Food Control最新文献_第5页

Optuna-optimized boosting models for predicting quality traits in multiple juice types using NIRS: Interpretability analysis via SHAP 利用近红外光谱（NIRS）预测多种果汁品质性状的optuna优化提升模型：通过SHAP进行可解释性分析

IF 6.3 1区农林科学 Q1 FOOD SCIENCE & TECHNOLOGY

Food Control

Pub Date : 2025-11-26 DOI: 10.1016/j.foodcont.2025.111878

Fangchen Ding , Miguel Ángel Rivero-Delgado , Rili Zha , Juan Francisco García-Martín

Near-infrared spectroscopy (NIRS) is a potential rapid and reagent-free technique for assessing the quality of fruit juices. However, most existing models focus on single juice type and rely on linear algorithms such as partial least squares regression (PLSR), which are often inadequate for handling the nonlinear and heterogeneous characteristics of diverse juice matrices. To address this challenge, this study developed boosting models optimized by Optuna, including XGBoost, AdaBoost, and CatBoost, to predict four key quality traits, namely acidity, total phenolic compounds (TPC), total flavonoid content (TFC), and vitamin C across 4 types of fruit juice. The boosting models consistently outperformed PLSR, particularly for acidity, TPC, and vitamin C, achieving R_p² values above 0.95 and RPD values exceeding 4.93. SHAP-based interpretability analysis further revealed that, in addition to typical NIRS absorption bands such as 1163 nm, 1169 nm, and 1193 nm located within the 1150–1210 nm region, non-classical regions including 1104 nm and several wavelengths between 1264 and 1322 nm also contributed positively to the model outputs. This demonstrates the capacity of boosting algorithms to capture informative spectral features from non-classical regions that are often overlooked by traditional linear models. Overall, this study demonstrates the value of combining automated hyperparameter optimization with interpretable machine learning, offering a robust and scalable framework for high-throughput, non-invasive quality control in the juice industry by NIRS.

近红外光谱（NIRS）是一种有潜力的快速、无试剂评价果汁质量的技术。然而，现有的模型大多集中于单一果汁类型，并依赖于线性算法，如偏最小二乘回归（PLSR），这往往不足以处理各种果汁矩阵的非线性和异质性特征。为了应对这一挑战，本研究开发了由Optuna优化的提升模型，包括XGBoost、AdaBoost和CatBoost，以预测4种果汁的酸度、总酚类化合物（TPC）、总黄酮含量（TFC）和维生素C四个关键品质性状。提升模型的性能一直优于PLSR，特别是在酸度、TPC和维生素C方面，Rp2值超过0.95，RPD值超过4.93。基于shap的可解释性分析进一步表明，除了位于1150-1210 nm区域的典型NIRS吸收带1163 nm、1169 nm和1193 nm外，包括1104 nm和1264 - 1322 nm之间的几个波长的非经典区域也对模型输出有积极贡献。这证明了增强算法从传统线性模型经常忽略的非经典区域捕获信息光谱特征的能力。总的来说，这项研究证明了将自动化超参数优化与可解释的机器学习相结合的价值，为近红外光谱在果汁行业的高通量、非侵入性质量控制提供了一个强大的、可扩展的框架。

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引用次数: 0

A non-destructive workflow integrating X-ray computed tomography and machine learning for multi-defect identification and kernel plumpness assessment of in-shell walnuts 一种集成x射线计算机断层扫描和机器学习的非破坏性工作流程，用于内壳核桃仁的多缺陷识别和饱满度评估

IF 6.3 1区农林科学 Q1 FOOD SCIENCE & TECHNOLOGY

Food Control

Pub Date : 2025-11-26 DOI: 10.1016/j.foodcont.2025.111879

Menglong Ma , Ming Zhang , Haitao Fu , Yixiao Wang , Ning Yang , Huang Dai , Fuwei Pi , Xiaodan Liu , Jiahua Wang

