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

Abstract

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 °C. 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.

Introduction

Cereal grains are considered to be one of the most important sources of dietary proteins, carbohydrates, vitamins, minerals and fiber for people all over the world. For Asian peoples, rice is the main staple food of them. It has many unique properties, such as bland taste and hypoallergenic properties (Kadan et al., 2001). However, the nutritional quality of cereals and the sensorial properties of products are sometimes inferior or poor in comparison with milk and milk products. The reasons behind this are the lower protein content, the deficiency of certain essential amino acids (lysine), the low starch availability, the absent of determined antinutrients (phytic acid, tannins and polyphenols) and the coarse nature of the grains (Wang et al., 2016). Fermentation as a biological technology is widely used in food processing. It is a prospective technology, which can achieve the proposed purpose without adding foreign materials which have health hazards (Lu et al., 2005). So it is widely to use waxy rice to get yin rice or “yinmi” in Chinese, which is a kind of the traditional rice food in south of china by natural fermentation in order to improve the shelf-life, digestibility and nutritional properties of waxy rice. Moreover, like china, there are many natural fermented rice foods in other countries, such as “cauim” beverage produced by Brazilian Amerindians (Almeida et al., 2007), “boza” beverage from Turkish, South Africa (Botes et al., 2007; Hancioğlu and Karapinar, 1997), “koji”, traditional fermented rice product in Japan (Hamajima et al., 2016), “banh deo”, yeast rice cakes from Vietnamese and so on (Thanh et al., 2016).

Accumulating evidence has identified that natural fermentation can enhance physico-chemical characteristics of rice, such as major chemical components, digestibility and intrinsic viscosity (Lu et al., 2005; Yang et al., 2011). However, natural fermentation yin rice is produced in small, labour-intensive factories, and the quality of natural fermentation yin rice varies with processing conditions. Thus, it is significant to use inoculation fermentation to shorten fermentation time and get stable quality yin rice. But there are few literatures about the inoculation fermentation and its effects on the properties of yin rice. The predominant microorganisms isolated from naturally fermented yin rice have been reported. The main fermenting strains are species of lactic acid bacteria (Lactobacillus plantarum, L.paracasei, L.pentosus), Saccharomyces cerevisiae and Rhizopus oryzae (Blandino et al., 2003; Rahayu, 2003). The viscosity of yin rice was decreased significantly after natural fermentation. Additionally, the value of viscosity reflected the composition of yin rice for a certain extent (Yang et al., 2011).

During inoculation fermentation processing, fermentation conditions are the crucial factors. Therefore, in order to reveal the relationship between inoculation fermentation parameters and properties of yin rice, artificial neural network and genetic algorithm (ANN-GA) model is developed.

Traditional modeling and optimization approaches for such as response surface methodology present restrictions for modeling highly complex systems (Rafigh et al., 2014). However, artificial neural network (ANN) is powerful tool to deal with the nonlinear and multiple processing systems (Ding et al., 2017). As artificial neural networks possess excellent ability of high learning and identifying, it can carry out complicated non-linear relationships between the input and output of a system with an appropriate choice of free parameters or weight easily (Watanabe et al., 2014). In the last decade, the ANN has already been successfully applied to chemistry (Cristea et al., 2003; Sun et al., 2011), food processing (Ding et al., 2016), microbiology (Ferrari et al., 2017), medicine (Amato et al., 2013), psychology (Levine, 2007) as well as various other fields. On the flip side, genetic algorithms (GA) mimic biological evolution process to choose the optimal value of a complex objective, and it is a global optimizing algorithm (Erenturk and Erenturk, 2007). Experiments show that GA can effective improve the productivity of fermentation (Kumar et al., 2015).

The aim of this present work was to build ANN-GA model to optimize the fermentation conditions of yin rice and the effects of inoculation fermentation on the physico-chemical properties of yin rice were discussed.

Materials and Methods

2.1 Materials    Waxy rice (Zhongnuo 2055) was harvested from the Chuhe farm (Hanchuan, Hubei, China). The lactic acid bacteria which were obtained from Beijing Chuanxiu science and technology limited company contained L. plantarum, L. paracasei, and L. pentosus (3:1:1), S. cerevisiae was from yellow rice wine yeast and R. oryzae was from rice wine starter. Both yellow rice wine yeast and rice wine starter were obtained from Hubei Angel Yeast stock limited company. All other chemicals used were analytical grade. Distilled water was used.

