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Technical Paper Artificial Neural Network–Genetic Algorithm to Optimize Yin Rice Inoculation Fermentation Conditions for Improving Physico-Chemical Characteristics

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Technical Paper Artificial Neural Network–Genetic Algorithm to Optimize Yin Rice Inoculation Fermentation Conditions for Improving Physico-Chemical Characteristics _ Food Science and Technology Research, 24 (4), 729 737, 2018 Copyright © 2018, Japanese Society for Food Science and Technology http://www.jsfst.or.jp doi: 10.3136/fstr.24.729 Technical paper Artificial Neural Network–Genetic Algorithm to Optimize Yin Rice Inoculation Fermentation Conditions for Improving Physico-chemical Characteristics 1,2# 1,3# 1,2# 1,2 4 1,2 1,2* Kaiqun HU , Cheng DING , Mengzhou ZHOU , Chao WANG , Bei HU , Yuanyuan CHEN , Qian WU 1* and Nianjie FENG 1Hubei University of Technology, Wuhan, Hubei 430068, China 2Key Laboratory of Fermentation Engineering (Ministry of Education), Hubei Key Laboratory of Industrial Microbiology, Hubei Provincial Cooperative Innovation Center of Industrial Fermentation, Wuhan, Hubei 430068, China 3State Key Laboratory of Environmental and Biological Analysis, Department of Chemistry, Hong Kong Baptist University, Hong Kong SAR, China 4College of Food Science and Technology, Huazhong Agricultural University, Wuhan, Hubei 430070, China Received August 18, 2017 ; Accepted April 2, 2018 In this research, a nonlinear model describing the relationship between the inoculation fermentation parameters and the quality of yin rice were investigated based on artificial neural network and genetic algorithm (ANN-GA) model. The ANN-GA model had excellent potential for predicting the viscosity property of yin rice, and fermentation parameters were optimized by using genetic algorithm. Through ANN-GA model, the optimized inoculation fermentation parameters were: 0.05 % lactic acid bacteria, 0.05 % Saccharomyces cerevisiae, 0.2 % Rhizopus oryzae, then fermenting for 48 h at 25 ℃ . The results were further validated by experiments. Moreover, it revealed that inoculation fermentation not only effectively improved physico-chemical characteristics of yin rice, but also shorten period of fermentation about 14 days compared to the natural fermentation. These results indicated that the accuracy and reliable of fermentation parameters optimized by ANN-GA model. Keywords: yin rice, fermentation, artificial neural network and genetic algorithm (ANN-GA) model, optimal parameters, physico-chemical properties Introduction sometimes inferior or poor in comparison with milk and milk Cereal grains are considered to be one of the most products. The reasons behind this are the lower protein content, important sources of dietary proteins, carbohydrates, vitamins, the deficiency of certain essential amino acids (lysine), the low minerals and fiber for people all over the world. For Asian starch availability, the absent of determined antinutrients peoples, rice is the main staple food of them. It has many (phytic acid, tannins and polyphenols) and the coarse nature of unique properties, such as bland taste and hypoallergenic the grains (Wang et al., 2016). Fermentation as a biological properties (Kadan et al., 2001). However, the nutritional technology is widely used in food processing. It is a quality of cereals and the sensorial properties of products are prospective technology, which can achieve the proposed *To whom correspondence should be addressed. E-mail: Nianjie Feng ; [email protected] Qian Wu ; [email protected] # Kaiqun Hu, Cheng Ding and Mengzhou Zhou have contributed equally to this work. 730 K. HU et al. purpose without adding foreign materials which have health the optimal value of a complex objective, and it is a global hazards (Lu et al., 2005). So it is widely to use waxy rice to get optimizing algorithm (Erenturk and Erenturk, 2007). yin rice or “yinmi” in Chinese, which is a kind of the traditional Experiments show that GA can effective improve the rice food in south of china by natural fermentation in order to productivity of fermentation (Kumar et al., 2015). improve the shelf-life, digestibility and nutritional properties of The aim of this present work was to build ANN-GA model waxy rice. Moreover, like china, there are many natural to optimize the fermentation conditions of yin rice and the fermented rice foods in other countries, such as “cauim” effects of inoculation fermentation on the physico-chemical beverage produced by Brazilian Amerindians (Almeida et al., properties of yin rice were discussed. 