Walnuts are highly valued globally for their nutritional benefits and economic importance; however, defects such as insect damage, kernel shrinkage, and shell cracks are critical concerns for quality control and food safety, while kernel plumpness directly determines grading and pricing. This study developed an efficient and non-destructive workflow based on X-ray computed tomography (CT) imaging and machine learning techniques to automatically identify defective walnuts and quantitatively assess kernel plumpness. Canny edge detection and closed contour analysis were employed to evaluate shell integrity, effectively identifying nuts with cracked shells or suture separations. A U-Net-based semantic segmentation model was trained to accurately delineate kernel and shell regions from CT images, achieving Dice coefficients of 0.89 and 0.95 for kernel segmentation and whole-nut segmentation, respectively. Twelve morphological features were extracted from the reconstructed 3D volume data to characterize the internal structure of the kernels. Using these features, ensemble machine learning models such as XGBoost and GBDT achieved 99 % classification accuracy in distinguishing healthy, withered, and insect-damaged kernels. The kernel-to-shell ratio (KSR) calculated from CT volumetric data showed strong agreement with destructive measurements (R² = 0.9839, mean absolute percentage error = 6.66 %). This approach demonstrates great potential as a non-destructive, and high-throughput tool for quality evaluation and intelligent sorting of in-shell walnuts.

核桃因其营养价值和经济重要性而在全球受到高度重视；然而，虫蛀、仁缩、壳裂等缺陷是质量控制和食品安全的关键问题，而仁饱满度直接决定了等级和价格。本研究开发了一种基于x射线计算机断层扫描（CT）成像和机器学习技术的高效无损工作流程，以自动识别缺陷核桃并定量评估核仁饱满度。采用精细边缘检测和闭合轮廓分析评估外壳完整性，有效识别外壳破裂或缝线分离的螺母。训练了基于u - net的语义分割模型，对CT图像的核和壳区域进行了准确的分割，核和全果分割的Dice系数分别为0.89和0.95。从重建的三维体数据中提取12个形态学特征来表征核的内部结构。使用这些特征，集成机器学习模型（如XGBoost和GBDT）在区分健康、枯萎和昆虫损坏的核方面达到了99%的分类准确率。CT体积数据计算的核壳比（KSR）与破坏性测量结果吻合较好（R2 = 0.9839，平均绝对百分比误差= 6.66%）。该方法作为一种无损、高通量的内壳核桃质量评价和智能分选工具具有很大的潜力。

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引用次数: 0

A facile identification strategy for coffee variety via the machine learning-assisted nanozyme sensor array 一种基于机器学习辅助纳米酶传感器阵列的咖啡品种简易识别策略

IF 6.3 1区农林科学 Q1 FOOD SCIENCE & TECHNOLOGY

Food Control

Pub Date : 2025-11-25 DOI: 10.1016/j.foodcont.2025.111875

Jie Li , Xingna Zheng , Siyu Wang , Yi Ru , Yongxin Li , Hui Huang

The growing concern over coffee adulteration demands advanced detection solutions. The rational selection of channels for the sensor array is crucial for achieving high-precision differentiation. This research presents a nanozyme-based colorimetric sensor array for effective coffee variety discrimination by analyzing characteristic components. Through machine learning-assisted screening of thirteen nanozymes, an optimized sensor array was developed, enabling the discrimination of coffee key compounds at the same concentration, binary/ternary mixture discrimination, and concentration-ignored discrimination within the concentration range of 0.8 μmol/L to 100 μmol/L. The sensing mechanism involves coffee components modulating nanozyme peroxidase-like activity through electron transfer and hydrophobic interactions. This technology successfully distinguished two main coffee categories and their subtypes with high accuracy. The development of a mobile APP has successfully achieved the adulteration identification of coffee varieties and the detection of semi-quantitative adulteration levels (Accuracy rate: 88.9 %). This portable platform demonstrates significant potential for commercial coffee quality monitoring, providing a reliable tool for authenticity verification in the coffee industry.

