Background Fiber maturity is a key cotton quality property,and its variability in a sample impacts fiber processing and dyeing performance.Currently,the maturity is determined by using established protocols in laborat...Background Fiber maturity is a key cotton quality property,and its variability in a sample impacts fiber processing and dyeing performance.Currently,the maturity is determined by using established protocols in laboratories under a controlled environment.There is an increasing need to measure fiber maturity using low-cost(in general less than $20000)and small portable systems.In this study,a laboratory feasibility was performed to assess the ability of the shortwave infrared hyperspectral imaging(SWIR HSI)technique for determining the conditioned fiber maturity,and as a comparison,a bench-top commercial and expensive(in general greater than $60000)near infrared(NIR)instrument was used.Results Although SWIR HSI and NIR represent different measurement technologies,consistent spectral characteristics were observed between the two instruments when they were used to measure the maturity of the locule fiber samples in seed cotton and of the well-defined fiber samples,respectively.Partial least squares(PLS)models were established using different spectral preprocessing parameters to predict fiber maturity.The high prediction precision was observed by a lower root mean square error of prediction(RMSEP)(<0.046),higher R_(p)^(2)(>0.518),and greater percentage(97.0%)of samples within the 95% agreement range in the entire NIR region(1000-2500 nm)without the moisture band at 1940 nm.Conclusion SWIR HSI has a good potential for assessing cotton fiber maturity in a laboratory environment.展开更多
为实现对多样本新鲜羊肉营养含量的快速检测,该研究利用近红外光谱(near-infrared reflectance spectroscopy,NIRS)技术构建了新鲜羊肉中6种营养成分的定量分析模型。于武威市民勤县采集203份新鲜羊肉,并测定其水分(moisture,MT)、粗脂...为实现对多样本新鲜羊肉营养含量的快速检测,该研究利用近红外光谱(near-infrared reflectance spectroscopy,NIRS)技术构建了新鲜羊肉中6种营养成分的定量分析模型。于武威市民勤县采集203份新鲜羊肉,并测定其水分(moisture,MT)、粗脂肪(ether extract,EE)、粗蛋白(crude protein,CP)、葡萄糖(glucose,Glu)、粗灰分(crude ash,Ash)及总磷(phosphorus,P)的含量。使用WINISI III与Foss Calibrator定标软件分别建立羊肉6种营养成分的NIRS模型并对其结果进行比较。WINISI III软件定标结果显示,羊肉MT、EE、CP预测模型的预测决定系数(coefficient of determination for validation,RSQ)和外部验证相对分析误差(ratio of performance to deviation for vali-dation,RPD)分别为0.83与2.47、0.90与3.60、0.81与2.79;Glu、Ash预测模型的RSQ和RPD分别为0.54与3.05、0.54与1.91;P预测模型的RSQ和RPD为0.45与1.80。Foss Calibrator软件定标结果显示,MT、EE、CP的交互验证均方根误差[root mean square error of cross-verification,RMSEP(cross)]和决定系数(coefficient of determination,R^(2))分别为0.631与0.84、0.326与0.87、0.468与0.83;Glu、Ash的RMSEP(cross)和R^(2)分别为0.127与0.53、0.179与0.51;P的RMSEP(cross)和R^(2)为0.086与0.33。2种定标软件得到的结论基本一致,均表明MT、EE、CP的预测模型可在实际生产中精确预测;Glu、Ash的预测模型可在大量样品的粗略分析与筛选时应用,但还需继续优化;P的预测模型相关性较差,不能在实际生产中应用。展开更多
基金supported partially by the USDA-ARS Research Project#6054-44000-080-00D.
文摘Background Fiber maturity is a key cotton quality property,and its variability in a sample impacts fiber processing and dyeing performance.Currently,the maturity is determined by using established protocols in laboratories under a controlled environment.There is an increasing need to measure fiber maturity using low-cost(in general less than $20000)and small portable systems.In this study,a laboratory feasibility was performed to assess the ability of the shortwave infrared hyperspectral imaging(SWIR HSI)technique for determining the conditioned fiber maturity,and as a comparison,a bench-top commercial and expensive(in general greater than $60000)near infrared(NIR)instrument was used.Results Although SWIR HSI and NIR represent different measurement technologies,consistent spectral characteristics were observed between the two instruments when they were used to measure the maturity of the locule fiber samples in seed cotton and of the well-defined fiber samples,respectively.Partial least squares(PLS)models were established using different spectral preprocessing parameters to predict fiber maturity.The high prediction precision was observed by a lower root mean square error of prediction(RMSEP)(<0.046),higher R_(p)^(2)(>0.518),and greater percentage(97.0%)of samples within the 95% agreement range in the entire NIR region(1000-2500 nm)without the moisture band at 1940 nm.Conclusion SWIR HSI has a good potential for assessing cotton fiber maturity in a laboratory environment.
文摘为实现对多样本新鲜羊肉营养含量的快速检测,该研究利用近红外光谱(near-infrared reflectance spectroscopy,NIRS)技术构建了新鲜羊肉中6种营养成分的定量分析模型。于武威市民勤县采集203份新鲜羊肉,并测定其水分(moisture,MT)、粗脂肪(ether extract,EE)、粗蛋白(crude protein,CP)、葡萄糖(glucose,Glu)、粗灰分(crude ash,Ash)及总磷(phosphorus,P)的含量。使用WINISI III与Foss Calibrator定标软件分别建立羊肉6种营养成分的NIRS模型并对其结果进行比较。WINISI III软件定标结果显示,羊肉MT、EE、CP预测模型的预测决定系数(coefficient of determination for validation,RSQ)和外部验证相对分析误差(ratio of performance to deviation for vali-dation,RPD)分别为0.83与2.47、0.90与3.60、0.81与2.79;Glu、Ash预测模型的RSQ和RPD分别为0.54与3.05、0.54与1.91;P预测模型的RSQ和RPD为0.45与1.80。Foss Calibrator软件定标结果显示,MT、EE、CP的交互验证均方根误差[root mean square error of cross-verification,RMSEP(cross)]和决定系数(coefficient of determination,R^(2))分别为0.631与0.84、0.326与0.87、0.468与0.83;Glu、Ash的RMSEP(cross)和R^(2)分别为0.127与0.53、0.179与0.51;P的RMSEP(cross)和R^(2)为0.086与0.33。2种定标软件得到的结论基本一致,均表明MT、EE、CP的预测模型可在实际生产中精确预测;Glu、Ash的预测模型可在大量样品的粗略分析与筛选时应用,但还需继续优化;P的预测模型相关性较差,不能在实际生产中应用。
