Food quality evaluation in drying: Structuring of measurable food attributes into multi-dimensional fuzzy sets

Abstract

Food quality is a fuzzy category, which could be evaluated using fuzzy logic. Our approach to food quality evaluation is based on mapping food quality attributes into a fuzzy domain as a multi-dimensional fuzzy sets. First, the data representing quality attributes are mapped into orthogonal coordinates using PCA to reduce dimensionality. Second, subtractive clustering (SC) is applied to determine a representative number of clusters. Each point in the dataset is associated with each cluster by credibilistic fuzzy C-means clustering (CFCM). After data organized in fuzzy clusters, an artificial neural network (ANN) is trained to associate each point in the dataset with its membership degree in each cluster. Trained ANN serves as a predictive model to convert real-time data stream into the multi-dimensional fuzzy domain. The application of this methodology is illustrated for real-time quality evaluation in shrimp batch drying. In this study 27 quality attributes have been merged into 9 orthonormal vectors, which have been clustered into 10 fuzzy sets. This structuring of the experimental fuzzy domain allowed the development of a multi-dimensional kinetic model, which improved the quality of shrimp drying. The computational time for quality identification in the fuzzy domain is below 1 sec, which is satisfactory for most real-time applications. This data-driven algorithm is completely automated and has unlimited potential for real-time fuzzy control and optimization.

Highlights

• Multi-dimensional fuzzy sets are unique identifiers of food quality

• Extracting principal information in orthonormal coordinates

• Using the artificial neural network for predicting membership functions

• A multi-dimensional fuzzy kinetics model was developed

• Structuring of fuzzy domain decreased computational time for fuzzy control

Keywords:

• Food quality control
• principal component analysis
• fuzzy clustering
• artificial neural network

Disclosure of Interest Statement

The authors report no conflicts of interest. The authors alone are responsible for the content and writing of the paper.

Notes

1 Fuzzy and crisp logic are different. Crisp logic is binary, assuming decision to be certain - either yes (1) or no (0). On the other hand, the fuzzy logic allows decision to be in the range between 0 to 1, which reflects uncertainty and risks, associated with decision-making.

Additional information

Funding

Canadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada.