In the present paper,the authors analysed 10 mineral elements in the roots of Rheum tanguticum collected from 30 different habitats.The mean concentration values of the 10 elements decreased as follows: Ca>Mg>K&...In the present paper,the authors analysed 10 mineral elements in the roots of Rheum tanguticum collected from 30 different habitats.The mean concentration values of the 10 elements decreased as follows: Ca>Mg>K>Fe>Mn>Cr>Zn>Ni>Cu>Se. Ca,Mg,K and Fe were abundant in this herb.Most elements varied over a wide range depending on the different habitats.The mineral element data were evaluated by principal component analysis to reveal the distribution pattern of elements in root.Four principal components(K-Ca factor,Cu factor,Mg factor and Zn-Se factor) of plant elements were selected.The authors' study provided a new scientific foundation for further studies and general application of this Chinese herb.展开更多
This paper, probing into heavy metal control in domestic rubbish by source screening and nutrient element analysis, revealed the feasibility of source control of heavy metals and the suitability of rubbish as turfgras...This paper, probing into heavy metal control in domestic rubbish by source screening and nutrient element analysis, revealed the feasibility of source control of heavy metals and the suitability of rubbish as turfgrass medium. Heavy metals in domestic rubbish were controlled by source screening before composting. The study consisted of a control with garden soil. The contents of main mineral elements and heavy metals in rubbish compost and control were determined by the method of ICP-AES. The results showed that heavy metal concentrations in rubbish were lower than those in garden soil, and little difference occurred between rubbish and garden soil in main mineral element concentrations. Based on this, it was concluded that rubbish compost was favorable for using as turfgrass medium and heavy metal control in rubbish by source screening was effective.展开更多
Background The geo-traceability of cotton is crucial for ensuring the quality and integrity of cotton brands. However, effective methods for achieving this traceability are currently lacking. This study investigates t...Background The geo-traceability of cotton is crucial for ensuring the quality and integrity of cotton brands. However, effective methods for achieving this traceability are currently lacking. This study investigates the potential of explainable machine learning for the geo-traceability of raw cotton.Results The findings indicate that principal component analysis(PCA) exhibits limited effectiveness in tracing cotton origins. In contrast, partial least squares discriminant analysis(PLS-DA) demonstrates superior classification performance, identifying seven discriminating variables: Na, Mn, Ba, Rb, Al, As, and Pb. The use of decision tree(DT), support vector machine(SVM), and random forest(RF) models for origin discrimination yielded accuracies of 90%, 87%, and 97%, respectively. Notably, the light gradient boosting machine(Light GBM) model achieved perfect performance metrics, with accuracy, precision, and recall rate all reaching 100% on the test set. The output of the Light GBM model was further evaluated using the SHapley Additive ex Planation(SHAP) technique, which highlighted differences in the elemental composition of raw cotton from various countries. Specifically, the elements Pb, Ni, Na, Al, As, Ba, and Rb significantly influenced the model's predictions.Conclusion These findings suggest that explainable machine learning techniques can provide insights into the complex relationships between geographic information and raw cotton. Consequently, these methodologies enhances the precision and reliability of geographic traceability for raw cotton.展开更多
基金The National Mid-West Fund of China(2001BA901A47)the Knowledge Innovation Programs of the Chinese Academy of Sciences(CXLY-2002-08)
文摘In the present paper,the authors analysed 10 mineral elements in the roots of Rheum tanguticum collected from 30 different habitats.The mean concentration values of the 10 elements decreased as follows: Ca>Mg>K>Fe>Mn>Cr>Zn>Ni>Cu>Se. Ca,Mg,K and Fe were abundant in this herb.Most elements varied over a wide range depending on the different habitats.The mineral element data were evaluated by principal component analysis to reveal the distribution pattern of elements in root.Four principal components(K-Ca factor,Cu factor,Mg factor and Zn-Se factor) of plant elements were selected.The authors' study provided a new scientific foundation for further studies and general application of this Chinese herb.
基金NationalNaturalScienceFoundation (59878033)Tianjin Science and Technology Developm entProgram (043100611).
文摘This paper, probing into heavy metal control in domestic rubbish by source screening and nutrient element analysis, revealed the feasibility of source control of heavy metals and the suitability of rubbish as turfgrass medium. Heavy metals in domestic rubbish were controlled by source screening before composting. The study consisted of a control with garden soil. The contents of main mineral elements and heavy metals in rubbish compost and control were determined by the method of ICP-AES. The results showed that heavy metal concentrations in rubbish were lower than those in garden soil, and little difference occurred between rubbish and garden soil in main mineral element concentrations. Based on this, it was concluded that rubbish compost was favorable for using as turfgrass medium and heavy metal control in rubbish by source screening was effective.
基金supported by Agricultural Science and Technology Innovation Program of Chinese Academy of Agricultural Science。
文摘Background The geo-traceability of cotton is crucial for ensuring the quality and integrity of cotton brands. However, effective methods for achieving this traceability are currently lacking. This study investigates the potential of explainable machine learning for the geo-traceability of raw cotton.Results The findings indicate that principal component analysis(PCA) exhibits limited effectiveness in tracing cotton origins. In contrast, partial least squares discriminant analysis(PLS-DA) demonstrates superior classification performance, identifying seven discriminating variables: Na, Mn, Ba, Rb, Al, As, and Pb. The use of decision tree(DT), support vector machine(SVM), and random forest(RF) models for origin discrimination yielded accuracies of 90%, 87%, and 97%, respectively. Notably, the light gradient boosting machine(Light GBM) model achieved perfect performance metrics, with accuracy, precision, and recall rate all reaching 100% on the test set. The output of the Light GBM model was further evaluated using the SHapley Additive ex Planation(SHAP) technique, which highlighted differences in the elemental composition of raw cotton from various countries. Specifically, the elements Pb, Ni, Na, Al, As, Ba, and Rb significantly influenced the model's predictions.Conclusion These findings suggest that explainable machine learning techniques can provide insights into the complex relationships between geographic information and raw cotton. Consequently, these methodologies enhances the precision and reliability of geographic traceability for raw cotton.
