Data Analysis in Newly Developed Milk Sensor Platforms: Good Practices, Common Pitfalls, and Hard-Earned Lessons from Field Application
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 det...
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MDPI AG
2025-05-01
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| Series: | Foods |
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| Online Access: | https://www.mdpi.com/2304-8158/14/10/1724 |
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| author | Francesco Martelli Claudia Giacomozzi Roberto Dragone Chiara Frazzoli Gerardo Grasso |
| author_facet | Francesco Martelli Claudia Giacomozzi Roberto Dragone Chiara Frazzoli Gerardo Grasso |
| author_sort | Francesco Martelli |
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| description | 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. |
| format | Article |
| id | doaj-art-a5fb6e135c2b420b8d75b0db68f160a9 |
| institution | OA Journals |
| issn | 2304-8158 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | MDPI AG |
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| series | Foods |
| spelling | doaj-art-a5fb6e135c2b420b8d75b0db68f160a92025-08-20T01:56:16ZengMDPI AGFoods2304-81582025-05-011410172410.3390/foods14101724Data Analysis in Newly Developed Milk Sensor Platforms: Good Practices, Common Pitfalls, and Hard-Earned Lessons from Field ApplicationFrancesco Martelli0Claudia Giacomozzi1Roberto Dragone2Chiara Frazzoli3Gerardo Grasso4Dipartimento Malattie Cardiovascolari ed Endocrino-Metaboliche, e Invecchiamento, Istituto Superiore di Sanità, Via Giano Della Bella, 34, 00162 Rome, ItalyDipartimento Malattie Cardiovascolari ed Endocrino-Metaboliche, e Invecchiamento, Istituto Superiore di Sanità, Via Giano Della Bella, 34, 00162 Rome, ItalyIstituto per Lo Studio Dei Materiali Nanostrutturati Sede Sapienza, Consiglio Nazionale delle Ricerche, P. le Aldo Moro 5, 00185 Rome, ItalyDipartimento Malattie Cardiovascolari ed Endocrino-Metaboliche, e Invecchiamento, Istituto Superiore di Sanità, Via Giano Della Bella, 34, 00162 Rome, ItalyIstituto per Lo Studio Dei Materiali Nanostrutturati Sede Sapienza, Consiglio Nazionale delle Ricerche, P. le Aldo Moro 5, 00185 Rome, ItalyIn 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.https://www.mdpi.com/2304-8158/14/10/1724animal welfarefood safetymilk chainmilk monitoringsensors |
| spellingShingle | Francesco Martelli Claudia Giacomozzi Roberto Dragone Chiara Frazzoli Gerardo Grasso Data Analysis in Newly Developed Milk Sensor Platforms: Good Practices, Common Pitfalls, and Hard-Earned Lessons from Field Application Foods animal welfare food safety milk chain milk monitoring sensors |
| title | Data Analysis in Newly Developed Milk Sensor Platforms: Good Practices, Common Pitfalls, and Hard-Earned Lessons from Field Application |
| title_full | Data Analysis in Newly Developed Milk Sensor Platforms: Good Practices, Common Pitfalls, and Hard-Earned Lessons from Field Application |
| title_fullStr | Data Analysis in Newly Developed Milk Sensor Platforms: Good Practices, Common Pitfalls, and Hard-Earned Lessons from Field Application |
| title_full_unstemmed | Data Analysis in Newly Developed Milk Sensor Platforms: Good Practices, Common Pitfalls, and Hard-Earned Lessons from Field Application |
| title_short | Data Analysis in Newly Developed Milk Sensor Platforms: Good Practices, Common Pitfalls, and Hard-Earned Lessons from Field Application |
| title_sort | data analysis in newly developed milk sensor platforms good practices common pitfalls and hard earned lessons from field application |
| topic | animal welfare food safety milk chain milk monitoring sensors |
| url | https://www.mdpi.com/2304-8158/14/10/1724 |
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