ArticleFood chemistry: X2024
Innovative strategies for protein content determination in dried laver (
Article in Food chemistry: X, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
4 citing papers in PubMed.
- Roasting-induced changes in the metabolite profile of laver (Food chemistry: X · 2026Article
- Spectroscopic Methods Applied in Food Quality Determination.Foods (Basel, Switzerland) · 2026Article
- Short-Wave Infrared Hyperspectral Image-Based Quality Grading of Dried Laver (Foods (Basel, Switzerland) · 2025Article
- Investigation of potential quality indicators for raw laver (Frontiers in nutrition · 2025Article
Corrections and comments
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
In this study, we explored the application of Short-Wave Infrared (SWIR) hyperspectral imaging combined with Competitive Adaptive Reweighted Sampling (CARS) and advanced regression models for the non-destructive assessment of protein content in dried laver. Utilizing a spectral range of 900-1700 nm, we aimed to refine the quality control process by selecting informative wavelengths through CARS and applying various preprocessing techniques (standard normal variate [SNV], Savitzky-Golay filtering [SG], Orthogonal Signal Correction [OSC], and StandardScaler [SS]) to enhance the model's accuracy. The SNV-OSC-StandardScaler- Support vector regression (SVR) model trained on CARS-selected wavelengths significantly outperformed the other configurations, achieving a prediction determination coefficient (Rp
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Registered trials
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