2007), “boza” beverage from Turkish, South Africa (Botes et al., 2007; Hancioğlu and Karapinar, 1997), “koji”, traditional Materials and Methods fermented rice product in Japan (Hamajima et al., 2016), “banh 2.1 Materials Waxy rice (Zhongnuo 2055) was harvested deo”, yeast rice cakes from Vietnamese and so on (Thanh et from the Chuhe farm (Hanchuan, Hubei, China). The lactic al., 2016). acid bacteria which were obtained from Beijing Chuanxiu Accumulating evidence has identified that natural science and technology limited company contained L. fermentation can enhance physico-chemical characteristics of plantarum, L. paracasei, and L. pentosus (3:1:1), S. cerevisiae rice, such as major chemical components, digestibility and was from yellow rice wine yeast and R. oryzae was from rice intrinsic viscosity (Lu et al., 2005; Yang et al., 2011). wine starter. Both yellow rice wine yeast and rice wine starter However, natural fermentation yin rice is produced in small, were obtained from Hubei Angel Yeast stock limited company. labour-intensive factories, and the quality of natural All other chemicals used were analytical grade. Distilled water fermentation yin rice varies with processing conditions. Thus, was used. it is significant to use inoculation fermentation to shorten 2.2 Preparation of yin rice flours by natural and fermentation time and get stable quality yin rice. But there are inoculation fermentation 250 g (14.22 %, wet basis) dehulled few literatures about the inoculation fermentation and its waxy rice was fermented at 25 ℃ for 16 days in 750 mL effects on the properties of yin rice. The predominant distilled water which was changed once every 5 days (Yang et microorganisms isolated from naturally fermented yin rice al., 2011). After fermentation, the rice grains were washed four have been reported. The main fermenting strains are species of times with distilled water, and dried at 40 ℃ for 12 h, then lactic acid bacteria (Lactobacillus plantarum, L.paracasei, milled using a grinder (JP-100A, Shanghai Jiupin limited L.pentosus), Saccharomyces cerevisiae and Rhizopus oryzae company, Shanghai, China). The rice flours (80 %, dry basis) (Blandino et al., 2003; Rahayu, 2003). The viscosity of yin rice were sieved through 100 meshs sieve (Shangyu Jinding was decreased significantly after natural fermentation. Standard Sieve Factory, Zhejiang, China), packed and sealed in Additionally, the value of viscosity reflected the composition polyethylene bags and stored at 4 ℃ in the dryer until use. The of yin rice for a certain extent (Yang et al., 2011). yin rice which was got in this process named natural During inoculation fermentation processing, fermentation fermentation yin rice. conditions are the crucial factors. Therefore, in order to reveal In this study, lactic acid bacteria (L.plantarum, L.paracasei, the relationship between inoculation fermentation parameters L.pentosus), S.cerevisiae and R.oryzae were chosen as and properties of yin rice, artificial neural network and genetic fermentation strains and viscosity value was selected as the algorithm (ANN-GA) model is developed. major index. The viscosity of natural fermentation yin rice was Traditional modeling and optimization approaches for such served as control. Lactic acid bacteria (1.45 x 108 CFU/g: as response surface methodology present restrictions for 0.05 %, 0.1 % and 0.15 %), S.cerevisiae (2.69 x 107 CFU/g: modeling highly complex systems (Rafigh et al., 2014). 0.05 %, 0.1 % and 0.15 %) and R.oryzae (3.01 x 1010 spore/g: However, artificial neural network (ANN) is powerful tool to 0.1 %, 0.2 % and 0.3 %) were added into dehulled waxy rice in deal with the nonlinear and multiple processing systems (Ding 750 mL distilled water before fermentation. Fermentation et al., 2017). As artificial neural networks possess excellent temperatures were controlled at 22, 25, 28 and 35 ℃ and the ability of high learning and identifying, it can carry out fermentation time were 42, 45, 48 h. After fermentation, the complicated non-linear relationships between the input and flours were prepared as above. output of a system with an appropriate choice of free All flours samples were stored at low temperature (4 ℃ ) parameters or weight easily (Watanabe et al., 2014). In the last for the following analysis. A schematic illustration representing decade, the ANN has already been successfully applied to the inoculation fermentation yin rice was shown in Figure 1A. chemistry (Cristea et al., 2003; Sun et al., 2011), food 2.3 Artificial neural networks (ANN) analysis processing (Ding et al., 2016), microbiology (Ferrari et al., 2.3.1 Modeling of artificial neural network Artificial 2017), medicine (Amato et al., 2013), psychology (Levine, neural network, as a novel information processing technique, 2007) as well as various other fields. On the flip side, genetic which is widely applied to various fields, consists of three algorithms (GA) mimic biological evolution process to choose layers: input, hidden, and output layers. Input layer consisting Yin Rice Inoculation Fermentation 731 Fig. 1. Schematic of inoculation fermentation yin rice (A) and optimization procedure of ANN-GA (B) of a group of processing units which are responsible for 2e2x tan sig (x) = ‒1 ······Eq. 2 acceptance of data imported to the network. Then hidden layers 1+e2x
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