Application of an adaptive neuro_fuzzy inference system (ANFIS) in the modeling of rapeseeds' oil extraction

Abstract

In the present study, the temperature and moisture content of the output seeds of the cooking pot were considered as inputs or independent variables and the insoluble fine partial content of the extracted oil, moisture content of the extracted oil and obtained meals, as well as the oil content of the achieved meals and acidity value of the extracted oil were considered as responses and were designed. Three different activation functions, including Gaussian membership and triangular as well as trapezoidal were applied and studied. The trapezoidal function with a 3–3 membership function for the three output variables including insoluble fine partials of oil, oil acidity and moisture content of the meals as well as the triangle membership function with 2–2 and 3–3 functions, respectively, for moisture content of the extracted oil and the oil content of the obtained meals were evaluated and detected as optimized models in the current study. The above mentioned models demonstrated higher correlation coefficients (R2) between the experimental and predicted values and the lowest root mean squared errors, confirming the adaptability of the applied models in the present study. Practical applications Today, because of the high demand for crops for extensive application in the human diet, increases in the efficiency of the processing are attracting much more attention. In this regard, discovering and detection of the optimized conditions for processing with the minimum wastes looks very important and economic. Therefore, the uses of predictive methods in different food processes have been considered appropriate tools for improving of the efficiency of the processes as well as the enhancement of the quality of the produced products. In this respect, the ANFIS design as a novel predictive analytic tool, along with Response Surface Methodology (RSM) and Artificial Neural Network (ANN), are applied extensively. Thus regarding the above mentioned content, ANFIS design was applied to predict and optimize some of the selected physico‐chemical properties of the extracted oil through the extraction process. Therefore prediction of the optimized conditions of oil extraction could improve the quality of the extracted oil and performance of the extraction process with the minimum wastes during short and logical extraction time.

Oxidative stability of virgin olive oil: evaluation and prediction with an adaptive neuro‐fuzzy inference system (ANFIS)

“…We therefore suggest applying the ANFIS model with generalized bell MFs to predict the oxidative stability of VOO. Similar results were found in studies conducted by several groups of scientists, applying artificial ANFIS to model the edible oil industry, for instance, Prediction impact of curcumin on soybean oil with a correlation coefficient (R 2 ≥ 0.98), prediction of rapeseeds oil extraction with a correlation coefficient (R 2 ≥ 0.998), fatty acid profile of vegetable oils based on rheological analysis with correlation coefficient (R 2 ≥ 0.995), effect of natural antioxidant added on hazelnut oil oxidation with correlation coefficient (R 2 ≥ 0.996), oxidation state of purified kilka fish oil with correlation coefficient (R 2 ≥ 0.993) …”

“…The application of the ANFIS in food science has been reported by several researchers in the field. It was used to predict the fatty acid profiles of vegetable oils, in rapeseed oil extraction, and for estimating the effect of antioxidant activity on hazelnut oil oxidation, and the oxidation behavior of purified kilka fish oil …”

Modeling of Linear and Non-linear Compression Processes of Sunflower Bulk Oilseeds

“…In the literature, it is obvious that many models have been applied extensively on the modeling of oilseeds or food processing engineering aimed at understanding the aerodynamics and biophysical or physical properties as well as optimizing the processing parameters. Some of these models include response surface methodology, artificial neural network, adaptive neuro-fuzzy inference system, fuzzy logic design [43][44][45][46][47][48][49]. However, the tangent curve mathematical model applied in this present study and previously published studies show reliability for describing the linear and non-linear compression processes of bulk oilseeds based on the experimental or model boundary conditions [35][36][37].…”

Babassu Oil (Orbynia phalerata), an Artisanal Product: Process Optimization of Seed Roasting on Yield, Phenolic Compounds, and Antioxidant Capacity

“…It may be related to favoring the disruption of the structure and cell membrane to which lipids can be associated and thereby increasing the availability of oil and consequently, the yield of the final product. [ 29 ]…”