肉类加工行业在维持绞碎鸡肉制品的凝胶品质方面面临挑战，而凝胶品质直接影响产品的感官吸引力和整体质量。本研究探讨了超声波处理对改善绞碎鸡肉凝胶品质的效果，并采用近红外（NIR）光谱与拉曼光谱技术，实现对凝胶强度的快速、无损评估。研究首先对鸡肉糜施加不同时间的超声波处理，结果表明：处理约30分钟时效果最佳，显著提升了凝胶强度和质构特性，同时明显降低了离心失水率。为全面评估凝胶强度，研究分别基于NIR光谱、拉曼光谱及其数据融合，构建了卷积神经网络（CNN）和长短期记忆网络（LSTM）模型。其中，采用NIR与拉曼融合数据的LSTM模型表现最优（预测决定系数 ( R_p^2 = 0.9882 )，残差预测偏差 RPD = 9.2091），优于单一光谱技术及基于CNN的模型。本研究表明，超声波处理能有效提升绞碎鸡肉的凝胶品质；同时，结合NIR与拉曼光谱并融合LSTM深度学习算法，可提供一种可靠、无损且高效的凝胶强度预测方法。该方法契合肉类加工行业对创新质量检测技术的迫切需求，有望提升加工鸡肉产品的品质与消费者满意度。

为建立一种快速、简便的方法来预测迷迭香中关键非挥发性成分的含量，本研究采用电子鼻（E-nose，含18个传感器S1–S18）、顶空固相微萃取-气相色谱-质谱联用技术（HS-SPME-GC-MS）以及液相色谱-质谱联用技术（LC-MS）对迷迭香中的化合物进行分析，并结合聚类分析和主成分分析（PCA）对数据进行处理。GC-MS共检测到161种挥发性化合物，包括：40种醇类、2种芳香烃、5种酚类化合物、2种呋喃类、1种含硫化合物、6种醚类、6种醛类、2种有机酸、5种萜烯、19种酮类、16种酯类，以及其他57种化合物。同时，利用LC-MS测定了迷迭香样品中咖啡酸、新泽兰素（nepetin）、木犀草素（luteolin）、芹菜素（apigenin）、地奥司明（diosmetin）、迷迭香酸（rosmarinic acid）、鼠尾草酸（carnosic acid）和鼠尾草酚（rosmanol）等关键非挥发性成分的含量。电子鼻用于分析迷迭香的气味特征，PCA结果表明，利用电子鼻区分迷迭香品质是可行的。此外，本研究基于电子鼻数据，分别构建了偏最小二乘法（PLS）模型和人工神经网络（ANN）模型，用于预测迷迭香中关键非挥发性成分的含量。与PLS模型相比，所构建的ANN模型具有更强的预测能力。结果表明，利用气味传感器（电子鼻）预测迷迭香中非气味性成分的含量是可行的。该研究为开发基于电子鼻的迷迭香快速检测方法提供了理论基础和技术支持。

英文摘要
In the European Union (EU) eggs from laying hens are sold in different husbandry systems: enriched cage, barn, free-range and organic. An analytical distinction between the husbandry systems is of interest for the food control authorities, as fraudsters can make high profits by mislabelling the husbandry system of the eggs. In this study, a total of 180 hen’s egg samples labelled to originate from barn, free-range and organic hens were taken from food retailers in Germany at monthly intervals over a period of 15 months. The eggs were analysed using stable isotope ratio analysis (IRMS) in order to identify potential influences of different husbandry methods. The analysis on a monthly basis showed that the feeding of the laying hens varied over the year, which makes it difficult to make a clear classification according to the type of husbandry. Temporary events such as the officially ordered poultry quarantine due to the avian influenza, had minor influence on the differentiation of the husbandry types. Various statistical models including linear discriminant analysis (LDA), random forest (RF) and multinomial logistic regression (Multinomial) based on a multi-element approach (δ13C, δ15N, δ34S) were used to classify the eggs according to the three types of husbandry. On average, the models achieved a correct classification of approximately 70%. The reduction of the prediction model to two classes (organic vs. conventional eggs) led to a correct classification of almost 80% across all samples. Additionally, significant differences in the mean values of the sulphur isotopes of the organic eggs showed a regional differentiation between Bavaria (southern Germany) and Schleswig-Holstein (northern Germany).
中文摘要
在欧盟（EU），产蛋鸡所产的鸡蛋根据不同的养殖方式分为富集笼养、舍饲、散养和有机四种类型进行销售。对食品监管部门而言，能够通过分析手段区分这些养殖方式具有重要意义，因为不法商家可通过错误标注鸡蛋的养殖类型获取高额利润。本研究在15个月期间，每月从德国食品零售商处采集共计180枚标称为舍饲、散养或有机养殖的鸡蛋样品。采用稳定同位素比值质谱法（IRMS）对这些鸡蛋进行分析，以探究不同养殖方式可能带来的影响。月度分析结果表明，蛋鸡的饲料组成随季节变化而波动，这使得依据养殖类型进行明确分类变得困难。此外，诸如因禽流感疫情而官方强制实施的家禽隔离等临时性事件，对养殖类型之间的区分影响较小。研究基于多元素同位素数据（δ¹³C、δ¹⁵N、δ³⁴S），构建了多种统计模型——包括线性判别分析（LDA）、随机森林（RF）和多项逻辑回归（Multinomial）——用于将鸡蛋归类至上述三种养殖类型。总体而言，这些模型的平均正确分类率约为70%。当将预测模型简化为两类（有机 vs. 常规鸡蛋）时，所有样本的平均正确分类率提升至近80%。此外，研究还发现有机鸡蛋中硫同位素（δ³⁴S）的平均值存在显著区域差异：德国南部巴伐利亚州与北部石勒苏益格-荷尔斯泰因州的有机鸡蛋表现出明显不同的硫同位素特征，表明该指标可用于区域溯源。
打标依据

