An Artificial Intelligence Approach Toward Food Spoilage Detection and Analysis

Aiming to increase the shelf life of food, researchers are moving toward new methodologies to maintain the quality of food as food grains are susceptible to spoilage due to precipitation, humidity, temperature, and a variety of other influences. As a result, efficient food spoilage tracking schemes are required to sustain food quality levels. We have designed a prototype to track food quality and to manage storage systems at home. Initially, we have employed a Convolutional Neural Network (CNN) model to detect the type of fruit and veggies. Then the proposed system monitors the gas emission level, humidity level, and temperature of fruits and veggies by using sensors and actuators to check the food spoilage level. This would additionally control the environment and avoid food spoilage wherever possible. Additionally, the food spoilage level is informed to the customer by an alert message sent to their registered mobile numbers based on the freshness and condition of the food. The model employed proved to have an accuracy rate of 95%. Finally, the experiment is successful in increasing the shelf life of some categories of food by 2 days.

Phytochemical compositions and antimicrobial activity of leaf extracts of Calotropis procera against food spoilage microorganisms

Background: The purpose of this study was to determine the phytochemical composition and antimicrobial potential of crude n-hexane, ethyl-acetate, methanol and aqueous extracts of Calotropis procera leaves against food spoilage microorganisms. Methods: Standard protocols were employed for the analysis of qualitative phytochemical compositions of the extracts, and antimicrobial activities against Staphylococcus aureus, Bacillus cereus, Pseudomonas aerugenosa and Aspergillus niger. Results: Phytochemical analysis revealed the presence of tannin, saponin, alkaloids, flavonoids, reducing sugar and phenolics. Terpenoids were absent in ethyl acetate and n-hexane extracts while cardiac glycoside was absent in all extracts. All extracts produced antimicrobial activity at a varying zone of inhibition. The widest inhibition zone was produced by methanol extract (21.35±0.43 mm) on staphylococcus aureus while the lowest inhibition zone (12.05±0.45 mm) was observed in the n-hexane fraction. Similarly, the widest inhibition zone (17.24±0.95 mm) was produced by methanol on A. niger while the lowest inhibition zone (5.45±0.42 mm) was produced on n-hexane on A. niger. However, the ethyl acetate extract showed no visible inhibitory zone on all the tested microorganisms. The minimum inhibitory concentration ranged from 32 mg/ml (S. aureus and B. cereus) for ethanol extracts to 128 mg/ml (B. cereus, P. aerugenosa and A. niger) for n-hexane extract. Conclusion: The result shows that the plant is a good source of bioactive compounds that can be used as a natural alternative to a chemical agent in preserving and controlling food poisoning organisms.

A Framework for an Artificial-Neural-Network-Based Electronic Nose

Machine odor detection has developed into an important aspect of our lives with various applications of it. From detecting food spoilage to diagnosis of diseases, it has been developed and tested in various fields and industries for specific purposes. This project, artificial-neural-network-based electronic nose (ANNeNose), is a machine-learning-based e-nose system that has been developed for detection of various types of odors for a general purpose. The system can be trained on any odor using various e-nose sensor types. It uses artificial neural network as its machine learning algorithm along with an OMX-GR semiconductor gas sensor for collecting odor data. The system was trained and tested with five different types of odors collected through a standard data collection method and then purified, which in turn had a result varying from 93% to 100% accuracy.

Misconception of consumers and retailers regarding fungal spoilage of onion and prevalence of fungi in rotten onion

Onion is an inevitable part of our food habits. Fungal infection is one of the major reasons of onion spoilage which not only cause economic losses but also may cause public health threat through mycoses and mycotoxicoses. A total 15 onion samples from different places in Chattagram, Bangladesh were collected to assess the fungal contaminants.Onion consumers along with retailers were interviewed to evaluate their awareness about fungal food spoilage and associated health risk. Three different fungal species were identified and isolated by analysing their macroscopic and microscopic features. These isolates were Aspergillus niger, Aspergillus flavus, and Penicillium spp. A. niger was the most prevalent and found in 14 samples out of 15. Biochemical characterization of the isolated fungi was also done to assess their ability to produce extracellular enzymes and amylase, protease, and cellulase activities were observed. Survey data showed that only around 20% of the interviewees had some idea about fungal contamination, while nearly about 80% of them believed that washing, sunburn and cooking can make the food safe. Bioresearch Commu. 8(1): 1068-1076, 2022 (January)