对咖啡掺假的日益关注需要先进的检测解决方案。传感器阵列通道的合理选择是实现高精度定位的关键。本研究提出了一种基于纳米酶的比色传感器阵列，通过分析特征成分对咖啡品种进行有效鉴别。通过机器学习辅助筛选13种纳米酶，开发出优化后的传感器阵列，可在0.8 μmol/L ~ 100 μmol/L浓度范围内，对咖啡关键化合物进行相同浓度下的识别、二元/三元混合物的识别、忽略浓度的识别。感知机制涉及咖啡成分通过电子转移和疏水相互作用调节纳米酶过氧化物酶样活性。该技术成功地以高精度区分了两种主要的咖啡类别及其亚型。开发了一款手机APP，成功实现了咖啡品种掺假鉴定和半定量掺假水平检测（准确率：88.9%）。这种便携式平台展示了商业咖啡质量监测的巨大潜力，为咖啡行业的真实性验证提供了可靠的工具。

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引用次数: 0

SS-CNN BruiseFinder: Hyperspectral imaging and CNN-driven spatial-spectral fusion for non-destructive plum bruise analysis SS-CNN BruiseFinder：高光谱成像和cnn驱动的空间光谱融合无损梅子瘀伤分析

IF 6.3 1区农林科学 Q1 FOOD SCIENCE & TECHNOLOGY

Food Control

Pub Date : 2025-11-25 DOI: 10.1016/j.foodcont.2025.111870

Shanthini K.S. , Sudhish N. George , Jobin Francis , Sony George

Plum fruit is susceptible to damage at various stages, from growth to packaging, and such bruising is often difficult to detect visually due to its subtle surface appearance. This research seeks to develop a convolutional neural network (CNN) model that leverages 3D convolutional layers to integrate spatial and spectral features from hyperspectral data, enabling accurate bruise analysis in plum fruit. In this study, plums sourced from a Norwegian fruit store were intentionally bruised and then imaged using hyperspectral technology at various time intervals (30 min to 48 h post-bruising). A novel CNN model, dubbed SS-CNN BruiseFinder, is developed to harness the spatial and spectral characteristics of these hyperspectral images for accurate bruise detection and classification. The SS-CNN BruiseFinder model demonstrates detection accuracy ranging from 68.5% to 91.5% and categorization accuracy between 67.39% and 98.16%. To further establish the effectiveness of this approach, three additional deep learning models – a custom spectral CNN, ResNet 101, and a bidirectional LSTM model – are developed and evaluated on the same dataset, providing a comprehensive validation of the proposed method’s superiority. Timely detection of bruising helps prevent contaminated plums from entering the supply chain during transportation or storage. By categorizing plums based on bruise age, retailers can offer consumers more accurate freshness and quality information, enabling them to make better-informed purchasing choices and ultimately enhancing the overall shopping experience. To encourage community engagement and re-implementation, our code is available at https://github.com/SS-CNN BruiseFinder.

李子果实在从生长到包装的各个阶段都很容易受到损害，由于其微妙的表面外观，这种瘀伤通常很难在视觉上发现。本研究旨在开发一种卷积神经网络（CNN）模型，该模型利用3D卷积层整合高光谱数据的空间和光谱特征，从而实现对李子果实瘀伤的准确分析。在这项研究中，来自挪威水果店的李子被故意擦伤，然后在不同的时间间隔（瘀伤后30分钟至48小时）使用高光谱技术成像。开发了一种新的CNN模型，称为SS-CNN BruiseFinder，用于利用这些高光谱图像的空间和光谱特征进行准确的瘀伤检测和分类。SS-CNN BruiseFinder模型检测准确率在68.5% ~ 91.5%之间，分类准确率在67.39% ~ 98.16%之间。为了进一步确定该方法的有效性，在同一数据集上开发并评估了另外三个深度学习模型——自定义谱CNN、ResNet 101和双向LSTM模型，全面验证了所提出方法的优越性。及时发现破损有助于防止污染的李子在运输或储存过程中进入供应链。通过对李子进行分类，零售商可以为消费者提供更准确的新鲜度和质量信息，使他们能够做出更明智的购买选择，最终提升整体购物体验。为了鼓励社区参与和重新实施，我们的代码可在https://github.com/SS-CNN BruiseFinder上获得。

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引用次数: 0

Assessing microplastic contamination and health risks in infant formula: A case study from Turkey 评估婴儿配方奶粉中的微塑料污染和健康风险：来自土耳其的案例研究

