ArticleSensors (Basel, Switzerland)2020
Nondestructive Detection for Egg Freshness Based on Hyperspectral Scattering Image Combined with Ensemble Learning.
Article in Sensors (Basel, Switzerland), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
What it found
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The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
8 citing papers in PubMed.
- Nondestructive Testing of Egg Freshness Based on Laser Doppler Vibrometry.Sensors (Basel, Switzerland) · 2026Article
- Advancing Egg Freshness Evaluation with Integrated AI and Spectroscopy.Foods (Basel, Switzerland) · 2026Review
- Advances in Hyperspectral Imaging for Nondestructive Food Quality and Safety Detection.Foods (Basel, Switzerland) · 2026Review
- Advances in Emerging Non-Destructive Technologies for Detecting Raw Egg Freshness: A Comprehensive Review.Foods (Basel, Switzerland) · 2024Review
- Nondestructive Detection of Egg Freshness Based on Infrared Thermal Imaging.Sensors (Basel, Switzerland) · 2023Article
- Current advances in detection of abnormal egg: a review.Journal of animal science and technology · 2022Review
- Development, Validation and Preliminary Experiments of a Measuring Technique for Eggs Aging Estimation Based on Pulse Phase Thermography.Sensors (Basel, Switzerland) · 2022Article
- The grades and freshness assessment of eggs based on density detection using machine vision and weighing sensor.Scientific reports · 2021Article
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
Scattering hyperspectral technology is a nondestructive testing method with many advantages. Here, we propose a method to improve the accuracy of egg freshness, research the influence of incident angles of light source on the accuracy, and explain its mechanism. A variety of weak classifiers classify eggs based on the spectra after preprocessing and feature wavelength extraction to obtain three classifiers with the highest accuracy. The three classifiers are used as metamodels of stacking ensemble learning to improve the highest accuracy from 96.25% to 100%. Moreover, the highest accuracy of scattering, reflection, transmission, and mixed hyperspectral of eggs are 100.00%, 88.75%, 95.00%, and 96.25%, respectively, indicating that the scattering hyperspectral for egg freshness detection is better than that of the others. In addition, the accuracy is inversely proportional to the angle of incidence, i.e., the smaller the incident angle, the camera collects a larger proportion of scattering light, which contains more biochemical parameters of an egg than that of reflection and transmission. These results are very important for improving the accuracy of non-destructive testing and for selecting the incident angle of a light source, and they have potential applications for online non-destructive testing.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.