Plan for Food Inspection for Inflated-Pareto Data Under Uncertainty Environment

Abstract

The existing sampling plans for food inspection have been designed under classical statistics. These sampling plans are applied in the food industry under the assumption that all observations are determined, clear and certain. The neutrosophic statistics (NS) which is the generalization of classical statistics applied under uncertainty environment. In this paper, we propose one of the simplest acceptance sampling plans namely, a single sampling plan for inspecting the quality of the raw materials where the quality characteristic follows inflated Pareto distribution under the NS. The neutrosophic plan parameters are determined under the neutrosophic statistical interval method (NISM). We provide the range/interval of the sample sizes and acceptance criteria which satisfy both producer and consumer expectations. The advantages of the proposed plan are given. An example from the food industry is selected to explain the proposed plan. INDEX TERMS Food inspection, sampling plan, neutrosophic statistics, risks, classical statistics. SAMINATHAN BALAMURALI is currently a Full Professor with the Department of Computer Applications, Kalasalingam University. He has published more than 100 articles in national and international well-reputed journals. His field of research interest includes statistical quality control. JEYADURGA PERIYASAMYPANDIAN is currently pursuing the Ph.D. degree with the Department of Computer Applications, Kalasalingam University. She has published several articles in national and international well-reputed journals. Her field of research interest includes statistical quality control.

Exploring heterogeneity in meat consumption and eating out by using a latent class model

“…What certainly came as a surprise, however, was the fact that one class mostly consisted of children with an apparently very low amount of meat consumption. Future research should devote more attention to this phenomenon, maybe also including neutrosophic statistics (Aslam et al ., 2019a, b). If today's children eat significantly less meat than today's adults do, it gives hope that the environmental footprint of mankind's future diet could shrink.…”

“…However, we do not have one single variable in our dataset indicating the heterogeneity of consumption patterns or different consumer classes. Therefore, different consumer classes are latent or unobservable in the data structure (see Aslam et al ., 2019a, b; Al-Marshadi and Aslam, 2021, for a broader approach about such uncertainties). To estimate latent consumer classes based on observed (explanatory) normally distributed (Leiva et al.…”