2.2 Preparation of yin rice flours by natural and inoculation fermentation    250 g (14.22 %, wet basis) dehulled waxy rice was fermented at 25 °C for 16 days in 750 mL distilled water which was changed once every 5 days (Yang et al., 2011). After fermentation, the rice grains were washed four times with distilled water, and dried at 40 °C for 12 h, then milled using a grinder (JP-100A, Shanghai Jiupin limited company, Shanghai, China). The rice flours (80 %, dry basis) were sieved through 100 meshs sieve (Shangyu Jinding Standard Sieve Factory, Zhejiang, China), packed and sealed in polyethylene bags and stored at 4 °C in the dryer until use. The yin rice which was got in this process named natural fermentation yin rice.

In this study, lactic acid bacteria (L.plantarum, L.paracasei, L.pentosus), S.cerevisiae and R.oryzae were chosen as fermentation strains and viscosity value was selected as the major index. The viscosity of natural fermentation yin rice was served as control. Lactic acid bacteria (1.45 × 10⁸ CFU/g: 0.05 %, 0.1 % and 0.15 %), S.cerevisiae (2.69 × 10⁷ CFU/g: 0.05 %, 0.1 % and 0.15 %) and R.oryzae (3.01 × 10¹⁰ spore/g: 0.1 %, 0.2 % and 0.3 %) were added into dehulled waxy rice in 750 mL distilled water before fermentation. Fermentation temperatures were controlled at 22, 25, 28 and 35 °C and the fermentation time were 42, 45, 48 h. After fermentation, the flours were prepared as above.

All flours samples were stored at low temperature (4 °C) for the following analysis. A schematic illustration representing the inoculation fermentation yin rice was shown in Figure 1A.

Fig. 1.

Schematic of inoculation fermentation yin rice (A) and optimization procedure of ANN-GA (B)

2.3 Artificial neural networks (ANN) analysis

2.3.1 Modeling of artificial neural network    Artificial neural network, as a novel information processing technique, which is widely applied to various fields, consists of three layers: input, hidden, and output layers. Input layer consisting of a group of processing units which are responsible for acceptance of data imported to the network. Then hidden layers of data from previous layer and the processing of each layer data by them corresponding weights, value of the sum, results using the nonlinear or linear activation function and accretion basis (Eq. 1):   

Where x and y are input and output, respectively; n is the number of input layer neurons; and wij and bj are associated with weight and bias, respectively. In the study, was used in the hidden layer was operated by the hyperbolic tangent function (Eq. 2), whereas a linear function was applied in the output layer. The Levenberg-Marquardt algorithm was used as training algorithm because of different ranges of input and output. According to Eq. (3), the inputs and outputs were normalized to [-1, 1] prior to entering the network.

  

  

The input layer consisted of five variables in the process, namely, lactic acid bacteria, S.cerevisiae, R.oryzae, fermentation temperature, fermentation time, and the output layer contained one (viscosity) variables. Figure 1B showed a summary of the network topology illustration.

We employed a range of retort processing conditions as listed in Table 1. The total runs of 324 experiments were obtained in accordance to general factorial design (3×3×3×4×3). In this study, the all data points (324) were randomly divided into three groups: training (60 %), validating (20 %), and testing data (20 %). The training data were used to train the network. The weight in the network was developed by the experimental output of the training data. The testing data was used to evaluate the predictive ability of the network. Training continued as long as the computed error between the actual and predicted outputs for the test set was decreased.

Table 1. A range of retort processing conditions of GA-ANN model

Parameter	viscosity
Mean square error	0.0387
Normalized mean square error	0.2481
Mean absolute error	0.0762
Minimum absolute error	0.0195
Maximum absolute error	19.8581
Linear Correlation coefficient	0.9746

Compared with other algorithms such as Bayesian regularization, gradient descent, Levenberg-Marquardt and BFGS quasi-Newton methodology, it showed that back-propagation (BP) network was effective, stable and consistent (Singh et al., 2009). Thus, we chose BP network to obtain the minimum sum of squared errors. Meanwhile, the BP neural network was trained from the vectors of input and corresponding target until it could approximate a prediction function (Annonymous, 2005). In the ANN, BP topology network had been greatly applied to the complex fermentation process modeling (Ma et al., 2011; Zhang et al., 2014). Hence, BP algorithm as the momentum learning rule was applied to implement supervised training network.

