Egg freshness prediction using a comprehensive analysis based on visible near infrared spectroscopy

Dong, Xiaoguang; Zhang, Beibei; Dong, Jun; Lu, Bing; Hu, Can; Tang, Xiuying

doi:10.1080/00387010.2020.1787455

Cited by 16 publications

(7 citation statements)

References 34 publications

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“…The HU and pH of the albumen had correlation coefficients of 0.842 and 0.867 between the predicted and the reference value, respectively. The comprehensive freshness indexes (shape, yolk, HU, albumen pH, air cell width, and eggshell thickness) were also reported in another study (Dong et al., 2020). The results showed that when compared to individual freshness indexes, the freshness indicator had a greater capacity to predict outcomes, with a predictive correlation value of 0.891 and a root mean square error (RMSE) of 1.0.…”

Section: Application Of I40 Technologies In the Egg Industrysupporting

confidence: 59%

Non‐destructive optical sensing technologies for advancing the egg industry toward Industry 4.0: A review

Ahmed,

Hossainy,

Khaliduzzaman

et al. 2023

Comp Rev Food Sci Food Safe

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The egg is considered one of the best sources of dietary protein, and has an important role in human growth and development. With the increase in the world's population, per capita egg consumption is also increasing. Ground‐breaking technological developments have led to numerous inventions like the Internet of Things (IoT), various optical sensors, robotics, artificial intelligence (AI), big data, and cloud computing, transforming the conventional industry into a smart and sustainable egg industry, also known as Egg Industry 4.0 (EI 4.0). The EI 4.0 concept has the potential to improve automation, enhance biosecurity, promote the safeguarding of animal welfare, increase intelligent grading and quality inspection, and increase efficiency. For a sustainable Industry 4.0 transformation, it is important to analyze available technologies, the latest research, existing limitations, and prospects. This review examines the existing non‐destructive optical sensing technologies for the egg industry. It provides information and insights on the different components of EI 4.0, including emerging EI 4.0 technologies for egg production, quality inspection, and grading. Furthermore, drawbacks of current EI 4.0 technologies, potential workarounds, and future trends were critically analyzed. This review can help policymakers, industrialists, and academicians to better understand the integration of non‐destructive technologies and automation. This integration has the potential to increase productivity, improve quality control, and optimize resource management toward sustainable development of the egg industry.

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Section: Application Of I40 Technologies In the Egg Industrysupporting

confidence: 59%

Non‐destructive optical sensing technologies for advancing the egg industry toward Industry 4.0: A review

Ahmed,

Hossainy,

Khaliduzzaman

et al. 2023

Comp Rev Food Sci Food Safe

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“…Therefore, it is desired to develop a non-invasive and rapid method to detect the S-ovalbumin content in eggs. Previous investigations on the non-destructive evaluation of egg qualities have used visible–near infrared spectroscopy [ 11 ], machine vision [ 12 ], electronic nose [ 13 ], and acoustic resonance [ 14 ]. However, the aforementioned methods have their own merits and drawbacks.…”

Section: Introductionmentioning

confidence: 99%

Development of Simplified Models for Non-Destructive Hyperspectral Imaging Monitoring of S-ovalbumin Content in Eggs during Storage

Yao

Sun

Cheng

et al. 2022

Foods

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S-ovalbumin content is an indicator of egg freshness and has an important impact on the quality of processed foods. The objective of this study is to develop simplified models for monitoring the S-ovalbumin content of eggs during storage using hyperspectral imaging (HSI) and multivariate analysis. The hyperspectral images of egg samples at different storage periods were collected in the wavelength range of 401–1002 nm, and the reference S-ovalbumin content was determined by spectrophotometry. The standard normal variate (SNV) was employed to preprocess the raw spectral data. To simplify the calibration models, competitive adaptive reweighted sampling (CARS) was applied to select feature wavelengths from the whole spectral range. Based on the full and feature wavelengths, partial least squares regression (PLSR) and least squares support vector machine (LSSVM) models were developed, in which the simplified LSSVM model yielded the best performance with a coefficient of determination for prediction (R2P) of 0.918 and a root mean square error for prediction (RMSEP) of 7.215%. By transferring the quantitative model to the pixels of hyperspectral images, the visualizing distribution maps were generated, providing an intuitive and comprehensive evaluation for the S-ovalbumin content of eggs, which helps to understand the conversion of ovalbumin into S-ovalbumin during storage. The results provided the possibility of implementing a multispectral imaging technique for online monitoring the S-ovalbumin content of eggs.

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“…Eggs are one of the staple foods for human and are rich in nutrients such as proteins, fatty acids, and many trace elements (Dong et al, 2020). Ovalbumin is the main component of fresh egg white proteins, accounting for about 54%, and is denatured at 80°C (Mann & Mann, 2011).…”

Section: Introductionmentioning

confidence: 99%

Nondestructive detection of S‐ovalbumin content in eggs using portable NIR spectrometer and MPA‐CARS

Yao

Sun

Cheng

et al. 2022

J Food Process Engineering

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The feasibility of applying a low‐cost and portable NIR spectrometer to detect the S‐ovalbumin content of eggs was investigated in this study, and a model population analysis based competitive adaptive reweighted sampling (MPA‐CARS) algorithm was proposed to reduce the dimensionality of spectral data. The NIR spectra of egg samples with different storage periods were collected in the wavelength range of 900–1700 nm. Standard normal variate (SNV) and Savitzky–Golay1st derivative were used to preprocess the raw spectral data. Then, CARS and the proposed MPA‐CARS were applied to select effective wavelengths from the whole spectral range. Statistical results showed that MPA‐CARS had better feature extraction performance than CARS and the number of selected feature wavelengths was less. Support vector machine (SVM), back propagation neural network (BPNN) and extreme gradient boosting (XGBoost) were used to establish calibration models for predicting S‐ovalbumin content, in which the simplified XGBoost model based on MPA‐CARS feature wavelengths yielded the best performance, with R2P of 0.906 and RMSEP of 7.799%. Therefore, portable NIR has the potential to be a useful tool for S‐ovalbumin content detection. This could help food processing industry to arrange miniaturized NIR sensors to detect egg quality at different points in the egg supply chain. Practical applications S‐ovalbumin content is a freshness index of eggs and can affect the quality of processed foods. This study demonstrated the feasibility of using portable NIR spectrometer to detect the S‐ovalbumin content of eggs, which can help food processing agencies to implement low‐cost NIR sensors to detect egg quality at different stages of the supply chain.

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Egg freshness prediction using a comprehensive analysis based on visible near infrared spectroscopy

Cited by 16 publications

References 34 publications

Non‐destructive optical sensing technologies for advancing the egg industry toward Industry 4.0: A review

Non‐destructive optical sensing technologies for advancing the egg industry toward Industry 4.0: A review

Development of Simplified Models for Non-Destructive Hyperspectral Imaging Monitoring of S-ovalbumin Content in Eggs during Storage

Nondestructive detection of S‐ovalbumin content in eggs using portable NIR spectrometer and MPA‐CARS

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