Analysis and Design of Adulteration Dairy Milk System

DOI

10.2991/978-94-6463-445-7_20How to use a DOI?

Keywords

dairy milk; dairy milk system; FAST technology

Abstract

Detection of milk adulteration can be done in several ways. The simplest and fastest one can be done without any tools, but the accuracy is limited since each milk cow produces is different in color. Furthermore, obtaining effective results requires a lot of effort and time; also, the observed milk cannot be reused for other analysis processes. Another approach to detecting milk adulteration is to consider several factors, such as pH, electrical conductivity (EC), and temperature. Research has been conducted using macroscopic observation utilizing IoT devices such as e-nose, e-tongue, pH, EC, NIR, and temperature sensors. Observation can also be done microscopically, but it takes a long time. To answer those problems, we used Framework for the Application of System Thinking (FAST) methodology. In this study, we tried to speed up the detection time for fake dairy milk using a digital microscope and smartphone. This research will discuss the design and architecture of a milk detection system. Microscopic images are taken by connecting a digital microscope to a smartphone as the first step in system design. The system is created using digital images captured by a microscope and processed using machine learning using a smartphone. This research begins with requirement analysis, business process design, and system architecture design and ends with prototype design.

Copyright

© 2024 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Download article (PDF)

TY  - CONF
AU  - Mohammad Zarkasi
AU  - Diah Ayu Retnani Wulandari
AU  - Saiful Bukhori
AU  - Yudha Alif Auliya
AU  - Tio Dharmawan
PY  - 2024
DA  - 2024/06/29
TI  - Analysis and Design of Adulteration Dairy Milk System
BT  - Proceedings of the 2nd International Conference on Neural Networks and Machine Learning 2023 (ICNNML 2023)
PB  - Atlantis Press
SP  - 190
EP  - 195
SN  - 1951-6851
UR  - https://doi.org/10.2991/978-94-6463-445-7_20
DO  - 10.2991/978-94-6463-445-7_20
ID  - Zarkasi2024
ER  -