Class-based physical properties of air-classified sunflower seeds and kernels

Munder, Simon; Argyropoulos, Dimitrios; Müller, Joachim

doi:10.1016/j.biosystemseng.2017.10.005

Cited by 29 publications

(35 citation statements)

References 20 publications

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“…The moisture content MC of seeds and hulls was measured from samples of 3 g by a standard thermogravimetric analysis in a convection oven at 103 ± 2 • C, according to ISO 665:2000 [23] and expressed in kg water per kg dry matter (kg·kg −1 ). All analyses were performed in triplicates, which is commonly applied in other studies [18,22].…”

Section: Moisture Content Determinationmentioning

confidence: 99%

“…The high oleic sunflower seeds used in this study are shaped like compressed, oval bodies with a sphericity of 0.45 to 0.55 and an equivalent diameter of 5.48 ± 0.64 mm [22]. Average moisture diffusivity D was assumed to remain constant during the relevant drying period [28].…”

Section: Analytical Estimation Of Diffusion Coefficientsmentioning

confidence: 99%

“…Both hulls and kernels show significantly different sorption behavior [2]. In addition, whole seeds and kernels are significantly different in most physical properties, such as volume and equivalent diameter [19,22]. Sunflower kernels show significantly slower moisture diffusivity compared to hulls and, thus, are the limiting factor in drying [3,19].…”

Section: Introductionmentioning

confidence: 99%

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Acquisition of Sorption and Drying Data with Embedded Devices: Improving Standard Models for High Oleic Sunflower Seeds by Continuous Measurements in Dynamic Systems

2018

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Innovative methods were used to determine both sorption and drying data at temperatures typically found in the handling of agricultural products. A robust sorption measurement system using multiple microbalances and a high precision through flow laboratory dryer, both with continuous data acquisition, were employed as the basis for a water vapor deficit based approach in modeling the sorption and drying behavior of high oleic sunflower seeds. A coherent set of data for sorption (Temperature T = 25-50 • C, water activity a w = 0.10-0.95) and for drying (T = 30-90 • C, humidity of the drying air x = 0.010-0.020 kg·kg −1 ) was recorded for freshly harvested material. A generalized single-layer drying model was developed and validated (R 2 = 0.99, MAPE = 8.3%). An analytical solution for predicting effective diffusion coefficients was also generated (R 2 = 0.976, MAPE of 6.33%). The water vapor pressure deficit-based approach allows for an easy integration of meaningful parameters recorded during drying while maintaining low complexity of the underlying equations in order for embedded microcontrollers with limited processing power to be integrated in current agro-industrial applications.

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Section: Moisture Content Determinationmentioning

confidence: 99%

Section: Analytical Estimation Of Diffusion Coefficientsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Acquisition of Sorption and Drying Data with Embedded Devices: Improving Standard Models for High Oleic Sunflower Seeds by Continuous Measurements in Dynamic Systems

2018

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“…In addition, it can be used in human and animal feeding (Carvalho et al, 2018). In addition to being considered as a major oilseed crop and source of vegetable oils, and to date, traditional sunflower varieties are the main commercially grown varieties, but sunflower varieties with high oleic capacity are of economic importance (Muder et al, 2017).…”

Section: Introductionmentioning

confidence: 99%

Influence of Dehydration in the Physical-Chemical Quality of Commercial Sunflower Almonds

Barros

Santos

Nascimento

et al. 2019

JAS

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Sunflower almonds are widely marketed and have numerous technological applications. Through the drying process occurs the reduction of the water content of the product, a factor that contributes to increase its useful life. However, nutritional losses and physical damage can also occur. In this context, the objective of this study was to evaluate the effect of different temperatures applied in the drying process on the physical-chemical characteristics of commercial sunflower almonds. Convective drying was performed using temperatures of 40, 50, 60 and 80 °C. Afterwards, the samples before and after the drying process were analyzed with respect to the following parameters: moisture, water activity, ash, lipids, proteins, carbohydrates and energetic value to observe the influence of different drying temperatures on these attributes. It was verified that the increase of the temperature of the drying air causes an increase in the ash content, total solids, lipids, carbohydrates and energetic value. However, the reduction of moisture content, water activity and proteins was observed. There was no significant difference between the samples in relation to pH.

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“…The beans initial moisture content was 0.129 kgw kgdm−1 and they have been roasted in a direct gas burning roaster. Munder et al [9] carried out the study to classify sunflower seeds via air-separation into different quality classes. They used seed mass, kernel mass, seed length, width and thickness as physical properties.…”

Section: Introductionmentioning

confidence: 99%

Determining the Carrot Volume via Radius and Length Using ANN

Örnek

Kahramanlı

2018

ijisae

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In this study a total of 464 carrots were taken from Kaşınhanı, where the most carrots in Turkey are produced. The radiuses with an interval of 5 cm, length and volume of each carrot were measured and recorded. Three different Artificial Neural Network models: BP, LM and PUNN were designed for predicting the carrot volume. To assess the success of the system, statistical measures such as Root Mean Squared Error, Mean Absolute Error and R 2 were used. The results showed that all three methods are successful in this problem, while LM and PUNN seems bit better.

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Class-based physical properties of air-classified sunflower seeds and kernels

Cited by 29 publications

References 20 publications

Acquisition of Sorption and Drying Data with Embedded Devices: Improving Standard Models for High Oleic Sunflower Seeds by Continuous Measurements in Dynamic Systems

Acquisition of Sorption and Drying Data with Embedded Devices: Improving Standard Models for High Oleic Sunflower Seeds by Continuous Measurements in Dynamic Systems

Influence of Dehydration in the Physical-Chemical Quality of Commercial Sunflower Almonds

Determining the Carrot Volume via Radius and Length Using ANN

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