ArticleFoods (Basel, Switzerland)2024
Quantitative Prediction of Protein Content in Corn Kernel Based on Near-Infrared Spectroscopy.
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 10 papers.
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Who cites it
10 citing papers in PubMed.
- Advances in extraction, structural characterization, and bioactivities of Armillaria mellea polysaccharides: insights into structure-activity relationships and future applications.Archives of microbiology · 2026Review
- Convolutional Neural Network-Based Models for Near-Infrared Prediction of Nutritional Quality in Multi-Product Animal Feeds.Animals : an open access journal from MDPI · 2026Article
- Near-Infrared Spectroscopy in Food Analysis: Applications, Chemometric Strategies, and Technological Advances.Foods (Basel, Switzerland) · 2026Review
- Foliar application of magnesium and amino acids to off-season maize: a strategy to improve photosynthetic and antioxidant metabolism.Frontiers in plant science · 2026Article
- Rapid Evaluation of Wet Gluten Content in Wheat Using Hyperspectral Technology Combined with Machine Learning Algorithms.Foods (Basel, Switzerland) · 2025Article
- A Cost-Effective and Scalable Machine Learning Approach for Quality Assessment of Fresh Maize Kernel Using NIR Spectroscopy.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
- Multi-Scale Feature Attention Network for Rapid and Non-Destructive Quantification of Aflatoxin BFoods (Basel, Switzerland) · 2025Article
- Rapid and Non-Destructive Assessment of Eight Essential Amino Acids in Foxtail Millet: Development of an Efficient and Accurate Detection Model Based on Near-Infrared Hyperspectral.Foods (Basel, Switzerland) · 2025Article
- Near-Infrared Spectroscopy and Machine Learning for Fast Quality Prediction of Bottle Gourd.Foods (Basel, Switzerland) · 2025Article
- Recent Advances in the Assessment of Cereal and Cereal-Based Product Quality.Foods (Basel, Switzerland) · 2025Article
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Authors and funding
7 authors.
Funding
Abstract
Rapid and accurate detection of protein content is essential for ensuring the quality of maize. Near-infrared spectroscopy (NIR) technology faces limitations due to surface effects and sample homogeneity issues when measuring the protein content of whole maize grains. Focusing on maize grain powder can significantly improve the quality of data and the accuracy of model predictions. This study aims to explore a rapid detection method for protein content in maize grain powder based on near-infrared spectroscopy. A method for determining protein content in maize grain powder was established using near-infrared (NIR) reflectance spectra in the 940-1660 nm range. Various preprocessing techniques, including Savitzky-Golay (S-G), multiplicative scatter correction (MSC), standard normal variate (SNV), and the first derivative (1D), were employed to preprocess the raw spectral data. Near-infrared spectral data from different varieties of maize grain powder were collected, and quantitative analysis of protein content was conducted using Partial Least Squares Regression (PLSR), Support Vector Machine (SVM), and Extreme Learning Machine (ELM) models. Feature wavelengths were selected to enhance model accuracy further using the Successive Projections Algorithm (SPA) and Uninformative Variable Elimination (UVE). Experimental results indicated that the PLSR model, preprocessed with 1D + MSC, yielded the best performance, achieving a root mean square error of prediction (RMSEP) of 0.3 g/kg, a correlation coefficient (R
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