Egg Freshness Detection Based on Hyperspectral Image Technology

Wang, Qiaohua; Zhou, Kuanjiu; Wang, Caiyun; Ma, Meihu

doi:10.19026/ajfst.7.1623

Cited by 11 publications

(10 citation statements)

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“…However, there are only a few papers using it to study the freshness of eggs. Among them, using reflectance hyperspectrum to detect egg freshness can achieve fast and non-destructive grading of egg freshness with a correlation coefficient of 0.93 [ 18 ]. In addition, by using the optimal classification model (IRIV GA-SVM), the classification accuracy on the training set and test set achieved 99.25% and 97.87% respectively [ 19 ].…”

Section: Introductionmentioning

confidence: 99%

Nondestructive Detection for Egg Freshness Based on Hyperspectral Scattering Image Combined with Ensemble Learning

Dai

Jiang

Lü

et al. 2020

Sensors

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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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Section: Introductionmentioning

confidence: 99%

Nondestructive Detection for Egg Freshness Based on Hyperspectral Scattering Image Combined with Ensemble Learning

Dai

Jiang

Lü

et al. 2020

Sensors

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“…At present, researchers have used hyperspectral technology to detect peanut protein content and rice moisture (Sun, Lu, Mao, Wu, & Gao, 2017;Yu, Wang, & Shi, 2017). Others have used the same technology to detect the freshness of eggs, hatching eggs, and blood spots (Wang, Zhou, Wang, & Ma, 2015;Zhu, Liu, & Ma, 2015). Hyperspectral imaging technology has been widely used in the food field.…”

Section: Introductionmentioning

confidence: 99%

Nondestructive prediction modeling of S‐ovalbumin content in stored eggs based on hyperspectral fusion information

Dandan

Wang

et al. 2019

J Food Process Engineering

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Hyperspectral imaging technology was used to nondestructively predict S‐ovalbumin content by obtaining transmission spectral and fused image data. Hyperspectral images of eggs with spectral wavelengths between 300 and 1,100 nm were collected. Three image features were selected to use as variables to model. After spectral pretreatment, 14 characteristic wavelengths were selected using competitive adaptive reweighted sampling algorithm (CARS). Nondestructive prediction models of S‐ovalbumin content were developed based on either image feature parameters, a combination of spectral characteristic wavelengths, or image‐spectral fusion data after dimensionality reduction. A partial least squares (PLS) model and genetic algorithm to optimize a back propagation neural network (GA‐BP) model was established based on these three kinds of data. From comparisons between PLS and GA‐BP models based on the same type of each of the three data types, we determined that hyperspectral fusion data and a GA‐BP model can improve the performance of a S‐ovalbumin nondestructive prediction model. It had a simpler structure with fewer variables. So, it can be used to nondestructively and rapidly predict protein content in egg white. Practical applications Current nondestructive methods to evaluate egg quality primarily focus on the changes of simple external and internal quality, such as egg weight and simple chemical indicators. Due to the individual differences between eggs, the above methods are under a limited application scope. Changes in S‐ovalbumin content do not depend on egg weight, and so forth, individual differences have little effect on it. Changes in S‐ovalbumin content of various varieties of eggs are alike because the biochemical processes that affect egg white protein content is fundamentally the same across egg varieties, and its traditional detection method is to perform complex biochemical tests after breaking. Therefore, if the egg's internal protein content can detected quickly without breaking it, it is more accurate and more profound from the biochemical essence and it can not only solve the shortcomings of biochemical detection, but also provide important theory and application technology for the detection and classification of egg quality.

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“…Up to now, major attention was paid to the development of rapid techniques able to assess egg products freshness, eg, using electronic noses or spectroscopic analyses . These techniques are particularly suitable for industrial screening purposes, due to the reduced time and costs.…”

Section: Introductionmentioning

confidence: 99%

Egg product freshness evaluation: A metabolomic approach

et al. 2018

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Egg products' freshness is a crucial issue for the production of safe and high-quality commodities. Up to now, this parameter is assessed with the quantification of few compounds, but the possibility to evaluate more molecules simultaneously could help to provide robust results. In this study, 31 compounds responsible of freshness and not freshness of egg products were selected with a metabolomic approach. After an ultrahigh-pressure liquid chromatography-high-resolution mass spectrometry (UHPLC-HRMS) analysis, different chemometric models were created to select gradually the most significant features that were finally extracted and identified through HRMS data. Sample lots were collected directly from their arrival at the production plant sites, extracted immediately after, then left at room temperature, and extracted again after 24 and 48 hours (first day and second day, respectively). A total amount of 79 samples was used for the model creation. Furthermore, the same compounds were detected in seven new egg products sample lots not used for the model creation and treated with the same experimental design (total amount of samples, 21). The results obtained clearly demonstrate that these 31 molecules can be considered real freshness or not freshness chemical markers. Furthermore, this UHPLC-HRMS metabolomic approach allows for the detection of a larger set of metabolites clearly related to possible microbial growth over time, which is a relevant point for also ensuring food safety.

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Egg Freshness Detection Based on Hyperspectral Image Technology

Cited by 11 publications

References 0 publications

Nondestructive Detection for Egg Freshness Based on Hyperspectral Scattering Image Combined with Ensemble Learning

Nondestructive Detection for Egg Freshness Based on Hyperspectral Scattering Image Combined with Ensemble Learning

Nondestructive prediction modeling of S‐ovalbumin content in stored eggs based on hyperspectral fusion information

Egg product freshness evaluation: A metabolomic approach

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