Martelli Francesco, Giacomozzi Claudia, Dragone Roberto, Frazzoli Chiara, Grasso Gerardo
Dipartimento Malattie Cardiovascolari ed Endocrino-Metaboliche, e Invecchiamento, Istituto Superiore di Sanità, Via Giano Della Bella, 34, 00162 Rome, Italy.
Istituto per Lo Studio Dei Materiali Nanostrutturati Sede Sapienza, Consiglio Nazionale delle Ricerche, P. le Aldo Moro 5, 00185 Rome, Italy.
Foods. 2025 May 13;14(10):1724. doi: 10.3390/foods14101724.
In the last decade, the demand for healthier and safer food has increased alongside greater consumer awareness of food consumption, particularly in developed countries. This trend has pushed the food industry to implement a wide range of food quality control measures and surveillance systems for detecting contaminants. While high-end laboratory techniques remain the gold standard detection techniques, there is a growing need for simpler, more robust diagnostic tools that can be applied in the early stages of the food production chain to promptly identify deviations that may compromise food safety or quality. A complementary approach using both techniques can result in an enhancement of the overall contaminant-detection effectiveness and a better balance between food safety decision-making and the preservation of production value. This need is particularly relevant in farming and in the dairy industry. Developing milk process analytics requires careful consideration of both the nature of the processed sample and the conditions under which it is collected. Moreover, newly introduced techniques require the development of sound methodologies for data collection, analysis, and statistical process control. For this reason, this paper presents a detailed analysis of our previous milk data-collection campaigns involving technological prototypes, aiming to identify and suggest ways to preventively minimize issues related to experimental data collection, interpretation, errors, and mishandling. This analysis resulted in a set of practical observations and recommendations reported in the paper.
在过去十年中，随着消费者对食品消费的意识增强，对更健康、更安全食品的需求不断增加，尤其是在发达国家。这一趋势促使食品行业实施广泛的食品质量控制措施和监测系统以检测污染物。虽然高端实验室技术仍然是金标准检测技术，但人们越来越需要更简单、更可靠的诊断工具，这些工具可应用于食品生产链的早期阶段，以便迅速识别可能危及食品安全或质量的偏差。同时使用这两种技术的互补方法可以提高整体污染物检测效率，并在食品安全决策和生产价值保护之间实现更好的平衡。这种需求在农业和乳制品行业尤为重要。开发牛奶过程分析需要仔细考虑加工样品的性质及其采集条件。此外，新引入的技术需要开发完善的数据收集、分析和统计过程控制方法。因此，本文对我们之前涉及技术原型的牛奶数据收集活动进行了详细分析，旨在识别并提出预防性措施，尽量减少与实验数据收集、解释、误差和处理不当相关的问题。该分析得出了一组实用的观察结果和建议，并在本文中进行了报道。
