The effect of sulfur addition/solids content(SA/SC)ratio on heavy metals(e.g.copper,zinc and lead)obtained from mine tailings by indigenous sulfur-oxidizing bacteria was studied,and the changes in the chemical forms o...The effect of sulfur addition/solids content(SA/SC)ratio on heavy metals(e.g.copper,zinc and lead)obtained from mine tailings by indigenous sulfur-oxidizing bacteria was studied,and the changes in the chemical forms of heavy metals after bioleaching were explored.The results show that the solubilization of metals is significantly influenced by SA/SC ratio,and SA/SC ratio of 2.50 is found to be the best for bacterial activity and metal solubilization among six SA/SC ratios tested(such as 1.00,1.33,1.50,1.67,2.00 and 2.50)under the chosen experimental conditions.The pH decreases fast and the maximum solubilizations of copper and zinc are respectively 81.76% and 84.35% while that of lead only reaches 40.36%.After bioleaching,the chemical forms of heavy metals have changed.The metals remained in mine tailings are mainly found in residual fractions,which is harmless to the surrounding environment.展开更多
【目的】针对传统化学方法测定猕猴桃品质存在工序复杂、费时费力、需破坏性检测等问题,提出一种基于高光谱技术的高效无损检测方法。【方法】以110个米良1号猕猴桃(Actinidia chinensis var.deliciosa‘Miliang-1’)样本为研究对象,利...【目的】针对传统化学方法测定猕猴桃品质存在工序复杂、费时费力、需破坏性检测等问题,提出一种基于高光谱技术的高效无损检测方法。【方法】以110个米良1号猕猴桃(Actinidia chinensis var.deliciosa‘Miliang-1’)样本为研究对象,利用高光谱仪采集不同贮藏时间果实的高光谱反射光谱。利用光谱-理化值共生距离法(sample set partitioning based on joint X-Y distance sampling,SPXY)将猕猴桃样本按照8∶3的数量比例划分为训练集和测试集,统一采用支持向量机(SVM)对比分析标准正态变换(SNV)、多元散射校正(MSC)、一阶导数(1st-D)、二阶导数(2nd-D)、平滑算法(SG)对原始光谱进行预处理。使用遗传算法(genetic algorithm,GA)和随机蛙跳(random frog,RF)对猕猴桃高光谱特征波长进行筛选,结合支持向量回归(SVR)、反向传播神经网络(BP)算法,组合构建猕猴桃品质的回归预测模型。【结果】在组合模型中,可溶性固形物含量的最优模型为1st-D+GA-BP,R^(2)为0.903,RMSE为1.731;可滴定酸含量的最优模型为1st-D+GA-BP,R^(2)为0.857,RMSE为0.225。【结论】应用高光谱技术对米良1号猕猴桃可溶性固形物含量、可滴定酸含量进行无损检测具有可行性。为进一步研究不同品种猕猴桃可溶性固形物含量、可滴定酸含量的无损检测模型奠定了基础。展开更多
[目的/意义]苹果“冰糖心”又称水心病,是一种常见的果实病害,严重的水心病果会随着储藏时间的增加发生霉变,造成食品安全隐患。为实现不同等级水心病苹果快速无损检测,本研究旨在构建有效的分级与可溶性固形物(Soluble Solids Content,...[目的/意义]苹果“冰糖心”又称水心病,是一种常见的果实病害,严重的水心病果会随着储藏时间的增加发生霉变,造成食品安全隐患。为实现不同等级水心病苹果快速无损检测,本研究旨在构建有效的分级与可溶性固形物(Soluble Solids Content,SSC)预测模型。[方法]本研究选取了230个富士苹果,其中正常、轻度、中度、重度水心苹果数量分别为113、61、47和9个,分别采集了400~1000 nm范围的反射光谱和X射线计算机断层成像(X-ray Computed Tomography,X-ray CT)数据,并测定了SSC含量。[结果和讨论]SSC随水心程度加剧呈上升趋势,重度水心苹果呈现更高的光谱反射率,X-ray CT扫描成像观察到水心区域的组织体积平均密度高于健康组织,基于三维重建算法实现不同等级水心苹果内部水心组织可视化分布。基于偏最小二乘判别分析(Partial Least Squares Discriminant Analysis,PLSDA)构建的不同水心程度苹果果实分级模型建模集和测试集准确率分别为98.7%和95.9%;构建不同水心程度苹果果实SSC回归模型,校正集决定系数(Correlation Coefficient of Calibration,R_(C)^(2))为0.962,均方根误差(Root Mean Squares Error of Calibration,RMSEC)为0.264,测试集决定系数(Correlation Coefficient of Prediction,R_(P)^(2))为0.879,均方根误差(Root Mean Squares Error of Prediction,RMSEP)为0.435。[结论]该研究构建的不同水心程度苹果果实分级模型能够实现苹果不同等级水心病的预测,构建的不同水心程度苹果果实SSC回归模型能够较好地预测苹果果实的SSC,为苹果水心病无损检测和品质评估提供了有效方法。展开更多
基金Project(11JJ2031)supported by the Key Project of Natural Fund of Hunan Province,ChinaProject(2009SK3029)supported by the Plan of Hunan Provincial Science and Technology Department,China
文摘The effect of sulfur addition/solids content(SA/SC)ratio on heavy metals(e.g.copper,zinc and lead)obtained from mine tailings by indigenous sulfur-oxidizing bacteria was studied,and the changes in the chemical forms of heavy metals after bioleaching were explored.The results show that the solubilization of metals is significantly influenced by SA/SC ratio,and SA/SC ratio of 2.50 is found to be the best for bacterial activity and metal solubilization among six SA/SC ratios tested(such as 1.00,1.33,1.50,1.67,2.00 and 2.50)under the chosen experimental conditions.The pH decreases fast and the maximum solubilizations of copper and zinc are respectively 81.76% and 84.35% while that of lead only reaches 40.36%.After bioleaching,the chemical forms of heavy metals have changed.The metals remained in mine tailings are mainly found in residual fractions,which is harmless to the surrounding environment.