IF 6.3 1区农林科学 Q1 FOOD SCIENCE & TECHNOLOGY

Food Control

Pub Date : 2025-11-25 DOI: 10.1016/j.foodcont.2025.111872

Murat Şirin , Tanju Mutlu , Ahmet Raif Eryaşar , Kenan Gedik

Microplastic (MP) contamination is an emerging concern for food safety and infant health. This study provides the first systematic assessment of MPs in infant formulas marketed in Turkey. A total of 36 samples from 12 commercial brands were analyzed using stereomicroscopy and micro-Raman spectroscopy. Analyses were performed with 532 and 785×nm lasers, 50 × magnification, 10 s exposure, a 300–3200 cm⁻¹ spectral range, and gratings of 600/1200 l/mm. Suspected particles were compared against the ST-Japan MP library, with a ≥70 % spectral match threshold applied for polymer identification. MPs were detected in 100 % of samples (n = 36), with 97 % of particles successfully characterized. Concentrations ranged from 14 to 52 MPs/100 g (mean 31.3 MPs/100 g). Fibers were the dominant form (58 %), followed by fragments and films. Nine polymers were identified, with polyethylene (PE), polyethylene terephthalate (PET), polypropylene (PP), and polyamide (PA) most abundant. Packaging materials, manufacturing processes, and feeding equipment were identified as likely contamination sources. Estimated daily intake for infants aged 0–6 months averaged 5.64 MP/kg bw/day (∼15,400 MPs annually). This annual exposure estimate was calculated based on an assumed body weight of 7.5 kg for a 6-month–old infant and a daily formula consumption of 135 g, as recommended in previous nutritional intake assessments. To enhance toxicological relevance, mass- and surface–area–based exposures were also calculated, averaging 326.77 μg/kg bw/day and 0.009 cm²/kg bw/day, respectively. The polymeric risk index (pR_i) ranged from 8.27 to 1647.65 (mean 472.12), classifying 50 % of samples as low risk, 33.3 % as high risk, and 8.3 % as very high risk. These findings confirm infant formulas as a consistent source of MP exposure and highlight the need for stricter production and packaging controls to reduce early–life risks.

微塑料污染是影响食品安全和婴儿健康的一个新问题。本研究首次对在土耳其销售的婴儿配方奶粉中的MPs进行了系统评估。采用立体显微镜和微拉曼光谱对12个商业品牌的36个样品进行了分析。分析使用532和785×nm激光器，50倍放大，10 s曝光，300-3200 cm−1光谱范围，光栅600/1200 l/mm。将可疑颗粒与ST-Japan MP文库进行比较，采用≥70%的光谱匹配阈值进行聚合物鉴定。100%的样品（n = 36）检测到MPs， 97%的颗粒成功表征。浓度范围为14至52 MPs/100 g（平均31.3 MPs/100 g）。纤维是主要形式（58%），其次是碎片和薄膜。共鉴定出9种聚合物，其中聚乙烯（PE）、聚对苯二甲酸乙二醇酯（PET）、聚丙烯（PP）和聚酰胺（PA）含量最多。包装材料、生产工艺和给料设备被确定为可能的污染源。0-6个月婴儿的估计每日摄入量平均为5.64毫微克/公斤体重/天（每年约15,400毫微克）。这一年度暴露估计是根据先前营养摄入评估中建议的6个月婴儿体重7.5公斤和每日配方奶粉摄入量135克的假设计算得出的。为了增强毒理学相关性，还计算了基于质量和表面积的暴露量，平均分别为326.77 μg/kg bw/day和0.009 cm2/kg bw/day。聚合风险指数（pRi）范围为8.27 ~ 1647.65（平均472.12），50%的样本为低风险，33.3%为高风险，8.3%为非常高风险。这些研究结果证实，婴儿配方奶粉始终是多聚氰胺暴露的来源，并强调需要对生产和包装进行更严格的控制，以减少生命早期的风险。

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引用次数: 0

Identification of risk factors associated with the presence of phthalates in raw milk obtained by manual and mechanical milking in Argentina 鉴定与阿根廷手工和机械挤奶获得的原料奶中邻苯二甲酸酯存在相关的风险因素

