A Meta-heuristic Approach for Design of Image Processing Based Model for Nitrosamine Identification in Red Meat Image

Abstract

Background: Nitrosamine is a chemical, commonly used as a preservative in red meat whose intake can cause serious carcinogenic effects on human health. The identification of such malignant chemicals in foodstuffs is an ordeal. Objective: The objective of the proposed research work presents a meta-heuristic approach for nitrosamine detection in red meat using a computer vision-based non-destructive method. Methods: 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 the strategic selection of discriminatory features of segmented images has been proposed. The significant features were determined by finding p-values using the Mann-Whitney U test at a 95% confidence interval, which were classified using partial least square-discriminant analysis (PLS-DA) algorithm. Subsequently, the predicted model was evaluated by the bootstrap technique, which projects an outline for preservative identification in meat samples. Results: The simulation results of the proposed meta-heuristic computer vision-based model demonstrate improved performance in comparison to the existing methods. Some of the prevailing machine learning-based methods were analyzed and compared from a survey of recent patents with the proposed technique in order to affirm new findings. The performance of the PLS-DA model was quantified by the receiver operating characteristics (ROC) curve at all classification thresholds. A maximum of 100% sensitivity and 71.21% specificity was obtained from the optimum threshold of 0.5964. The concept of bootstrapping was used for evaluating the predicted model. Nitrosamine content in the meat samples was predicted with a 0.8375 correlation coefficient and 0.109 bootstrap error. Conclusion: The proposed method comprehends the 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.