# A Rice Hazards Risk Assessment Method for a Rice Processing Chain Based on a Multidimensional Trapezoidal Cloud Model

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## Abstract

**:**

## 1. Introduction

## 2. Materials and Methods

#### 2.1. Data Characteristics and Processing

_{1}(AFB

_{1}) and zealenone (ZEA) are common mycotoxins in rice and are highly toxic to both humans and animals [30]. These five indexes widely exist in real production, do strong harm, and are easier to detect than other factors. Therefore, these five indexes are selected as risk indexes to judge the safety degree of the rice processing chain.

_{ij}is the original value of risk index I in processing chain j. A sample of rice hazard testing data is shown in Table 2.

#### 2.2. Risk Assessment Method of a Rice Processing Chain Based on a Multidimensional Trapezoidal Cloud Model

_{1}, mercury, lead, and chromium, were adopted as the main indexes of the risk assessment. Thus, a five-dimensional trapezoidal cloud model was constructed. First, the AIVILNs are used to determine the parameters of cloud model. According to the rice hazard data, the expectation, upper and lower limits of the expectation, entropy, and excess entropy of the multidimensional trapezoidal cloud model are determined by the AIVILNs. Second, the comprehensive weights of the risk indexes are obtained through the method of combining static and dynamic weights. Finally, the above model parameters, risk index weights, and rice hazard data are all input into the multidimensional trapezoidal cloud model to obtain the corresponding level of the membership degree. The final evaluation result is the level with the maximum membership degree. The specific stage is shown in Figure 1.

#### 2.2.1. Construction of Risk Assessment Index System for Rice Hazards

#### 2.2.2. Weighting Method

_{0}, which represents the fixed weight of each risk index. It represents the subjective weight [32] and is obtained through AHP [33] based on expert opinion. Let the dynamic weight be w

_{d}, which changes constantly according to the different rice hazard data. In this paper, the exponential function is adopted for modeling, and the coefficient of variation method is combined for calculation. Let the combined weight obtained from the combination of the two be W, which makes the weight more accurate.

_{0}. The dynamic weight w

_{d}is modeled by an exponential function, which can be expressed as follows:

_{ij}is the normalized result of the impact factor, a

_{ij}is the bottom value of the exponential function of the dynamic weight of risk index i in stage j, and a

_{ij}> 1. Different hazards have different values, which are determined by the impact of hazard i on the security of this stage. The greater the impact, the greater the value. The bottom value of the dynamic weight exponential function represents a kind of deviation, which is the degree of deviation of the detection value from 0. The greater the difference between the detection values of risk index i, the greater the value of a

_{ij}should be. Therefore, the coefficient of the variation method is adopted to obtain the value. The coefficient of variation method is an objective weighting method. The index with a greater difference in the evaluation index system can better reflect the gap of the risk index, and the weight of such an index is larger in the index system. The dynamic weight is calculated as follows:

_{d}

_{1}. The weight vector of paddy rice is as follows: w

_{d}

_{1}= (0.1984, 0.2007, 0.1938, 0.1953, 0.2117).

_{1}sampling data. The weight W is the comprehensive weight obtained by combining static and dynamic weights. Through the comprehensive weight, we can intuitively see the relative proportion of current risk indexes. It can be seen from the above table that when the rice hazard data change, the corresponding weight also changes dynamically. The parameter ZEA in data sets 1 and 2 changes significantly. When the data of risk index ZEA decreases, the weight of ZEA also decreases. The parameters of AFB

_{1}and mercury in data sets 3 and 4 vary greatly. As their data increase, their weights also increase. In data sets 5, 6, 7, and 8, three parameters show obvious changes. It can be seen that when the parameter data sets are larger, the weight coefficient increases, and when the parameter data sets are smaller, the weight coefficient decreases.

_{0}= (0.4757, 0.2856, 0.1377, 0.0314, 0.0695).