2.3.2 Genetic algorithm to optimize fermentation parameters    Genetic algorithm was an optimization method based on the concept of natural selection (Aggarwa et al., 2014). In this study, we used the GA-ANN model linking input layer (lactic acid bacteria, S.cerevisiae, R.oryzae, fermentation temperature and fermentation time) to output layer (viscosity). Genetic algorithm was a parameter searching and optimization technique based on emulation of natural evolutionary processes. According to the selection, crossover and mutation operation, GA could discover the optimal fitness individual. Selection was the first operation that can be chose to be the best individual through roulette wheel and fitness function. Then the two randomly individuals transform into two new individuals. Finally, based on the mutation probability, the composed of each individual of the chromosome randomly alters through the mutation operation. After the ANN model was established, according to the input variables, GA could continuously optimize the fermentation parameters, until get the optimal solution, as shown in Figure 1B. The operation parameters of genetic algorithm were assigned as follows: number of individuals: 60, maximum number of generations: 100, number of variables: 5, crossover probability: 90 %, mutation probability: 0.01. The optimization process was run several times with various initial populations to avoid local optimum.

2.4 Viscosity determination    The viscosity was measured with rotary viscosimeter (NDJ-8S, Shanghai, China). The 5 % (w/w) rice paste was prepared by kneading well with distilled water. The samples were heated at 100 °C for 10 minutes to gel. Then they were cooled for 20 minutes, and measured the viscosity with rotary viscosimeter (Ohishi et al., 2007). The parameter of measuring viscosity was rotor 4, 60 r/min.

  

When the parameter of measuring viscosity was rotor 4, 60 r/min, the conversion factors K = 100. η was value of the shear rate.

2.5 Chemical analysis    The moisture content, ash content and lipid content of native rice flours, natural fermentation yin rice flours and inoculation fermentation yin rice flours were determined. The amylose content was examined using the method of Xie et al (2014). The total starch content and resistant starch content were examined using the method of Jiang et al (2013). Crude protein (n×5.95) was determined by the Shaikh et al (2014). The results were expressed on a dry basis except for amylose content, which was based on total starch. Total sugar was examined using the method of Miller (1959).

2.6 Size distribution    A laser particle size analyzer BT-9300H (Bettersize, China) was used to determine the size distribution of rice flours. A mount of rice flours were dispersed in ethanol regularly for the analysis of size distribution (Wang et al., 2012).

2.7 Scanning electron microscopy    Scanning electron micrographs were obtained with a scanning microscope (JSM-6390/LV, Japan). The rice flours were set to the mold and then observed. Micrographs were taken at 1000 × magnification.

2.8 X-ray diffraction and relative crystallinity    Rice flours were equilibrated in a relative humidity (5 % RH) at room temperature. X-ray diffraction analysis was performed with a D/max-RA III X-ray diffractometer (Rigaku Corporation, Tokyo, Japan). Rice flours were tightly packed into the sample holder. The diffractometer was operated at 40 kV and 50 mA with the Cu Kα radiation. The diffraction data were collected over an angular range from 3 to 50° (2θ). Step width 0.02°, and a scan rate of 15°/min (Wang et al., 2012). The crystallinity (%) of the rice flours were calculated following the method of Woranuch et al (2017) using a computer program (Origin 6.0, Microcal, Northampton, MA).

2.9 Differential scanning calorimetry (DSC)    Thermal properties of rice flours were measured using a differential scanning calorimeter (DSC 200F3, Suzhou Kaidi Deri instrument limited company, Europe). Rice flours (5 mg, db) were weighed into an aluminium specimen box with 10 µL of distilled water. The samples were sealed and equilibrated at room temperature for 12 h, and then heated from 30 to 130 °C at a rate of 5 °C/min (Chung et al., 2011).

2.10 Statistical analysis    All statistical analyses were performed using Origin software 8.0. All data were presented as means ± standard deviation (SD) and calculated using one-way ANOVA of SPSS 17.0 followed by the Tukey's multiple-range test. The statistical significance was defined as p < 0.05 or p < 0.01.

Results and Discussions

3.1 Evaluation of artificial neural network    Based on the fermentation parameters and viscosity, the reliable back-propagation ANN model was established. According to widely accepted empirical rule, as follows, the maximum number of hidden layer was obtained (Nagata and Chu, 2003).

  

Where N^TR and N^Inputs were the number of training samples and input nodes output of neurons, respectively. Therefore, in the network, the maximum value for hidden layer was 32. Further, the regression plots of the ANN model were depicted in Figure 2, which presented the prediction values versus experimental values for training, validation and test data.

Fig. 2.