IF 6.3 1区农林科学 Q1 FOOD SCIENCE & TECHNOLOGY

Food Control

Pub Date : 2025-11-25 DOI: 10.1016/j.foodcont.2025.111874

Pablo L. Pisano , Santiago A. Bortolato , Miguel Taverna , Marcelo Signorini , Dianela Costamagna

This study aimed to estimate the prevalence of phthalates in raw milk and identify the risk factors associated with their presence to scientifically support the management measures to be applied at the primary milk production level. Conducted from December 2021 to February 2022 in a key Argentine dairy region, the cross-sectional study included 60 dairy farms. The presence and quantification of dimethyl phthalate (DMP), diethyl phthalate (DEP), diisobutyl phthalate (DiBP), di-n-butyl phthalate (DnBP), benzylbutyl phthalate (BzBP), di (2-ethylhexyl) phthalate (DEHP), dicyclohexyl phthalate (DCHP) and di-n-octyl phthalate (DnOP) in manually and mechanically obtained raw cow's milk was performed. Potential explanatory variables were gathered using a checklist completed by each farm owner. The quantitative prevalence of phthalates in milk was determined using high-performance liquid chromatography with a diode array detector and chemometric modeling (Multivariate Curve Resolution assisted by Alternating Least Squares). The developed chemometric method successfully detected most target analytes. The overall prevalence of phthalates, regardless of the specific compound detected, was 10.8 %, higher in manually obtained milk (15.0 %) than mechanically obtained milk (6.7 %). DiBP was the most prevalent phthalate (11.7 %). Concentrations were mostly below the limit of quantification. No explanatory variable was associated with the presence of phthalates in milk. In general, the prevalence of phthalates was low and its high presence in manually obtained milk revealed that environmental contamination, probably through food intake, would be the main source of phthalates compared to the contact materials used during the mechanical milking process.

本研究旨在估计原料奶中邻苯二甲酸盐的流行程度，并确定与其存在相关的危险因素，以科学地支持在初级奶生产层面应用的管理措施。该横断面研究于2021年12月至2022年2月在阿根廷一个主要的奶牛场进行，包括60个奶牛场。对手工和机械制得的原料牛奶中邻苯二甲酸二甲酯（DMP）、邻苯二甲酸二乙酯（DEP）、邻苯二甲酸二异丁酯（DiBP）、邻苯二甲酸二丁酯（DnBP）、邻苯二甲酸二丁酯（BzBP）、邻苯二甲酸二（2-乙基己基）酯（DEHP）、邻苯二甲酸二环己酯（DCHP）和邻苯二甲酸二辛酯（DnOP）的存在和定量进行了研究。使用每个农场主填写的清单收集潜在的解释变量。采用高效液相色谱二极管阵列检测器和化学计量学模型（交替最小二乘法辅助多元曲线分辨率）确定牛奶中邻苯二甲酸酯的定量流行率。所建立的化学计量学方法成功地检测了大多数目标分析物。无论检测到何种特定化合物，邻苯二甲酸酯的总体流行率为10.8%，手工获得的牛奶（15.0%）高于机械获得的牛奶（6.7%）。邻苯二甲酸酯以DiBP最常见（11.7%）。浓度大多低于定量限。没有解释变量与牛奶中邻苯二甲酸盐的存在有关。总的来说，邻苯二甲酸酯的含量很低，而人工获得的牛奶中邻苯二甲酸酯的含量很高，这表明与机械挤奶过程中使用的接触材料相比，环境污染（可能是通过食物摄入）将是邻苯二甲酸酯的主要来源。

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引用次数: 0

Leveraging smart technologies to enhance safety in milk and milk-based products 利用智能技术加强奶类及奶类产品的安全

IF 6.3 1区农林科学 Q1 FOOD SCIENCE & TECHNOLOGY

Food Control

Pub Date : 2025-11-24 DOI: 10.1016/j.foodcont.2025.111871

Shubham Singh Patel , Aarti Bains , Ravinder Kaushik , Sanju Bhala Dhull , Rupak Nagraik , Mohammad Fareed , Sandeep Janghu , Prince Chawla

