Prediction of moisture ratio and drying rate of orange slices using machine learning approaches

Çetin, Necati

doi:10.1111/jfpp.17011

Cited by 7 publications

(14 citation statements)

References 59 publications

(86 reference statements)

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“…A computer vision system based on CNN or ML models, such as SVM, KNN, J48, and RF, has been seen as a potential technique for automatic food classification, adulterant quantification, and feature extraction [ 72 , 84 , 103 , 104 , 145 , 158 , 160 , 161 ]. ML algorithms have also been used to improve the effectiveness of the drying system for orange slices [ 82 ]. Swarm intelligence (SI), a subfield of AI, was employed to provide an efficient approach to fresh food distribution [ 133 ].…”

Section: Discussionmentioning

confidence: 99%

Artificial Intelligence in Food Safety: A Decade Review and Bibliometric Analysis

Liu

Wang

Zhang

et al. 2023

Foods

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Artificial Intelligence (AI) technologies have been powerful solutions used to improve food yield, quality, and nutrition, increase safety and traceability while decreasing resource consumption, and eliminate food waste. Compared with several qualitative reviews on AI in food safety, we conducted an in-depth quantitative and systematic review based on the Core Collection database of WoS (Web of Science). To discover the historical trajectory and identify future trends, we analysed the literature concerning AI technologies in food safety from 2012 to 2022 by CiteSpace. In this review, we used bibliometric methods to describe the development of AI in food safety, including performance analysis, science mapping, and network analysis by CiteSpace. Among the 1855 selected articles, China and the United States contributed the most literature, and the Chinese Academy of Sciences released the largest number of relevant articles. Among all the journals in this field, PLoS ONE and Computers and Electronics in Agriculture ranked first and second in terms of annual publications and co-citation frequency. The present character, hot spots, and future research trends of AI technologies in food safety research were determined. Furthermore, based on our analyses, we provide researchers, practitioners, and policymakers with the big picture of research on AI in food safety across the whole process, from precision agriculture to precision nutrition, through 28 enlightening articles.

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Section: Discussionmentioning

confidence: 99%

Artificial Intelligence in Food Safety: A Decade Review and Bibliometric Analysis

Liu

Wang

Zhang

et al. 2023

Foods

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“…The D eff was determined by solving the Fick equation. For a blade piece of saffron petals using Equations (11)–(13) (Çetin, 2022; Kalantari et al, 2023).

MR = \frac{8}{π^{2}} \sum_{n = 0}^{\infty} \frac{1}{((), 2 n goodbreak+ 1)} \exp (- \frac{{((), 2 n goodbreak+ 1)}^{2} {normalπ}^{2} D_{eff} t}{4 L^{2}})

MR = \frac{M_{normalt} - M_{normale}}{M_{0} - M_{normale}} = \frac{8}{π^{2}} \exp (- \frac{{normalπ}^{2} D_{eff} t}{4 L^{2}})

K = \frac{{normalπ}^{2} D_{eff}}{4 L^{2}}

where K is the slope of the line and L is the half of the sample thickness (m).…”

Section: Methodsmentioning

confidence: 99%

“…The D eff was determined by solving the Fick equation. For a blade piece of saffron petals using Equations ( 11)-( 13) (Çetin, 2022;Kalantari et al, 2023).…”

Section: Determining the D Effmentioning

confidence: 99%

Infrared and hot drying of saffron petal (Crocus sativus L.): Effect on drying, energy, color, and rehydration

Sharifian

Gharkhloo

Yamchi

et al. 2023

J Food Process Engineering

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In this study, the effect of infrared (IR) and hot air dryers on the drying properties of saffron petals was investigated. Drying of saffron petals was done under three level temperature 40, 50, and 60 C and distance from IR lamps 10, 14, and 20 cm and the main physical and thermal properties were measured. The highest value effective moisture diffusion (D eff ) was evaluated for IR-dried samples at 60 C and 10 cm with 5.57 Â 10 À7 m 2 /s. The lowest value of color changes is related to the sample dried at 40 C at a distance of 20 cm with a color change of 11.99 in the IR dryer. Increasing the drying temperature reduced the drying time and energy consumption and increased the drying rate and D eff . Also, the results showed that Midilli model was selected as the best model for drying saffron petals with an IR and hot air dryer. Practical ApplicationsDue to the importance and sensitivity of saffron petals to the application of heat in the drying process, it is necessary to dry this product like other medicinal plants scientifically. Theoretical research, with the help of mathematical modeling and numerical simulation, provides a powerful and fast tool for analyzing the process of mass and heat transfer under natural drying conditions. Careful study of the drying kinetics of the product, evaluation of parameters affecting the physical quality properties, and the concomitant and reciprocal effects of these parameters is necessary. It can be used in the drying industry of medicinal plants.

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“…These algorithms provide very successful results for drying characteristics that occur in non‐linear complex drying processes. In some studies, similar models were developed and performed well in drying processes (Çetin, 2022a; Sağlam & Çetin, 2022). Four characteristics including drying methods, drying time, moisture content (dry basis), and drying rate were used as the inputs for estimation the moisture ratio, and three characteristics (drying method, drying time, and drying rate) were employed as input for estimation the moisture content.…”

Section: Methodsmentioning

confidence: 99%

“…The main key variables in the drying process are moisture ratio, moisture content, and drying rate. 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).…”

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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Prediction of moisture ratio and drying rate of orange slices using machine learning approaches

Cited by 7 publications

References 59 publications

Artificial Intelligence in Food Safety: A Decade Review and Bibliometric Analysis

Artificial Intelligence in Food Safety: A Decade Review and Bibliometric Analysis

Infrared and hot drying of saffron petal (Crocus sativus L.): Effect on drying, energy, color, and rehydration

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

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