[Objective] The aim was to provide reference for the production technolo- gy of cold fresh chicken. [Method] Lactic acid was used as the disinfectant in cooling water, so as to study the effect of disinfection time on...[Objective] The aim was to provide reference for the production technolo- gy of cold fresh chicken. [Method] Lactic acid was used as the disinfectant in cooling water, so as to study the effect of disinfection time on the microbial content and quality of cold fresh chicken. [Result] With disinfection of 20 min, the removal rates of the total colonies, coliforms and moulds reached over 95%. As the disinfec- tion time was further extended, it had no significant effect on the removal effects of the microbial content and could affect the tenderness and color of cold fresh chick- en. [Cendusion] The study provides references for the design and optimization of the production process of cold fresh chicken.展开更多
The aim of this study was to assess the hygienic properties of swabs taken from the arm and coat of workers, work surfaces, refrigerated display cases, trays and packaging materials. As well as the safe properties of ...The aim of this study was to assess the hygienic properties of swabs taken from the arm and coat of workers, work surfaces, refrigerated display cases, trays and packaging materials. As well as the safe properties of samples of fresh chicken meat (wing, drumstick, chest) and chicken internal organs (heart and liver) from two butcheries. One of the goals was to determine whether education on hygienic conditions was carried out, influenced the hygienic condition of swabs and safety samples of fresh chicken meat offal. The results of the analysis showed that on the first day of sampling, the largest number of <i><span style="font-family:Verdana;">aerobic</span></i><span> <i><span style="font-family:Verdana;">mesophilic</span></i> <i><span style="font-family:Verdana;">bacteria</span></i></span><span style="font-family:Verdana;"> were found in the swabs of the arm (butcher A-8.15 × 10</span><sup><span style="font-family:Verdana;">2</span></sup><span style="font-family:Verdana;"> CFU/ml), butcher (B-17.88 × 10</span><sup><span style="font-family:Verdana;">2</span></sup><span style="font-family:Verdana;"> CFU/ml) and the smallest in the packaging material (0.03 × 10</span><sup><span style="font-family:Verdana;">2</span></sup><span style="font-family:Verdana;"> CFU/ml). Comparing the results of the first and thirtieth sampling day was </span><span style="font-family:Verdana;"><span style="font-family:Verdana;"><span style="font-family:Verdana;">a </span></span></span><span style="font-family:Verdana;"><span style="font-family:Verdana;"><span style="font-family:Verdana;">visible decreas</span></span></span><span style="font-family:Verdana;"><span style="font-family:Verdana;"><span style="font-family:Verdana;">e</span></span></span><span><span><span style="font-family:;" "=""><span style="font-family:Verdana;"> in the number of </span><i><span style="font-family:Verdana;">aerobic</span></i><span> <i><span style="font-family:Verdana;">mesophilic</span></i> <i><span style="font-family:Verdana;">bacteria</span></i></span><span style="font-family:Verdana;"> and </span><i><span style="font-family:Verdana;">Enterobacteriaceae</span></i><span style="font-family:Verdana;"> for all examined swabs from both butchers. In the samples of fresh chicken meat, the number of </span><i><span style="font-family:Verdana;">aerobic</span></i><span> <i><span style="font-family:Verdana;">mesophilic</span></i> <i><span style="font-family:Verdana;">bacteria</span></i></span><span style="font-family:Verdana;"> during the first sampling was the highest in the drumstick samples (4.75 × 10</span><sup><span style="font-family:Verdana;">3</span></sup><span style="font-family:Verdana;"> CFU/ml) slightly less in the samples of the wing (3.78 × 10</span><sup><span style="font-family:Verdana;">3</span></sup><span style="font-family:Verdana;"> CFU/ml) in the butcher A, in </span></span></span></span><span style="font-family:Verdana;"><span style="font-family:Verdana;"><span style="font-family:Verdana;">the </span></span></span><span><span><span style="font-family:;" "=""><span style="font-family:Verdana;">same time in the butcher B was 3.82 × 10</span><sup><span style="font-family:Verdana;">3</span></sup><span style="font-family:Verdana;"> CFU/ml. Microbiological analyzes samples of </span></span></span></span><span style="font-family:Verdana;"><span style="font-family:Verdana;"><span style="font-family:Verdana;">the </span></span></span><span><span><span style="font-family:;" "=""><span style="font-family:Verdana;">thirtieth day showed that </span><i><span style="font-family:Verdana;">Escherichia</span></i><span style="font-family:Verdana;"> <i>coli</i> </span><span style="font-family:Verdana;">has been found in both butchers in drumsticks A-0.8 CFU/ml, B butcher 0.4 CFU/ml. and offal butcher A 0.6 CFU/ml, butcher B 0.7 CFU/ml, but less than </span></span></span></span><span style="font-family:Verdana;"><span style="font-family:Verdana;"><span style="font-family:Verdana;">the </span></span></span><span><span><span>first day. It is important to say that education of workers in butcher shops has shown that it can improve good hygiene practices in butcher shops and safety of fresh chicken meat. Education in Good Hygiene Practice has positive impact on safety of fresh chicken meat in examined butcheries.</span></span></span>展开更多
