Detection of blood spots in eggs by hyperspectral transmittance imaging

Feng, Zhe; Ding, Chengqiao; Li, Weihao; Cui, Di

doi:10.25165/j.ijabe.20191206.5376

Cited by 6 publications

(11 citation statements)

References 1 publication

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“…In addition, bloody eggs were detected using the HSI system [ 16 ]. Hyperspectral images of intact brown eggs and dried brown eggshell fragments were acquired from a spectrum range between 450 nm and 1,000 nm.…”

Section: Detection Technologies For Internal Defects Of Eggsmentioning

confidence: 99%

“…Studies to develop abnormal egg detection technology using a modified pressure technology [9][10][11][12] and hyperspectral imaging technology [13][14][15][16] have also been conducted in addition to the currently commercialized technology. All techniques for detecting defective eggs are non-destructive and require a defective egg identification algorithm.…”

Section: Introductionmentioning

confidence: 99%

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Current advances in detection of abnormal egg: a review

So¹,

Joe²,

Hwang³

et al. 2022

J Anim Sci Technol

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Internal and external defects of eggs should be detected to prevent cross-contamination of intact eggs by abnormal eggs during storage. Emerging detection technologies for abnormal eggs were introduced as an alternative to human inspection. The advanced technologies could rapidly detect abnormal eggs. Abnormal egg detection technologies using acoustic response, machine vision, and spectroscopy have been commercialized in the poultry industry. Non-destructive egg quality assessment methods meanwhile could preserve the value of eggs and improve detection efficiency. In order to improve detection efficiency, it is essential to select a proper algorithm for classifying the types of abnormal eggs. This review deals with the performance of the detection technologies for various types of abnormal eggs in recently published resources. In addition, the discriminant methods and detection algorithms of abnormal eggs reported in the published literature were investigated. Although the majority of the studies were conducted on a laboratory scale, the developed detection technologies for internal and external defects in eggs were technically feasible to obtain the excellent detection accuracy. To apply the developed detection technologies to the poultry industry, it is necessary to achieve the detection rates required from the industry.

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Section: Detection Technologies For Internal Defects Of Eggsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Current advances in detection of abnormal egg: a review

So¹,

Joe²,

Hwang³

et al. 2022

J Anim Sci Technol

View full text Add to dashboard Cite

show abstract

“…Moreover, the influence of different sample batches and the acquisition speed of spectrum was fully considered and overcome to a certain degree in the detection system. According to the statement in Feng et al (2019), a hyperspectral transmittance imaging technique was used to detect blood-spot eggs and reached 96.34% accuracy.…”

Section: Discrimination Accuracy Of the Bpnn After Lm Conversionmentioning

confidence: 99%

“…After, blood value discriminant method, which is based on the ratio of absorption peaks of hemoglobin spectra, was put forward (Bamelis, Tona, De Baerdemaeker, & Decuypere, 2002;Gielen, Jong, & Kerkvliet, 1979;Usui, Nakano, & Mizutani, 2005;Usui, Nakano, & Motonaga, 2003;Usui, Nakano, & Saitou, 2006;Xu, Xu, & Chen, 2014;Zhu, Xie, & Li, 2015). Hyperspectral spectroscopy technique also proved to be effective (96.43% accuracy) for blood-spot detection when combined with support vector machine (Feng, Ding, Li, & Cui, 2019). However, most of the methods mentioned above are only available for static detection.…”

mentioning

confidence: 99%

Online detection of blood‐spot eggs based on a novel method of spectral amplitude space conversion

Zhu

Yang

et al. 2021

J Food Process Engineering

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To improve the robustness of the online detection system for blood-spot eggs, a linear converter was established based on Levenberg-Marquardt (LM) algorithm to transform spectral signals in the amplitude space, so that error introduced from the variation of sample, mechanical device, and other factors can be reduced to a certain degree. First, the transmission spectrum in the 500-599 nm bandwidth of each sample was selected. After preprocessed with the moving average filtering, the spectrum was transformed in the space of spectral amplitude using the LM algorithm. Then, the converted spectrum was utilized to establish the backpropagation neural network model. As the result, the classification model using the transformed spectra realized more than 96% accuracy. When the detection system dealt with different batches of samples produced on different dates, the discriminant accuracy using LM converter was significantly higher than that without (p value < .05). Meanwhile, when the detection system ran at different speeds, the model still showed good robustness, and the accuracy was increased to some degree with LM converter. The research indicated the technical superiority of the model built with the proposed spectra converter over the model without and provided a feasible method to detect blood-spot eggs online for commercial usage.

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“…HSI integrates traditional imaging and spectroscopy techniques, which can synchronously obtain the spatial and spectral information of the target [ 19 , 20 ]. In terms of egg quality assessment, HSI has been successfully used for the detection of the Haugh unit (HU), albumen pH, bubbles, scattered yolk, and blood spots [ 21 , 22 , 23 , 24 ]. However, research on the detection and visualization of S-ovalbumin content in eggs has not been reported in the literature.…”

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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Detection of blood spots in eggs by hyperspectral transmittance imaging

Cited by 6 publications

References 1 publication

Current advances in detection of abnormal egg: a review

Current advances in detection of abnormal egg: a review

Online detection of blood‐spot eggs based on a novel method of spectral amplitude space conversion

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

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