Determining food safety in canned food using fuzzy logic based on sulphur dioxide, benzoic acid and sorbic acid concentration.

Canned food market demand has arisen due to the higher need for instant and ready-to-eat food. Food preservatives are often added to canned and processed foods to prolong their shelf life and help to sustain the quality, taste, color, and food texture. However, excessive usage of such food preservatives can lead to various diseases and health issues including palpitations, allergies, and cancer. Therefore, food preservative detection in food samples is essential for safe consumption and health well-being. This paper proposed a fuzzy logic framework to determine the safety of food products based on the concentration of sulphur dioxide (SD), benzoic acid (BA), and sorbic acid (SA) in five different food categories as referred to the Food Acts 1983 and Food Regulations 1985 in Malaysia. The fuzzy logic framework comprises of Mamdani inference system design with 90 fuzzy rules, 15 and 5 membership functions for both the input and output parameters respectively. 50 random values and 10 lab analysis results based on the industrial samples were used to validate the developed algorithms in ensuring the safety of the food products. The membership functions generated for the three inputs (SD, BA, and SA) during the fuzzification steps are based on the maximum allowable limit from the food acts. The defuzzification of fuzzy logic gave an average output value of 0.1565, 0.1350, 0.1150, 0.1100, and 0.1550 for chicken curry with potatoes, satay sauce, sardine in tomato sauce, anchovies paste, and sardine spread accordingly. Results obtained from the fuzzy logic framework concluded that all the industrial samples are safe to be eaten and comply with the Sixth Schedule, Regulation 20 in both Acts.

Multifactor assessments to determine the overall performance of supercritical fluid extraction from Gynura procumbens essential oil.

Gynura procumbens is a medicinal herb that contains bioactive compounds that can relieve coughs and prevent liver cancer. Supercritical fluid extraction (SFE) was suggested as one of the techniques that can be used to extract the valuable compounds from the G. procumbens. SFE was widely applied in extracting medicinal ingredients from herbs. However, most of them were performed only at the laboratory scale. Moreover, study to increase the yield performance, economic studies and safety assessments of the SFE process were also performed; however, these tests were conducted individually. Moreover, to date, there is no integration study between all the factors stated for determining the overall performance of SFE with herbs specifically G. procumbens. The integration between all the factors is beneficial because the data on the overall performance can assist in developing the SFE process with G. procumbens at the pilot or industrial scale. Therefore, this study incorporated a multifactor approach to measure the overall performance of the SFE process towards G. procumbens by using a rating and index approach. A summary of factors, such as the solubility of G. procumbens in CO2, operational cost and safety assessment elements, were taken into consideration as the main influences that determine the overall performance index of this study. Iperformance or overall performance of SFE from G. procumbens was successfully assessed and compared with response surface methodology (RSM). Overall, the results from Iperformance exhibit satisfactory solubility values when compared to the optimized value from RSM when considering the lowest operational costs in the safest SFE environment.

Tropospheric Ozone Formation Estimation in Urban City, Bangi, Using Artificial Neural Network (ANN).

Due to the rapid development of economy and society around the world, the most urban city is experiencing tropospheric ozone or commonly known as ground-level air pollutants. The concentration of air pollutants must be identified as an early precaution step by the local environmental or health agencies. This work aims to apply the artificial neural network (ANN) in estimating the ozone concentration forecast in Bangi. It consists of input variables such as temperature, relative humidity, concentration of nitrogen dioxide, time, UVA and UVB rays obtained from routine monitoring, and data recorded. Ten hidden layer is utilized to obtain the optimized ozone concentration, which is the output layer of the ANN framework. The finding showed that the meteorology condition and emission patterns play an important part in influencing the ozone concentration. However, a single network is sufficient enough to estimate the concentration despite any circumstances. Thus, it can be concluded that ANN is able to give reliable and satisfactory estimations of ozone concentration for the following day.