ArticleFood chemistry: X2023
Determination of corn protein content using near-infrared spectroscopy combined with A-CARS-PLS.
Article in Food chemistry: X, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed, 41 citations in OpenAlex.
- Key Indicator Detection and Authenticity Identification of Beer Based on Near-Infrared Spectroscopy Combined with Multi-Task Feature Extraction.Molecules (Basel, Switzerland) · 2026Article
- Quantitative Detection of Key Parameters and Authenticity Verification for Beer Using Near-Infrared Spectroscopy.Foods (Basel, Switzerland) · 2025Article
- Hyperspectral magnetite grade based on characteristic band screening inversion study.Scientific reports · 2025Article
- Corn Seed Freezing Damage Identification of Different Sides Based on Hyperspectral Imaging and SPA-2DCOS Fusion Algorithm.Molecules (Basel, Switzerland) · 2025Article
- Quantitative Prediction of Protein Content in Corn Kernel Based on Near-Infrared Spectroscopy.Foods (Basel, Switzerland) · 2024Article
- A Nondestructive Detection Method for the Muti-Quality Attributes of Oats Using Near-Infrared Spectroscopy.Foods (Basel, Switzerland) · 2024Article
- Rapid Determination of Crude Protein Content in Alfalfa Based on Fourier Transform Infrared Spectroscopy.Foods (Basel, Switzerland) · 2024Article
- Establishment and comparison ofFrontiers in nutrition · 2024Article
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Authors and funding
6 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
In order to quickly and accurately determine the protein content of corn, a new characteristic wavelength selection algorithm called anchor competitive adaptive reweighted sampling (A-CARS) was proposed in this paper. This method first lets Monte Carlo synergy interval PLS (MC-siPLS) to select the sub-intervals where the characteristic variables exist and then uses CARS to screen the variables further. A-CARS-PLS was compared with 6 methods, including 3 feature variable selection methods (GA-PLS, random frog PLS, and CARS-PLS) and 2 interval partial least squares methods (siPLS and MWPLS). The results showed that A-CARS-PLS was significantly better than other methods with the results: RMSECV = 0.0336, R
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