Regression plots of training, validation and test data of artificial neural network

The ANN results showed that correlation coefficient of training, validation and test were 0.9777, 0.9889, and 0.9746, respectively. Table 1 showed the mean square error (MSE), normalized mean square error (NMSE), mean absolute error (MAE), minimum absolute error, maximum absolute error and the linear correlation coefficient from the testing process. In the GA-ANN model, the values of the linear correlation coefficient, MSE, NMSE, and MAE were found to be close to 1, 0, 0, and 0, respectively, which represented the optimum of GA-ANN model (Sahoo and Ray, 2006). These results indicated that GA-ANN model provided an accurate prediction method for viscosity based on fermentation parameters. A sensitivity analysis was applied to select the largest contribution of output layers in ANN. Figure 3 showed that the variation of each output layers with respect to the variation of each input. Among input variables, fermentation temperature exhibited the most significant effect on viscosity of yin rice, followed by R. oryzae, lactic acid bacteria, S. cerevisiae and fermentation time.

Fig. 3.

Significance analysis on the optimized neural network sensitivity. L: lactic acid bacteria; Y: S.cerevisiae; R: R.oryzae; FT: Fermentation temperature; FM: Fermentation time

3.2 Optimization of process parameters by GA    Based on fermentation parameters (lactic acid bacteria, S.cerevisiae, R.oryzae, fermentation temperature and fermentation time), optimization conditions were continuously chosen in the ANN-GA model. The model optimal value of viscosity was 146.8 ± 0.8, which was nearly to that of natural fermentation yin rice (148.9 ± 1.3). The optimized process conditions were as follow: the content of lactic acid bacteria, S.cerevisiae and R.oryzae, were 0.05 %, 0.05 % and 0.2 %, respectively, the fermentation temperature and time were 25 °C and 48 h, respectively. Furthermore, the optimization condition of inoculation fermentation yin rice obtained from ANN-GA model was validated by experiments.

3.3 Chemical components    Table 2 showed the chemical components of native rice, natural fermentation yin rice and inoculation fermentation yin rice. After fermentation, the contents of moisture, total starch, resistant starch, protein, lipid and ash in waxy rice decreased, whereas the contents of total sugar and amylose increased. Theoretically speaking, the decrease of resistant starch, protein and lipid content would improve the digestibility of waxy rice. Meanwhile, the contents of total sugar and protein had no significant differences between inoculation fermentation yin rice and natural fermentation yin rice. These results revealed that the optimization of inoculation fermentation could obtain the similar characteristics to natural fermentation yin rice to some extent.

Table 2. Chemical components of rice flours

Rice flours	Native	Natural fermentation	Inoculation fermentation
Moisture (%)	11.97 ± 0.07	8.93 ± 0.08	6.77 ± 0.06**
Total sugar (%)	6.49 ± 0.18	8.52 ± 0.62	8.59 ± 0.67
Total starch (%)	76.76 ± 1.03	73.26 ± 0.60	81.24 ± 0.87*
Amylose (%)	3.56 ± 0.14	4.51 ± 0.11	5.85 ± 0.19**
Resistant starch (%)	7.46 ± 0.15	4.41 ± 0.09	5.06 ± 0.05**
Protein (%)	6.21 ± 0.03	5.10 ± 0.02	4.95 ± 0.06
Lipid (%)	8.89 ± 0.13	6.82 ± 0.05	4.02 ± 0.09**
Ash (%)	0.41 ± 0.01	0.16 ± 0.01	0.27 ± 0.01**
Relative crystallinity (%)	30.39	33.18	35.54

Amylose content was calculated on the basis of total starch. All data are a mean of three values µ standard deviation.

*  p < 0.01;

**  p < 0.05

3.4 Granule size distribution, thermal properties and scanning electron microscopy survey    The granule size distributions and thermal properties of rice flours were presented in Table 3. It was clear that the granule distribution of the optimization of inoculation fermentation yin rice flours was more intensive than natural fermentation yin rice flours. The major range of granule size distribution for inoculation fermentation yin rice flours was just <2.76 µm. Smaller granular size would result in energy efficient when the yin rice was cooked. Within the smaller granular size, the rice flours would be more easily to gel. So it was meaningful to utilize inoculation fermentation yin rice flours to make products, like rice cake.

