Comparative analysis of visible and near-infrared (Vis-NIR) spectroscopy and prediction of moisture ratio using machine learning algorithms for jujube dried under different conditions

Dried fruits are considered healthy because they are high in fiber and carbohydrates and contain low fat. In this study, jujube slices were dried using three different methods (open sun, closed shade, and microwave). Then, characteristics, such as color, spectral reflectance, vegetation indices (VIs...

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Main Authors: Seda Günaydın, Necati Çetin, Cevdet Sağlam, Kamil Sacilik, Ahmad Jahanbakhshi
Format: Article
Language:English
Published: Elsevier 2025-06-01
Series:Applied Food Research
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Online Access:http://www.sciencedirect.com/science/article/pii/S2772502225000095
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author Seda Günaydın
Necati Çetin
Cevdet Sağlam
Kamil Sacilik
Ahmad Jahanbakhshi
author_facet Seda Günaydın
Necati Çetin
Cevdet Sağlam
Kamil Sacilik
Ahmad Jahanbakhshi
author_sort Seda Günaydın
collection DOAJ
description Dried fruits are considered healthy because they are high in fiber and carbohydrates and contain low fat. In this study, jujube slices were dried using three different methods (open sun, closed shade, and microwave). Then, characteristics, such as color, spectral reflectance, vegetation indices (VIs), rehydration rate (RR), drying kinetics, moisture ratio (MR), and moisture content (MC) were measured and compared after using the above-mentioned drying methods. Also, the MR was predicted by the MC, and the drying rate (DR), drying times, and final thickness were predicted using the multi-layer perceptron (MLP), gaussian process (GP), k-nearest neighbors (KNN), random forest (RF), and support vector regression (SVR) algorithms. The drying times for jujube slices dried through the open sun closed shade, and microwave methods were 1680, 1140, and 24 min, respectively. Among the six mathematical thin-layer models, the Jena & Das model obtained a high coefficient of determination (R2) with experimental data in the three drying methods (open sun, closed shade, and microwave). The results showed that closed shade drying had the highest RR (4.58), followed by open sun drying (4.40) and microwave drying (3.33). The microwave drying technique produced the lowest color change, with a value of 10.67. Shade drying resulted in the least light reflection across the spectrum, suggesting that the color of the product darkened as drying time increased. For MR prediction, the highest R-values belonged to MLP and RF, with values of 0.9997 and 0.9968, respectively. Regarding the attributes under study, microwave drying is a promising method for drying the jujube slices.
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issn 2772-5022
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publishDate 2025-06-01
publisher Elsevier
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series Applied Food Research
spelling doaj-art-5a85852b3f53480faea78d8bf8106b6d2025-01-22T05:44:22ZengElsevierApplied Food Research2772-50222025-06-0151100699Comparative analysis of visible and near-infrared (Vis-NIR) spectroscopy and prediction of moisture ratio using machine learning algorithms for jujube dried under different conditionsSeda Günaydın0Necati Çetin1Cevdet Sağlam2Kamil Sacilik3Ahmad Jahanbakhshi4Department of Biosystems Engineering, Faculty of Agriculture, University of Erciyes, Kayseri, TürkiyeDepartment of Agricultural Machinery and Technologies Engineering, Faculty of Agriculture, Ankara University, Ankara, Türkiye; Corresponding authors.Department of Biosystems Engineering, Faculty of Agriculture, University of Erciyes, Kayseri, TürkiyeDepartment of Agricultural Machinery and Technologies Engineering, Faculty of Agriculture, Ankara University, Ankara, TürkiyeDepartment of Biosystems Engineering, Faculty of Agriculture, Lorestan University, Khorramabad, Iran; Corresponding authors.Dried fruits are considered healthy because they are high in fiber and carbohydrates and contain low fat. In this study, jujube slices were dried using three