Adaptive neuro-fuzzy interface system and neural network modeling for the drying kinetics of instant controlled pressure drop treated parboiled rice

Abstract

Hot air drying kinetics of paddy grains during instant controlled pressure drop (ICPD) assisted parboiling process and its impact on the quality and micro-structural properties of milled rice were investigated. Among five mathematical models, Midilli model showed best fitted outcomes for prediction of adequate drying behavior. For the mapping of moisture ratio (MR) as a function of treatment pressure (TP), decompressed state duration (DD) and drying time (DT), artificial neural network (ANN) and adaptive neuro-fuzzy interface system (ANFIS) were applied. ANFIS model (5-5-5) with Gaussian membership function demonstrated best performance when contrasted with 3-5-1 ANN architecture. Effective diffusivity of the drying process varied from 2.8 × 10⁻⁰⁹ to 7.0 × 10⁻⁰⁹ m²/s with the increase of TP and DD. In comparison of quality parameters with the variation of TP and DD, positive impacts on head rice yield (HRY), redness (a*) and yellowness (b*) values and negative consequences on cooking time (CT) and brightness (L*) value were observed. The outcomes additionally uncovered that parboiled rice obtained at 0.6 MPa TP, indicated best quality in terms of improved process performance, HRY, CT, color and micro-structural properties.

Keywords

Instant controlled pressure drop neuro-fuzzy neural network parboiling drying

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References

Ali

Ojha

TP.

(1977). Cooking quality of raw rice and parboiled rice. Rice Process Engineering Centre Reporter 3(1): 22–25.

Bahmani

Sabzi

Bahmani

(2015). Prediction of solubility of sulfur dioxide in ionic liquids using artificial neural network. Journal of Molecular Liquids 211: 395–400.

Bai

Xiao

Zhou

CS.

(2018). Artificial neural network modeling of drying kinetics and color changes of ginkgo biloba seeds during microwave drying process. Journal of Food Quality 2018: 1–8.

Bezbaruah

Hazarika

MK.

(2014). Generalization of temperature and thickness effects in kinetic studies of turmeric (Curcuma longa) slices drying. International Food Research Journal 21(4): 1529.

Bhadra

Bandyopadhyay

Chakraborty

Roy

Kumar

(2017). Development and testing of an ANN model for estimation of runoff from a snow covered catchment. Journal of the Institution of Engineers (India): Series A 98(1-2): 29–39.

Chakraborty

Gautam

Das

Hazarika

MK.

(2019). Instant controlled pressure drop (DIC) treatment for improving process performance and milled rice quality. Journal of the Institution of Engineers (India): Series A 100(4): 683–695.

Chakraborty

Sarma

Bora

Faisal

Hazarika

MK.

(2016). Generalization of drying kinetics during thin layer drying of paddy. Agricultural Engineering International: CIGR Journal 8(4): 177–189.

Chen

Chang

HC.

(2012). Evaluation of physicochemical properties of plasma treated brown rice. Food Chemistry 135(1): 74–79.

Das

Bal

(2009). Drying kinetics of high moisture paddy undergoing vibration-assisted infrared (IR) drying. Journal of Food Engineering 95(1): 166–171.

10.

Dash

Chakraborty

Singh

YR.

(2020). Modeling of microwave vacuum drying kinetics of bael (Aegle marmelos L.) pulp by using artificial neural network. Journal of the Institution of Engineers (India): Series A 101(2): 343–349.

11.

Graham-Acquaah

Manful

Ndindeng

Tchatcha

DA.

(2015). Effects of soaking and steaming regimes on the quality of artisanal parboiled rice. Journal of Food Processing and Preservation 39(6): 2286–2296.

12.

Guine

Cruz

Mendes

(2014). Convective drying of apples: Kinetic study, evaluation of mass transfer properties and data analysis using artificial neural networks. International Journal of Food Engineering 10(2): 281–299.

13.

