A comparative study of RSM and ANN models for predicting spray drying conditions for encapsulation of Lactobacillus casei

IF 2.2 4区 农林科学 Q3 CHEMISTRY, APPLIED
Poorva Sharma, Michael T. Nickerson, Darren R. Korber
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引用次数: 0

Abstract

Background and Objectives

The aim of this study was to develop a wall material using pea protein isolate and pectin to optimize the encapsulation of Lactobacillus casei by spray drying. Response surface methodology (RSM) and artificial neural network (ANN) were used to analyze the effect of processing parameters.

Findings

The results showed that both RSM and ANN could be used to successfully characterize the experimental data, although ANN demonstrated greater predictive accuracy than RSM due to a higher R2 and lower mean square error (MSE).

Conclusion

ANN was observed to show more suitability than RSM. The encapsulation efficiency (90.7%), yield (45.5%), and wettability (169 s) of spray-dried probiotic powder obtained under optimal spray drying conditions (inlet air temperature (132°C); feed flow rate (9.5 mL/min) and pea protein isolate concentration (7.1%)) were observed to be not significantly different (p < .05) from predicted values for all three parameters, demonstrating the validity of applied model.

Significance and Novelty

In this study, production technology of vegan base probiotic powder has been developed using mathematical modeling through the spray-drying method. Therefore, this data can be useful for food processing industries to develop a high-quality probiotic powder through spray drying.

用于预测包裹干酪乳杆菌的喷雾干燥条件的 RSM 和 ANN 模型比较研究
背景与目的 本研究旨在开发一种使用豌豆蛋白分离物和果胶的壁材,以优化喷雾干燥法对干酪乳杆菌的封装。采用响应面法(RSM)和人工神经网络(ANN)分析了加工参数的影响。 研究结果 结果表明,RSM 和 ANN 都能成功地描述实验数据的特征,但 ANN 的 R2 和均方误差(MSE)更低,因此比 RSM 显示出更高的预测准确性。 结论 ANN 比 RSM 更适用。在最佳喷雾干燥条件(进气温度(132°C)、进料流速(9.5 mL/min)和豌豆蛋白分离物浓度(7.1%))下,观察到喷雾干燥益生菌粉的封装效率(90.7%)、产量(45.5%)和润湿性(169 s)与所有三个参数的预测值均无显著差异(p <.05),证明了所应用模型的有效性。 意义和新颖性 本研究利用数学模型,通过喷雾干燥法,开发了素基益生菌粉的生产技术。因此,这些数据对食品加工业通过喷雾干燥法研制优质益生菌粉很有帮助。
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来源期刊
Cereal Chemistry
Cereal Chemistry 工程技术-食品科技
CiteScore
5.10
自引率
8.30%
发文量
110
审稿时长
3 months
期刊介绍: Cereal Chemistry publishes high-quality papers reporting novel research and significant conceptual advances in genetics, biotechnology, composition, processing, and utili­zation of cereal grains (barley, maize, millet, oats, rice, rye, sorghum, triticale, and wheat), pulses (beans, lentils, peas, etc.), oil­seeds, and specialty crops (amaranth, flax, quinoa, etc.). Papers advancing grain science in relation to health, nutrition, pet and animal food, and safety, along with new methodologies, instrumentation, and analysis relating to these areas are welcome, as are research notes and topical review papers. The journal generally does not accept papers that focus on nongrain ingredients, technology of a commercial or proprietary nature, or that confirm previous research without extending knowledge. Papers that describe product development should include discussion of underlying theoretical principles.
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