What is it about?
This paper presents an analytical approach for assessing the quality of meat samples upon storage (24, 48, 72 and 96 hours). A novel machine learning-based method involving strategic selection of discriminatory features of segmented images has been proposed. The proposed method comprehends double-cross validation technique which makes it more comprehensive in discriminating between the edibility of foodstuff which can certainly reinstate conventional methods and ameliorate existing computer-vision methods.
Featured Image
Why is it important?
Nitrosamine is a chemical, commonly used as preservative in red meat whose intake can cause serious carcinogenic effects on human health. Identification of such malignant chemicals in foodstuffs is an ordeal. The objective of the proposed research work presents a meta-heuristic approach for nitrosamine detection in red meat using computer vision-based non-destructive method.
Perspectives
Experimenting red meat for quality check was a very challenging task. Applying image processing on the same made it even more challenging and at the same time extremely exciting. This article will definitely help food industry to perform the quality and safety of food items. Non-destructive quality analysis of red meat can be done in real-time which will ensure better human health.
Dr Monika Arora
EXL Service
Read the Original
This page is a summary of: A Meta-heuristic Approach for Design of Image Processing Based Model for Nitrosamine Identification in Red Meat Image, Recent Patents on Engineering, July 2020, Bentham Science Publishers,
DOI: 10.2174/1872212114999200719145022.
You can read the full text:
Contributors
The following have contributed to this page