Hasanipanah

Armaghani

Khamesi

Amnieh

Ghoraba

(2016). Several non-linear models in estimating air-overpressure resulting from mine blasting. Engineering with Computers 32(3): 441–455.

14.

Itagi

Singh

(2015). Status in physical properties of colored rice varieties before and after inducing retro-gradation. Journal of Food Science and Technology 52(12): 7747–7758.

15.

Kar

Jain

Srivastav

PP.

(1999). Parboiling of dehusked rice. Journal of Food Engineering 39(1): 17–22.

16.

Kaveh

Jahanbakhshi

Abbaspour-Gilandeh

Taghinezhad

Moghimi

MBF.

(2018). The effect of ultrasound pre-treatment on quality, drying, and thermodynamic attributes of almond kernel under convective dryer using ANNs and ANFIS network. Journal of Food Process Engineering 41(7): e12868.

17.

Khawas

Dash

Das

Deka

SC.

(2016). Modeling and optimization of the process parameters in vacuum drying of culinary banana (Musa ABB) slices by application of artificial neural network and genetic algorithm. Drying Technology 34(4): 491–503.

18.

Louka

Allaf

(2004). Expansion ratio and color improvement of dried vegetables texturized by a new process “controlled sudden decompression to the vacuum”: Application to potatoes, carrots and onions. Journal of Food Engineering 65(2): 233–243.

19.

Manikantan

M R

Barnwal

Goyal

R K.

(2014). Drying characteristics of paddy in an integrated dryer. Journal of Food Science and Technology 51(4): 813–819.

20.

Mounir

Allaf

Mujumdar

Allaf

(2012). Swell drying: Coupling instant controlled pressure drop DIC to standard convection drying processes to intensify transfer phenomena and improve quality—An overview. Drying Technology 30(14): 1508–1531.

21.

Mounir

Besombes

Al-Bitar

Allaf

(2011). Study of instant controlled pressure drop DIC treatment in manufacturing snack and expanded granule powder of apple and onion. Drying Technology 29(3): 331–341.

22.

Pilatowski

Mounir

Haddad

Cong

Allaf

(2010). The instant controlled pressure drop process as a new post-harvesting treatment of paddy rice: Impacts on drying kinetics and end product attributes. Food and Bioprocess Technology 3(6): 901–907.

23.

Rodríguez

Clemente

Sanjuán

Bon

(2014). Modelling drying kinetics of thyme (Thymus vulgaris L.): Theoretical and empirical models, and neural networks. Food Science and Technology International = Ciencia y Tecnologia de Los Alimentos Internacional 20(1): 13–22.

24.

Sarimeseli

Coskun

Yuceer

(2014). Modeling microwave drying kinetics of thyme (Thymus vulgaris L.) leaves using ANN methodology and dried product quality. Journal of Food Processing and Preservation 38(1): 558–564.

25.

Soponronnarit

Nathakaranakule

Jirajindalert

Taechapairoj

(2006). Parboiling brown rice using super heated steam fluidization technique. Journal of Food Engineering 75(3): 423–432.

26.

Suganthi

Natarajan

Sathiyamurthy

Chidambaram

(2013). Prediction of quality responses in micro-EDM process using an adaptive neuro-fuzzy inference system (ANFIS) model. The International Journal of Advanced Manufacturing Technology 68(1–4): 339–347.

27.

Tao

Zhou

Chu

Han

, et al. (2016). Neuro-fuzzy modeling to predict physicochemical and microbiological parameters of partially dried cherry tomato during storage: Effects on water activity, temperature and storage time. Journal of Food Science and Technology 53(10): 3685–3694.

28.

Thai

Allaf

(2014). DIC-assisted parboiling of paddy rice. In: Instant Controlled Pressure Drop (DIC) in Food Processing. New York, NY: Springer, pp. 57–66.

29.

Wahengbam

Hazarika

MK.

(2019). Quality of ready-to-eat komal chawal produced by brown rice parboiling method. Journal of Food Science and Technology 56(1): 187–199.

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