Milk and milk-based products are essential to the global food industry, providing key nutrients such as calcium, vitamins, and high-quality proteins. However, concerns regarding contamination and adulteration pose significant risks to consumer health. Traditional detection methods, including chemical and microbiological assays, are often time-consuming, costly, and require specialized expertise. Integrating smart technologies offers a promising solution to enhance safety and quality in the dairy industry. This review explores the application of smart technologies like artificial intelligence (AI), big data (BD), blockchain technology (BT), the Internet of Things (IoT), hyperspectral imaging analysis (HSIA), and digital image analysis (DIA) in monitoring milk safety. AI and machine learning models allow for rapid detection of adulterants and contaminants, while IoT-based sensor systems enable real-time tracking of milk quality and storage conditions. BT enhances traceability and transparency in the dairy supply chain, and BD-driven risk assessment aids in identifying potential hazards. HSIA and DIA provide non-destructive methods for identifying impurities in dairy products. Their advantages and disadvantages for integrating into conventional milk production and processing practices provide an interesting insight and arguments for constructive analysis. The integration of these technologies aligns with sustainable development goals (SDGs) by improving food safety, reducing waste, and optimizing dairy production. This review highlights recent advancements in smart technologies and their applications in combating adulteration and contamination of milk and milk-based products.

牛奶和乳制品对全球食品工业至关重要，提供钙、维生素和高质量蛋白质等关键营养素。然而，对污染和掺假的担忧对消费者健康构成重大风险。传统的检测方法，包括化学和微生物分析，往往耗时，昂贵，并需要专门的专业知识。集成智能技术为提高乳制品行业的安全和质量提供了一个有前途的解决方案。本文综述了人工智能（AI）、大数据（BD）、区块链技术（BT）、物联网（IoT）、高光谱成像分析（HSIA）、数字图像分析（DIA）等智能技术在牛奶安全监测中的应用。人工智能和机器学习模型可以快速检测掺假和污染物，而基于物联网的传感器系统可以实时跟踪牛奶质量和储存条件。BT提高了乳制品供应链的可追溯性和透明度，bd驱动的风险评估有助于识别潜在危害。HSIA和DIA提供了非破坏性的方法来鉴别乳制品中的杂质。它们融入传统牛奶生产和加工实践的优缺点为建设性分析提供了有趣的见解和论据。这些技术的整合通过改善食品安全、减少浪费和优化乳制品生产，符合可持续发展目标（sdg）。本综述重点介绍了智能技术的最新进展及其在打击掺假和污染牛奶和乳基产品中的应用。

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引用次数: 0

Individual and combined decontamination effect of aqueous ozone and plant extracts on fungi and aflatoxins in brown rice 水臭氧和植物提取物对糙米真菌和黄曲霉毒素的单独和联合去污作用

IF 6.3 1区农林科学 Q1 FOOD SCIENCE & TECHNOLOGY

Food Control

Pub Date : 2025-11-24 DOI: 10.1016/j.foodcont.2025.111873

Iqra Naeem , Amir Ismail , Yun Yun Gong , Muhammad Riaz , Muhammad Arif Shahzad , Aneela Hameed , Mubashir Aziz , Asif Mahmood , Waheed Al Masry , Muhammad Latif , Sher Ali , Carlos A.F. Oliveira

This study aimed to investigate the effect of aqueous ozone (AO), alone or in combination with plant aqueous extracts (PAE) of Mentha arvensis, Chenopodium album, and Eucalyptus camaldulensis on fungal load and aflatoxins (AFs) at 100 or 200 ng/g in brown rice. Significant reductions in AFs levels (up to 100 %) and fungal counts (up to 0.60 log CFU/g) were observed in treated brown rice, with higher reductions achieved with AO combined with M. arvensis' extract at 10 % during 40 min exposure. Notably, this treatment improved the cooking quality and retained key physicochemical properties of brown rice, including fatty acid value, total phenolic contents, antioxidant activity, and color (L∗ value). AO in combination with PAE, particularly M. arvensis, is a sustainable approach for AFs removal in brown rice. Further studies are needed to optimize the AFs decontamination process and evaluate long-term effects of combined AO and PAE treatments on brown rice's quality.