文摘【目的】针对传统化学方法测定猕猴桃品质存在工序复杂、费时费力、需破坏性检测等问题,提出一种基于高光谱技术的高效无损检测方法。【方法】以110个米良1号猕猴桃(Actinidia chinensis var.deliciosa‘Miliang-1’)样本为研究对象,利用高光谱仪采集不同贮藏时间果实的高光谱反射光谱。利用光谱-理化值共生距离法(sample set partitioning based on joint X-Y distance sampling,SPXY)将猕猴桃样本按照8∶3的数量比例划分为训练集和测试集,统一采用支持向量机(SVM)对比分析标准正态变换(SNV)、多元散射校正(MSC)、一阶导数(1st-D)、二阶导数(2nd-D)、平滑算法(SG)对原始光谱进行预处理。使用遗传算法(genetic algorithm,GA)和随机蛙跳(random frog,RF)对猕猴桃高光谱特征波长进行筛选,结合支持向量回归(SVR)、反向传播神经网络(BP)算法,组合构建猕猴桃品质的回归预测模型。【结果】在组合模型中,可溶性固形物含量的最优模型为1st-D+GA-BP,R^(2)为0.903,RMSE为1.731;可滴定酸含量的最优模型为1st-D+GA-BP,R^(2)为0.857,RMSE为0.225。【结论】应用高光谱技术对米良1号猕猴桃可溶性固形物含量、可滴定酸含量进行无损检测具有可行性。为进一步研究不同品种猕猴桃可溶性固形物含量、可滴定酸含量的无损检测模型奠定了基础。
文摘[目的/意义]苹果“冰糖心”又称水心病,是一种常见的果实病害,严重的水心病果会随着储藏时间的增加发生霉变,造成食品安全隐患。为实现不同等级水心病苹果快速无损检测,本研究旨在构建有效的分级与可溶性固形物(Soluble Solids Content,SSC)预测模型。[方法]本研究选取了230个富士苹果,其中正常、轻度、中度、重度水心苹果数量分别为113、61、47和9个,分别采集了400~1000 nm范围的反射光谱和X射线计算机断层成像(X-ray Computed Tomography,X-ray CT)数据,并测定了SSC含量。[结果和讨论]SSC随水心程度加剧呈上升趋势,重度水心苹果呈现更高的光谱反射率,X-ray CT扫描成像观察到水心区域的组织体积平均密度高于健康组织,基于三维重建算法实现不同等级水心苹果内部水心组织可视化分布。基于偏最小二乘判别分析(Partial Least Squares Discriminant Analysis,PLSDA)构建的不同水心程度苹果果实分级模型建模集和测试集准确率分别为98.7%和95.9%;构建不同水心程度苹果果实SSC回归模型,校正集决定系数(Correlation Coefficient of Calibration,R_(C)^(2))为0.962,均方根误差(Root Mean Squares Error of Calibration,RMSEC)为0.264,测试集决定系数(Correlation Coefficient of Prediction,R_(P)^(2))为0.879,均方根误差(Root Mean Squares Error of Prediction,RMSEP)为0.435。[结论]该研究构建的不同水心程度苹果果实分级模型能够实现苹果不同等级水心病的预测,构建的不同水心程度苹果果实SSC回归模型能够较好地预测苹果果实的SSC,为苹果水心病无损检测和品质评估提供了有效方法。
