Doping perylene diimide(PDI)into a polymer matrix is a simple strategy to prepare near-infrared(NIR)reflective materials,but the mechanical properties and NIR reflectance properties are significantly compromised due t...Doping perylene diimide(PDI)into a polymer matrix is a simple strategy to prepare near-infrared(NIR)reflective materials,but the mechanical properties and NIR reflectance properties are significantly compromised due to macro-phase separation.In this study,a novel polymer(denoted as PU-PDI)with intrinsic NIR reflective proper⁃ties was synthesized by covalent incorporation of PDI units into polyurethane chains.Its photophysical characteris⁃tics,mechanical property and NIR reflectance property are investigated in detail.The results show that covalent in⁃corporation reduces the severe aggregation of PDI units,thereby endows PU-PDI with excellent mechanical property.The elongation at break of PU-PDI can reach more than 700%,and the breaking strength is 34.11 MPa.Moreover,compared to the blending system,PU-PDI possesses enhanced NIR reflection ability due to the better dispersion of PDI units.展开更多
Background:Gossypol found in cottonseeds is toxic to human beings and monogastric animals and is a primary parameter for the integrated utilization of cottonseed products.It is usually determined by the techniques rel...Background:Gossypol found in cottonseeds is toxic to human beings and monogastric animals and is a primary parameter for the integrated utilization of cottonseed products.It is usually determined by the techniques relied on complex pretreatment procedures and the samples after determination cannot be used in the breeding program,so it is of great importance to predict the gossypol content in cottonseeds rapidly and nondestructively to substitute the traditional analytical method.Results:Gossypol content in cottonseeds was investigated by near-infrared spectroscopy(NIRS)and high-performance liquid chromatography(HPLC).Partial least squares regression,combined with spectral pretreatment methods including Savitzky-Golay smoothing,standard normal variate,multiplicative scatter correction,and first derivate were tested for optimizing the calibration models.NIRS technique was efficient in predicting gossypol content in intact cottonseeds,as revealed by the root-mean-square error of cross-validation(RMSECV),root-mean-square error of prediction(RMSEP),coefficient for determination of prediction(R_(p)^(2)),and residual predictive deviation(RPD)values for all models,being 0.05∼0.07,0.04∼0.06,0.82∼0.92,and 2.3∼3.4,respectively.The optimized model pretreated by Savitzky-Golay smoothing+standard normal variate+first derivate resulted in a good determination of gossypol content in intact cottonseeds.Conclusions:Near-infrared spectroscopy coupled with different spectral pretreatments and partial least squares(PLS)regression has exhibited the feasibility in predicting gossypol content in intact cottonseeds,rapidly and non destructively.It could be used as an alternative method to substitute for traditional one to determi ne the gossypol content in intact cottonseeds.展开更多
