The study was conducted to evaluate the hygienic practices, handling methods, microbial quality, and safety of raw camel milk at various supply points during the dry and wet seasons in the Borana zone. A total of 370 respondents were interviewed for hygienic camel milk handling along the supply chains (farms, collection points, and open markets), and 192 camel milk samples were collected from surveyed respondents of camel herders, primary collectors, and retailers. Milk sampling, sample collection, handling, transportation, and analysis followed standard procedures. Unhygienic practices, poor handling procedures of camel milk, and lack of standard milk containers were observed across supply chains, which contributed to microbial contaminations. Significant (p < 0.05) increases in counts of total bacteria, total coliform, yeast and mold, Enterobacteriaceae, and Staphylococcus aureus of raw camel milk samples from farms during the wet season to markets during the dry season with mean log10 CFU/mL of 5.21–8.35, 4.99–7.24, 3.86–7.29, 4.30–7.12, and 3.31–5.32 were observed, respectively. The overall average count of total bacteria, total coliform, yeast and mold, Enterobacteriaceae, and Staphylococcus aureus was 7.04, 6.09, 5.45, 5.75, and 4.30 log10 CFU/mL, respectively. These findings indicate that raw camel milk in the Borana zone is highly contaminated, with microbial loads exceeding established raw milk standards. Such high microbial contaminations justify the need for proper heat treatment of raw camel milk before consumption. In addition, community training and awareness creation on improving hygienic milk handling practices and the introduction of dairy material across milk supply chains are crucial to enhancing the quality of camel milk.
{"title":"Hygienic Practices and Determination of Microbial Quality and Safety of Raw Camel Milk in Borana Zone, Southern Ethiopia","authors":"Birhanu Bekele, Mitiku Eshetu, Takele Wolkero, Tesfemariam Berhe, Ulfina Galmessa, Sileshi Gadissa","doi":"10.1155/jfq/2486717","DOIUrl":"https://doi.org/10.1155/jfq/2486717","url":null,"abstract":"<p>The study was conducted to evaluate the hygienic practices, handling methods, microbial quality, and safety of raw camel milk at various supply points during the dry and wet seasons in the Borana zone. A total of 370 respondents were interviewed for hygienic camel milk handling along the supply chains (farms, collection points, and open markets), and 192 camel milk samples were collected from surveyed respondents of camel herders, primary collectors, and retailers. Milk sampling, sample collection, handling, transportation, and analysis followed standard procedures. Unhygienic practices, poor handling procedures of camel milk, and lack of standard milk containers were observed across supply chains, which contributed to microbial contaminations. Significant (<i>p</i> < 0.05) increases in counts of total bacteria, total coliform, yeast and mold, <i>Enterobacteriaceae</i>, and <i>Staphylococcus aureus</i> of raw camel milk samples from farms during the wet season to markets during the dry season with mean log<sub>10</sub> CFU/mL of 5.21–8.35, 4.99–7.24, 3.86–7.29, 4.30–7.12, and 3.31–5.32 were observed, respectively. The overall average count of total bacteria, total coliform, yeast and mold, <i>Enterobacteriaceae</i>, and <i>Staphylococcus aureus</i> was 7.04, 6.09, 5.45, 5.75, and 4.30 log<sub>10</sub> CFU/mL, respectively. These findings indicate that raw camel milk in the Borana zone is highly contaminated, with microbial loads exceeding established raw milk standards. Such high microbial contaminations justify the need for proper heat treatment of raw camel milk before consumption. In addition, community training and awareness creation on improving hygienic milk handling practices and the introduction of dairy material across milk supply chains are crucial to enhancing the quality of camel milk.</p>","PeriodicalId":15951,"journal":{"name":"Journal of Food Quality","volume":"2025 1","pages":""},"PeriodicalIF":2.9,"publicationDate":"2025-12-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/jfq/2486717","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145887755","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Ümit Murat Akkaya, Mutlu Pilavtepe-Çelik, Habil Kalkan
