Fuzzy logic is now a wide field of study and different tools have been developed over the last 10 years. Its implementation in food quality control for the food industry has been highlighted by several authors that have focused on different applications designed specifically for this task. This is especially true in the case of taking into account the reasoning process, expressed in linguistic terms, of operators and experts. Nevertheless, applications are still limited and few reviews on this topic are available. Consequently, the aim of this paper is to provide an overview of the application of fuzzy concepts to the control of the product quality in the food industry over the past 10 years. Future interesting developments and trends in this area are also emphasized.
In the food industry, end-products must achieve a compromise between several properties, including sensory, sanitary
and technological properties. Among the latter, sensory and sanitary properties are essential because they influence
consumer choice and preference. Nevertheless, managing these properties right fromthe fabrication stage with the aim
of controlling them is no easy task for several reasons:
• The food industry works with many parameters that must be taken into account in parallel. Asingle sensory property
like colour or texture can be linked individually to several dimensions registered by the human brain.
• The food industry works with non-uniform, variable raw materials that, when processed, should lead to a product
that satisfies a fixed standard.
• The phenomena involved in the processing are highly non-linear and variables are coupled.
• The food industry operates with very diverse processes and products and has requirements in terms of the portability
and adaptability of the systems developed.
• Little data are available in traditional manufacturing plants that produce, for example, sausage or cheese and this
situation is general throughout the food industry. Furthermore, even when databases do exist, it is not always possible
to use them for controlling food product quality.
In this context, despite the fact that the design of standards and reliable procedures for controlling the quality of products
is a major objective for the food industry, automation is limited:
(i) Few sensors are available to carry out such measurements. Although new sensors have been developed such as
artificial noses, the road is difficult and long and inaccessible for SMEs.
(ii) For several processes, it is difficult to established models sufficiently representative of the phenomenon involved,
even for control purposes.
(iii) Classical automated approaches are limited for the reasons mentioned below.
At present many production processes rely to a great extent on the skill and experience of the operator, something that
no system will be capable of replacing in the foreseeable future. Consequently, in practice, operators often play an
important role and cooperate with automation so as to (1) make on-line evaluations of the sensory properties of the
product and/or (2) adjust the on-line process. Moreover, experienced operators make macroscopic interpretations of
the physicochemical phenomena that appear during processing, which can act in synergy with classical engineering
knowledge on the process.
Integrating operator and expert skill in a control framework is a relevant direction, especially for traditional processes.
Nevertheless, it leads to designing mathematical tools that have to integrate (i) reasoning based on the use of linguistic
symbols such as “over-coated”, “good colour”, etc., expressed not on a numerical scale but on a discontinuous graduated
scale and referring to an evaluation of a deviation in comparison to a set point; (ii) an uncertainty on these symbols that
is translated after fusion in a specific action; and (iii) an action that is the result of an implicit or explicit interpolation
between two specific state recorded by the operator over time.
Fuzzy sets and possibility theories were introduced by Zadeh in 1965 [92] as an extension of the set theory by the
replacement of the characteristic function of a set by a membership function whose values range from 0 to 1. It is now
a wide field of study that has seen the development of different tools over the last 10 years. Applied to the control of
product quality in the food industry, it has been considered as pertinent by several authors for different applications
and especially for taking into account the reasoning process, expressed in linguistic terms, of operators and experts
[18,24,48,66,77,94]. Nevertheless, applications are still limited and few reviews on this topic are available.
In this framework, the aimof this paper is to provide an overviewof the application of fuzzy concepts for controlling
product quality in the food industry over the last 10 years.
The first papers on this topic appeared 15 years ago although the volume of literature really began to increase from
1996 (Fig. 1). All in all, 78 applications have been dedicated to this topic over the last 12 years.
This topic involves different subjects: (1) representation of the descriptive sensory evaluation performed by a quality
team, anoperator, or a consumer; (2) indirect measurement of the properties of a foodproduct; (3) diagnosis, supervision,
and control of food quality. The proportion of papers dedicated to each of these research fields is illustrated in Fig. 2,
which shows more than 80%of papers being dedicated to fields (2) and (3), thus they are well represented in comparison
As we have seen, fuzzy logic is used in food applications to (i) capture and formalise the descriptive sensory evaluation
performed by a quality team, an operator, or a consumer, (ii) develop an indirect measurement of the properties of a
food product, and (iii) control food processes. Fig. 6 presents a classification of the different papers written in this area
on the different research topics.
If we focus on the type of approaches developed (Fig. 7), on the one hand, 33 papers deal exclusively with data-driven
approaches including fuzzy PID. A total of 66%of these papers are above all dedicated to indirect measurement tasks,
while 34%are dedicated to the control and modelling of processes. This category represents only a small percentage out
of the total, which can be explained by a key difficulty in the food industry, especially in traditional manufacturing plants:
that of constituting databases that can be easily used for control purposes. On the other hand, “expert knowledge”-driven
approaches are dealt with in 35 papers. Mixed approaches are encountered in seven papers.
If we focus on the type of fuzzy concepts applied (Fig. 8), 74% of the applications dealt with stem from the fuzzy
set theory and most of them implement classical fuzzy logical functions (Mamdani type). On the contrary, the theory
of possibility is hardly used, being dedicated to a restricted task in only three of the total of 78 papers [39,37,23].
Nevertheless, the authors underline a really interesting and open field of research. For example, in De Silva et al. [23],
a firmness sensor for an automated herring roe grader is developed using fuzzy concepts. The theory of possibility is
used in this case to estimate the fuzzy membership functions of the fuzzy decision-making system. The results show
that this approach is more efficient than the use of classical trapezoidal membership functions.
