Development of a Novel Acoustic Spectroscopy Method for Detection of Eggshell Cracks

Development of Cracked Egg Detection Device Using Electric Discharge Phenomenon

Eggs are a highly nutritious food; however, those are also fragile and susceptible to cracks, which can lead to bacterial contamination and economic losses. Traditional methods for detecting cracks, particularly in processed eggs, often fall short due to changes in the eggs' physical properties during processing. This study was aimed at developing a novel device for detecting egg cracks using electric discharge phenomena. The system was designed to apply a high-voltage electric field to the eggs, where sparks were generated at crack locations due to the differences in electrical conductivity between the insulative eggshell and the more conductive inner membrane exposed by the cracks. The detection apparatus consisted of a custom-built high-voltage power supply, flexible electrode pins, and a rotation mechanism to ensure a complete 360-degree inspection of each egg. Numerical simulations were performed to analyze the distribution of the electric field and charge density, confirming the method's validity. The results demonstrated that this system could efficiently detect cracks in both raw and processed eggs, overcoming the limitations of existing detection technologies. The proposed method offers high precision, reliability, and the potential for broader application in the inspection of various poultry products, representing a significant advancement in food safety and quality control.

Robust Detection of Cracked Eggs Using a Multi-Domain Training Method for Practical Egg Production

The presence of cracks reduces egg quality and safety, and can easily cause food safety hazards to consumers. Machine vision-based methods for cracked egg detection have achieved significant success on in-domain egg data. However, the performance of deep learning models usually decreases under practical industrial scenarios, such as the different egg varieties, origins, and environmental changes. Existing researches that rely on improving network structures or increasing training data volumes cannot effectively solve the problem of model performance decline on unknown egg testing data in practical egg production. To address these challenges, a novel and robust detection method is proposed to extract max domain-invariant features to enhance the model performance on unknown test egg data. Firstly, multi-domain egg data are built on different egg origins and acquisition devices. Then, a multi-domain trained strategy is established by using Maximum Mean Discrepancy with Normalized Squared Feature Estimation (NSFE-MMD) to obtain the optimal matching egg training domain. With the NSFE-MMD method, the original deep learning model can be applied without network structure improvements, which reduces the extremely complex tuning process and hyperparameter adjustments. Finally, robust cracked egg detection experiments are carried out on several unknown testing egg domains. The YOLOV5 (You Only Look Once v5) model trained by the proposed multi-domain training method with NSFE-MMD has a detection mAP of 86.6% on the unknown test Domain 4, and the YOLOV8 (You Only Look Once v8) model has a detection mAP of 88.8% on Domain 4, which is an increase of 8% and 4.4% compared to the best performance of models trained on a single domain, and an increase of 4.7% and 3.7% compared to models trained on all domains. In addition, the YOLOV5 model trained by the proposed multi-domain training method has a detection mAP of 87.9% on egg data of the unknown testing Domain 5. The experimental results demonstrate the robustness and effectiveness of the proposed multi-domain training method, which can be more suitable for the large quantity and variety of egg detection production.

Effects of Zinc, Manganese, and Taurine on Egg Shell Microstructure in Commercial Laying Hens After Peak Production

Much strive has been made to improve egg shell quality in laying hens. This study was conducted to evaluate the effects of two microminerals, zinc and manganese, besides taurine semi-essential amino acid on eggshell quality after peak production of Hy-line laying hens. A total of 720 laying hens were assigned to 18 treatments in a completely randomized design (3 × 3 × 2 factorial) at week 71. Experimental period included 8-week adaptation and using 18 dietary treatments for about 6 weeks. Dietary treatments included Zn (0, 80, and 160 mg/kg), Mn (0, 90, and 180 mg/kg), and taurine (0 and 1960 mg/kg). Supplementation of 90 mg Mn and 1960 mg taurine in laying hens' diet after peak of production improved egg shell quality without any negative effect on the internal quality of the egg. Egg specific gravity significantly increased in response to Zn, Mn, and taurine in comparison with control treatment (P < 0.05). Applying 1960 mg taurine/kg diet significantly improved calcite crystal's structure and eggshell strength in comparison with control treatment (P < 0.05). It was concluded that adding Mn and taurine can positively affect eggshell quality of laying hens post peak period.

Damage Detection of Unwashed Eggs through Video and Deep Learning

Broken eggs can be harmful to human health but are also unfavorable for transportation and production. This study proposes a video-based detection model for the real-time detection of broken eggs regarding unwashed eggs in dynamic scenes. A system capable of the continuous rotation and translation of eggs was designed to display the entire surface of an egg. We added CA into the backbone network, fusing BiFPN and GSConv with the neck to improve YOLOv5. The improved YOLOV5 model uses intact and broken eggs for training. In order to accurately judge the category of eggs in the process of movement, ByteTrack was used to track the eggs and assign an ID to each egg. The detection results of the different frames of YOLOv5 in the video were associated by ID, and we used the method of five consecutive frames to determine the egg category. The experimental results show that, when compared to the original YOLOv5, the improved YOLOv5 model improves the precision of detecting broken eggs by 2.2%, recall by 4.4%, and mAP:0.5 by 4.1%. The experimental field results showed an accuracy of 96.4% when the improved YOLOv5 (combined with ByteTrack) was used for the video detection of broken eggs. The video-based model can detect eggs that are always in motion, which is more suitable for actual detection than a single image-based detection model. In addition, this study provides a reference for the research of video-based non-destructive testing.

Non-Destructive Measurement of Egg's Haugh Unit by Vis-NIR with iPLS-Lasso Selection

Egg freshness is of great importance to daily nutrition and food consumption. In this work, visible near-infrared (vis-NIR) spectroscopy combined with the sparsity of interval partial least square regression (iPLS) were carried out to measure the egg's freshness by semi-transmittance spectral acquisition. A fiber spectrometer with a spectral range of 550-985 nm was embedded in the developed spectral scanner, which was designed with rich light irradiation mode from another two reflective surfaces. The semi-transmittance spectra were collected from the waist of eggs and monitored every two days. Haugh unit (HU) is a key indicator of egg's freshness, and ranged 56-91 in 14 days after delivery. The profile of spectra was analyzed the relation to the changes of egg's freshness. A series of iPLS models were constructed on the basis of spectral intervals at different divisions of the spectral region to predict the egg's HU, and then the least absolute shrinkage and selection operator (Lasso) was used to sparse the number of iPLS member models acting as a role of model selection and fusion regression. By optimization of the number of spectral intervals in the range of 1 to 40, the 26^th fusion model obtained the best performance with the minimum root mean of squared error of prediction (RMSEP) of 5.161, and performed the best among the general PLS model and other intervals-combined PLS models. This study provided a new, rapid, and reliable method for the non-destructive and in-site determination of egg's freshness.

Current advances in detection of abnormal egg: a review

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.