本研究旨在研究水溶液臭氧（AO）单独或与薄荷、Chenopodium album和camaldulensis植物水提取物（PAE）在100或200 ng/g浓度下对糙米真菌负荷和黄曲霉毒素（AFs）的影响。在处理过的糙米中观察到AFs水平（高达100%）和真菌计数（高达0.60 log CFU/g）的显著降低，在暴露40分钟期间，AO与M. arvensis提取物联合降低10%的效果更高。值得注意的是，这种处理改善了糙米的蒸煮品质，并保留了糙米的关键理化特性，包括脂肪酸值、总酚含量、抗氧化活性和颜色（L *值）。AO与PAE联合使用，特别是M. arvensis，是去除糙米中AFs的可持续方法。需要进一步研究优化AFs去污工艺，并评价AO和PAE联合处理对糙米品质的长期影响。

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引用次数: 0

Microbial safety in cold-preserved foods: risks, regulatory gaps, and mitigation strategies 冷保存食品中的微生物安全：风险、监管缺口和缓解策略

IF 6.3 1区农林科学 Q1 FOOD SCIENCE & TECHNOLOGY

Food Control

Pub Date : 2025-11-22 DOI: 10.1016/j.foodcont.2025.111869

Piyush Kumar Jha , Aurelie Hanin , Brijesh K. Tiwari , Heni Dallagi

The global expansion of cold chain systems is critical for ensuring food security and reducing post-harvest losses. This review examines the microbiological risks associated with cold-preserved foods, focusing on psychrotrophic pathogens including bacteria (L. monocytogenes, Salmonella enteritidis, etc.), molds that contribute to food spoilage and mycotoxin production, as well as viruses like Norovirus (NoV) and Hepatitis A virus (HAV). It highlights contamination routes across the supply chain, from primary production to processing, storage, and distribution, emphasizing surface hygiene and design, inadequate cleaning, and process water as key contributors. Traditional cleaning and disinfection strategies, including clean-in-place, clean-out-of-place, and open surface cleaning using chemical detergents and disinfectants, are reviewed along with their effectiveness and limitations, such as persistent contamination, high resource demands, and occupational risks. While low temperatures in cold processing limit or halt microbial growth, they cannot be considered true mitigation methods. Similarly, blanching may cause partial microbial inactivation but is not reliable for significantly reducing microbial loads. Emerging interventions including antimicrobial gases, irradiation, light-based methods, cold plasma, dry ice blasting, high-pressure freezing, antimicrobial packaging, and essential oils (EOs) offer promising complementary tools by enhancing microbial control without compromising product quality. This review also discusses how international regulatory frameworks, including Codex Alimentarius standards, European Union regulations, and ISO methods, shape requirements for microbiological criteria, control measures such as freezing for parasites, and general hygiene practices throughout the cold chain. Integrating these elements into multi-hurdle strategies, alongside robust hygienic design, targeted monitoring, and careful management of critical control points (CCPs), could substantially improve food safety and sustainability in cold storage environments. The analysis underscores the need for tailored, science-based interventions to close gaps in current practices, safeguard public health, and optimize resource efficiency.

冷链系统的全球扩展对于确保粮食安全和减少收获后损失至关重要。本文综述了与冷保存食品相关的微生物风险，重点研究了精神营养病原体，包括细菌（单核细胞增生乳杆菌、肠炎沙门氏菌等）、导致食物变质和产生霉菌毒素的霉菌，以及诺如病毒（NoV）和甲型肝炎病毒（HAV）等病毒。它强调了从初级生产到加工、储存和分销的整个供应链的污染路线，强调表面卫生和设计、清洁不足和工艺用水是主要贡献者。传统的清洁和消毒策略，包括就地清洁、非就地清洁和使用化学清洁剂和消毒剂的露天表面清洁，以及它们的有效性和局限性，如持续污染、高资源需求和职业风险，都进行了审查。虽然冷加工中的低温限制或停止了微生物的生长，但它们不能被认为是真正的缓解方法。同样，烫烫可能导致部分微生物失活，但不可靠的显着减少微生物负荷。新兴的干预措施包括抗菌气体、辐照、光基方法、冷等离子体、干冰爆破、高压冷冻、抗菌包装和精油（EOs），通过在不影响产品质量的情况下加强微生物控制，提供了有希望的补充工具。本综述还讨论了包括食品法典标准、欧盟法规和ISO方法在内的国际监管框架如何制定微生物标准要求、寄生虫冷冻等控制措施以及整个冷链的一般卫生做法。将这些要素整合到多障碍策略中，再加上稳健的卫生设计、有针对性的监测和对关键控制点（ccp）的精心管理，可以大大提高冷藏环境中的食品安全性和可持续性。该分析强调需要有针对性的、基于科学的干预措施，以缩小当前做法中的差距，保障公众健康，并优化资源效率。