With the rise in global meat consumption and chicken becoming a principal source of white meat,methods for efficiently and accurately determining the freshness of chicken are of increasing importance,since traditional...With the rise in global meat consumption and chicken becoming a principal source of white meat,methods for efficiently and accurately determining the freshness of chicken are of increasing importance,since traditional detection methods fail to satisfy modern production needs.A non-destructive method based on machine vision and machine learning technology was proposed for detecting chicken breast freshness.A self-designed machine vision system was first used to collect images of chicken breast samples stored at 4℃ for 1-7 d.The Region of Interest(ROI)for each image was then extracted and a total of 700 ROI images were obtained.Six color features were extracted from two different color spaces RGB(red,green,blue)and HSI(hue,saturation,intensity).Six main Gray Level Co-occurrence Matrix(GLCM)texture feature parameters were also calculated from four directions.Principal Component Analysis(PCA)was used to reduce the dimension of these 30 extracted feature parameters for multiple features image fusion.Four principal components were taken as input and chicken breast freshness level as output.A 10-fold cross-validation was used to partition the dataset.Four machine learning methods,Particle Swarm Optimization-Support Vector Machine(PSO-SVM),Random Forest(RF),Gradient Boosting Decision Tree(GBDT),and Naive Bayes Classifier(NBC),were used to establish a chicken breast freshness level prediction model.Among these,SVM had the best prediction effect with prediction accuracy reaching 0.9867.The results proved the feasibility of using a detection method based on multiple features image fusion and machine learning,providing a theoretical reference for the nondestructive detection of chicken breast freshness.展开更多
基金Supported by the Jiangsu Agricultural Science and Technology Innovation Foundation(CX(15)1009)~~
文摘[Objective] The aim was to provide reference for the production technolo- gy of cold fresh chicken. [Method] Lactic acid was used as the disinfectant in cooling water, so as to study the effect of disinfection time on the microbial content and quality of cold fresh chicken. [Result] With disinfection of 20 min, the removal rates of the total colonies, coliforms and moulds reached over 95%. As the disinfec- tion time was further extended, it had no significant effect on the removal effects of the microbial content and could affect the tenderness and color of cold fresh chick- en. [Cendusion] The study provides references for the design and optimization of the production process of cold fresh chicken.
文摘The aim of this study was to assess the hygienic properties of swabs taken from the arm and coat of workers, work surfaces, refrigerated display cases, trays and packaging materials. As well as the safe properties of samples of fresh chicken meat (wing, drumstick, chest) and chicken internal organs (heart and liver) from two butcheries. One of the goals was to determine whether education on hygienic conditions was carried out, influenced the hygienic condition of swabs and safety samples of fresh chicken meat offal. The results of the analysis showed that on the first day of sampling, the largest number of <i><span style="font-family:Verdana;">aerobic</span></i><span> <i><span style="font-family:Verdana;">mesophilic</span></i> <i><span style="font-family:Verdana;">bacteria</span></i></span><span style="font-family:Verdana;"> were found in the swabs of the arm (butcher A-8.15 × 10</span><sup><span style="font-family:Verdana;">2</span></sup><span style="font-family:Verdana;"> CFU/ml), butcher (B-17.88 × 10</span><sup><span style="font-family:Verdana;">2</span></sup><span style="font-family:Verdana;"> CFU/ml) and the smallest in the packaging material (0.03 × 10</span><sup><span style="font-family:Verdana;">2</span></sup><span style="font-family:Verdana;"> CFU/ml). Comparing the results of the first and thirtieth sampling day was </span><span style="font-family:Verdana;"><span style="font-family:Verdana;"><span