Mohamad A Mohamed Razali Yaacob

Bio: Mohamad A Mohamed Razali Yaacob is an academic researcher from Universiti Putra Malaysia. The author has contributed to research in topics: Food safety. The author has an hindex of 1, co-authored 1 publications receiving 2 citations.

Data mining techniques in food safety

Abstract: Food safety is an important consideration because the reduced quality of freshness may result in food poisoning that can threaten our health. There are many methods used to test the freshness of food such as visual appearance as well using a variety of devices. One of the devices that can be used to test the freshness of food is the E-Nose. E-nose is an instrument that enables the discrimination of gas and odor in food industry for quality and safety purposes. It is a well-established instrument to detect odor and aroma not only in the food industry, but also in health-diagnosis, defense, and environmental industry. Generally, E-nose mimics human olfactory sense to detect and discriminate gasses or volatile organic compound from a few objects such as food, chemicals, explosive etc. Thus, E-nose can be used to measure gas emitted from food due to its ability to measure gas and odor. Principally, the E-nose operates by using a number of sensors to response to molecules from vaporous compound. Each sensor will respond to their specific gas respectively. These sensors are the major component in electronic nose to sense and obtain percentage of gases release by the compound samples. All gases detected by sensors will be recorded, that to be analyzed using classification method. Classification is a way to distinguish a mixture odor/aroma obtained from gas sensors using a method of machine learning. In this paper, we discussed briefly about electronic nose, it’s principle of work and classification method and in order to classify food freshness.

Application of Artificial Intelligence in Food Industry—a Guideline

Abstract: Artificial intelligence (AI) has embodied the recent technology in the food industry over the past few decades due to the rising of food demands in line with the increasing of the world population. The capability of the said intelligent systems in various tasks such as food quality determination, control tools, classification of food, and prediction purposes has intensified their demand in the food industry. Therefore, this paper reviews those diverse applications in comparing their advantages, limitations, and formulations as a guideline for selecting the most appropriate methods in enhancing future AI- and food industry–related developments. Furthermore, the integration of this system with other devices such as electronic nose, electronic tongue, computer vision system, and near infrared spectroscopy (NIR) is also emphasized, all of which will benefit both the industry players and consumers.

Smell identification using a thick-film hybrid gas sensor

Abstract: An analytical method and experimental results of identifying and quantifying smells using an electronic system composed of an integrated sensor and a microcomputer are described. The integrated sensor with six different elements on an alumina substrate was fabricated by using thick-film techniques. The elements are kept at around 400°C by a Pt heater mounted on the sensor back. Since each element was made from different semiconductor oxides, they possess different sensitivities to material odors and the integrated sensor can develop specific patterns corresponding to each odor as a histogram of conductance ratios for each element. The microcomputer identifies the scent on the basis of similarities calculated by comparing standard patterns stored in the memory and a sample pattern developed by the integrated sensor. The scent is then quantified by using the sensor element with the highest sensitivity to the smell identified. The experimental results show that smells can be successfully identified and quantified with the electronic System.