对传统方法、传感器诱导方法以及基于机器学习的方法在牛奶质量分析和掺假物检测中的系统研究
A systematic study of conventional, sensor-induced, and machine learning-based methods for milk quality analysis and adulterant detection.
摘要(中文译文)
牛奶是全球日常饮食的重要组成部分。由于乳品行业中存在不合标准的操作、频繁的掺假行为、微生物污染、贮藏及气候条件等多重问题，牛奶质量受到损害。由于缺乏便携式的牛奶质量评估与检测解决方案，这一问题变得更加严重。目前，牛奶质量检测仍然以集中化和实验室检测为主。因此，当务之急是研究并找出便携、准确且能及时提供结果的解决方案。本研究试图通过分析牛奶检测领域的最新进展来弥合这一差距，尤其是基于传感器的技术与基于机器学习的分类策略相结合。研究旨在寻找一种可靠、一体化、低成本、便携、随处可用、用户界面友好且能实时运行的牛奶质量检测方案。该方案应采用 connectivity 以及新型人工智能和机器学习方法等前沿技术。研究结论表明，融合传感器的机器学习解决方案相较于传统实验室中缓慢且昂贵的方案具有明显优势。物联网和基于传感器的技术正在为实时便携式牛奶检测套件开辟道路，借助机器学习方法的后端能力，该套件正展现出良好的应用前景。本研究以社会影响为导向，尤其是在食品安全和农村赋能领域。在线版本包含补充材料，可访问 10.1007/s13197-026-06746-0 获取。
Abstract
Milk is important constituent of daily food worldwide. Quality of milk is compromised because of multiple issues like sub-standard practices in dairy industry, frequent adulterations, microbial contaminations, storage and climatic conditions. The problem gets multifold grave because of lack of portable solutions of milk quality assessment and testing. Still the milk quality testing remains centralized and lab oriented. So, the need of the hour is to study and figure out solutions which can be portable, accurate and provide results in timely fashion. The current study tries to bridge this gap by analyzing recent development in the field of milk testing especially sensor-based techniques coupled with the power of Machine Learning based classification strategies. The search is for a milk quality testing solution which is reliable, all-in-one testing solution, low cost, portable, anywhere accessible, friendly user interface and operates in real time. It should employ state of the art technology like connectivity and novel Artificial Intelligence and Machine learning methods. The study concludes that sensor infused machine learning solutions provides an upper edge with respect to traditional lab based slow and costly solutions. Internet of Things and Sensor-based technology is opening doors for real time portable milk testing kit which is showing promising result with backend prowess of Machine learning methods. Our study is driven of societal impact especially in the area of food safety and rural empowerment. The online version contains supplementary material available at 10.1007/s13197-026-06746-0.
如何引用
AMA
Satya Prakash, Kirti Amresh Gautam, Krishna Kumar Gupta, Prateek Singh Raghav, Annu Kumari. A systematic study of conventional, sensor-induced, and machine learning-based methods for milk quality analysis and adulterant detection.. Journal of food science and technology. 2026; doi:10.1007/s13197-026-06746-0.
APA
Satya Prakash, Kirti Amresh Gautam, Krishna Kumar Gupta, Prateek Singh Raghav, Annu Kumari (2026). A systematic study of conventional, sensor-induced, and machine learning-based methods for milk quality analysis and adulterant detection.. Journal of food science and technology. https://doi.org/10.1007/s13197-026-06746-0
GB/T 7714
Satya Prakash, Kirti Amresh Gautam, Krishna Kumar Gupta, Prateek Singh Raghav, Annu Kumari. A systematic study of conventional, sensor-induced, and machine learning-based methods for milk quality analysis and adulterant detection.[J]. Journal of food science and technology, 2026 doi:10.1007/s13197-026-06746-0.
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