THE DESIGN OF HACCP PLAN FOR CHICKEN BALL PLANT IN BANGLADESH

Abstract The purpose of this study is to design Hazard Analysis and Critical Control Point (HACCP) plan for chicken ball production based on actual conditions in the plant. A specific model has been developed to boost the safety and quality of chicken ball product in this plant. The spread of some diseases by unsafe products due to Listeria monocytogenes, Staphylococcus aureus, Salmonella enteritidis, Escherichia coli, adulterants and chemical residue reported makes it important to pay attention to the potential contamination in chicken ball production. The prerequisite program, hazards, critical control point, preventive measure, critical limits, monitoring procedure and corrective actions have been designed in this HACCP plan. The production process of the product was also analyzed scrupulously for this HACCP plan

A Nondestructive Eggshell Thickness Measurement Technique Using Terahertz Waves

Eggshells play a number of important roles in the avian and reptile kingdom: protection of internal contents and as a major source of minerals for developing embryos. However, when researching these respective roles, eggshell thickness measurement remains a bottleneck due to the lack of a non-destructive measurement techniques. As a result, many avian and reptile research protocols omit consideration of eggshell thickness bias on egg or embryo growth and development. Here, we validate a non-destructive method to estimate eggshell thickness based on terahertz (THz) reflectance spectroscopy using chicken white coloured eggs. Since terahertz waves are reflected from outer air-eggshell interface, as well as the inner eggshell-membrane boundary, the resulting interference signals depend on eggshell thickness. Thus, it is possible to estimate shell thickness from the oscillation distance in frequency-domain. A linear regression-based prediction model for non-destructive eggshell thickness measurement was developed, which had a coefficient of determination (R2) of 0.93, RMSEP of 0.009, RPD of 3.45 and RER 13.67. This model can estimate eggshell thickness to a resolution of less than 10 μm. This method has the potential to expand the protocols in the field of avian and reptile research, as well as be applied to industrial grading of eggs

K-Means Segmentation Based-on Lab Color Space for Embryo Detection in Incubated Egg

The quality of the hatching process influences the success of the hatch rate besides the inherent egg factors. Eliminating infertile or dead eggs and monitoring embryonic growth are very important factors in efficient hatchery practices. This process aims to sort eggs that only have embryos to remain in the incubator until the end of the hatching process. This process aims to sort eggs with embryos to remain hatched until the end. Maximum checking is done the first week in the hatching period. This study aims to detect the presence of embryos in eggs. Detection of the existence of embryos is processed using segmentation. Egg images are segmented using the K-means algorithm based on Lab color images. The results of the image acquisition are converted into Lab color space images. The results of Lab color space images are processed using K-means for each color. The K-means process uses cluster k=3, where this cluster divides the image into three parts: background, eggs, and yolk. Egg yolks are part of eggs that have embryonic characteristics. This study applies the concept of color in the initial segmentation and grayscale in the final stages. The initial phase results show that the image segmentation results using k-means clustering based on Lab color space provide a grouping of three parts. At the grayscale image processing stage, the results of color image segmentation are processed with grayscaling, image enhancement, and morphology. Thus, it seems clear that the yolk segmented shows the presence of egg embryos. Based on this process and results, the initial stages of the embryo detection process used K-means segmentation based on Lab color space. The evaluation uses MSE and MSSIM, with values of 0.0486 and 0.9979; this can be used as a reference that the results obtained can detect embryos in egg yolk. This protocol could be used in a non-destructive quantitative study on embryos and their morphology in a precision poultry production system in the future

Association of fruit, pericarp, and epidermis traits with surface autofluorescence in green peppers

We investigated the association of blue fluorescence (excitation at 365 nm) with the traits of the fruit, pericarp, and epidermis in green peppers. The fruits were manually classified into two groups based on fluorescence brightness. The dark fluorescence group showed the accumulation of blue-absorbing pigments and a thicker cuticular structure, suggesting epidermal development

Nondestructive Chicken Egg Fertility Detection Using CNN-Transfer Learning Algorithms