Color, as an essential visual quality characteristic of fish that determines consumer preferences and purchasing decisions, serves as a quick indicator of freshness. However, determining whether fish has undergone previous freezing poses a challenge. Traditional methods such as sensory evaluation, physicochemical, biochemical, and microbiological analyses, despite being time-consuming and costly, have been employed for this purpose among various fish species. This study aims to differentiate frozen-thawed (at −20°C and −60°C) and fresh fish samples through deep learning-based image analysis. A dataset comprising frozen-thawed and fresh Vermilion snapper (Rhomboplites aurorubens) images was used to extract features with four pretrained CNN models (VGG16, ResNet50, InceptionV3, and EfficientNetB0), followed by a neural network for classification. For comparison, the same classifier was applied to traditional L∗, a∗, and b∗ color values (commonly used in conventional analyses) extracted from the images. The CNN-based models consistently outperformed the traditional approach. In the binary classification task (fresh vs. frozen), VGG16 achieved the highest accuracy of 96.8%, compared to 80.0% using color features. In the more challenging three-class classification (fresh vs. −20°C vs. −60°C), EfficientNetB0 achieved an 86.9% accuracy, outperforming the 64.0% accuracy of the color-based approach. Additionally, for left- vs. right-side classification, ResNet50 reached a 96.0% accuracy, while color-based classification achieved only 52.0%. These findings suggest that CNN-based image analysis can be a valuable tool for the seafood industry, providing a nondestructive, faster, more reliable alternative to conventional methods for assessing the freshness and freezing history of fish products.
颜色,作为鱼的基本视觉质量特征,决定了消费者的偏好和购买决定,作为新鲜度的快速指标。然而,确定鱼是否经历过先前的冷冻是一个挑战。传统的方法,如感官评价、物理化学、生物化学和微生物分析,尽管耗时和昂贵,但已被用于各种鱼类的这一目的。本研究旨在通过基于深度学习的图像分析来区分冻融鱼(- 20°C和- 60°C)和新鲜鱼样本。利用冻融和新鲜朱红鲷鱼(Rhomboplites aurorubens)图像组成的数据集,通过四个预训练的CNN模型(VGG16、ResNet50、InceptionV3和EfficientNetB0)提取特征,然后使用神经网络进行分类。为了进行比较,将相同的分类器应用于从图像中提取的传统L∗,a∗和b∗颜色值(通常用于常规分析)。基于cnn的模型一直优于传统方法。在二元分类任务(新鲜与冷冻)中,VGG16达到了96.8%的最高准确率,而使用颜色特征的准确率为80.0%。在更具挑战性的三类分类(新鲜vs. - 20°C vs. - 60°C)中,EfficientNetB0达到了86.9%的准确率,优于基于颜色的方法的64.0%的准确率。此外,对于左侧和右侧分类,ResNet50达到96.0%的准确率,而基于颜色的分类仅达到52.0%。这些发现表明,基于cnn的图像分析可以成为海产品行业的一个有价值的工具,为评估鱼类产品的新鲜度和冷冻历史提供了一种非破坏性、更快、更可靠的替代方法。
{"title":"Convolutional Neural Network (CNN)-Based Image Analysis for Differentiating Fresh and Frozen-Thawed Vermilion Snapper (Rhomboplites aurorubens)","authors":"Ümit Murat Akkaya, Mutlu Pilavtepe-Çelik, Habil Kalkan","doi":"10.1155/jfq/2782474","DOIUrl":"https://doi.org/10.1155/jfq/2782474","url":null,"abstract":"<p>Color, as an essential visual quality characteristic of fish that determines consumer preferences and purchasing decisions, serves as a quick indicator of freshness. However, determining whether fish has undergone previous freezing poses a challenge. Traditional methods such as sensory evaluation, physicochemical, biochemical, and microbiological analyses, despite being time-consuming and costly, have been employed for this purpose among various fish species. This study aims to differentiate frozen-thawed (at −20°C and −60°C) and fresh fish samples through deep learning-based image analysis. A dataset comprising frozen-thawed and fresh Vermilion snapper (<i>Rhomboplites aurorubens</i>) images was used to extract features with four pretrained CNN models (VGG16, ResNet50, InceptionV3, and EfficientNetB0), followed by a neural network for classification. For comparison, the same classifier was applied to traditional <i>L</i><sup>∗</sup>, <i>a</i><sup>∗</sup>, and <i>b</i><sup>∗</sup> color values (commonly used in conventional analyses) extracted from the images. The CNN-based models consistently outperformed the traditional approach. In the binary classification task (fresh vs. frozen), VGG16 achieved the highest accuracy of 96.8%, compared to 80.0% using color features. In the more challenging three-class classification (fresh vs. −20°C vs. −60°C), EfficientNetB0 achieved an 86.9% accuracy, outperforming the 64.0% accuracy of the color-based approach. Additionally, for left- vs. right-side classification, ResNet50 reached a 96.0% accuracy, while color-based classification achieved only 52.0%. These findings suggest that CNN-based image analysis can be a valuable tool for the seafood industry, providing a nondestructive, faster, more reliable alternative to conventional methods for assessing the freshness and freezing history of fish products.