Predicting the yield of pomegranate oil from supercritical extraction using artificial neural networks and an adaptive-network-based fuzzy inference system

Sargolzaei, Javad; Moghaddam, Amin Hedayati

doi:10.1007/s11705-013-1336-3

Cited by 14 publications

(5 citation statements)

References 30 publications

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“…The work in [6] compares simulation systems for predicting the mass yield of pomegranate oil from super-critical extraction. The pomegranate seed contains oil with lipids on a dry basis ranging from 66g to 193g per kg of fruit.…”

Section: Related Workmentioning

confidence: 99%

“…Given the temperature, extraction time, and solvent of an extraction process, the proposed architecture makes use of our fuzzy sets and fuzzy rules in a fuzzy inference method to return the predicted mass yield. The use of fuzzy methods has been a promising approach when applied to other organic matrices, such as seeds of almond [4], sandbox [5], pomegranate [6], and dragon fruit peel [7]. In this paper, we expand the applicability of fuzzy inference methods to another organic matrix that is important for the food industry.…”

Section: Introductionmentioning

confidence: 99%

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Applying Fuzzy Inference Systems in the Extraction of Chia Cake Extract: Predicting the Mass Yield

Johann

Santos

Montanher

et al. 2021

2021 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)

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Chia extract has been increasingly used in the food industry since it is rich in bioactive compounds, such as fatty acids, omega-3 fatty, antioxidants, proteins, vitamins, minerals, and dietary fiber. This extract can be obtained by using conventional extraction techniques (e.g., pressure) on chia seeds. Unfortunately, such techniques are insufficient to access all chemical components present in the seeds matrix, producing a by-product named chia cake that is usually discarded. On the other hand, since chia cake contains significant nutraceutical properties, it is still viable and beneficial to perform extractions of chia extract from chia cake. A typical objective of an extraction is to gather a high mass yield of chia (cake) extract. Since the extraction process is complex and expensive (e.g., in terms of laboratory resources), there is an increasing interest in determining the mass yield based on variables of the extraction like temperature, extraction time, and solvent. In this paper, we study the viability of applying traditional fuzzy inference systems (e.g., based on Mamdani's method) and adaptive neurofuzzy inference systems (ANFIS) for this problem. We propose a fuzzy inference architecture that predicts the mass yield of chia cake extract based on temperature, extraction time, and solvent. Our architecture makes use of fuzzy sets and fuzzy rules in the context of fuzzy inference methods. To design them, we create and use a dataset that contains the mass yield of real extractions conducted in the laboratory under different configurations. Hence, it represents another contribution of this paper and serves as the needed foundation to build the proposed architecture. Further, we conduct a performance evaluation to choose the fuzzy inference system that better fits the architecture. Based on our analysis, ANFIS was the best inference method since it delivered the lesser errors and greater correlations between predicted and observed values. We conclude that fuzzy inference systems are powerful tools for the food industry since they can capture the intrinsic imprecise nature of the extraction process, model the existing non-linear relations of the variables, and represent the expert domain knowledge.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Applying Fuzzy Inference Systems in the Extraction of Chia Cake Extract: Predicting the Mass Yield

Johann

Santos

Montanher

et al. 2021

2021 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)

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“…In SC‐CO 2 extraction, the extraction pressure was shown to be the dominant factor to affect the PSO yield (Guangmin and others ), and the PSO possessed high contents of PA and γ‐tocopherol which were slightly increased by increasing extraction pressure and temperature. Sargolzaei and Moghaddam () developed an effective intelligent system for predicting the effects of temperature and pressure on PSO yield by SC‐CO 2 process and reported that properly developed back‐propagation neural network and radial basis function neural network could be used as successful predicting tools for SC‐CO 2 oil extraction. Ahangari and Sargolzaei () reported the superiority of PSO extracted with hexane over SC‐CO 2 and subcritical propane extracted PSO.…”

Section: Effect Of Extraction Proceduresmentioning

confidence: 99%

Health Benefits of Punicic Acid: A Review

Pamisetty

Venkataramanamma

Singh

et al. 2015

Comp Rev Food Sci Food Safe

118

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Punicic acid (PA) is a polyunsaturated fatty acid (18:3 n-5), which is classified as a conjugated linolenic acid. PA is also referred as a "super CLnA" whose effect is even more potent than that of an ordinary CLnA. It is found mainly in the seeds of pomegranate fruit (Punica granatum) and Trichoxanthes kirilowii and some other minor sources. It possesses a wide array of biological properties including antidiabetic, antiobesity, antiproliferative, and anticarcinogenic activity against various forms of cancer. In spite of this, PA has not been explored as a nutraceutical or as an ingredient of food products which can be aimed at specific consumer target groups. This review details the various health-beneficial properties of PA and explores the possibilities of its utilization as an active ingredient in various food products.

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“…Table-3 describes the and mass of the extract 62,63 . Some studies even compare the results of ANN and ANFIS implementation to identify the best system that can represent the data and is reliable for optimization processes research that applies ANFIS and its hybrid for yield and solubility predictions is shown in Table Table- represent the data and is reliable for optimization processes [53][54][55]63 . A detailed description of the prior research that applies ANFIS and its hybrid for yield and solubility predictions is shown in Table 56 Glycyrrhizic…”

Section: Fuzzy Inference System (Anfis)mentioning

confidence: 99%

AN OVERVIEW OF THE Gynura procumbens LEAVES EXTRACTION AND POTENTIAL OF HYBRID PREDICTIVE TOOLS APPLICATION FOR PREDICTION AND SIMULATION IN SUPERCRITICAL FLUID EXTRACTION

Idris

Markom²

2019

RJC

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Supercritical Fluid (SCF) technology has been applied in many areas, such as the pharmaceutical and food sectors due to its outstanding features. It is an efficient technology that performs extraction and leaves none or less organic residues compared to conventional processes. Recently, the simulation and prediction of process output from supercritical fluid extraction (SFE) have been determined using intelligent system predictive tools, such as artificial neural networks. The prediction of the set of results from SFE for designing and scale up purposes is because apart from reducing the usage of extraction solvent, and the energy and time of the process, it can also generate a solution for problems that a complex mathematical model cannot solve. For example, the prediction of solubility is important because this particular fundamental value contributes to the optimizing process. A neural network is considered as one of the artificially intelligent systems, and furthermore a key technology in Industry 4.0. Moreover, the use of hybrid predictive tools is also a developing area in the prediction and simulation of supercritical fluid extraction (SFE), which would be discussed further in this paper. Currently, a limited number of studies related to the analysis of processing technology on extracting Gynura procumbens leaves can be found. Most of the research was focused on pre-clinical studies of Gynura procumbens extracts using conventional methods to prove the effectiveness of the herbal product application. Therefore, this overview will discuss and describe previous studies using Gynura procumbens and several recommendations for subsequent analysis.

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Predicting the yield of pomegranate oil from supercritical extraction using artificial neural networks and an adaptive-network-based fuzzy inference system

Cited by 14 publications

References 30 publications

Applying Fuzzy Inference Systems in the Extraction of Chia Cake Extract: Predicting the Mass Yield

Applying Fuzzy Inference Systems in the Extraction of Chia Cake Extract: Predicting the Mass Yield

Health Benefits of Punicic Acid: A Review

AN OVERVIEW OF THE Gynura procumbens LEAVES EXTRACTION AND POTENTIAL OF HYBRID PREDICTIVE TOOLS APPLICATION FOR PREDICTION AND SIMULATION IN SUPERCRITICAL FLUID EXTRACTION

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