harvest year of maize seeds has a significant impact on seed vitality and maize yield. Therefore, it is vital to identify new seeds. In this study, an on-line near-infrared (NIR) spectra collection device (899–1715 nm) was designed and employed for distinguishing maize seeds harvested in different years. Compared with least squares support vector machine (LS-SVM), k-nearest neighbor (KNN), and extreme learning machine (ELM), the partial least squares discriminant analysis (PLS-DA) model has the optimal recognition performance for maize seed harvest years. Six different preprocessing methods, including Savitzky–Golay smoothing (SGS), standard normal variate transformation (SNV), multiplicative scatter correction (MSC), Savitzky–Golay 1 derivative (SG-D1), Savitzky–Golay 2 derivative (SG-D2), and normalization (Norm), were used to improve the quality of the spectra. The Monte Carlo cross-validation uninformative variable elimination (MC-UVE), competitive adaptive reweighted sampling (CARS), bootstrapping soft shrinkage (BOSS), successive projections algorithm (SPA), and their combinations were used to obtain effective wavelengths and decrease spectral dimensionality. The MC-UVE-BOSS-PLS-DA model achieved the classification with an accuracy of 88.75% using 93 features based on Norm preprocessed spectral data. This study showed that the self-designed NIR collection system could be used to identify the harvested years of maize seed.

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Section: Introductionmentioning

confidence: 99%