ArticleFoods (Basel, Switzerland)2020
Integration of Partial Least Squares Regression and Hyperspectral Data Processing for the Nondestructive Detection of the Scaling Rate of Carp (
Article in Foods (Basel, Switzerland), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Near-Infrared Hyperspectral Imaging for Non-Destructive Detection of Old Rice in Freshly Milled Rice.Foods (Basel, Switzerland) · 2026Article
- Deep Learning-Enhanced UV Fluorescence for Automated Detection of Foreign Bodies in Tilapia Fillets.Foods (Basel, Switzerland) · 2026Article
- Advancing Real-Time Food Inspection: An Improved YOLOv10-Based Lightweight Algorithm for Detecting Tilapia Fillet Residues.Foods (Basel, Switzerland) · 2025Article
- Rapid Identification ofMolecules (Basel, Switzerland) · 2025Article
- Rapid and Non-Invasive Assessment of Texture Profile Analysis of Common Carp (Foods (Basel, Switzerland) · 2023Article
- Post-heating Fluorescence-based Alteration and Adulteration Detection of Extra Virgin Olive Oil.Journal of fluorescence · 2023Article
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Authors and funding
6 authors.
Funding
Abstract
The scaling rate of carp is one of the most important factors restricting the automation and intelligence level of carp processing. In order to solve the shortcomings of the commonly-used manual detection, this paper aimed to study the potential of hyperspectral technology (400-1024.7 nm) in detecting the scaling rate of carp. The whole fish body was divided into three regions (belly, back, and tail) for analysis because spectral responses are different for different regions. Different preprocessing methods, including Savitzky-Golay (SG), first derivative (FD), multivariate scattering correction (MSC), and standard normal variate (SNV) were applied for spectrum pretreatment. Then, the successive projections algorithm (SPA), regression coefficient (RC), and two-dimensional correlation spectroscopy (2D-COS) were applied for selecting characteristic wavelengths (CWs), respectively. The partial least square regression (PLSR) models for scaling rate detection using full wavelengths (FWs) and CWs were established. According to the modeling results, FD-RC-PLSR, SNV-SPA-PLSR, and SNV-RC-PLSR were determined to be the optimal models for predicting the scaling rate in the back (the coefficient of determination in calibration set (
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