Investigating the Physicochemical Properties of Strawberries and Classification by an E‐Nose During Storage.
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| Title: | Investigating the Physicochemical Properties of Strawberries and Classification by an E‐Nose During Storage. |
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| Authors: | Gholami, Rashid1 (AUTHOR) r.gholami@razi.ac.ir, Aghilinategh, Nahid1 (AUTHOR), Rabbani, Hekmat2 (AUTHOR), Hebishy, Essam (AUTHOR) ehebishy+eic@wiley.com |
| Source: | Journal of Food Processing & Preservation. 2/28/2025, Vol. 2025, p1-12. 12p. |
| Subjects: | Artificial neural networks, Controlled atmosphere packaging, Packaging film, Computer vision, Temperature control |
| Abstract: | This study involved packaging strawberries using conventional polyethylene (PE) and advanced nanofilm. Modified atmosphere packaging (MAP) and conventional atmospheric conditions were employed. The strawberries were stored under ambient and refrigerated conditions (4°C) throughout the 12 days. Periodic assessments of chemical attributes such as pH, total soluble solids (TSS), vitamin C content, and antioxidant capacity were conducted, and quality evaluations using electronic olfaction techniques and machine vision were performed every 3 days for all treatment variations. The study shows that packaging film, internal atmosphere, storage temperature, duration, and their interactions significantly affect the chemical properties of strawberries (p < 0.01). Using MAP with nanofilm and temperature control helps preserve strawberry quality during storage. Additionally, it was noted that the classification accuracy achieved by the adaptive neurofuzzy inference system (ANFIS) remained consistently at 100% throughout all storage periods. In contrast, in the artificial neural network (ANN), the highest accuracy was attained during the 3‐ and 6‐day storage intervals (84%), with the lowest accuracy recorded during the 9‐day storage period (68%). The ANFIS model achieved the highest accuracy in predicting vitamin C content with an R2 value of 1 and an Root Mean Squar Error (RMSE) of 0.62, while in the neural network model, the highest accuracy was achieved with an R2 value of 0.98 and an RMSE of 0.86. [ABSTRACT FROM AUTHOR] |
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| Database: | Engineering Source |
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| Abstract: | This study involved packaging strawberries using conventional polyethylene (PE) and advanced nanofilm. Modified atmosphere packaging (MAP) and conventional atmospheric conditions were employed. The strawberries were stored under ambient and refrigerated conditions (4°C) throughout the 12 days. Periodic assessments of chemical attributes such as pH, total soluble solids (TSS), vitamin C content, and antioxidant capacity were conducted, and quality evaluations using electronic olfaction techniques and machine vision were performed every 3 days for all treatment variations. The study shows that packaging film, internal atmosphere, storage temperature, duration, and their interactions significantly affect the chemical properties of strawberries (p < 0.01). Using MAP with nanofilm and temperature control helps preserve strawberry quality during storage. Additionally, it was noted that the classification accuracy achieved by the adaptive neurofuzzy inference system (ANFIS) remained consistently at 100% throughout all storage periods. In contrast, in the artificial neural network (ANN), the highest accuracy was attained during the 3‐ and 6‐day storage intervals (84%), with the lowest accuracy recorded during the 9‐day storage period (68%). The ANFIS model achieved the highest accuracy in predicting vitamin C content with an R2 value of 1 and an Root Mean Squar Error (RMSE) of 0.62, while in the neural network model, the highest accuracy was achieved with an R2 value of 0.98 and an RMSE of 0.86. [ABSTRACT FROM AUTHOR] |
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| ISSN: | 01458892 |
| DOI: | 10.1155/jfpp/2322442 |