Background:Manga nese(Mn)is an essential microelement in cotton seeds,which is usually determined by the techniques relied on hazardous reagents and complex pretreatment procedures.Therefore a rapid,low-cost,and reage...Background:Manga nese(Mn)is an essential microelement in cotton seeds,which is usually determined by the techniques relied on hazardous reagents and complex pretreatment procedures.Therefore a rapid,low-cost,and reagent-free analytical way is demanded to substitute the traditional analytical method.Results:The Mn content in cottonseed meal was investigated by near-infrared spectroscopy(NIRS)and chemometrics techniques.Standard normal variate(SNV)combined with first derivatives(FD)was the optimal spectra pre-treatment method.Monte Carlo uninformative variable elimination(MCUVE)and successive projections algorithm method(SPA)were employed to extract the informative variables from the full NIR spectra.The lin ear and non linear calibration models for cott on seed Mn content were developed.Finally,the optimal model for cottonseed Mn content was obtained by MCUVE-SPA-LSSVM,with root mean squares error of prediction(RMSEP)of 1.994 6,coefficient of determination(R^2)of 0.949 3,and the residual predictive deviation(RPD)of 4.370 5,respectively.Conclusions:The MCUVE-SPA-LSSVM model is accuracy enough to measure the Mn content in cottonseed meal,which can be used as an alter native way to substitute for traditional analytical method.展开更多
微生物细胞的傅里叶变换近红外光谱(Fourier transform near infrared spectroscopy,FT-NIR)反映了细胞成分的分子振动信息,具有的高度特异性,为寻求一种基于FT-NIR的微生物快速鉴定方法提供了可能。文章通过采集1株酵母和5株细菌标准...微生物细胞的傅里叶变换近红外光谱(Fourier transform near infrared spectroscopy,FT-NIR)反映了细胞成分的分子振动信息,具有的高度特异性,为寻求一种基于FT-NIR的微生物快速鉴定方法提供了可能。文章通过采集1株酵母和5株细菌标准菌株的近红外漫反射光谱,采用主成分分析法对光谱数据进行了分析,构建了基于FT-NIR的微生物快速鉴定模型。分析结果表明:①光谱鉴别指数Dy1y2值范围为1.61±1.05～10.97±6.65,重现性良好;②建立的基于线性判别分析模型的鉴定准确率为100%,基于人工神经网络模型的预测结果平均相对误差为5.75%,预测准确率高。研究结果证实该方法可以实现基于FT-NIR结合多元数学统计方法的微生物快速鉴定,并具有广阔的产业应用前景。展开更多
利用可见/近红外光谱技术对冷却肉菌落总数和颜色进行快速、无损检测。采用400~1 100 nm可见/近红外光谱成像系统,获取54个冷却肉样本表面的光谱图像,采用主成分分析结合马氏距离方法对异常光谱进行判别及剔除。通过Gompertz分布函数对...利用可见/近红外光谱技术对冷却肉菌落总数和颜色进行快速、无损检测。采用400~1 100 nm可见/近红外光谱成像系统,获取54个冷却肉样本表面的光谱图像,采用主成分分析结合马氏距离方法对异常光谱进行判别及剔除。通过Gompertz分布函数对散射特征曲线进行拟合,得到表征光谱信息的Gompertz参数,结合支持向量机算法建立冷却肉菌落总数和肉色L*的预测模型。α、β、θ、δ组合和α、β、δ组合建模对细菌总数预测效果最好,预测相关系数分别为0.937和0.935,预测标准差为0.600 lg CFU/g和0.702 lg CFU/g。β、δ组合建模对肉色L*预测效果较好,预测相关系数达到0.930,预测标准差为1.515。研究结果表明利用Vis/NIR光谱散射特征结合支持向量机可以实现冷却肉品质的快速、高效、无损伤检测。展开更多
文摘Doping perylene diimide(PDI)into a polymer matrix is a simple strategy to prepare near-infrared(NIR)reflective materials,but the mechanical properties and NIR reflectance properties are significantly compromised due to macro-phase separation.In this study,a novel polymer(denoted as PU-PDI)with intrinsic NIR reflective proper⁃ties was synthesized by covalent incorporation of PDI units into polyurethane chains.Its photophysical characteris⁃tics,mechanical property and NIR reflectance property are investigated in detail.The results show that covalent in⁃corporation reduces the severe aggregation of PDI units,thereby endows PU-PDI with excellent mechanical property.The elongation at break of PU-PDI can reach more than 700%,and the breaking strength is 34.11 MPa.Moreover,compared to the blending system,PU-PDI possesses enhanced NIR reflection ability due to the better dispersion of PDI units.
基金The research work was funded by The National Key Technology R&D Program of China(2016YFD0101404)China Agriculture Research System(CARS-18-25)Jiangsu Collaborative Innovation Center for Modern Crop Production.