Table 3. Physical properties of fermented and control rice flours

Rice flours	Native	Natural fermentation	Inoculation fermentation
Size distribution (µm) %
0–2.76	12.65 ± 1.61	18.33 ± 0.67	78.12 ± 4.88
2.76–21.12	61.42 ± 0.70	61.15 ± 0.68	16.49 ± 0.38
21.12–40.15	19.64 ± 1.32	15.61 ± 0.71	4.96 ± 0.19
40.15–61.62	5.20 ± 0.48	4.02 ± 0.43	0.23 ± 0.09
61.62–84.96	1.05 ± 0.13	0.86 ± 0.15	0
84.96–94.56	0.04 ± 0.08	0.04 ± 0.07	0
>94.56	0	0	0
Thermal properties
To (°C)	75.50 ± 0.40	74.01 ± 0.38	75.11 ± 1.51*
Tp (°C)	80.21 ± 0.31	78.80 ± 0.12	80.02 ± 1.30**
Tc (°C)	85.91 ± 0.31	85.01 ± 0.27	87.20 ± 1.14**
ΔH (J/g)	9.62 ± 1.02	10.61 ± 0.88	11.51 ± 0.90
Tp-To (°C)	4.70 ± 0.10	4.73 ± 0.32	4.92 ± 0.27
Tc-To (°C)	10.30 ± 0.15	11.00 ± 0.64	12.13 ± 0.19*

All data are a mean of three values ± standard deviation.

*  p < 0.01;

**  p < 0.05

The gelatinization enthalpy (ΔH) and Tp-To (°C) varied no significantly among the optimization of inoculation fermentation rice flours and the natural fermentation rice flours. From the Table 3, it was easy to obtain the result that the T_o and T_p of the fermentation rice flours were decreased, but the gelatinization enthalpy and the range of gelatinization temperature were increased after fermentation. Gelatinization temperatures enhanced with increasing amylose content in various rice starches (Park et al., 2007). During fermentation, the amorphous zone of starch changed and the hydration ability of starch molecular enhanced because of acid and enzyme produced by the microbe, so the gelatinization enthalpy (ΔH) of yin rice flours increased. In addition, the process of fermentation could destruct the combination of fat, protein and starch and might led to the decrease of T_c which was contributed to decreasing the energy during food process.

The granule shapes of different rice flours were presented in Figure 4. From the figure, it could obtain the result that the optimization of inoculation fermentation rice flours granule would more homogeneous than the natural fermentation rice flours granule. The range of granule size of native rice flours was wider than fermentation rice flours. Meanwhile, granules had slight superficial corrosion after fermentation.

Fig. 4.

Scanning electron micrograph of the rice flour A: native rice flours; B: natural fermentation yin rice flours; C: inoculation fermentation yin rice flours

3.5 X-ray diffraction and relative crystallinity    The X-ray diffraction patterns of rice flours were showed in Figure 5. The crystallinity level calculated from the ratio of area of crystalline diffraction peak and total diffraction peaks area were given in Table 2. Rice flours exhibited strong diffraction peaks at 2θ with values of around 15.61°, 17.58°, 18.35°, 23.44° and 27°. These results indicated that the crystal type of rice flours was a characteristic A-type. No significant differentia was observed between the X-ray diffraction patterns of different rice flours. As shown in Table 2, the relative crystallinity of different rice flours ranged from 30.39 % to 35.54 %. So the differences in relative crystallinity among the rice flours could not be attributed to differences in crystallite size since the sharpness in X-ray pattern was identical in all rice flours (Wang et al., 2012).

Fig. 5.

X-ray diffraction patterns of rice flours

In the tested rice samples, fermentation yin rice flours showed the higher value of relative crystallinity than the native yin rice flours. During the process of fermentation, the metabolism of microbial might produce acids and enzymes which might influence the amorphous portion of starch granules. As the position of the characteristic peaks of rice flours was quite similar, so it was easy to obtain the result that fermentation hadn't change the crystal type of the rice flours.

Conclusion

In the present work, the excellent predictive capabilities of ANN model were established and optimization of fermentation conditions were carried out by coupling with GA. Optimization of fermentation process parameters based on the ANN-GA, the reliability of network model was further verified by experiments. Results showed that the optimization of inoculation fermentation effectively improved physico-chemical properties of yin rice compared to the natural fermentation. The results revealed that the optimization of inoculation fermentation may change the crystalline structure of rice flours and amorphous region of the starch granule as well as the chemical components and it may modify the physical properties of yin rice. Therefore, ANN-GA was an optimization method which provided valuable information for inoculation fermentation yin rice.

Acknowledgments    The authors wish to acknowledge the National Key Research and Development Program of China (No: 2016YFD0400701-05), Doctoral Start-up Funding of Hubei University of Technology (No: 4301/00047, BSQD2017016), and Nature Science Foundation of Hubei Province (No: 4115/00051).

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