{"title":"通过矿物元素分析预测中国牡蛎地理来源的可解释机器学习","authors":"Xuming Kang , Yanfang Zhao , Lin Yao , Zhijun Tan","doi":"10.1016/j.crfs.2024.100738","DOIUrl":null,"url":null,"abstract":"<div><p>The traceability of geographic origin is essential for guaranteeing the quality, safety, and protection of oyster brands. However, the current outcomes of traceability lack credibility as they do not adequately explain the model's predictions. Consequently, we conducted a study to evaluate the efficacy of utilizing explainable machine learning combined with mineral elements analysis. The study findings revealed that 18 elements have the ability to determine regional orientation. Simultaneously, individuals should pay closer attention to the potential risks associated with oyster consumption due to the regional differences in essential and toxic elements they contain. Light gradient boosting machine (LightGBM) model exhibited indistinguishable performance, achieving flawless accuracy, precision, recall, F1 score and AUC, with values of 96.77%, 96.43%, 98.53%, 97.32% and 0.998, respectively. The SHapley Additive exPlanations (SHAP) method was used to evaluate the output of the LightGBM model, revealing differences in feature interactions among oysters from different provinces. Specifically, the features Na, Zn, V, Mg, and K were found to have a significant impact on the predictive process of the model. Consistent with existing research, the use of explainable machine learning techniques can provide insights into the complex connections between important product attributes and relevant geographical information.</p></div>","PeriodicalId":10939,"journal":{"name":"Current Research in Food Science","volume":null,"pages":null},"PeriodicalIF":6.2000,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2665927124000649/pdfft?md5=aff510fde53b3e34edb47de00f9695a0&pid=1-s2.0-S2665927124000649-main.pdf","citationCount":"0","resultStr":"{\"title\":\"Explainable machine learning for predicting the geographical origin of Chinese Oysters via mineral elements analysis\",\"authors\":\"Xuming Kang , Yanfang Zhao , Lin Yao , Zhijun Tan\",\"doi\":\"10.1016/j.crfs.2024.100738\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>The traceability of geographic origin is essential for guaranteeing the quality, safety, and protection of oyster brands. However, the current outcomes of traceability lack credibility as they do not adequately explain the model's predictions. Consequently, we conducted a study to evaluate the efficacy of utilizing explainable machine learning combined with mineral elements analysis. The study findings revealed that 18 elements have the ability to determine regional orientation. Simultaneously, individuals should pay closer attention to the potential risks associated with oyster consumption due to the regional differences in essential and toxic elements they contain. Light gradient boosting machine (LightGBM) model exhibited indistinguishable performance, achieving flawless accuracy, precision, recall, F1 score and AUC, with values of 96.77%, 96.43%, 98.53%, 97.32% and 0.998, respectively. The SHapley Additive exPlanations (SHAP) method was used to evaluate the output of the LightGBM model, revealing differences in feature interactions among oysters from different provinces. Specifically, the features Na, Zn, V, Mg, and K were found to have a significant impact on the predictive process of the model. Consistent with existing research, the use of explainable machine learning techniques can provide insights into the complex connections between important product attributes and relevant geographical information.</p></div>\",\"PeriodicalId\":10939,\"journal\":{\"name\":\"Current Research in Food Science\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":6.2000,\"publicationDate\":\"2024-01-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.sciencedirect.com/science/article/pii/S2665927124000649/pdfft?md5=aff510fde53b3e34edb47de00f9695a0&pid=1-s2.0-S2665927124000649-main.pdf\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Current Research in Food Science\",\"FirstCategoryId\":\"97\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S2665927124000649\",\"RegionNum\":2,\"RegionCategory\":\"农林科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"FOOD SCIENCE & TECHNOLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Current Research in Food Science","FirstCategoryId":"97","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2665927124000649","RegionNum":2,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"FOOD SCIENCE & TECHNOLOGY","Score":null,"Total":0}
引用次数: 0
摘要
地理原产地的可追溯性对于保证牡蛎品牌的质量、安全和保护至关重要。然而,目前的溯源结果缺乏可信度,因为它们不能充分解释模型的预测。因此,我们开展了一项研究,评估利用可解释机器学习结合矿物元素分析的功效。研究结果表明,18 种元素具有确定区域方位的能力。同时,由于牡蛎所含的必需元素和有毒元素存在地区差异,人们应更密切地关注食用牡蛎的潜在风险。光梯度增强机(LightGBM)模型表现出无差别的性能,准确度、精确度、召回率、F1得分和AUC均无懈可击,其值分别为96.77%、96.43%、98.53%、97.32%和0.998。使用 SHapley Additive exPlanations(SHAP)方法评估了 LightGBM 模型的输出结果,发现了不同省份牡蛎之间特征交互作用的差异。具体而言,Na、Zn、V、Mg 和 K 等特征对模型的预测过程有显著影响。与现有研究一致,使用可解释机器学习技术可以深入了解重要产品属性与相关地理信息之间的复杂联系。
Explainable machine learning for predicting the geographical origin of Chinese Oysters via mineral elements analysis
The traceability of geographic origin is essential for guaranteeing the quality, safety, and protection of oyster brands. However, the current outcomes of traceability lack credibility as they do not adequately explain the model's predictions. Consequently, we conducted a study to evaluate the efficacy of utilizing explainable machine learning combined with mineral elements analysis. The study findings revealed that 18 elements have the ability to determine regional orientation. Simultaneously, individuals should pay closer attention to the potential risks associated with oyster consumption due to the regional differences in essential and toxic elements they contain. Light gradient boosting machine (LightGBM) model exhibited indistinguishable performance, achieving flawless accuracy, precision, recall, F1 score and AUC, with values of 96.77%, 96.43%, 98.53%, 97.32% and 0.998, respectively. The SHapley Additive exPlanations (SHAP) method was used to evaluate the output of the LightGBM model, revealing differences in feature interactions among oysters from different provinces. Specifically, the features Na, Zn, V, Mg, and K were found to have a significant impact on the predictive process of the model. Consistent with existing research, the use of explainable machine learning techniques can provide insights into the complex connections between important product attributes and relevant geographical information.
期刊介绍:
Current Research in Food Science is an international peer-reviewed journal dedicated to advancing the breadth of knowledge in the field of food science. It serves as a platform for publishing original research articles and short communications that encompass a wide array of topics, including food chemistry, physics, microbiology, nutrition, nutraceuticals, process and package engineering, materials science, food sustainability, and food security. By covering these diverse areas, the journal aims to provide a comprehensive source of the latest scientific findings and technological advancements that are shaping the future of the food industry. The journal's scope is designed to address the multidisciplinary nature of food science, reflecting its commitment to promoting innovation and ensuring the safety and quality of the food supply.