</p>","PeriodicalId":15951,"journal":{"name":"Journal of Food Quality","volume":"2025 1","pages":""},"PeriodicalIF":2.9,"publicationDate":"2025-12-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/jfq/2782474","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145824787","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Zhengrui Liao, Thaigarajan Parumasivam, Wee Yin Koh, Thuan-Chew Tan, Xiaotong Zhu, Muhammad H. Alu’datt, Ali Madi Almajwal, Mohammad Alrosan
Medium-chain dicarboxylic acids (MCDAs), including glutaric acid (GLA), succinic acid (SUA), and azelaic acid (AZA), have been extensively studied for pharmaceutical applications but remain underutilized in the meat industry. This study evaluated the antibacterial properties and antioxidant potential of AZA, SUA, and GLA. Their ability to enhance the quality and safety of sliced and minced beef was also assessed. The results revealed that the minimum inhibitory concentration (MIC) and minimum bactericidal concentration (MBC) values for these MCDAs generally ranged between 500 and 2000 μg/mL, with AZA exhibiting lower MIC and MBC values than SUA and GLA. However, the antioxidant activity of these MCDAs was found to be limited. Application of these MCDAs to refrigerated raw beef demonstrated their efficacy in maintaining freshness and extending shelf life. These MCDAs were particularly beneficial in preserving color, pH, and moisture levels while reducing spoilage markers such as total volatile base nitrogen and bacterial counts. Overall, the findings suggest that these MCDAs exert dose-dependent effects in refrigerated sliced and minced beef models, with AZA delivering the most pronounced benefits.
{"title":"Enhancing the Shelf-Life and Quality of Raw Beef During Refrigerated Storage Utilizing Medium-Chain Dicarboxylic Acids","authors":"Zhengrui Liao, Thaigarajan Parumasivam, Wee Yin Koh, Thuan-Chew Tan, Xiaotong Zhu, Muhammad H. Alu’datt, Ali Madi Almajwal, Mohammad Alrosan","doi":"10.1155/jfq/5570496","DOIUrl":"https://doi.org/10.1155/jfq/5570496","url":null,"abstract":"<p>Medium-chain dicarboxylic acids (MCDAs), including glutaric acid (GLA), succinic acid (SUA), and azelaic acid (AZA), have been extensively studied for pharmaceutical applications but remain underutilized in the meat industry. This study evaluated the antibacterial properties and antioxidant potential of AZA, SUA, and GLA. Their ability to enhance the quality and safety of sliced and minced beef was also assessed. The results revealed that the minimum inhibitory concentration (MIC) and minimum bactericidal concentration (MBC) values for these MCDAs generally ranged between 500 and 2000 μg/mL, with AZA exhibiting lower MIC and MBC values than SUA and GLA. However, the antioxidant activity of these MCDAs was found to be limited. Application of these MCDAs to refrigerated raw beef demonstrated their efficacy in maintaining freshness and extending shelf life. These MCDAs were particularly beneficial in preserving color, pH, and moisture levels while reducing spoilage markers such as total volatile base nitrogen and bacterial counts. Overall, the findings suggest that these MCDAs exert dose-dependent effects in refrigerated sliced and minced beef models, with AZA delivering the most pronounced benefits.</p>","PeriodicalId":15951,"journal":{"name":"Journal of Food Quality","volume":"2025 1","pages":""},"PeriodicalIF":2.9,"publicationDate":"2025-12-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/jfq/5570496","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145824789","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}