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引用次数: 0

Residual lead in edible tissues of red-legged partridge (Alectoris rufa) following removal of detectable pellets 去除可检测的铅丸后，红腿鹧鸪可食用组织中的残留铅

IF 6.3 1区农林科学 Q1 FOOD SCIENCE & TECHNOLOGY

Food Control

Pub Date : 2025-11-21 DOI: 10.1016/j.foodcont.2025.111865

Mário Quaresma , António Almeida , João Pinto , Óscar Gamboa , Maria Leonor Nunes , Cristina Roseiro , Luísa Martins , Miguel Mourato

In the European Union, game hunting is practiced by over 7 million hunters and generates more than €16 billion annually. However, the widespread use of lead (Pb) ammunition has made game meat the primary source of dietary Pb exposure, raising significant public health concerns. This study aimed to quantify and map the distribution of embedded Pb pellets in red-legged partridges and assess residual Pb content in prime edible meat portions (breast and leg), following the manual removal of all visible intact and nearly-intact Pb pellets. Each of the 40 specimens was radiographed in two anatomical planes to localize Pb contamination, followed by pellet removal and Pb quantification using ICP-OES. A total of 267 contamination points were identified, including 190 intact pellets and 77 fragmentation centers. Of these, 172 pellets and 48 fragmentation centers were located in breast and leg meat. Despite visible pellet removal, 57.5 % of breast and 85 % of leg samples exceeded the EU's Maximum Residue Level (MRL) for Pb in livestock meat (0.1 mg/kg). Notably, 10 % of breast and 37.5 % of leg samples contained Pb levels exceeding 100 times the MRL, and up to 1000 times in some cases. Our findings demonstrate that even thorough manual removal of Pb pellets is insufficient to ensure the safety of game meat, which may pose risks of acute Pb poisoning, particularly in children. These results emphasize the urgent need to ban Pb-based ammunition and adopt non-toxic alternatives to protect consumers and public health.

在欧盟，有超过700万猎人从事狩猎活动，每年产生超过160亿欧元的收入。然而，铅（Pb）弹药的广泛使用使野味肉成为膳食铅暴露的主要来源，引起了重大的公共卫生问题。本研究旨在量化和绘制嵌入铅颗粒在红腿鹧鸪中的分布，并评估在人工去除所有可见的完整和几乎完整的铅颗粒后，主要可食用肉部分（胸脯和腿）中的残留铅含量。40个标本分别在两个解剖平面上进行x线摄影以定位铅污染，然后去除颗粒并使用ICP-OES进行铅定量。总共鉴定出267个污染点，包括190个完整的颗粒和77个碎裂中心。其中172个颗粒和48个碎片中心位于胸肉和腿肉中。尽管去除了可见的颗粒，但57.5%的胸肉和85%的腿肉样本超过了欧盟对牲畜肉中铅的最大残留水平（0.1 mg/kg）。值得注意的是，10%的乳房和37.5%的腿部样本的铅含量超过了MRL的100倍，在某些情况下高达1000倍。我们的研究结果表明，即使完全手工去除铅颗粒也不足以确保野味的安全，这可能会造成急性铅中毒的风险，特别是对儿童。这些结果强调，迫切需要禁止含铅弹药并采用无毒替代品，以保护消费者和公众健康。

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引用次数: 0