Authenticating the geographical origin of the Chinese yam (Tiegun) with stable isotopes and multiple elements

Xiong, Feng; Lyu, Chao-Geng; Kang, Chuan-Zhi; Wan, Xiu-Fu; Sun, Jie; Wang, Tielin; Wang, Sheng; Li, Haiyan; Yang, Jian; Guo, Lanping

doi:10.1016/j.fochx.2023.100678

Cited by 3 publications

(1 citation statement)

References 22 publications

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“…Yam, a candidate for “the homology of medicine and food” published by China in 2020, was first recorded in “Shennong’s Classic Material Medical” in China. With its main edible part being its underground tuber, yam is effective at improving blood circulation and enhancing spleen, lung, stomach, and kidney channel functions, and yam has a long history of cultivation and medicinal use in China. , Wencheng is located in Wenzhou City, Zhejiang province, China (27°34′–27°59′N, 119°46′–120°15′E). The mountainous area within Wencheng County accounts for 82.5% of the total area of the county, and most of the landforms are typical mountainous areas.…”

Section: Introductionmentioning

confidence: 99%

2 Year Wencheng Waxy Yam Pesticide Residue Investigation and Quality Evaluation

Chen,

Liu,

Zheng

et al. 2024

ACS Omega

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Wencheng waxy yam is famous for its glutinous and resilient taste, similar to waxy rice, but there is currently a lack of systematic research on the quality of this featured product, and little is known about its pesticide residues. We carried out a 2 year investigation of Wencheng waxy yam at seven sites from 2021 to 2022 to determine the oxidase content and phytochemical characteristics, namely, amylose, amylopectin, protein, reducing sugar, and mineral contents, such as K, Fe, and Zn, including the status of pesticide residues. The results showed that the oxidase content was affected by rainfall, and adequate water reduced the production of oxidase, including polyphenol oxidase, peroxidase, and superoxide dismutase, during the late growth stage of waxy yam, which was beneficial for reducing browning in yam processing. Radar map analysis showed that, with comprehensive evaluation, standardized production sites 1 and 2 had a relatively higher quality than 3−7 with small farmers. The results of pesticide multiresidue testing showed that no pesticides were detected in 64.29% of the samples, and the detected residues in the samples were very low, making the consumption of yam safe for consumers. These findings could be beneficial for the exploitation of the health benefits of waxy yam tubers and the innovation of yam-based functional products.

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Section: Introductionmentioning

confidence: 99%

2 Year Wencheng Waxy Yam Pesticide Residue Investigation and Quality Evaluation

Chen,

Liu,

Zheng

et al. 2024

ACS Omega

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Tracing the origin of isatidis radix based on multivariate data fusion combined with DBN classification algorithm

Chen,

Huang,

Fei

et al. 2024

Chemometrics and Intelligent Laboratory Systems

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Enhancing Yam Quality Detection through Computer Vision in IoT and Robotics Applications

Audu,

Adegbenjo,

Ajisegiri

et al. 2023

Preprint

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This study introduces a comprehensive framework aimed at automating the process of detecting yam tuber quality attributes. This is achieved through the integration of Internet of Things (IoT) devices and robotic systems. The primary focus of the study is the development of specialized computer codes that extract relevant image features and categorize yam tubers into one of three classes: "Good," "Diseased," or "Insect Infected." By employing a variety of machine learning algorithms, including tree algorithms, support vector machines (SVMs), and k-nearest neighbors (KNN), the codes achieved an impressive accuracy of over 90% in effective classification. Furthermore, a robotic algorithm was designed utilizing an artificial neural network (ANN), which exhibited a 92.3% accuracy based on its confusion matrix analysis. The effectiveness and accuracy of the developed codes were substantiated through deployment testing. Although a few instances of misclassification were observed, the overall outcomes indicate significant potential for transforming yam quality assessment and contributing to the realm of precision agriculture. This study is in alignment with prior research endeavors within the field, highlighting the pivotal role of automated and precise quality assessment. The integration of IoT devices and robotic systems in agricultural practices presents exciting possibilities for data-driven decision-making and heightened productivity. By minimizing human intervention and providing real-time insights, the study approach has the potential to optimize yam quality assessment processes. Therefore, this study successfully demonstrates the practical application of IoT and robotic technologies for the purpose of yam quality detection, laying the groundwork for progress in the agricultural sector.

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Authenticating the geographical origin of the Chinese yam (Tiegun) with stable isotopes and multiple elements

Cited by 3 publications

References 22 publications

2 Year Wencheng Waxy Yam Pesticide Residue Investigation and Quality Evaluation

2 Year Wencheng Waxy Yam Pesticide Residue Investigation and Quality Evaluation

Tracing the origin of isatidis radix based on multivariate data fusion combined with DBN classification algorithm

Enhancing Yam Quality Detection through Computer Vision in IoT and Robotics Applications

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