#### 2.2.3. Construction of the Evaluation Model of the Processing Chain

_{n}is the entropy, and H

_{e}is the excess entropy.

_{nn}is a normal random number generated by E

_{n}and H

_{e}, and f is the comprehensive weight of each hazard index. We have i = 1, 2, …, 5, which represents the five risk indexes.

Method flow | |

Step 1 | if $\underset{\_}{{E}_{{x}_{1}}}\le {x}_{1}\le \overline{{E}_{{x}_{1}}}\&\&\underset{\_}{{E}_{{x}_{2}}}\le {x}_{2}\le \overline{{E}_{{x}_{2}}}\&\&\dots \&\&\underset{\_}{{E}_{{x}_{5}}}\le {x}_{5}\le \overline{{E}_{{x}_{5}}}$ |

Step 2 | μ = 1 |

Step 3 | else |

Step 4 | for i = 1:5 |

Step 5 | if $\underset{\_}{{E}_{{x}_{i}}}\le {x}_{i}\le \overline{{E}_{{x}_{i}}}$ |

Step 6 | ${E}_{{x}_{i}}={E}_{{x}_{i}}$ |

Step 7 | else if ${x}_{i}>\overline{{E}_{{x}_{i}}}$ |

Step 8 | ${E}_{{x}_{i}}=\stackrel{\_}{{E}_{{x}_{i}}}$ |

Step 9 | Else |

Step 10 | ${E}_{{x}_{i}}=\underset{\_}{{E}_{{x}_{i}}}$ |

Step 11 | End |

Step 12 | End |

Step 13 | $\mu =\mathrm{exp}\left[-{\displaystyle \sum _{i=1}^{5}\left({f}_{i}\frac{{(x-{E}_{{x}_{i}})}^{2}}{2{({E}_{n{n}_{i}})}^{2}}\right)}\right]$ |

Step 14 | End |

#### 2.2.4. Construction of AIVILNs and the Evaluation Model Parameter Calculation

_{j}represents the rice safety level. Because there are six levels in Table 3, the value of j ranges from 1 to 6. The language item set of the rice safety level in this paper is constructed as $H=\{{h}_{1},{h}_{2},{h}_{3},{h}_{4},{h}_{5},{h}_{6}\}$. The AIVILNs are implemented as follows.

_{j}is mapped from the language item set of the rice safety level. The formula is as follows:

_{max}and X

_{min}are the maximum and minimum values of the measured indexes, respectively.

_{nj}and H

_{ej}of the trapezoidal cloud model are as follows:

_{nj}and H

_{ej}are the entropy and excess entropy of each level, respectively.

## 3. Experiments and Results

#### 3.1. Risk Safety Evaluation in the Rice Processing Chain

#### 3.2. Comparison Experiments

## 4. Conclusions

## Author Contributions

## Funding

## Institutional Review Board Statement

## Informed Consent Statement

## Data Availability Statement

## Conflicts of Interest

## References

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Types of Evaluation Methods | Risk Assessment Methodology | Advantages | Disadvantages |
---|---|---|---|

Qualitative assessment methods | Index scoring method [5] | Clear quantitative metrics | Difficult to define indicator weights |

Delphi [6] | Relatively simplified relationships between system elements | Complex and time-consuming for collecting expert opinions | |

HACCP [6,7] | Multilevel and multi-indicator evaluation | Complex implementation | |

Quantitative assessment methods | Random forest algorithm [11] | Simple calculation | Prone to overfitting |

SVM [12] | High generalization ability | Unsuitable for classification of large data sets | |

BP [13] | High nonlinear mapping capability | Prone to local miniaturization problems | |

Qualitative and quantitative comprehensive analysis method | AHP [16] | A clear hierarchy of indicators and a wide range of applications | Reliance on the accuracy of expert assessment results |

Fuzzy integrated evaluation [17] | Excellent evaluation results for fuzzy objects | Complex calculation and subjective determination of weights | |