style="font-family:Verdana;">a </span></span></span><span style="font-family:Verdana;"><span style="font-family:Verdana;"><span style="font-family:Verdana;">visible decreas</span></span></span><span style="font-family:Verdana;"><span style="font-family:Verdana;"><span style="font-family:Verdana;">e</span></span></span><span><span><span style="font-family:;" "=""><span style="font-family:Verdana;"> in the number of </span><i><span style="font-family:Verdana;">aerobic</span></i><span> <i><span style="font-family:Verdana;">mesophilic</span></i> <i><span style="font-family:Verdana;">bacteria</span></i></span><span style="font-family:Verdana;"> and </span><i><span style="font-family:Verdana;">Enterobacteriaceae</span></i><span style="font-family:Verdana;"> for all examined swabs from both butchers. In the samples of fresh chicken meat, the number of </span><i><span style="font-family:Verdana;">aerobic</span></i><span> <i><span style="font-family:Verdana;">mesophilic</span></i> <i><span style="font-family:Verdana;">bacteria</span></i></span><span style="font-family:Verdana;"> during the first sampling was the highest in the drumstick samples (4.75 × 10</span><sup><span style="font-family:Verdana;">3</span></sup><span style="font-family:Verdana;"> CFU/ml) slightly less in the samples of the wing (3.78 × 10</span><sup><span style="font-family:Verdana;">3</span></sup><span style="font-family:Verdana;"> CFU/ml) in the butcher A, in </span></span></span></span><span style="font-family:Verdana;"><span style="font-family:Verdana;"><span style="font-family:Verdana;">the </span></span></span><span><span><span style="font-family:;" "=""><span style="font-family:Verdana;">same time in the butcher B was 3.82 × 10</span><sup><span style="font-family:Verdana;">3</span></sup><span style="font-family:Verdana;"> CFU/ml. Microbiological analyzes samples of </span></span></span></span><span style="font-family:Verdana;"><span style="font-family:Verdana;"><span style="font-family:Verdana;">the </span></span></span><span><span><span style="font-family:;" "=""><span style="font-family:Verdana;">thirtieth day showed that </span><i><span style="font-family:Verdana;">Escherichia</span></i><span style="font-family:Verdana;"> <i>coli</i> </span><span style="font-family:Verdana;">has been found in both butchers in drumsticks A-0.8 CFU/ml, B butcher 0.4 CFU/ml. and offal butcher A 0.6 CFU/ml, butcher B 0.7 CFU/ml, but less than </span></span></span></span><span style="font-family:Verdana;"><span style="font-family:Verdana;"><span style="font-family:Verdana;">the </span></span></span><span><span><span>first day. It is important to say that education of workers in butcher shops has shown that it can improve good hygiene practices in butcher shops and safety of fresh chicken meat. Education in Good Hygiene Practice has positive impact on safety of fresh chicken meat in examined butcheries.</span></span></span>
基金funded by the International Science and Technology Cooperation Program of Jiangsu Province(Grant No.BZ2023013)the Xuzhou Key Research and Development Project(Modern Agriculture)(Grant No.KC21135).
文摘With the rise in global meat consumption and chicken becoming a principal source of white meat,methods for efficiently and accurately determining the freshness of chicken are of increasing importance,since traditional detection methods fail to satisfy modern production needs.A non-destructive method based on machine vision and machine learning technology was proposed for detecting chicken breast freshness.A self-designed machine vision system was first used to collect images of chicken breast samples stored at 4℃ for 1-7 d.The Region of Interest(ROI)for each image was then extracted and a total of 700 ROI images were obtained.Six color features were extracted from two different color spaces RGB(red,green,blue)and HSI(hue,saturation,intensity).Six main Gray Level Co-occurrence Matrix(GLCM)texture feature parameters were also calculated from four directions.Principal Component Analysis(PCA)was used to reduce the dimension of these 30 extracted feature parameters for multiple features image fusion.Four principal components were taken as input and chicken breast freshness level as output.A 10-fold cross-validation was used to partition the dataset.Four machine learning methods,Particle Swarm Optimization-Support Vector Machine(PSO-SVM),Random Forest(RF),Gradient Boosting Decision Tree(GBDT),and Naive Bayes Classifier(NBC),were used to establish a chicken breast freshness level prediction model.Among these,SVM had the best prediction effect with prediction accuracy reaching 0.9867.The results proved the feasibility of using a detection method based on multiple features image fusion and machine learning,providing a theoretical reference for the nondestructive detection of chicken breast freshness.