This study explores the application of CNN-Transfer Learning for nondestructive chicken egg fertility detection. Four models, VGG16, ResNet50, InceptionNet, and MobileNet, were trained and evaluated on a dataset using augmented images. The training results demonstrated that all models achieved high accuracy, indicating their ability to accurately learn and classify chicken eggs’ fertility state. However, when evaluated on the testing set, variations in accuracy and performance were observed. VGG16 achieved a high accuracy of 0.9803 on the testing set but had challenges in accurately detecting fertile eggs, as indicated by a NaN sensitivity value. ResNet50 also achieved an accuracy of 0.98 but struggled to identify fertile and non-fertile eggs, as suggested by NaN values for sensitivity and specificity. However, InceptionNet demonstrated excellent performance, with an accuracy of 0.9804, a sensitivity of 1 for detecting fertile eggs, and a specificity of 0.9615 for identifying non-fertile eggs. MobileNet achieved an accuracy of 0.9804 on the testing set; however, it faced challenges in accurately classifying the fertility status of chicken eggs, as indicated by NaN values for both sensitivity and specificity. While the models showed promise during training, variations in accuracy and performance were observed during testing. InceptionNet exhibited the best overall performance, accurately classifying fertile and non-fertile eggs. Further optimization and fine-tuning of the models are necessary to address the limitations in accurately detecting fertile and non-fertile eggs. This study highlights the potential of CNN-Transfer Learning for nondestructive fertility detection and emphasizes the need for further research to enhance the models’ capabilities and ensure accurate classification

Chick Embryo Growth Modeling Using Near-Infrared Sensor and Non-Linear Least Square Fitting of Egg Opacity Values

Non-destructive monitoring of chick embryonic growth can provide vital management insights for poultry farmers and other stakeholders. Although non-destructive studies on fertility, hatching time and gender have been conducted recently, there has been no available method for embryonic growth observation, especially during the second half of incubation. Therefore, this work investigated the feasibility of using near-infrared (NIR) sensor-based egg opacity values—the amount of light lost when passing through the egg—for indirectly observing embryo growth during incubation. ROSS 308 eggs were selected based on size, mass and shell color for this experiment. To estimate the embryo size precisely, we fit various mathematical growth functions during incubation, based on the opacity value of incubated eggs. Although all the growth models tested performed similarly in fitting the data, the exponential and power functions had better performances in terms of co-efficient of determination (0.991 and 0.994 respectively) and RMSE to explain embryo growth during incubation. From these results, we conclude that the modeling paradigm adopted provides a simple tool to non-invasively investigate embryo growth. These models could be applied to resolving developmental biology, embryonic pathology, industrial and animal welfare issues in the near future

Research Note: Nondestructive detection of super grade chick embryos or hatchlings using near-infrared spectroscopy

ABSTRACT: Some unresolved questions in poultry science were addressed: what determines the yield of chick embryos or hatchlings; what kind of influence does egg yolk content have on embryonic development; and how to detect eggs producing super grade chicks? Since the yolk acts as a vital energy and nutrient reservoir for embryos, we hypothesized that a higher yolk content of similar sizes eggs would play an important role in embryo or chick viability during incubation, as well as at hatch. As experimental sample, we used ROSS 308 (broiler line) and a nondestructive spectroscopic absorbance method. The influence of high yolk content to embryonic heartbeat and chick yield (i.e., chick weight/egg weight) were then investigated. Embryonic heartbeat signal was measured indirectly using a prototype near-infrared sensor during incubation period. A positive influence was found in both cases. Similar size eggs with higher yolk content were found to significantly (P-value < 0.05) promote higher chick yield at hatch. This methodology may have the potential to be used to precision poultry production system, ornithology, developmental, or evolutionary biology in the near future

Nondestructive Chicken Egg Fertility Detection Using CNN-Transfer Learning Algorithms

This study explored the application of CNN-Transfer Learning for nondestructive chicken egg fertility detection for precise poultry hatchery practices. Four models, VGG16, ResNet50, InceptionNet, and MobileNet, were trained and evaluated on a dataset (200 single egg images) using augmented images (rotation, flip, scale, translation, and reflection). The training results demonstrated that all models achieved high accuracy, indicating their ability to learn and classify chicken eggs’ fertility. However, variations in accuracy and performance were observed when these models were evaluated on the testing datasets. The InceptionNet exhibited the best overall performance, accurately classifying fertile and non-fertile eggs. It demonstrated excellent performance in all parameters of the evaluation metrics for both training and testing datasets. When evaluated on the testing datasets, it achieved an accuracy of 0.98, a sensitivity of 1 for detecting fertile eggs, and a specificity of 0.96 for identifying non-fertile eggs. The higher performance is attributed to its unique architecture, efficiently capturing features at different scales, which leads to improved accuracy and robustness. Further optimization and fine-tuning of the models might be necessary to address the limitations in accurately detecting fertile and non-fertile eggs using other models. This study highlighted the potential of CNN-transfer learning for nondestructive fertility detection and emphasized the need for further research to enhance the models’ capabilities and to ensure accurate classification