Cloud model [18] | Enables conversion of quantitative risk values to qualitative language sets | Difficulty in determining numerical characteristics |

Province | Stage | Sampling Date | Hazards | ||||
---|---|---|---|---|---|---|---|

ZEA (μg/kg) | AFB_{1} (μg/kg) | Mercury (mg/kg) | Lead (mg/kg) | Chromium (mg/kg) | |||

Anhui | Paddy rice | 20210304 | 30.756 | 0.671 | 0.009 | 0.137 | 1.300 |

Anhui | Husking | 20210313 | 0.857 | 0.143 | 0.007 | 0.057 | 0.495 |

Jiangsu | Paddy rice | 20210411 | 32.149 | 0.678 | 0.059 | 0.126 | 7.860 |

Jiangsu | Polished rice | 20210413 | 0.517 | 0.200 | 0.008 | 0.051 | 0.156 |

Heilongjiang | Polishing | 20211006 | 0.640 | 0.235 | 0.005 | 0.050 | 0.188 |

Heilongjiang | Polished rice | 20211009 | 0.361 | 0.187 | 0.007 | 0.065 | 0.269 |

Level | Evaluation Parameters | ||||
---|---|---|---|---|---|

ZEA (μg/kg) | AFB_{1} (μg/kg) | Mercury (mg/kg) | Lead (mg/kg) | Chromium (mg/kg) | |

I | ≤12.0 | ≤2.0 | ≤0.004 | ≤0.04 | ≤0.2 |

II | ≤24.0 | ≤4.0 | ≤0.008 | ≤0.08 | ≤0.4 |

III | ≤36.0 | ≤6.0 | ≤0.012 | ≤0.12 | ≤0.6 |

IV | ≤48.0 | ≤8.0 | ≤0.016 | ≤0.16 | ≤0.8 |

V | ≤60.0 | ≤10.0 | ≤0.02 | ≤0.2 | ≤1.0 |

VI | >64.0 | >10.0 | >0.02 | >0.2 | >1.0 |

Number | Data | Comprehensive Weight Vector | Stage |
---|---|---|---|

(ZEA, AFB_{1}, Mercury, Lead, Chromium) * | (ZEA, AFB_{1}, Mercury, Lead, Chromium) * | ||

1 | (29.581, 0.691, 0.016, 0.159, 5.634) | (0.3635, 0.1796, 0.0988, 0.0437, 0.3414) | paddy rice |

2 | (15.231, 0.724, 0.009, 0.19, 6.112) | (0.3008, 0.1773, 0.0989, 0.0469, 0.3671) | paddy rice |

3 | (6.17, 0.577, 0.008, 0.074, 0.571) | (0.4251, 0.2176, 0.1341, 0.0817, 0.1145) | husking |

4 | (5.878, 3.514, 0.608, 0.048, 0.498) | (0.4176, 0.2761, 0.1286, 0.074, 0.1037) | husking |

5 | (0.713, 0.212, 0.008, 0.055, 0.513) | (0.3458, 0.2423, 0.1622, 0.1095, 0.1402) | polishing |

6 | (0.681, 3.179, 0.007, 1.352, 2.473) | (0.3075, 0.3004, 0.1295, 0.0851, 0.1775) | polishing |

7 | (0.512, 0.19, 0.007, 0.047, 0.226) | (0.3475, 0.2424, 0.1637, 0.1114, 0.1351) | polished rice |

8 | (0.113, 1.89, 0.007, 0.832, 0.226) | (0.3258, 0.2805, 0.1541, 0.1147, 0.1249) | polished rice |

Stage | ZEA (μg/kg) | AFB_{1} (μg/kg) | Mercury (mg/kg) | Lead (mg/kg) | Chromium (mg/kg) |
---|---|---|---|---|---|

