Optimization of the pistachio nut roasting process using response surface methodology and gene expression programming

Abstract

Roasted pistachio nuts are consumed as snack foods and used as ingredients in confectionery, chocolates and ice-cream industries. Response surface methodology (RSM) and Gene Expression Programming (GEP) were used to optimize the roasting process for production of the pistachios in shell, kernel, and ground-kernel forms over a range of temperature (100–180 °C) and for various times (10–60 min). The moisture content and color parameters (L, a, b and yellowness index (YI)) were evaluated during roasting and modeled by RSM and GEP. The moisture content changes of the pistachios during roasting were successfully described by RSM and GEP models. The results showed that the L, a and b values could be used as parameters for the development of the predictive models during roasting of in shell pistachios, but the color of kernel and ground-kernel pistachios could be monitored by measuring only a and a, b values, respectively. The quadratic models developed by RSM adequately described the changes in selected color parameters during roasting. The GEP models were found to be slightly better than RSM models. The response surface of desirability function was used successfully in optimization procedure of pistachio nut roasting.

Introduction

Pistachio nut (Pistacia vera L.), known as green gold due to its high economical value, is one of the deciduous and popular nuts in the world (Tokatli, Ozudogru, & Akcin, 2005). The global pistachio production volume increases steadily, estimated by FAO as 489,000 Mt in 2005 having risen from 191,000 Mt in the 1985 (FAO, 2006). The elevation in the global production is probably due to its good nutritional properties and the reports on its health-promoting effects (Kocyigit, Koylu, & Keles, 2006) and this rising entails more researches on the pistachio nut processing, which determines the final quality as food product. A large percentage of pistachios are consumed as roasted product. In pistachio, the characteristic aroma, color, and texture are developed during roasting, which is the crucial step in the pistachio nut processing as in the case of other nuts and coffee processing (Nicoli, Anese, Manzocco, & Lerici, 1997; Pittia, Rosa, & Lerici 2001; Saklar, Katnas, & Ungan, 2001). Roasting is also one of the effective physical methods to reduce aflatoxins content in pistachio nuts (Yazdanpanah, Mohammadi, Abouhossain, & Cheraghali, 2005). In the literature, there are some studies on the effect of roasting on pistachio nut quality (Koroglu, Okay, & Koksal, 2000), on the chemical constituents (Kashani & Valadon (1983), Kashani & Valadon (1984); Luh, Wung, & El-Shimini, 1982). Also the sorption behavior and drying process of pistachio were studied (Maskan & Karataş, 1999; Midilli & Kucuk, 2003; Nejad, Tabiil, Mortavazi, & Kordi, 2003).

At present, the control of pistachio roasting process relay on the large extent of skill and experience of operators rather than the utilization of a clear scientific understanding of the process. In reality, the transition from “art” to “science” is still objective for many small to medium food enterprises (McGrath, O’Connor, & Cummins, 1998). The novel and classical computational methods are extensively used for the “art” to “science” transformation and to simulate the food process. Two major types of models are recognized. Empirical models are derived from an essentially pragmatic perspective. They simply describe the data in convenient mathematical relationship and consequently often give little or no insight into underlying process. Mechanistic, or deterministic, models are built up from theoretical bases and, if they are correctly formulated, may allow the interpretation of modeled response in terms of known phenomena and processes (McMeekin, Olley, Ross, & Ratkowsky, 1993). Response surface methodology (RSM) and artificial intelligence techniques (such as fuzzy logic, neural networks and genetic algorithms (GA)) are also empirical models widely used in modeling of food processing due to the complexity of reactions and non-homogeneous structure of food products.

The effectiveness of RSM in optimization of processing conditions in food technology from raw to final products has been documented in the literature (Gan et al., 2007; Madamba, 2002) and it has been successfully applied to many roasting process modeling and optimization problems. Saklar et al. (2001) used the RSM for the determination of the optimum hazelnut roasting conditions, Ozdemir and Devres (2000) also obtained successful predictive models for describing the color changes in hazelnuts during roasting. Similarly, Kahyaoglu and Kaya (2006) reported the optimum roasting ranges for hulled sesame seed could be found using RSM.

Besides statistical techniques as RSM, recently soft-computing methods such as fuzzy logic, neural network and genetic algorithms offer novel solutions to improve the control and modeling tools in food processing. In the literature, there are many studies on the use of neural networks in use of food process modeling and optimization (Liu, Chen, Wu, & Peng, 2007). Trelea, Courtois, and Trystram (1997) used neural networks for modeling the moisture content of corn during thin-layer drying process. Davidson, Brown, and Landman (1999) developed a successful fuzzy control system for continuous, cross-flow peanut roasting. Genetic Programming (GP), one of the soft-computing methods, is derived from GA that simulates biological evolution process (evolutionary algorithms). GEP, which is an extension of GP, uses population of individuals (populations of model and solutions), selects and reproduces them according to fitness, and introduces genetic variations one or more genetic operators such as mutation or recombination (Ferreira, 2006). In this research, GEP software is used for the evolutionary algorithms.

This study aimed to characterize and model the moisture and color changes of the pistachio nut in various forms during roasting processing order to establish optimum operating conditions. RSM and GEP were used for modeling.