食品与发酵工业 >

2026 , Vol. 52 >Issue 9: 431 - 440

DOI: https://doi.org/10.13995/j.cnki.11-1802/ts.044781

综述与专题评论

人工智能算法下的国内外白砂糖产品标准的对比分析

  • 张尤佳 ,
  • 宋晓杰 ,
  • 王琳 ,
  • 王媛媛 ,
  • 胡晓雨 ,
  • 毕园 ,
  • 李国明
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  • 1(国家粮食和物资储备局科学研究院,北京,100037)
    2(中国食品发酵工业研究院有限公司,北京,100015)
第一作者：硕士,研究实习员(宋晓杰副研究员和李国明教授级高级工程师为共同通信作者,E-mail:sxj@ags.ac.cn;gml_1002@163.com)

收稿日期: 2025-10-11

  修回日期: 2025-12-16

  网络出版日期: 2026-06-03

基金资助

中央级公益性事业单位基本科研业务费课题(ZX2423)

收起

Comparative analysis of domestic and international standards for white granulated sugar using artificial intelligence algorithms

  • ZHANG Youjia ,
  • SONG Xiaojie ,
  • WANG Lin ,
  • WANG Yuanyuan ,
  • HU Xiaoyu ,
  • BI Yuan ,
  • LI Guoming
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  • 1(Academy of National Food and Strategic Reserves Administration, Beijing 100037, China)
    2(China National Research Institute of Food & Fermentation Industries Co. Ltd., Beijing 100015, China)

Received date: 2025-10-11

  Revised date: 2025-12-16

  Online published: 2026-06-03

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摘要

白砂糖作为保障民生的国家战略物资与支撑食品、制药工业的核心原料,其标准体系的科学性与适用性直接决定产业竞争力与国际贸易安全。该研究立足全球白砂糖标准化发展视野,通过历史文献溯源、标准文本解构、抽检数据核验与舆情风险挖掘,对比我国白砂糖标准(GB/T 317—2018《白砂糖》) 与欧盟(Codex Stan 212-1999)、泰国(TIS 56-2552)、巴西(ICUMSA体系)的指标差异,阐明区域产业定位、消费需求与工艺水平对标准设计的驱动机制。针对多源异构的白砂糖标准差异造成国际贸易中质量溯源、市场监管、技术壁垒等问题,基于2014—2024年不合格抽检数据及舆情风险信息,识别出“色值-还原糖分-二氧化硫残留”的核心风险链与农药残留、重金属及理化质量指标等区域风险,以7项核心指标为输入,创新性地构建3层神经网络判别模型,经Levenberg-Marquardt算法训练后,对巴西、欧盟、泰国标准的判别准确率分别达92.3%、93.2%、88.5%,验证集无过拟合(val fail=0)且均方误差低至 0.000 65,有效突破传统人工判别效率低、主观性强的行业痛点。该研究为全球白砂糖贸易中的标准协同与智能化风险防控提供重要参考,对推动行业从合规导向向质量效益导向转型具有关键意义。

关键词： 白砂糖; 产品标准; 抽检分析; 国际比对; 对策建议; 人工智能

本文引用格式

张尤佳 , 宋晓杰 , 王琳 , 王媛媛 , 胡晓雨 , 毕园 , 李国明 . 人工智能算法下的国内外白砂糖产品标准的对比分析[J]. 食品与发酵工业, 2026 , 52(9) : 431 -440 . DOI: 10.13995/j.cnki.11-1802/ts.044781

Abstract

As a national strategic material for safeguarding people’s livelihood and a core raw material supporting the food and pharmaceutical industries, the scientificity and applicability of white granulated sugar’s standard system directly determine industrial competitiveness and international trade security.Based on a global perspective of white granulated sugar standardization development, this study compares the index differences between China’s white sugar standard (GB/T 317—2018 White granulated sugar) and those of the European Union (Codex Stan 212-1999), Thailand (TIS 56-2552), and Brazil (ICUMSA system) through historical document tracing, standard text deconstruction, sampling inspection data verification, and public opinion risk mining, and clarifies the driving mechanism of regional industrial positioning, consumer demand, and technological level on standard design.Based on unqualified sampling inspection data and public opinion risk data from 2014 to 2024, the core risk chain of color value-reducing sugar-sulfur dioxide residue and regional risks such as pesticide residues, heavy metals, and physical and chemical quality indicators are identified.Taking 7 core indicators as inputs, an innovative 3-layer neural network discriminant model is constructed.After training with the Levenberg-Marquardt algorithm, the discrimination accuracy rates for Brazilian, EU, and Thai standards reach 92.3%, 93.2%, and 88.5% respectively.The validation set shows no overfitting (val fail=0) with a mean squared error (MSE) as low as 0.00065, effectively breaking through the industry pain points of low efficiency and strong subjectivity in traditional manual discrimination.This study provides important references for standard coordination and intelligent risk prevention and control in global white granulated sugar trade, and plays a key role in promoting the industry’s transformation from compliance-oriented to quality and efficiency-oriented development.

Key words： white granulated sugar; product standards; random inspection analysis; international comparison; countermeasures and suggestions; artificial intelligence

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