Paddy rice | 0.3635 | 0.1769 | 0.0988 | 0.0465 | 0.3144 |

Husking | 0.4521 | 0.2176 | 0.1341 | 0.0817 | 0.1145 |

Polishing | 0.3458 | 0.2423 | 0.1622 | 0.1095 | 0.1402 |

Polished rice | 0.3475 | 0.2624 | 0.7771 | 0.1114 | 0.1351 |

Level | AIVILNs | |||
---|---|---|---|---|

Paddy Rice | Husking | Polishing | Polished Rice | |

I | $<{h}_{1},[0.74,0.75],[0.1,0.24]>$ | $<{h}_{1},[0.97,0.98],[0,0.02]>$ | $<{h}_{1},[0.96,0.98],[0,0.02]>$ | $<{h}_{1},[0.96,0.98],[0,0.02]>$ |

II | $<{h}_{2},[0.88,0.89],[0,0.11]>$ | $<{h}_{2},[0.94,0.95],[0,0.05]>$ | $<{h}_{2},[0.93,0.95],[0,0.05]>$ | $<{h}_{2},[0.94,0.95],[0,0.05]>$ |

III | $<{h}_{3},[0.96,0.97],[0,0.03]>$ | $<{h}_{3},[0.91,0.92],[0,0.08]>$ | $<{h}_{3},[0.78,0.79],[0,0.11]>$ | $<{h}_{3},[0.91,0.92],[0,0.08]>$ |

IV | $<{h}_{4},[0.81,0.82],[0.05,0.18]>$ | $<{h}_{4},[0.86,0.87],[0.07,0.13]>$ | $<{h}_{4},[0.66,0.67],[0.12,0.33]>$ | $<{h}_{4},[0.85,0.87],[0.07,0.13]>$ |

V | $<{h}_{5},[0.69,0.70],[0.12,0.30]>$ | $<{h}_{5},[0.75,0.76],[0.1,0.24]>$ | $<{h}_{5},[0.62,0.63],[0.22,0.37]>$ | $<{h}_{5},[0.75,0.77],[0.15,0.23]>$ |

VI | $<{h}_{6},[0.55,0.56],[0.3,0.44]>$ | $<{h}_{6},[0.51,0.52],[0.3,0.48]>$ | $<{h}_{6},[0.49,0.5],[0.3,0.5]>$ | $<{h}_{6},[0.51,0.52],[0.3,0.48]>$ |

Level | ZEA | AFB_{1} | Mercury | Lead | Chromium |
---|---|---|---|---|---|

I | (2.6, 2.9, 210/6, 4/18) | (0.43, 0.45, 158/6, 1/18) | (0.00087, 0.00088, 10/6, 0.01) | (0.0087, 0.0088, 6/6, 0.12) | (0.043, 0.045, 30/6, 0.03) |

II | (16.56, 16.8, 178/6, 8/18) | (2.76, 2.8, 153/6, 1/18) | (0.0055, 0.0057, 10/6, 0.01) | (0.055, 0.057, 6/6, 0.03) | (0.276, 0.28, 28/6, 0.01) |

III | (29.4, 30.6, 145/6, 15/18) | (4.9, 5.1, 147/6, 3/18) | (0.0098, 0.0102, 10/6, 0.002) | (0.098, 0.102, 6/6, 0.03) | (0.49, 0.51, 28/6, 0.01) |

IV | (42.6, 44, 112/6, 12/18) | (7.09, 7.34, 142/6, 4/18) | (0.0142, 0.0147, 10/6, 0.03) | (0.142, 0.147, 6/6, 0.02) | (0.71, 0.73, 27/6, 0.02) |

V | (55.8, 59.2, 110/6, 9/18) | (9.3, 9.8, 137/6, 2/18) | (0.0186, 0.0197, 10/6, 0.01) | (0.186, 0.197, 6/6, 0.10) | (0.93, 0.99, 27/6, 0.03) |

VI | (6, 6.8, 2110/6, 5/18) | (10, 10.6, 135/6, 2/18) | (0.02, 0.028, 10/6, 0.01) | (0.2, 0.28, 6/6, 0.03) | (1, 1.04, 26/6, 0.01) |

