ArticleFood chemistry: X2022
Research on physicochemical properties, microscopic characterization and detection of different freezing-damaged corn seeds.
Article in Food chemistry: X, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Detection of Frost-Damaged Maize Kernels for Food Quality Screening Using Hyperspectral Imaging and 2D-COS-Assisted Deep Learning.Food science & nutrition · 2026Article
- RGSGAN-MACRNet: A More Accurate Recognition Method for Imperfect Corn Kernels Under Sample-Size-Limited Conditions.Foods (Basel, Switzerland) · 2025Article
- Corn Seed Freezing Damage Identification of Different Sides Based on Hyperspectral Imaging and SPA-2DCOS Fusion Algorithm.Molecules (Basel, Switzerland) · 2025Article
- Application of Hyperspectral Imaging and Deep Convolutional Neural Network for Freezing Damage Identification on Embryo and Endosperm Side of Single Corn Seed.Foods (Basel, Switzerland) · 2025Article
- Corn variety identification based on improved EfficientNet lightweight neural network.Frontiers in plant science · 2025Article
- Metabolomic and Physiological Analyses Reveal the Effects of Different Storage Conditions onMetabolites · 2024Article
- Isolation and Functional Analysis ofInternational journal of molecular sciences · 2022Article
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Seed freezing damage is an agricultural disaster. To explore how frostbite affects the growth and development of corn seeds, the germination conditions, and the biological indicators including the activities of related enzymes (SOD, POD, CAT, and AMS) of different frozen corn seeds (normal, -10 °C,10 h, and -20 °C,10 h) were measured. The texture of seed coat and the cell structure of seed embryo were observed by scanning electron microscope and transmission electron microscope respectively. The texture and cell structural changes reflect the influence of frostbite on corn seeds. To propose a quick, accurate and non-destructive method to identify the freezing-damaged corn seeds, near-infrared spectroscopy was used the identify the different frozen corn seeds. Different pretreatments, feature extraction methods and modeling methods were applied, result showed that in the case of standard normal variation pretreatment combined with principal component analysis feature extraction method and K-nearest neighbor model, 99.4 % and 100 % classification results of the training set and testing set were obtained respectively.
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