## 1 Introduction  
The transport phenomena, mechanical behavior, and sensory characteristics of food products depend on their structure and how they are modified during the production processes, which conforms to the structure-property-process relationships [1, 2]; see Figure 1. Examples of characteristics analyzed in food products in the course of processing include, for instance, the level of oil absorption during frying of tortilla chips [3], the presence of pathogens in papaya fruits [4], among others. Hence, relationships in Figure 1 are important research areas in food engineering, due to their possible use in the development of models to predict the properties of food products at different structural levels (i.e., molecular, microscopic, mesoscopic, and macroscopic scales) [5]. The main aim of this research is to construct classification models of food products and process for the morphological analysis of microstructures [2].  
The structure of foods of vegetable-origin is determined by how cells, intercellular spaces, and interconnections are distributed in the whole food [5; 6]. Although visual inspection of microstructures commonly provides rich information to understand the behavior of food products, it requires time and effort that can be alleviated by automated systems. Consequently, it is necessary to develop techniques and methodologies to analyze the distribution of the different microstructural elements in food tissues.  
The techniques that are commonly used for microstructural analysis are performed over micrographs obtained by optical, electronic, confocal, or atomic force microscopes. However, this analysis process is usually highly complex because plant tissues have interconnected structural elements; see Figure 2. Therefore, some authors have proposed that this task could be carried out semi-automatically by combining computing capabilities through specialized software and trained operators [6; 7; 8; 9].  
Researchers such as [PERSON] et al. [9] or [PERSON] and [PERSON] [6] have tested the feasibility of machine learning techniques as neural networks and Bayesian networks, respectively, to discriminate microstructural elements in apple tissues. In both cases, the  
Figure 1: Relationships in process conditions, as well as food structure and property.  
Figure 2: Representation of cells (C) and intercellular spaces (ICS) in vegetal tissue.  
discrimination was performed using the morfogeometric parameters in a semi-automatized manner and obtained around 90% accuracy.  
More recently, deep learning techniques have shown good performance in pattern recognition literature, using different types of neural networks, such as Convolutional Neural Networks (CNNs) [10; 11]. These types of neural networks have been applied to discriminate between normal and abnormal red blood cells [12], cells infected with malaria [13], among other applications. The architecture of a neural network is suitable to code enough information to extract different characteristics that represent the elements to be classified directly from the samples [10]. However, up to now, neural networks have not been reported to be used to discriminate micro-structures in plant-based food.  
In this paper, classification models based on radial basis neural networks and convolutional neural networks for the discrimination of microstructures in vegetal foods are compared. The paper is organized as follows: The materials and experimental methodology used in the comparison are described in Section 2. Section 3 contains the experimental results and the discussion of the relevant findings and their impact on practice. Finally, Section 4 draws the conclusion and makes some recommendations for future work.