ArticleFoods (Basel, Switzerland)2023
Non-Destructive Detection of Meat Quality Based on Multiple Spectral Dimension Reduction Methods by Near-Infrared Spectroscopy.
Article in Foods (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed, 32 citations in OpenAlex.
- Non-Destructive Assessment of Beef Freshness Using Visible and Near-Infrared Spectroscopy with Interpretable Machine Learning.Foods (Basel, Switzerland) · 2026Article
- Prediction Model for Quality Changes in Repeatedly Frozen-Thawed Pork Based on MRI Scans and Chemometrics.Foods (Basel, Switzerland) · 2026Article
- Non-destructive identification of commercial jerky types based on multi-band hyperspectral imaging with machine learning.Food chemistry: X · 2025Article
- Quantitative Classification and Prediction of Starkrimson Pear Maturity by Near-Infrared Spectroscopy.Foods (Basel, Switzerland) · 2024Article
- Evaluation of Dual-Band Near-Infrared Spectroscopy and Chemometric Analysis for Rapid Quantification of Multi-Quality Parameters of Soy Sauce Stewed Meat.Foods (Basel, Switzerland) · 2023Article
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Authors and funding
4 authors at 1 institution in 1 country.
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Abstract
The potential of four dimension reduction methods for near-infrared spectroscopy was investigated, in terms of predicting the protein, fat, and moisture contents in lamb meat. With visible/near-infrared spectroscopy at 400-1050 nm and 900-1700 nm, respectively, calibration models using partial least squares regression (PLSR) or multiple linear regression (MLR) between spectra and quality parameters were established and compared. The MLR prediction models for all three quality parameters based on the wavelengths selected by stepwise regression achieved the best results in the spectral region of 400-1050 nm. As for the spectral region of 900-1700 nm, the PLSR prediction model based on the raw spectra or high-correlation spectra achieved better results. The results of this study indicate that sampling interval shortening and of peak-to-trough jump features are worthy of further study, due to their great potential in explaining the quality parameters.
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