ArticleFoods (Basel, Switzerland)2024
The Study on Nondestructive Detection Methods for Internal Quality of Korla Fragrant Pears Based on Near-Infrared Spectroscopy and Machine Learning.
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 8 papers.
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8 citing papers in PubMed.
- Use of Machine Learning Techniques for Fertilization Traceability Discrimination via Core Quality Indicators of Korla Fragrant Pear Fruits.Foods (Basel, Switzerland) · 2026Article
- Rapid Determination of Soybean Protein Content by Near-Infrared Spectroscopy Coupled with Multi-Learner Ensemble Wavelength Selection.Foods (Basel, Switzerland) · 2026Article
- Hyperspectral imaging with machine learning for non-destructive prediction of quality attributes in Lanzhou frozen pears.Food chemistry: X · 2026Article
- Prediction of Total Soluble Solids in Apricot Using Adaptive Boosting Ensemble Model Combined with NIR and High-Frequency UVE-Selected Variables.Molecules (Basel, Switzerland) · 2025Article
- Colour detection method of Korla fragrant pear based on dielectric spectroscopy technology.Frontiers in plant science · 2025Article
- Critical Issues Faced by Industries Associated with Food Science and Technology: A Delphi Analysis.Foods (Basel, Switzerland) · 2024Article
- Detection of Pear Quality Using Hyperspectral Imaging Technology and Machine Learning Analysis.Foods (Basel, Switzerland) · 2024Article
- Quantitative Classification and Prediction of Starkrimson Pear Maturity by Near-Infrared Spectroscopy.Foods (Basel, Switzerland) · 2024Article
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10 authors.
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Abstract
Quality control and grading of Korla fragrant pears significantly impact their commercial value. Rapid and non-destructive detection of soluble solids content (SSC) and firmness is crucial to improving this. This study proposes a method combining near-infrared spectroscopy (NIRS) with machine learning for the rapid, non-destructive detection of SSC and firmness in Korla pears. By analyzing absorbance in the 900-1800 nm range, six preprocessing methods-Savitzky-Golay derivative (SGD), standard normal variate (SNV), multiplicative scatter correction (MSC), Savitzky-Golay smoothing (SGS), vector normalization (VN), and min-max normalization (MMN)-were applied to the raw spectral data. uninformative variable elimination (UVE) and successive projections algorithm (SPA) were then used to extract effective wavelengths. Partial least squares regression (PLSR) models were developed for SSC and firmness based on the extracted data. The results showed that all preprocessing and wavelength-extraction methods improved model accuracy. The optimal SSC prediction model was MSC-SPA-PLSR (R = 0.93, RMSE = 0.195), and the best hardness prediction model was MSC-UVE-PLSR (R = 0.83, RMSE = 0.249). This research aids in establishing a non-destructive testing system, offering producers a rapid and accurate quality assessment tool, and provides the food industry with better production control measures to enhance standardization and market competitiveness of Korla pears.
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