The grades and freshness assessment of eggs based on density detection using machine vision and weighing sensor

Estimating yolk weight of duck eggs using VIS-NIR Spectroscopy and RGB images and whole egg weights

Duck eggs are widely-consumed food and cooking ingredient. The heavier yolk weight (YW) corresponds to a larger size and greater value. However, there is no nondestructive method available to estimate the weight of the yolk. Accurate weight prediction of duck egg yolks must combine both phenotypic and internal information. In this research, we used Visible-Near Infrared (VIS-NIR) spectroscopy to obtain internal information of duck eggs, and a high-definition camera to capture their phenotypic features. YW was predicted by combining the reduced spectral and RGB image information with the whole egg weight. We also investigated the impact of color and thickness of the duck egg on spectral transmittance (ST), as these factors can influence the extent of ST. The results showed that the spectral curves of duck eggs produced 2 peaks and 1 valley, which may be caused by the dual-frequency absorption of the C-H group and O-H group, and can be used to symbolize the internal information of duck eggs. The ST was somewhat affected by the color and thickness of the duck eggshell. Before modelling, Principal component analysis (PCA) was used to significantly reduce the dimensionality of the RGB image with spectral data. A partial least squares regression (PLSR) model was utilized to fit all the features. The test set yielded a coefficient of determination (R2) of 0.82 and a Root Mean Squared Error (RMSE) of 1.05 g. After removing the eggshell's color and thickness features, the model showed an R2 of 0.79 and an RMSE of 1.11 g. This study demonstrated that the yolk weight of duck eggs can be estimated using VIS-NIR spectroscopy, RGB images and whole egg weight. Furthermore, the effects of shell color and thickness can be neglected.

Predicting Egg Storage Time with a Portable Near-Infrared Instrument: Effects of Temperature and Production System

Determining egg freshness is critical for ensuring food safety and security and as such, different methods have been evaluated and implemented to accurately measure and predict it. In this study, a portable near-infrared (NIR) instrument combined with chemometrics was used to monitor and predict the storage time of eggs under two storage conditions-room temperature (RT) and cold (CT) storage-from two production systems: cage and free-range. A total of 700 egg samples were analyzed, using principal component analysis (PCA) and partial least squares (PLS) regression to analyze the NIR spectra. The PCA score plot did not show any clear separation between egg samples from the two production systems; however, some egg samples were grouped according to storage conditions. The cross-validation statistics for predicting storage time were as follows: for cage and RT eggs, the coefficient of determination in cross validation (R2CV) was 0.67, with a standard error in cross-validation (SECV) of 7.64 days and residual predictive deviation (RPD) of 1.8; for CT cage eggs, R2CV of 0.84, SECV of 5.38 days and RPD of 3.2; for CT free-range eggs, R2CV of 0.83, SECV of 5.52 days and RPD of 3.2; and for RT free-range eggs, R2CV of 0.82, SECV of 5.61 days, and RPD of 3.0. This study demonstrated that NIR spectroscopy can predict storage time non-destructively in intact egg samples. Even though the results of the present study are promising, further research is still needed to further extend these results to other production systems, as well as to explore the potential of this technique to predict other egg quality parameters associated with freshness.

Recent Advance of Intelligent Packaging Aided by Artificial Intelligence for Monitoring Food Freshness

Food safety is a pressing concern for human society, as it directly impacts people's lives, while food freshness serves as one of the most crucial indicators in ensuring food safety. There exist diverse techniques for monitoring food freshness, among which intelligent packaging based on artificial intelligence technology boasts the advantages of low cost, high efficiency, fast speed and wide applicability; however, it is currently underutilized. By analyzing the current research status of intelligent packaging both domestically and internationally, this paper provides a clear classification of intelligent packaging technology. Additionally, it outlines the advantages and disadvantages of using intelligent packaging technology for food freshness detection methods, while summarizing the latest research progress in applying artificial intelligence-based technologies to food freshness detection through intelligent packaging. Finally, the author points out the limitations of the current research, and anticipates future developments in artificial intelligence technology for assisting freshness detection in intelligent packaging. This will provide valuable insights for the future development of intelligent packaging in the field of food freshness detection.

Nondestructive Detection of Egg Freshness Based on Infrared Thermal Imaging

In this paper, we proposed a nondestructive detection method for egg freshness based on infrared thermal imaging technology. We studied the relationship between egg thermal infrared images (different shell colors and cleanliness levels) and egg freshness under heating conditions. Firstly, we established a finite element model of egg heat conduction to study the optimal heat excitation temperature and time. The relationship between the thermal infrared images of eggs after thermal excitation and egg freshness was further studied. Eight values of the center coordinates and radius of the egg circular edge as well as the long axis, short axis, and eccentric angle of the egg air cell were used as the characteristic parameters for egg freshness detection. After that, four egg freshness detection models, including decision tree, naive Bayes, k-nearest neighbors, and random forest, were constructed, with detection accuracies of 81.82%, 86.03%, 87.16%, and 92.32%, respectively. Finally, we introduced SegNet neural network image segmentation technology to segment the egg thermal infrared images. The SVM egg freshness detection model was established based on the eigenvalues extracted after segmentation. The test results showed that the accuracy of SegNet image segmentation was 98.87%, and the accuracy of egg freshness detection was 94.52%. The results also showed that infrared thermography combined with deep learning algorithms could detect egg freshness with an accuracy of over 94%, providing a new method and technical basis for online detection of egg freshness on industrial assembly lines.

