Hafiza Hina Ibraheem, Muhammad Rizwan Tariq, Shinawar Waseem Ali, Zujaja Umer, Zunaira Basharat, Azeem Intisar, Tariq Mahmood, Gulzar Ahmad Nayik, Seema Ramniwas, Saleh Alfarraj, Mohammad Javed Ansari
{"title":"评估基于功能性芦荟(Aloe barbadensis)的番石榴果酱的营养探测和储存稳定性:预测建模的机器学习方法","authors":"Hafiza Hina Ibraheem, Muhammad Rizwan Tariq, Shinawar Waseem Ali, Zujaja Umer, Zunaira Basharat, Azeem Intisar, Tariq Mahmood, Gulzar Ahmad Nayik, Seema Ramniwas, Saleh Alfarraj, Mohammad Javed Ansari","doi":"10.1111/ijfs.17209","DOIUrl":null,"url":null,"abstract":"<div>\n \n <p>This research aimed to explore the influence of nutritional adjustments and storage stability on a functional, reduced-calorie guava jam incorporating <i>Aloe vera</i>. Over two months, comprehensive analysis assessed physicochemical properties, sensory traits, microbial stability, and shelf-life. The addition of <i>Aloe vera</i> gel resulted in significant improvements in pH (3.00 to 3.65), total soluble solids (40.10 to 42.20°Brix), antioxidant activity (36.85% to 81.09%), moisture content (29.48% to 38.82%), water activity (0.78 to 0.84), ash content (0.29% to 0.45%), fat content (0.14% to 0.19%), fibre content (1.05% to 1.86%), and the colour values. Moreover, <i>b</i>* scores for colour indication improved from 15.09 to 18.86. Texture attributes of cohesiveness and firmness improved significantly. Sensory evaluation favoured the T2 variant (20% <i>Aloe vera</i> gel), suggesting it as the optimal formulation. Furthermore, artificial neural networks (ANNs), a technique of machine learning, were utilised to predict guava jam behaviour, with 99% accuracy. The study discovered substantial changes in pH, total soluble solids, antioxidant activity, moisture content, and textural qualities, indicating that <i>Aloe vera</i> supplementation could improve guava jam quality and shelf-life. The results of ANN predictions about antioxidants and cohesiveness provide information about the product's performance during storage.</p>\n </div>","PeriodicalId":181,"journal":{"name":"International Journal of Food Science & Technology","volume":null,"pages":null},"PeriodicalIF":2.6000,"publicationDate":"2024-05-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Assessing nutritional probing and storage stability of functional Aloe vera (Aloe barbadensis) based guava jam: a machine learning approach for predictive modelling\",\"authors\":\"Hafiza Hina Ibraheem, Muhammad Rizwan Tariq, Shinawar Waseem Ali, Zujaja Umer, Zunaira Basharat, Azeem Intisar, Tariq Mahmood, Gulzar Ahmad Nayik, Seema Ramniwas, Saleh Alfarraj, Mohammad Javed Ansari\",\"doi\":\"10.1111/ijfs.17209\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div>\\n \\n <p>This research aimed to explore the influence of nutritional adjustments and storage stability on a functional, reduced-calorie guava jam incorporating <i>Aloe vera</i>. Over two months, comprehensive analysis assessed physicochemical properties, sensory traits, microbial stability, and shelf-life. The addition of <i>Aloe vera</i> gel resulted in significant improvements in pH (3.00 to 3.65), total soluble solids (40.10 to 42.20°Brix), antioxidant activity (36.85% to 81.09%), moisture content (29.48% to 38.82%), water activity (0.78 to 0.84), ash content (0.29% to 0.45%), fat content (0.14% to 0.19%), fibre content (1.05% to 1.86%), and the colour values. Moreover, <i>b</i>* scores for colour indication improved from 15.09 to 18.86. Texture attributes of cohesiveness and firmness improved significantly. Sensory evaluation favoured the T2 variant (20% <i>Aloe vera</i> gel), suggesting it as the optimal formulation. Furthermore, artificial neural networks (ANNs), a technique of machine learning, were utilised to predict guava jam behaviour, with 99% accuracy. The study discovered substantial changes in pH, total soluble solids, antioxidant activity, moisture content, and textural qualities, indicating that <i>Aloe vera</i> supplementation could improve guava jam quality and shelf-life. The results of ANN predictions about antioxidants and cohesiveness provide information about the product's performance during storage.</p>\\n </div>\",\"PeriodicalId\":181,\"journal\":{\"name\":\"International Journal of Food Science & Technology\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":2.6000,\"publicationDate\":\"2024-05-17\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"International Journal of Food Science & Technology\",\"FirstCategoryId\":\"1\",\"ListUrlMain\":\"https://onlinelibrary.wiley.com/doi/10.1111/ijfs.17209\",\"RegionNum\":3,\"RegionCategory\":\"农林科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"FOOD SCIENCE & TECHNOLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Journal of Food Science & Technology","FirstCategoryId":"1","ListUrlMain":"https://onlinelibrary.wiley.com/doi/10.1111/ijfs.17209","RegionNum":3,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"FOOD SCIENCE & TECHNOLOGY","Score":null,"Total":0}
Assessing nutritional probing and storage stability of functional Aloe vera (Aloe barbadensis) based guava jam: a machine learning approach for predictive modelling
This research aimed to explore the influence of nutritional adjustments and storage stability on a functional, reduced-calorie guava jam incorporating Aloe vera. Over two months, comprehensive analysis assessed physicochemical properties, sensory traits, microbial stability, and shelf-life. The addition of Aloe vera gel resulted in significant improvements in pH (3.00 to 3.65), total soluble solids (40.10 to 42.20°Brix), antioxidant activity (36.85% to 81.09%), moisture content (29.48% to 38.82%), water activity (0.78 to 0.84), ash content (0.29% to 0.45%), fat content (0.14% to 0.19%), fibre content (1.05% to 1.86%), and the colour values. Moreover, b* scores for colour indication improved from 15.09 to 18.86. Texture attributes of cohesiveness and firmness improved significantly. Sensory evaluation favoured the T2 variant (20% Aloe vera gel), suggesting it as the optimal formulation. Furthermore, artificial neural networks (ANNs), a technique of machine learning, were utilised to predict guava jam behaviour, with 99% accuracy. The study discovered substantial changes in pH, total soluble solids, antioxidant activity, moisture content, and textural qualities, indicating that Aloe vera supplementation could improve guava jam quality and shelf-life. The results of ANN predictions about antioxidants and cohesiveness provide information about the product's performance during storage.
期刊介绍:
The International Journal of Food Science & Technology (IJFST) is published for the Institute of Food Science and Technology, the IFST. This authoritative and well-established journal publishes in a wide range of subjects, ranging from pure research in the various sciences associated with food to practical experiments designed to improve technical processes. Subjects covered range from raw material composition to consumer acceptance, from physical properties to food engineering practices, and from quality assurance and safety to storage, distribution, marketing and use. While the main aim of the Journal is to provide a forum for papers describing the results of original research, review articles are also welcomed.