Data Fusion Approaches as a Novel Strategy in Multivariate Analysis of Spectroscopic and Spectral Imaging Information for Non-destructive Food Microbial and Fungal Assessment

Microbial and fungal contamination of agricultural products as major safety challenges are known in recent years. Non-destructive methods for food quality assessment are warmly welcomed by the food industry. Spectroscopic and optical methods provide a large variety of measurement techniques like optical and near-infrared spectroscopy and imaging which have especially high potential for various food quality assessment. The integration of data and knowledge from several sources is known as data fusion. Data fusion approaches have been introduced as powerful and novel strategies for obtaining more reliable authentication models with respect to the results showed using each method separately. In this paper, we have shortly described the data fusion principles and the most prominent application examples in this rapidly growing strategy of knowledge in microbial and fungal quality assessment of agricultural food products.

Keywords:

Data fusion , Chemometrics , Microbial assessment , Food quality , Multi-way classification