Discriminating Drying Method of Tarhana Using Computer Vision

Kurtulmuş, Ferhat; Gürbüz, Ozan; Değirmencioğlu, Nurcan

doi:10.1111/jfpe.12092

Cited by 12 publications

(7 citation statements)

References 41 publications

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“…The type of ingredients might vary from region to region in Turkey (Ç olak et al 2012). The mixture is fermented for 1-7 days after than dried under the sun or at oven (Köse and Ç agındı 2002;Tarakçı et al 2004;Kilci and Gocmen 2014a;Kurtulmuş et al 2014). Lactic acid bacteria and yeast are responsible for the fermentation and at the end of fermentation is formed an acidic and sour taste (Ş engün et al 2009; Ö zdestan and Ü ren 2013).…”

Section: Introductionmentioning

confidence: 99%

Composition of volatile aromatic compounds and minerals of tarhana enriched with cherry laurel (Laurocerasus officinalis)

Temiz

Tarakçı

2017

J Food Sci Technol

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Different concentrations of cherry laurel pulp (0, 5, 10, 15 and 20%) were used to produce tarhana samples. Volatile aromatic compounds and minor mineral content were investigated. Volatile aromatic compounds were analyzed by using GC-MS with SPME fiber and minor mineral values were evaluated with inductively coupled plasma optical emission spectrometer. The statistical analysis showed that addition of pulp affected volatile aromatic compounds and minor mineral content significantly. Thirty five volatile aromatic compounds were found in tarhana samples. The octanoic acid from acids, benzaldehyde (CAS) phenylmethanal from aldehydes, 6-methyl-5-hepten-2-one from ketones, octadecane (CAS) -octadecane form terpenes, ethyl caprylate from esters and benzenemethanol (CAS) benzyl alcohol from alcohols had the highest percentage of volatile aromatic compounds. Tarhana samples were rich source of Mn, Cu and Fe content.

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Section: Introductionmentioning

confidence: 99%

Composition of volatile aromatic compounds and minerals of tarhana enriched with cherry laurel (Laurocerasus officinalis)

Temiz

Tarakçı

2017

J Food Sci Technol

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“…A few investigations have considered the drying characteristics estimation using machine learning approaches including for pineapple cubes (Meerasri & Sothornvit, 2022), apple slices (Sağlam & Çetin, 2022), pomelo fruit (Kırbaş et al, 2019), mushroom (Tarafdar et al, 2019), cocoyam slices (Onu et al, 2022), apricot slices (Satorabi et al, 2021), orange slices (Çetin, 2022a), orange‐fleshed sweet potato (Okonkwo et al, 2022), banana (Trivedi et al, 2023), cantaloupe (Zadhossein et al, 2023). In addition, there are rare discrimination research about drying of agriculture product in the past studies such as dried strawberry (Przybył et al, 2020), freeze‐dried beetroot (Ropelewska & Wrzodak, 2022), dried tarhana (Kurtulmuş et al, 2014), and dried garlic (Makarichian et al, 2021). These studies disclose that there are limited available research about the modeling machine learning based of drying parameters particularly moisture content and moisture ratio estimation of apricot.…”

Section: Introductionmentioning

confidence: 99%

Machine learning approaches for estimating apricot drying characteristics in various advanced and conventional dryers

Kaveh,

Çetin,

Khalife

et al. 2023

J Food Process Engineering

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Drying plays a crucial role in preserving the quality of agricultural products. Nevertheless, suboptimal conditions in drying systems have an adverse effect on drying characteristics and energy efficiency. Machine learning approaches are innovative and reliable that have been successfully used to solve such challenges and achieve optimization in drying processes. In this study, five machine learning approaches (multilayer perceptron [MLP], gaussian processes [GP], support vector regression [SVR], k‐nearest neighbors [kN], and random forest [RF]) were used to estimate moisture content and moisture ratio of apricot in five various dryers (convective [CV], microwave [MW], infrared [IR], microwave‐convective [MW‐CV], and infrared‐convective [IR‐CV]). Also, the values of specific energy consumption (SEC) and effective moisture diffusivity (Deff) were calculated in these dryers. Accordingly, the best result of the Deff (3.14 × 10−10 m2/s) and the minimum value of the drying time (130 min) and SEC (18.67 MJ/kg) were obtained using MW‐CV hybrid dryer. While the lowest values of Deff (2.09 × 10−11 m2/s) and highest drying time (18.5 h) and SEC (209.34 MJ/kg) were detected in CV dryer at 50°C. The best correlation coefficients (R) for the estimation of moisture content were gained using RF technique for k‐fold cross validation and train‐test split with the values of 0.9908 and 0.9912, respectively. Moreover, moisture ratio results showed that the MLP achieved the highest R value over 0.9985 for both validation methodologies. In the discrimination of the drying methods, the MLP had the greatest accuracy as 82.00% and 86.00% for k‐fold cross validation and train‐test split, respectively. The results showed that the RF and ML models could potentially be used for estimation and discrimination for drying applications.Practical ApplicationsRecently, there has been an increased interest in healthy food choices such as foodstuffs, snacks, and dried products. This trend has captured the attention of both dietitians and conscious consumers. Apricots are a prime example of a valuable dried product that can be dry in various conditions. Machine learning techniques can be used for rapid and non‐destructive determination of drying characteristics and such techniques yield objective and accurate results. Present findings revealed that texture machine learning models could be used as an effective and reliable discrimination tool for dried products.

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“…In agri-food industry, sorting is playing an important role [ 17 , 18 , 19 ] in increasing the value of the products in the market and marketing capability [ 20 , 21 , 22 ]. The sorting process is providing uniformity in size and shape, decreasing the costs related to the packaging and transportation, and offering the most ideal packaging configuration [ 23 , 24 , 25 ].…”

Section: Introductionmentioning

confidence: 99%

A Multi-Flow Production Line for Sorting of Eggs Using Image Processing

Akkoyun

Özçelik

Arpacı

et al. 2022

Sensors

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In egg production facilities, the classification of eggs is carried out either manually or by using sophisticated systems such as load cells. However, there is a need for the classification of eggs to be carried out with faster and cheaper methods. In the agri-food industry, the use of image processing technology is continuously increasing due to the data processing speed and cost-effective solutions. In this study, an image processing approach was used to classify chicken eggs on an industrial roller conveyor line in real-time. A color camera was used to acquire images in an illumination cabinet on a motorized roller conveyor while eggs are moving on the movement halls. The system successfully operated for the grading of eggs in the industrial multi-flow production line in real-time. There were significant correlations among measured weights of the eggs after image processing. The coefficient of linear correlation (R2) between measured and actual weights was 0.95.

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Discriminating Drying Method of Tarhana Using Computer Vision

Cited by 12 publications

References 41 publications

Composition of volatile aromatic compounds and minerals of tarhana enriched with cherry laurel (Laurocerasus officinalis)

Composition of volatile aromatic compounds and minerals of tarhana enriched with cherry laurel (Laurocerasus officinalis)

Machine learning approaches for estimating apricot drying characteristics in various advanced and conventional dryers

A Multi-Flow Production Line for Sorting of Eggs Using Image Processing

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