Majid Arabameri1, Roshanak Rafiei Nazari2, Anna Abdolshahi3, Mohammad Abdollahzadeh4, Solmaz Mirzamohammadi1,5, Nabi Shariatifar6,7,8, Francisco J Barba9, Amin Mousavi Khaneghah10. 1. Vice-chancellery of Food and Drug, Shahroud University of Medical Sciences, Shahroud, Iran. 2. Department of Physics, South Tehran Branch, Islamic Azad University, Tehran, Iran. 3. Food Safety Research Center(salt), School of Nutrition and Food Sciences, Semnan University of Medical Sciences, Semnan, Iran. 4. Vice-Chancellery of Food and Drug, Sabzevar University of Medical Sciences, Sabzevar, Iran. 5. School of Medicine, Shahroud University of Medical Sciences, Shahroud, Iran. 6. Department of Environmental Health, School of Public Health, Tehran University of Medical Sciences, Tehran, Iran. 7. Halal research center of IRI.FDA.MOH, Tehran, Iran. 8. Food safety research center, Shahid Beheshti University of Medical Science, Tehran, Iran. 9. Universitat de València, Faculty of Pharmacy, Preventive Medicine and Public Health, Food Science, Toxicology and Forensic Medicine Department, Nutrition and Food Science Area, Avda. Vicent Andrés Estellés, s/n, 46100 Burjassot, València, Spain. 10. Department of Food Science, Faculty of Food Engineering, State University of Campinas (UNICAMP), Monteiro Lobato, 80. Caixa. CEP: 13083-862, Campinas, São Paulo.
Abstract
BACKGROUND: An adaptive neuro-fuzzy inference system (ANFIS) was employed to predict the oxidative stability of virgin olive oil (VOO) during storage as a function of time, storage temperature, total polyphenol, α-tocopherol, fatty acid profile, ultraviolet (UV) extinction coefficient (K268 ), and diacylglycerols (DAGs). RESULTS: The mean total quantities of polyphenols and DAGs were 1.1 and 1.9 times lower in VOOs stored at 25 °C than in the initial samples, and the mean total quantities of polyphenols and DAGs were 1.3 and 2.26 times lower in VOOs stored at 37 °C than in the initial samples, respectively. In a single sample, α-tocopherol was reduced by between 0.52 and 0.91 times during storage, regardless of the storage temperature. The mean specific UV extinction coefficients (K268 ) for VOO stored at 25 and 37 °C were reported as 0.15 (ranging between 0.06-0.39) and 0.13 (ranging between 0.06-0.35), respectively. The ANFIS model created a multi-dimensional correlation function, which used compositional variables and environmental conditions to assess the quality of VOO. The ANFIS model, with a generalized bell-shaped membership function and a hybrid learning algorithm (R2 = 0.98; MSE = 0.0001), provided more precise predictions than other algorithms. CONCLUSION: Minor constituents were found to be the most important factors influencing the preservation status and freshness of VOO during storage. Relative changes (increases and reductions) in DAGs were good indicators of oil oxidative stability. The observed effectiveness of ANFIS for modeling oxidative stability parameters confirmed its potential use as a supplemental tool in the predictive quality assessment of VOO.
BACKGROUND: An adaptive neuro-fuzzy inference system (ANFIS) was employed to predict the oxidative stability of virgin olive oil (VOO) during storage as a function of time, storage temperature, total polyphenol, α-tocopherol, fatty acid profile, ultraviolet (UV) extinction coefficient (K268 ), and diacylglycerols (DAGs). RESULTS: The mean total quantities of polyphenols and DAGs were 1.1 and 1.9 times lower in VOOs stored at 25 °C than in the initial samples, and the mean total quantities of polyphenols and DAGs were 1.3 and 2.26 times lower in VOOs stored at 37 °C than in the initial samples, respectively. In a single sample, α-tocopherol was reduced by between 0.52 and 0.91 times during storage, regardless of the storage temperature. The mean specific UV extinction coefficients (K268 ) for VOO stored at 25 and 37 °C were reported as 0.15 (ranging between 0.06-0.39) and 0.13 (ranging between 0.06-0.35), respectively. The ANFIS model created a multi-dimensional correlation function, which used compositional variables and environmental conditions to assess the quality of VOO. The ANFIS model, with a generalized bell-shaped membership function and a hybrid learning algorithm (R2 = 0.98; MSE = 0.0001), provided more precise predictions than other algorithms. CONCLUSION: Minor constituents were found to be the most important factors influencing the preservation status and freshness of VOO during storage. Relative changes (increases and reductions) in DAGs were good indicators of oil oxidative stability. The observed effectiveness of ANFIS for modeling oxidative stability parameters confirmed its potential use as a supplemental tool in the predictive quality assessment of VOO.
Authors: Samin Moradi; Nabi Shariatifar; Behrouz Akbari-Adergani; Ebrahim Molaee Aghaee; Majid Arbameri Journal: J Environ Health Sci Eng Date: 2021-06-21
Authors: Maria Tarapoulouzi; Sofia Agriopoulou; Anastasios Koidis; Charalampos Proestos; Hesham Ali El Enshasy; Theodoros Varzakas Journal: Biomolecules Date: 2022-08-25