مفاهیم فازی اعمال شده به محصول مواد غذایی کنترل کیفیت: نقد و بررسی
|کد مقاله||سال انتشار||تعداد صفحات مقاله انگلیسی||ترجمه فارسی|
|4742||2006||10 صفحه PDF||سفارش دهید|
نسخه انگلیسی مقاله همین الان قابل دانلود است.
هزینه ترجمه مقاله بر اساس تعداد کلمات مقاله انگلیسی محاسبه می شود.
این مقاله تقریباً شامل 5594 کلمه می باشد.
هزینه ترجمه مقاله توسط مترجمان با تجربه، طبق جدول زیر محاسبه می شود:
- تولید محتوا با مقالات ISI برای سایت یا وبلاگ شما
- تولید محتوا با مقالات ISI برای کتاب شما
- تولید محتوا با مقالات ISI برای نشریه یا رسانه شما
پیشنهاد می کنیم کیفیت محتوای سایت خود را با استفاده از منابع علمی، افزایش دهید.
Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)
Journal : Fuzzy Sets and Systems, Volume 157, Issue 9, 1 May 2006, Pages 1145–1154
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  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
نتیجه گیری انگلیسی
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. , 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.