Stage | Membership Degrees | |||||
---|---|---|---|---|---|---|

I | II | III | IV | V | VI | |

paddy rice | 0.7389 | 0.8137 | 0.8456 | 0.7646 | 0.5965 | 0.5336 |

husking | 0.9972 | 0.9635 | 0.8091 | 0.4200 | 0.1882 | 0.1405 |

polishing | 0.9991 | 0.9957 | 0.7807 | 0.4143 | 0.2064 | 0.1611 |

polished rice | 0.9992 | 0.9632 | 0.7771 | 0.4083 | 0.2028 | 0.1570 |

Algorithm | I | II | III | IV | V | VI | Evaluation Results |
---|---|---|---|---|---|---|---|

Algorithm 1 | 0.6067 | 0.6124 | 0.6262 | 0.6443 | 0.6401 | 0.5841 | IV |

Algorithm 2 | 0.8337 | 0.9305 | 0.9606 | 0.8483 | 0.5877 | 0.4951 | III |

The algorithm of the proposed method | 0.7189 | 0.8137 | 0.9256 | 0.7646 | 0.5965 | 0.4336 | III |

Algorithm | I | II | III | IV | V | VI | Evaluation Results |
---|---|---|---|---|---|---|---|

Algorithm 1 | 0.9941 | 0.9961 | 0.9722 | 0.9109 | 0.8764 | 0.6298 | II |

Algorithm 2 | 0.9975 | 0.9891 | 0.8542 | 0.8340 | 0.7036 | 0.6615 | I |

The algorithm of the proposed method | 0.9972 | 0.9635 | 0.8091 | 0.4083 | 0.1882 | 0.1405 | I |

Algorithm | I | II | III | IV | V | VI | Evaluation Results |
---|---|---|---|---|---|---|---|

Algorithm 1 | 0.9907 | 0.9919 | 0.9502 | 0.8799 | 0.7563 | 0.5871 | II |

Algorithm 2 | 0.9912 | 0.9641 | 0.7833 | 0.7690 | 0.6438 | 0.6004 | I |

The algorithm of the proposed method | 0.9991 | 0.9557 | 0.7807 | 0.4143 | 0.2064 | 0.1611 | I |

Algorithm | I | II | III | IV | V | VI | Evaluation Results |
---|---|---|---|---|---|---|---|

Algorithm 1 | 0.9946 | 0.9923 | 0.9541 | 0.8735 | 0.7586 | 0.5703 | I |

Algorithm 2 | 0.9871 | 0.9592 | 0.8746 | 0.7468 | 0.6111 | 0.4753 | I |

The algorithm of the proposed method | 0.9992 | 0.9632 | 0.7771 | 0.4083 | 0.2028 | 0.1570 | I |

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## Share and Cite

**MDPI and ACS Style**

Yu, J.; Chen, H.; Zhang, X.; Cui, X.; Zhao, Z.
A Rice Hazards Risk Assessment Method for a Rice Processing Chain Based on a Multidimensional Trapezoidal Cloud Model. *Foods* **2023**, *12*, 1203.
https://doi.org/10.3390/foods12061203

**AMA Style**

Yu J, Chen H, Zhang X, Cui X, Zhao Z.
A Rice Hazards Risk Assessment Method for a Rice Processing Chain Based on a Multidimensional Trapezoidal Cloud Model. *Foods*. 2023; 12(6):1203.
https://doi.org/10.3390/foods12061203

**Chicago/Turabian Style**

Yu, Jiabin, Huimin Chen, Xin Zhang, Xiaoyu Cui, and Zhiyao Zhao.
2023. "A Rice Hazards Risk Assessment Method for a Rice Processing Chain Based on a Multidimensional Trapezoidal Cloud Model" *Foods* 12, no. 6: 1203.
https://doi.org/10.3390/foods12061203