ArticleFoods (Basel, Switzerland)2024
Discrimination of New and Aged Seeds Based on On-Line Near-Infrared Spectroscopy Technology Combined with Machine Learning.
Article in Foods (Basel, Switzerland), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed.
- Progeny Differentiation in Faba Bean Using Hyperspectral Images and Machine Learning.Plant direct · 2026Article
- Dual-Level Probability Consistency FS-PFCE for Robust Transfer Classification of Rice Seed Vigor Using Near-Infrared Hyperspectral Imaging.Plants (Basel, Switzerland) · 2026Article
- Visible-Near-Infrared Hyperspectral Imaging Enables Nondestructive Identification of Bean Accessions via 1D Spectral Reflectance Analysis.ACS omega · 2026Article
- Rapid Determination of Soybean Protein Content by Near-Infrared Spectroscopy Coupled with Multi-Learner Ensemble Wavelength Selection.Foods (Basel, Switzerland) · 2026Article
- VIS-NIR-SWIR Hyperspectral Imaging and Advanced Machine and Deep Learning Algorithms for a Controlled Benchmark of Bean Seed Identification and Classification.Plants (Basel, Switzerland) · 2026Article
- Non-Destructive Geographical Traceability and Quality Control ofFoods (Basel, Switzerland) · 2026Article
- From optical architectures to actual deployment a review of online process spectroscopy in industrial environments.Frontiers in chemistry · 2026Review
- MHENet: a multimodal hybrid enhancement network for accurate classification of unsound maize kernels.Frontiers in plant science · 2026Article
- WSPSorter: a real-time integrated system for non-destructive purity sorting of watermelon seeds based on rotary singulation and near infrared spectroscopy.Frontiers in plant science · 2026Article
- Research Progress in the Detection of Mycotoxins in Cereals and Their Products by Vibrational Spectroscopy.Foods (Basel, Switzerland) · 2025Review
- Applications of Artificial Intelligence in Food Industry.Foods (Basel, Switzerland) · 2025Article
- The Study on Nondestructive Detection Methods for Internal Quality of Korla Fragrant Pears Based on Near-Infrared Spectroscopy and Machine Learning.Foods (Basel, Switzerland) · 2024Article
- Rapid Determination of Crude Protein Content in Alfalfa Based on Fourier Transform Infrared Spectroscopy.Foods (Basel, Switzerland) · 2024Article
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Authors and funding
6 authors.
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Abstract
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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