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优化博尔-特克勒(Bor-Thekera)果汁的品质与保质期延长:一种基于人工神经网络建模的热超声处理方法

Optimizing Quality and Shelf-Life Extension of Bor-Thekera () Juice: A Thermosonication Approach with Artificial Neural Network Modeling.

作者信息

Gogoi Shikhapriyom, Das Puja, Nayak Prakash Kumar, Sridhar Kandi, Sharma Minaxi, Sari Thachappully Prabhat, Kesavan Radha Krishnan, Bhaswant Maharshi

机构信息

Department of Food Engineering and Technology, Central Institute of Technology, Kokrajhar 783370, India.

Department of Food Technology, Karpagam Academy of Higher Education (Deemed to Be University), Coimbatore 641021, India.

出版信息

Foods. 2024 Feb 4;13(3):497. doi: 10.3390/foods13030497.

Abstract

This study investigated the quality characteristics of pasteurized and thermosonicated bor-thekera (Garcinia pedunculata) juices (TSBTJs) during storage at 4 °C for 30 days. Various parameters, including pH, titratable acidity (TA), total soluble content (TSSs), antioxidant activity (AA), total phenolic content (TPC), total flavonoid content (TFC), ascorbic acid content (AAC), cloudiness (CI) and browning indexes (BI), and microbial activity, were analyzed at regular intervals and compared with the quality parameters of fresh bor-thekera juice (FBTJ). A multi-layer artificial neural network (ANN) was employed to model and optimize the ultrasound-assisted extraction of bor-thekera juice. The impacts of storage time, treatment time, and treatment temperature on the quality attributes were also explored. The TSBTJ demonstrated the maximum retention of nutritional attributes compared with the pasteurized bor-thekera juice (PBTJ). Additionally, the TSBTJ exhibited satisfactory results for microbiological activity, while the PBTJ showed the highest level of microbial inactivation. The designed ANN exhibited low mean squared error values and high R values for the training, testing, validation, and overall datasets, indicating a strong relationship between the actual and predicted results. The optimal extraction parameters generated by the ANN included a treatment time of 30 min, a frequency of 44 kHz, and a temperature of 40 °C. In conclusion, thermosonicated juices, particularly the TSBTJ, demonstrated enhanced nutritional characteristics, positioning them as valuable reservoirs of bioactive components suitable for incorporation in the food and pharmaceutical industries. The study underscores the efficacy of ANN as a predictive tool for assessing bor-thekera juice extraction efficiency. Moreover, the use of thermosonication emerged as a promising alternative to traditional thermal pasteurization methods for bor-thekera juice preservation, mitigating quality deterioration while augmenting the functional attributes of the juice.

摘要

本研究调查了巴氏杀菌和热超声处理的博尔泰克拉(山竹)汁(TSBTJ)在4℃储存30天期间的品质特性。定期分析了各种参数,包括pH值、可滴定酸度(TA)、总可溶性固形物(TSSs)、抗氧化活性(AA)、总酚含量(TPC)、总黄酮含量(TFC)、抗坏血酸含量(AAC)、浊度(CI)和褐变指数(BI)以及微生物活性,并与新鲜博尔泰克拉汁(FBTJ)的品质参数进行比较。采用多层人工神经网络(ANN)对博尔泰克拉汁的超声辅助提取进行建模和优化。还探讨了储存时间、处理时间和处理温度对品质属性的影响。与巴氏杀菌博尔泰克拉汁(PBTJ)相比,TSBTJ在营养属性保留方面表现最佳。此外,TSBTJ在微生物活性方面表现出令人满意的结果,而PBTJ的微生物失活水平最高。所设计的ANN在训练、测试、验证和整体数据集方面均表现出较低的均方误差值和较高的R值,表明实际结果与预测结果之间存在很强的相关性。ANN生成的最佳提取参数包括处理时间30分钟、频率44kHz和温度40℃。总之,热超声处理的果汁,特别是TSBTJ,具有增强的营养特性,使其成为适合食品和制药行业的生物活性成分的宝贵来源。该研究强调了ANN作为评估博尔泰克拉汁提取效率的预测工具的有效性。此外,热超声处理作为博尔泰克拉汁保存的传统热巴氏杀菌方法的一种有前途的替代方法出现,在减轻品质劣化的同时增强了果汁的功能属性。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7c5a/10855326/b459cf6d2f7d/foods-13-00497-g001.jpg

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