文摘Background:Gossypol found in cottonseeds is toxic to human beings and monogastric animals and is a primary parameter for the integrated utilization of cottonseed products.It is usually determined by the techniques relied on complex pretreatment procedures and the samples after determination cannot be used in the breeding program,so it is of great importance to predict the gossypol content in cottonseeds rapidly and nondestructively to substitute the traditional analytical method.Results:Gossypol content in cottonseeds was investigated by near-infrared spectroscopy(NIRS)and high-performance liquid chromatography(HPLC).Partial least squares regression,combined with spectral pretreatment methods including Savitzky-Golay smoothing,standard normal variate,multiplicative scatter correction,and first derivate were tested for optimizing the calibration models.NIRS technique was efficient in predicting gossypol content in intact cottonseeds,as revealed by the root-mean-square error of cross-validation(RMSECV),root-mean-square error of prediction(RMSEP),coefficient for determination of prediction(R_(p)^(2)),and residual predictive deviation(RPD)values for all models,being 0.05∼0.07,0.04∼0.06,0.82∼0.92,and 2.3∼3.4,respectively.The optimized model pretreated by Savitzky-Golay smoothing+standard normal variate+first derivate resulted in a good determination of gossypol content in intact cottonseeds.Conclusions:Near-infrared spectroscopy coupled with different spectral pretreatments and partial least squares(PLS)regression has exhibited the feasibility in predicting gossypol content in intact cottonseeds,rapidly and non destructively.It could be used as an alternative method to substitute for traditional one to determi ne the gossypol content in intact cottonseeds.
基金funded by The National Key Technology R&D program of China(2016YFD0101404)China Agriculture Research System(CARS-18-25)Jiangsu Collaborative Innovation Center for Modern Crop Production
文摘Background:Manga nese(Mn)is an essential microelement in cotton seeds,which is usually determined by the techniques relied on hazardous reagents and complex pretreatment procedures.Therefore a rapid,low-cost,and reagent-free analytical way is demanded to substitute the traditional analytical method.Results:The Mn content in cottonseed meal was investigated by near-infrared spectroscopy(NIRS)and chemometrics techniques.Standard normal variate(SNV)combined with first derivatives(FD)was the optimal spectra pre-treatment method.Monte Carlo uninformative variable elimination(MCUVE)and successive projections algorithm method(SPA)were employed to extract the informative variables from the full NIR spectra.The lin ear and non linear calibration models for cott on seed Mn content were developed.Finally,the optimal model for cottonseed Mn content was obtained by MCUVE-SPA-LSSVM,with root mean squares error of prediction(RMSEP)of 1.994 6,coefficient of determination(R^2)of 0.949 3,and the residual predictive deviation(RPD)of 4.370 5,respectively.Conclusions:The MCUVE-SPA-LSSVM model is accuracy enough to measure the Mn content in cottonseed meal,which can be used as an alter native way to substitute for traditional analytical method.
文摘微生物细胞的傅里叶变换近红外光谱(Fourier transform near infrared spectroscopy,FT-NIR)反映了细胞成分的分子振动信息,具有的高度特异性,为寻求一种基于FT-NIR的微生物快速鉴定方法提供了可能。文章通过采集1株酵母和5株细菌标准菌株的近红外漫反射光谱,采用主成分分析法对光谱数据进行了分析,构建了基于FT-NIR的微生物快速鉴定模型。分析结果表明:①光谱鉴别指数Dy1y2值范围为1.61±1.05～10.97±6.65,重现性良好;②建立的基于线性判别分析模型的鉴定准确率为100%,基于人工神经网络模型的预测结果平均相对误差为5.75%,预测准确率高。研究结果证实该方法可以实现基于FT-NIR结合多元数学统计方法的微生物快速鉴定,并具有广阔的产业应用前景。
文摘利用可见/近红外光谱技术对冷却肉菌落总数和颜色进行快速、无损检测。采用400~1 100 nm可见/近红外光谱成像系统,获取54个冷却肉样本表面的光谱图像,采用主成分分析结合马氏距离方法对异常光谱进行判别及剔除。通过Gompertz分布函数对散射特征曲线进行拟合,得到表征光谱信息的Gompertz参数,结合支持向量机算法建立冷却肉菌落总数和肉色L*的预测模型。α、β、θ、δ组合和α、β、δ组合建模对细菌总数预测效果最好,预测相关系数分别为0.937和0.935,预测标准差为0.600 lg CFU/g和0.702 lg CFU/g。β、δ组合建模对肉色L*预测效果较好,预测相关系数达到0.930,预测标准差为1.515。研究结果表明利用Vis/NIR光谱散射特征结合支持向量机可以实现冷却肉品质的快速、高效、无损伤检测。
