Impact of thermal and thermosonication treatments of amora (Spondius pinnata) juice and prediction of quality changes using artificial neural networks

被引:15
|
作者
Nayak, Prakash Kumar [1 ]
Chandrasekar, Chandra Mohan [2 ]
Gogoi, Shikharpiyom [1 ]
Kesavan, Radha krishnan [1 ]
机构
[1] Cent Inst Technol, Dept Food Engn & Technol, Kokrajhar 783370, Assam, India
[2] Anna Univ, Ctr Food Technol, Chennai 600025, Tamil Nadu, India
关键词
Thermosonication; quality; juice; ANN; modelling; storage study; ULTRASOUND TREATMENT; BIOACTIVE COMPOUNDS; MICROBIAL QUALITY; STABILITY; FRUIT; SONICATION; PARAMETERS; INACTIVATION; OPTIMIZATION; EXTRACTION;
D O I
10.1016/j.biosystemseng.2022.02.012
中图分类号
S2 [农业工程];
学科分类号
0828 ;
摘要
The impact of thermal and non-thermal treatments on the quality of amora (Spondius pinnata) juice during refrigerated storage (4 +/- 1 degrees C) for 30 days was investigated. Freshly prepared juices from amora fruits were thermosonicated at the frequency of 44 kHz for 30, 45 and 60 min at 40 degrees C and also pasteurised at 90 degrees C for 1 min. Changes in the quality parameters, such as, pH, total soluble solids, titratable acidity, total phenolic contents, total flavonoid contents. Antioxidant activity, ascorbic acid content, browning index, cloud values and microbial populations were checked periodically. The experimental results indicated a significant increase in total phenolic contents, total flavonoid contents, anti-oxidant activity and the sensorial properties of thermosonicated juices during storage their period when compared to fresh and thermally treated amora juices. Maximum reduction in the microbial populations was accomplished after thermal and thermosonication for 60 min treatments. The physicochemical properties of thermosonicated juices exhibited minimal changes in contrast to other samples. The prediction of the quality changes in thermosonicated juices during storage was carried out using an artificial neural network model. From this study thermosonication at 40 degrees C can be recommended as a substitute to thermal processing and it may be employed to amora juice production to decrease the microbial population and improve functional and sensorial properties.(c) 2022 IAgrE. Published by Elsevier Ltd. All rights reserved.
引用
收藏
页码:169 / 181
页数:13
相关论文
共 50 条
  • [21] Thermal properties of ethylic biodiesel blends and solid fraction prediction using artificial neural networks
    Magalhaes, Ana M. S.
    Brentan, Bruno M.
    Meirelles, Antonio J. A.
    Maximo, Guilherme J.
    FLUID PHASE EQUILIBRIA, 2023, 574
  • [22] Prediction of thermal conductivity of ethylene glycol-water solutions by using artificial neural networks
    Kurt, Hueseyin
    Kayfeci, Muhammet
    APPLIED ENERGY, 2009, 86 (10) : 2244 - 2248
  • [23] Thermal Conductivity Prediction of Soil in Complex Plant Soil System using Artificial Neural Networks
    Wardani, A. K.
    Purqon, A.
    6TH ASIAN PHYSICS SYMPOSIUM, 2016, 739
  • [24] Improving Quality of Long-Term Bond Price Prediction Using Artificial Neural Networks
    Verner, Robert
    Tkac, Michal, Sr.
    Tkac, Michal, Jr.
    QUALITY INNOVATION PROSPERITY-KVALITA INOVACIA PROSPERITA, 2021, 25 (01): : 103 - 123
  • [25] A practical low-cost model for prediction of the groundwater quality using artificial neural networks
    Heidarzadeh, Nima
    JOURNAL OF WATER SUPPLY RESEARCH AND TECHNOLOGY-AQUA, 2017, 66 (02): : 86 - 95
  • [26] Prediction of wheat quality parameters using near-infrared spectroscopy and artificial neural networks
    Ayse C. Mutlu
    Ismail Hakki Boyaci
    Huseyin E. Genis
    Rahime Ozturk
    Nese Basaran-Akgul
    Turgay Sanal
    Asuman Kaplan Evlice
    European Food Research and Technology, 2011, 233 : 267 - 274
  • [27] Quality Prediction in Directed Energy Deposition Using Artificial Neural Networks Based on Process Signals
    Marko, Angelina
    Baehring, Stefan
    Raute, Julius
    Biegler, Max
    Rethmeier, Michael
    APPLIED SCIENCES-BASEL, 2022, 12 (08):
  • [28] Prediction of wheat quality parameters using near-infrared spectroscopy and artificial neural networks
    Mutlu, Ayse C.
    Boyaci, Ismail Hakki
    Genis, Huseyin E.
    Ozturk, Rahime
    Basaran-Akgul, Nese
    Sanal, Turgay
    Evlice, Asuman Kaplan
    EUROPEAN FOOD RESEARCH AND TECHNOLOGY, 2011, 233 (02) : 267 - 274
  • [29] Prediction of quality performance using artificial neural networks Evidence from Indian construction projects
    Jha, K. N.
    Chockalingam, C. T.
    JOURNAL OF ADVANCES IN MANAGEMENT RESEARCH, 2009, 6 (01) : 70 - U94
  • [30] Prediction of the Physicochemical Properties of Spray-Dried Black Mulberry (Morus nigra) Juice using Artificial Neural Networks
    Mahboubeh Fazaeli
    Zahra Emam-Djomeh
    Mahmoud Omid
    Ahmad Kalbasi-Ashtari
    Food and Bioprocess Technology, 2013, 6 : 585 - 590