Abstract
Recently, machine vision is gaining attention in food science as well as in food industry concerning food quality assessment and monitoring. Into the framework of implementation of Process Analytical Technology (PAT) in the food industry, image processing can be used not only in estimation and even prediction of food quality but also in detection of adulteration. Towards these applications on food science, we present here a novel methodology for automated image analysis of several kinds of food products e.g. meat, vanilla crème and table olives, so as to increase objectivity, data reproducibility, low cost information extraction and faster quality assessment, without human intervention. Image processing’s outcome will be propagated to the downstream analysis. The developed multispectral image processing method is based on unsupervised machine learning approach (Gaussian Mixture Models) and a novel unsupervised scheme of spectral band selection for segmentation process optimization. Through the evaluation we prove its efficiency and robustness against the currently available semi-manual software, showing that the developed method is a high throughput approach appropriate for massive data extraction from food samples.
Highlights
Food spoilage may be defined as the outcome of the biochemical activity of microbial processes, which will eventually dominate [1,2]
The whole system, is developed in order to guarantee the reproducibility of the collected images, and so it can be used in comparative studies of time series studies, or across a large variety of different samples
A first analysis is done based on the informative areas sizes
Summary
Food spoilage may be defined as the outcome of the biochemical activity of microbial processes, which will eventually dominate [1,2]. The factors that can influence the microbial activity and as a consequence, contribute in the food deterioration can be distinguished in two categories: (i) intrinsic (e.g. water activity, acidity, redox potential, available nutrients and natural antimicrobial substances) and (ii) extrinsic (storage conditions of temperature, humidity, atmosphere composition and packaging) [2,3]. Following this inherent complexity in food quality assessment, the acquisition of reliable information is a major challenge of the food industry, throughout the production, processing, and distribution chain [2,4,5,6]. A promising new technique is the use of multi- and hyper-
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