Screening of the aerodynamic and biophysical properties of barley malt

Ghodsvali, Alireza; Farzaneh, Vahid; Bakhshabadi, Hamid; Zare, Zahra; Karami, Zahra; Mokhtarian, Mohsen; Carvalho, Isabel S.

doi:10.1515/intag-2016-0017

Cited by 10 publications

(6 citation statements)

References 20 publications

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“…(2009) found that extrusion treatment could change the distribution of the molecular weight and the ratios of the (1/3) and (1/4) chemical bonds in oat bran (OB) SDF. Accumulating evidence has identified that some thermal processing can increase the extraction rate of nutrients and increase their functionality and improve the quality of food (Dolatabadi et al., 2016; Farzaneh and Carvalho, 2017; Ghodsvali et al., 2016; Jabrayili et al., 2016; Ozyurt and Ötles, 2016; Zhang et al., 2011). Available evidence suggested that the high temperatures and high pressures can break covalent bonds and disrupt physical structures of macromolecules leading to a change in their functional properties (Kim et al., 2006; Singh et al., 2007).…”

Section: Introductionmentioning

confidence: 99%

Effects of thermal processing on the structural and functional properties of soluble dietary fiber from whole grain oats

Dong

Yang

Shen

et al. 2018

Food sci. technol. int.

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Normal pressure steaming, high pressure steaming, microwave, and frying are widely used to deactivate enzyme in the oats, but these thermal processing methods may affect the structural and functional properties of soluble dietary fiber, which contribute greatly to the health benefits of oat foods. The objective of this study was to evaluate the effects of four different thermal processing methods on the structural and functional properties of soluble dietary fiber from whole grain oats. The results showed that the thermal processing resulted in changes on nutritional components of whole grain oats. Especially dietary fiber components, the total dietary fiber, insoluble dietary fiber, and soluble dietary fiber content of heat-treated oats were significantly increased ( p < 0.05). Moreover, thermal processing can not only result in an increase in molecular weight and particle size, but also cause molecular aggregation and different functional properties of soluble dietary fiber. High pressure steaming-treated oat soluble dietary fiber displayed significantly higher swelling and emulsifying ( p < 0.05), but microwave-treated oat soluble dietary fiber exhibited the highest glucose, cholesterol, and sodium cholate adsorption capacities. These results might provide basic information to help to better understand the functionality of oat soluble dietary fiber and improve the process efficiency of oat foods with high nutritional qualities.

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Section: Introductionmentioning

confidence: 99%

Effects of thermal processing on the structural and functional properties of soluble dietary fiber from whole grain oats

Dong

Yang

Shen

et al. 2018

Food sci. technol. int.

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“…Other researchers applied a modeling phase to provide an automatic monitoring system based on some physical properties of chocolate (Davidson, Ryks, & Chu, ). Other related studies have been performed with the use of statistical tools by (Bakhshabadi et al, ; Dolatabadi et al, ; Farzaneh et al, ; Farzaneh & Carvalho, ; Ghods Vali et al, ; Jabrayili et al, ; Rostami, Farzaneh, Boujmehrani, Mohammadi, & Bakhshabadi, ) on the optimization of different processes. However, the authors of the present study have not found any studies performed on the optimization of oil extraction of rapeseed by ANFIS system.…”

Section: Introductionmentioning

confidence: 99%

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

Farzaneh

Bakhshabadi

Gharekhani

et al. 2017

J Food Process Engineering

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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.

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“…Conceivably, this outcome is a result of the ANN model being able to better capture the degree of spectral inference between pigments than the three tested parametric approaches. The superiority of ANN over response surface in capturing non-linear behavior was previously reported in several other [ 25 , 29 , 41 , 42 ], but not all [ 43 ], evaluated systems.…”

Section: Resultsmentioning

confidence: 59%

“…Moreover, its versatility and model-free approach allows the modeling of underlying processes without restrictive assumptions [ 23 ]. In plant research, modeling with neural networks has been successfully used in a wide range of applications, including optimizing growth media [ 24 , 25 ], classifying cell wall architecture [ 26 ], identifying diseases [ 27 , 28 ], analyzing biophysical properties [ 29 ], forecasting pH and electrical conductivity [ 30 , 31 ], evaluating post-harvest changes and product quality [ 32 , 33 , 34 ], and characterizing and authenticating plant products [ 35 ]. Because of the unknown extent of spectral interference between the two pigments, an ANN appears to be a convenient method for modeling CMQ coordinates, using chlorophyll and anthocyanin content as input parameters and qL* , qa* , and qb* values as output parameters.…”

Section: Introductionmentioning

confidence: 99%

Predictive Modeling of a Leaf Conceptual Midpoint Quasi-Color (CMQ) Using an Artificial Neural Network

Šimko

2020

Sensors

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The color of plant leaves is moderated by the content of pigments, which can show considerable dorsiventral distribution. Two typical examples are leafy vegetables and ornamentals, wherein red and green color surfaces can be seen on the same leaf. The proof of concept is provided for predictive modeling of a leaf conceptual mid-point quasi-color (CMQ) from the content of pigments. The CMQ idea is based on the hypothesis that the content of pigments in leaves is associated with the combined color from both surfaces. The CMQ, which is calculated from CIELab color coordinates at adaxial and abaxial antipodes, is thus not an actual color, but a notion that can be used in modeling. The CMQ coordinates, predicted from the content of chlorophylls and anthocyanins by means of an artificial neural network (ANN), matched well with the CMQ coordinates empirically found on photosynthetically active leaves of lettuce (Lactuca sativa L.), but also with other plant species with comparable leaf attributes. Modeled values of lightness (qL*) decreased with the increasing content of both pigments, while the redness or greenness (qa*) and yellowness or blueness (qb*) of the CMQ were affected more by a relative content of chlorophylls and anthocyanins in leaves. The highest vividness of quasi-colors (qC*) was modeled for leaves with a high content of either pigment alone. The model predicted a substantially duller quasi-color for leaves with chlorophylls and anthocyanins present together, particularly when both pigments were present at very high levels.

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Screening of the aerodynamic and biophysical properties of barley malt

Cited by 10 publications

References 20 publications

Effects of thermal processing on the structural and functional properties of soluble dietary fiber from whole grain oats

Effects of thermal processing on the structural and functional properties of soluble dietary fiber from whole grain oats

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

Predictive Modeling of a Leaf Conceptual Midpoint Quasi-Color (CMQ) Using an Artificial Neural Network

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