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...
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2025-06-01
|
| Series: | Applied Food Research |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2772502225000095 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832591769458442240 |
|---|---|
| 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. |
| format | Article |
| id | doaj-art-5a85852b3f53480faea78d8bf8106b6d |
| institution | Kabale University |
| issn | 2772-5022 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Elsevier |
| record_format | Article |
| 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 |
| work_keys_str_mv | AT sedagunaydın comparativeanalysisofvisibleandnearinfraredvisnirspectroscopyandpredictionofmoistureratiousingmachinelearningalgorithmsforjujubedriedunderdifferentconditions AT necaticetin comparativeanalysisofvisibleandnearinfraredvisnirspectroscopyandpredictionofmoistureratiousingmachinelearningalgorithmsforjujubedriedunderdifferentconditions AT cevdetsaglam comparativeanalysisofvisibleandnearinfraredvisnirspectroscopyandpredictionofmoistureratiousingmachinelearningalgorithmsforjujubedriedunderdifferentconditions AT kamilsacilik comparativeanalysisofvisibleandnearinfraredvisnirspectroscopyandpredictionofmoistureratiousingmachinelearningalgorithmsforjujubedriedunderdifferentconditions AT ahmadjahanbakhshi comparativeanalysisofvisibleandnearinfraredvisnirspectroscopyandpredictionofmoistureratiousingmachinelearningalgorithmsforjujubedriedunderdifferentconditions |