different methods (open sun, closed shade, and microwave). Then, characteristics, such as color, spectral reflectance, vegetation indices (VIs), rehydration rate (RR), drying kinetics, moisture ratio (MR), and moisture content (MC) were measured and compared after using the above-mentioned drying methods. Also, the MR was predicted by the MC, and the drying rate (DR), drying times, and final thickness were predicted using the multi-layer perceptron (MLP), gaussian process (GP), k-nearest neighbors (KNN), random forest (RF), and support vector regression (SVR) algorithms. The drying times for jujube slices dried through the open sun closed shade, and microwave methods were 1680, 1140, and 24 min, respectively. Among the six mathematical thin-layer models, the Jena & Das model obtained a high coefficient of determination (R2) with experimental data in the three drying methods (open sun, closed shade, and microwave). The results showed that closed shade drying had the highest RR (4.58), followed by open sun drying (4.40) and microwave drying (3.33). The microwave drying technique produced the lowest color change, with a value of 10.67. Shade drying resulted in the least light reflection across the spectrum, suggesting that the color of the product darkened as drying time increased. For MR prediction, the highest R-values belonged to MLP and RF, with values of 0.9997 and 0.9968, respectively. Regarding the attributes under study, microwave drying is a promising method for drying the jujube slices.http://www.sciencedirect.com/science/article/pii/S2772502225000095Fruit dryingVis-NIR spectroscopyQualitative propertiesNon-destructive assessmentArtificial intelligence
spellingShingle Seda Günaydın
Necati Çetin
Cevdet Sağlam
Kamil Sacilik
Ahmad Jahanbakhshi
Comparative analysis of visible and near-infrared (Vis-NIR) spectroscopy and prediction of moisture ratio using machine learning algorithms for jujube dried under different conditions
Applied Food Research
Fruit drying
Vis-NIR spectroscopy
Qualitative properties
Non-destructive assessment
Artificial intelligence
title Comparative analysis of visible and near-infrared (Vis-NIR) spectroscopy and prediction of moisture ratio using machine learning algorithms for jujube dried under different conditions
title_full Comparative analysis of visible and near-infrared (Vis-NIR) spectroscopy and prediction of moisture ratio using machine learning algorithms for jujube dried under different conditions
title_fullStr Comparative analysis of visible and near-infrared (Vis-NIR) spectroscopy and prediction of moisture ratio using machine learning algorithms for jujube dried under different conditions
title_full_unstemmed Comparative analysis of visible and near-infrared (Vis-NIR) spectroscopy and prediction of moisture ratio using machine learning algorithms for jujube dried under different conditions
title_short Comparative analysis of visible and near-infrared (Vis-NIR) spectroscopy and prediction of moisture ratio using machine learning algorithms for jujube dried under different conditions
title_sort comparative analysis of visible and near infrared vis nir spectroscopy and prediction of moisture ratio using machine learning algorithms for jujube dried under different conditions
topic Fruit drying
Vis-NIR spectroscopy
Qualitative properties
Non-destructive assessment
Artificial intelligence
url http://www.sciencedirect.com/science/article/pii/S2772502225000095
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AT necaticetin comparativeanalysisofvisibleandnearinfraredvisnirspectroscopyandpredictionofmoistureratiousingmachinelearningalgorithmsforjujubedriedunderdifferentconditions
AT cevdetsaglam comparativeanalysisofvisibleandnearinfraredvisnirspectroscopyandpredictionofmoistureratiousingmachinelearningalgorithmsforjujubedriedunderdifferentconditions
AT kamilsacilik comparativeanalysisofvisibleandnearinfraredvisnirspectroscopyandpredictionofmoistureratiousingmachinelearningalgorithmsforjujubedriedunderdifferentconditions
AT ahmadjahanbakhshi comparativeanalysisofvisibleandnearinfraredvisnirspectroscopyandpredictionofmoistureratiousingmachinelearningalgorithmsforjujubedriedunderdifferentconditions