英文摘要
In this study, chitosan (CS) films with different concentrations of clove essential oil (CEO) microemulsions were prepared. The composite film with a 0.8% microemulsion concentration exhibited the highest DPPH free radical clearance (77.44 ± 1.10%). When applied to six types of pork with different n-3 polyunsaturated fatty acid (PUFA) content stored at 4 °C, the composite films with 0.6% and 0.8% CEO microemulsions effectively preserved the pork and extended its shelf life by 9 days. Additionally, the deep-learning model successfully monitored the freshness levels of different n-3 pork samples. The CS/CEO microemulsion composite films, in conjunction with deep learning, demonstrate significant potential for pork preservation and freshness monitoring.
中文摘要
本研究制备了含有不同浓度丁香精油（CEO）微乳液的壳聚糖（CS）复合膜。其中，CEO微乳液浓度为0.8%的复合膜表现出最高的DPPH自由基清除率（77.44 ± 1.10%）。将该复合膜应用于6种不同n-3多不饱和脂肪酸（PUFA）含量的猪肉样品，并在4°C下贮藏，结果表明：含0.6%和0.8% CEO微乳液的复合膜能有效保鲜猪肉，使其货架期延长9天。此外，所构建的深度学习模型成功实现了对不同n-3含量猪肉样品新鲜度等级的实时监测。综上所述，壳聚糖/丁香精油微乳液复合膜结合深度学习技术，在猪肉保鲜与新鲜度智能监控方面展现出显著的应用潜力。打标依据

英文摘要
Quality control in the brewing industry is essential to ensure the consistency and excellence of beer products. A simple, economical and reliable methodology was developed based on digital images captured with a smartphone for the multiparametric quantification in blond beers. For this objective, PLS was used to construct multivariate calibration models using grayscale color histograms, RGB (red-green-blue) channels, HSI (hue-saturation-intensity) and their combinations as analytical information. Through these calibrations, the alcohol content, pH, total acidity, bitterness and polyphenols have been successfully quantified with a relative prediction error between 3.90% and 12.60%, demonstrating satisfactory results. In addition, the data were processed using PLS-DA to develop a multivariate classification model that allowed discriminating beer samples according to their production method (industrial or craft) and between alcoholic and non-alcoholic beers. The error rates were of 9% and 2%, respectively. These methods hold promise for improving quality control of blond beers.
中文摘要
啤酒酿造行业的质量控制对于确保产品的一致性与高品质至关重要。本研究开发了一种基于智能手机拍摄数字图像的简便、经济且可靠的多参数定量分析方法，用于淡色啤酒的质量评估。为实现该目标，研究采用偏最小二乘法（PLS）构建多元校正模型，以灰度颜色直方图、RGB（红-绿-蓝）通道、HSI（色调-饱和度-亮度）及其不同组合形式作为分析信息源。通过这些校正模型，成功实现了对酒精含量、pH值、总酸度、苦味值和多酚含量的定量预测，其相对预测误差介于3.90%至12.60%之间，结果令人满意。此外，研究还利用偏最小二乘判别分析（PLS-DA）对数据进行处理，构建了多元分类模型，能够有效区分不同生产工艺（工业啤酒 vs. 精酿啤酒）以及含酒精与无酒精啤酒。两类判别任务的错误率分别为9%和2%。上述方法在淡色啤酒的质量控制中展现出良好的应用前景，有望为啤酒生产提供一种快速、低成本且高效的现场检测工具。
打标依据