Single-View Measurement Method for Egg Size Based on Small-Batch Images

Egg size is a crucial indicator for consumer evaluation and quality grading. The main goal of this study is to measure eggs' major and minor axes based on deep learning and single-view metrology. In this paper, we designed an egg-carrying component to obtain the actual outline of eggs. The Segformer algorithm was used to segment egg images in small batches. This study proposes a single-view measurement method suitable for eggs. Experimental results verified that the Segformer could obtain high segmentation accuracy for egg images in small batches. The mean intersection over union of the segmentation model was 96.15%, and the mean pixel accuracy was 97.17%. The R-squared was 0.969 (for the long axis) and 0.926 (for the short axis), obtained through the egg single-view measurement method proposed in this paper.

Ascaridia galli: A report of erratic migration in eggs for human consumption in Bucaramanga, Colombia - case report

ABSTRACT This case report describes how an erratic specimen of Ascaridia galli in the adult phase was recovered in an unusual way from a hen’s egg intended for human consumption. Although the literature on similar events is limited, this appears to be the first case reported in Bucaramanga, Colombia. The parasite was identified directly under a light microscope as an adult female A. galli, 6.5-cm long with 3 trilobed lips. In addition, the remaining eggs of the same group were examined to determine if there were more cases of erratic migration in that same batch. This nematode can cause various pathological conditions, including enteritis, hemorrhage, diarrhea, anemia, weakness, and emaciation, that can lead to huge economic and production losses in the poultry industry.

Molecularly Imprinted Polymer-Based Electrochemical Sensor for Rapid and Selective Detection of Hypoxanthine

In this paper, we report on the coupling of an electrochemical transducer with a specifically designed biomimetic and synthetic polymeric layer that serves as a recognition surface that demonstrates the molecular memory necessary to facilitate the stable and selective identification of the meat-freshness indicator hypoxanthine. Consumer preferences and the food safety of meat products are largely influenced by their freshness, so it is crucial to monitor it so as to quickly identify when it deteriorates. The sensor consists of a glassy-carbon electrode, which can be regenerated in situ continuously, functionalized with molecularly imprinted polymers (MIPs) and a nanocomposite of curcumin-coated iron oxide magnetic nanospheres (C-IO-MNSs) and multiwalled carbon nanotubes (MWCNTs) that enhance the surface area as well as the electroactive characteristics. The electrochemical behavior of the fabricated sensor was analyzed by both cyclic voltammetry (CV) and electrochemical impedance spectroscopy (EIS). Differential pulse voltammetric studies revealed the rapid response of the proposed sol-gel-MIP/MWCNT/C-IO-MNS/GCE sensor to hypoxanthine in a concentration range of 2-50 µg/mL with a lower limit of detection at 0.165 μg/mL. Application of the newly fabricated sensor demonstrated acceptable recoveries and satisfactory accuracy when used to measure hypoxanthine in different meat samples.

Fast and Non-Destructive Quail Egg Freshness Assessment Using a Thermal Camera and Deep Learning-Based Air Cell Detection Algorithms for the Revalidation of the Expiration Date of Eggs

Freshness is one of the most important parameters for assessing the quality of avian eggs. Available techniques to estimate the degradation of albumen and enlargement of the air cell are either destructive or not suitable for high-throughput applications. The aim of this research was to introduce a new approach to evaluate the air cell of quail eggs for freshness assessment as a fast, noninvasive, and nondestructive method. A new methodology was proposed by using a thermal microcamera and deep learning object detection algorithms. To evaluate the new method, we stored 174 quail eggs and collected thermal images 30, 50, and 60 days after the labeled expiration date. These data, 522 in total, were expanded to 3610 by image augmentation techniques and then split into training and validation samples to produce models of the deep learning algorithms, referred to as "You Only Look Once" version 4 and 5 (YOLOv4 and YOLOv5) and EfficientDet. We tested the models in a new dataset composed of 60 eggs that were kept for 15 days after the labeled expiration label date. The validation of our methodology was performed by measuring the air cell area highlighted in the thermal images at the pixel level; thus, we compared the difference in the weight of eggs between the first day of storage and after 10 days under accelerated aging conditions. The statistical significance showed that the two variables (air cell and weight) were negatively correlated (R² = 0.676). The deep learning models could predict freshness with F1 scores of 0.69, 0.89, and 0.86 for the YOLOv4, YOLOv5, and EfficientDet models, respectively. The new methodology for freshness assessment demonstrated that the best model reclassified 48.33% of our testing dataset. Therefore, those expired eggs could have their expiration date extended for another 2 weeks from the original label date.

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.