Milk Assessment using Potentiometric and Gas Sensors in Conjunction With Neural Network

IoT-Enabled Electronic Nose System for Beef Quality Monitoring and Spoilage Detection

“…However, most of the e-nose systems were designed to monitor the gases produced by either a specific type of food (i.e., chicken or meat) or by a limited selection of perishables, such as climacteric fruits. For instance, J. Brezmes et al [42] and L. Ma et al [43] highlighted e-nose systems that mainly monitor ethylene gas production to evaluate the ripeness levels and shelf life of a variety of fruits (i.e., apples, peaches, nectarines, pears, and kiwifruit), M. A. Putra [44] developed an e-nose system to assess the quality of milk by monitoring the NH 3 and H 2 S levels, and Rivai et al [29] evaluated meat freshness by monitoring the H 2 S and NH 3 production [29,[42][43][44]. Therefore, we developed a cost-effective, noninvasive, and versatile food quality monitoring system that can evaluate the freshness of a variety of food items, including meats, fruits, and vegetables.…”

Milk Purity Recognition Software through Image Processing

“…For the detection of adulterated milk with water, there are many jobs as in [5] where an efficiency of 95% was obtained through the study of the density of the sample; as well as in [6], where a 99% success rate was obtained using sensors and infrared light; in [9] they show an accuracy of approximately www.ijacsa.thesai.org 95% with a deviation of 1%; there are research as in [2] that by carrying out a study of the electrical impedance they obtain an efficiency of 94.9% applying the KNN algorithm; in [12] a distinction is made between milk of three categories, cow's milk, buffalo milk, skim milk, obtaining an accuracy of approximately 95%; finally in [13] they mention that they have obtained an efficiency percentage of 83% using neural networks; in our case, it obtains similar results by applying the KNN method, image processing and Classification Learner application, which it obtains efficiency in the classification of 95.4%.…”

2.3 - MOS Sensors for Spoilage Detection of Milk using GC-MS and Human Perception as Reference

“…Besides microbiological methods, that often include use of a growth medium and incubation times (often several hours) [4] and are, thus, not suitable for rapid testing, spoilage of milk can be measured, e.g., via its pH value [5] or using infrared spectroscopy [6]. The use of different kinds of gas sensors or sensor arrays to measure gases or volatile organic compounds (VOCs) which are, e.g., produced by microorganisms involved in spoilage was also reported [7,8], as well as investigations of actual milk spoiling over time using gas sensor arrays [9,10]. Measuring microbial (by)products is closely related to the mentioned spoilage detection using the human nose, and it is supposed to be conclusive as the concentration of several VOCs was reported to correlate with microbial counts in milk samples under various storage conditions [11].…”