The prediction of seedy grape drying rate using a neural network method

Çakmak, Gülşah; Yıldız, Cengiz

doi:10.1016/j.compag.2010.10.008

Cited by 92 publications

(36 citation statements)

References 10 publications

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“…To increase the shelf life of foods, drying is the oldest process (Cakmak & Yıldız, 2011). Dried fruits and vegetables have been considered as an alternative fat-free snack and have gained much more attention recently (Devahastin & Niamnuy, 2010).…”

Section: Introductionmentioning

confidence: 99%

Impact of different drying parameters on color, β-carotene, antioxidant activity and minerals of apricot (Prunus armeniacaL.)

et al. 2016

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Apricot is one of the fruits dried by using different methods, such as sun, convective or microwave drying. The effects of drying methods on the components of this fruit differ depending upon the temperature or time parameters. In this research, the impacts of convective, microwave and microwave-convective drying techniques on color, β-carotene, minerals and antioxidant activity of apricots were investigated. The color values (L*, b*, ΔE ab, h° and C* ab ) of dried fruit were decreased, while the a* values increased. Compared with a fresh sample, the dried apricots showed a 1.4-3.9-fold proportional increase in β-carotene based on the increment of dry matter. The samples dried at high temperature and microwave levels, at 75 °C+90 watt and 75 °C+160 watt, showed lower antioxidant activity. Df the different drying treatments, the microwave-convective method (50 °C+160 watt) obtained a higher β-carotene content while maintaining antioxidant activity with a short drying time.Keywords: apricot; drying; color; β-carotene; antioxidant activity.Practical Application: This paper presents different drying methods of apricots in addition to optimization of the power level and temperature conditions. These methods make it possible to select the best way to provide high quality and nutritional dried apricots.

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Section: Introductionmentioning

confidence: 99%

Impact of different drying parameters on color, β-carotene, antioxidant activity and minerals of apricot (Prunus armeniacaL.)

et al. 2016

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“…Mantarları, Midilli et al (1999) laboratuar tipi bir kurutucuda, Toğrul et al (2005) infrared kurutucuda, Xanthopoulos et al (2007) ince katmanlı kurutucuda, Lidhoo et al (2008) mantar dilimlerini haşlayarak ve Kulshreshtha et al (2009) akışkan yataklı bir kurutucuda sıcak hava kullanarak kurutmuşlardır. Jambrak et al (2007) tarafından yapılan çalışmada 9.7 g nem (g kuru 20 (2014) (Chen et al 2001), pirinç (Zhang et al 2002), cassava ve mango (Hernandez-Perez et al 2004), kereste (Wu & Avramidis 2006), domates (Movagharnejad & Nikzad 2007), fıstık (Omid et al 2009;Balbay et al 2011), elma (Khoshhal et al 2010, üzüm (Cakmak & Yildiz 2011) Sistemde kontrol işlevi; kontrol elemanları, sistemin yönlendirilmesin senkronize çalışan kontrol kartları, sistem yazılımı ve benzeri ekipmanların bir dizi elektro-mekanik kontrol elemanları tarafından sağlanmaktadır. Si kontrolünün anlık olarak bilgisayar ekranından takibine ve kontrolüne sırasında da bütün kontroller bilgisayar ekranından yapılmıştır.…”

Section: Introductionunclassified

Güneş destekli ısı pompalı bir kurutucuda mantarın kuruma davranışlarının yapay sinir ağı kullanılarak modellenmesi

AKTAŞ¹

2014

Tarım Bilimleri Dergisi

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Dryer was tested by drying mushroom with solar energy and solar assisted heat pump separately at 45 °C and 55 °C drying air temperature and 0.9 m s -1 and 1.2 m s -1 drying air velocities. Moisture content (MC), moisture ratio (MR) and drying rate (DR) which were obtained from experiments were modeled by using Levenberg-Marquardt (LM) the backpropagation learning algorithm and fermi transfer function with artificial neural networks (ANNs). The coefficient of multiple determination (R 2 ), the root means square error (RMSE) and the mean absolute percentage error (MAPE) were used for the determination of statistical validity of the developed model. R 2 , RMSE and MAPE were determined for MC

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“…The grapes were dried through a solar air collector. It was proved that drying prediction could be made via neural networks, and an alternative approach was presented [13].…”

Section: Literature Reviewmentioning

confidence: 99%

“…Likewise, the more parameters are taken as an output, the more ganglions are formed. In the hidden layer where the neural network is trained, ganglions are determined by trial and error depending on the degree of difficulty of the problem [13]. The gradient descent algorithm was used in the training of the neural network.…”

Section: Figure 5 Neural Networkmentioning

confidence: 99%

The Determination of the Developments of Beehives via Artificial Neural Networks

Bayır

Albayrak

2018

Teh. vjesn.

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Honeybees provide great benefits for people both with the foods they produce and as a pollinator. It is known that they pass the whole year with the foods they collect in spring and summer months. Beekeepers also benefit from the honey produced in these periods. Whether a beehive works adequately or not and its status of development can be understood through the observations by beekeepers. In this study, an Arduino-supported neural network model was developed in order to obtain information about the general situations of beehives. The three-input and three-output neural networks were embedded in a board after the training and testing stage. While temperature, humidity, and weight refer to inputs, good situation, stable situation, and bad situation represent outputs. The real-time model has an accuracy of 99.84%.

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The prediction of seedy grape drying rate using a neural network method

Cited by 92 publications

References 10 publications

Impact of different drying parameters on color, β-carotene, antioxidant activity and minerals of apricot (Prunus armeniacaL.)

Impact of different drying parameters on color, β-carotene, antioxidant activity and minerals of apricot (Prunus armeniacaL.)

Güneş destekli ısı pompalı bir kurutucuda mantarın kuruma davranışlarının yapay sinir ağı kullanılarak modellenmesi

The Determination of the Developments of Beehives via Artificial Neural Networks

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