Pseudomonas spp.and Enterobacteriaceae are dominant spoilage bacteria in chicken during cold storage(0°C-4°C).In this study,high resolution spectra in the range of 900-1700 nm were acquired and preprocessed ...Pseudomonas spp.and Enterobacteriaceae are dominant spoilage bacteria in chicken during cold storage(0°C-4°C).In this study,high resolution spectra in the range of 900-1700 nm were acquired and preprocessed using Savitzky-Golay convolution smoothing(SGCS),standard normal variate(SNV)and multiplicative scatter correction(MSC),respectively,and then mined using partial least squares(PLS)algorithm to relate to the total counts of Pseudomonas spp.and Enterobacteriaceae(PEC)of fresh chicken breasts to predict PEC rapidly.The results showed that with full 900-1700 nm range wavelength,MSC-PLS model built with MSC spectra performed better than PLS models with other spectra(RAW-PLS,SGCS-PLS,SNV-PLS),with correlation coefficient(RP)of 0.954,root mean square error of prediction(RMSEP)of 0.396 log10 CFU/g and residual predictive deviation(RPD)of 3.33 in prediction set.Based on the 12 optimal wavelengths(902.2 nm,905.5 nm,923.6 nm,938.4 nm,946.7 nm,1025.7 nm,1124.4 nm,1211.6 nm,1269.2 nm,1653.7 nm,1691.8 nm and 1693.4 nm)selected from MSC spectra by successive projections algorithm(SPA),SPA-MSC-PLS model had RP of 0.954,RMSEP of 0.397 log10 CFU/g and RPD of 3.32,similar to MSC-PLS model.The overall study indicated that NIR spectra combined with PLS algorithm could be used to detect the PEC of chicken flesh in a rapid and non-destructive way.展开更多
Soil texture is an indicator of soil physical structure which delivers many ecological functions of soils such as thermal regime, plant growth, and soil quality. However, traditional methods for soil texture measureme...Soil texture is an indicator of soil physical structure which delivers many ecological functions of soils such as thermal regime, plant growth, and soil quality. However, traditional methods for soil texture measurement are time-consuming and labor-intensive. This study attempts to explore an indirect method for rapid estimating the texture of three subgroups of purple soils (i.e. calcareous, neutral, and acidic). 190 topsoil (0 - 10 cm) samples were collected from sloping croplands in Tongnan and Beibei Districts of Chongqing Municipality in China. Vis-NIR spectrum was measured and processed, and stepwise multiple linear regression (SMLR), partial least squares regression (PLSR), and back propagation neural network (BPNN) models were constructed to inform the soil texture. The clay fractions ranged from 4.40% to 27.12% while sand fractions ranged from 0.34% to 36.57%, hereby soil samples encompass three textural classes (i.e. silt, silt loam, and silty clay loam). For the original spectrum, the texture of calcareous and neutral purple soils was not significantly correlated with spectral reflectance and linear models (SMLR and PLSR) exhibited low prediction accuracy. The correlation coefficients and the goodness-of-fits between soil texture and the transformed spectra of all soil groups increased by continuum-removal (CR), first-order differential (R'), and second-order differential (R") transformations. Among them, the R" had the best performance in terms of improving the correlation coefficients and the goodness-of-fits. For the calcareous purple soil, the SMLR exceeds PLSR and BPNN with a higher coefficient of determination (R<sup>2</sup>) and the ratio of performance to inter-quartile distance (RPIQ) values and lower root mean square error of validation (RMSEV), but for the neutral and acidic purple soils, the PLSR model has a better prediction accuracy. In summary, the linear methods (SMLR and PLSR) are more reliable in estimating the texture of the three purple soil groups when using Vis-NIR spectroscopy inversion.展开更多
【目的】快速、准确地监测土壤有机质对于精准农业的发展具有重要意义。可见光-近红外(visible and near-infrared,Vis-NIR)光谱技术在土壤属性估算、数字化土壤制图等方面应用较为广泛,然而,在田间进行光谱测量,易受土壤含水量(soil mo...【目的】快速、准确地监测土壤有机质对于精准农业的发展具有重要意义。可见光-近红外(visible and near-infrared,Vis-NIR)光谱技术在土壤属性估算、数字化土壤制图等方面应用较为广泛,然而,在田间进行光谱测量,易受土壤含水量(soil moisture,SM)、温度、土壤表面状况等因素的影响,导致光谱信息中包含大量干扰信息,其中,SM变化是影响光谱观测结果最为显著的因素之一。此研究的目的是探讨OSC算法消除其影响,提升Vis-NIR光谱定量估算土壤有机质(soil organic matter,SOM)的精度。【方法】以江汉平原公安县和潜江市为研究区域,采集217份耕层(0—20 cm)土壤样本,进行风干、研磨、过筛等处理,采用重铬酸钾-外加热法测定SOM;将总体样本划分为3个互不重叠的样本集:建模集S^0(122个样本)、训练集S^1(60个样本)、验证集S^2(35个样本);设计SM梯度试验(梯度间隔为4%),在实验室内获取S^1和S^2样本集的9个梯度SM(0%—32%)的土壤光谱数据;分析SM对土壤Vis-NIR光谱反射率的影响,采用外部参数正交化算法(external parameter orthogonalization,EPO)、正交信号校正算法(orthogonal signal correction,OSC)消除SM对土壤光谱的干扰;利用主成分分析(principal component analysis,PCA)的前两个主成分得分和光谱相关系数两种方法检验消除SM干扰前、后的效果;基于偏最小二乘回归(partial least squares regression,PLSR)方法建立EPO和OSC处理前、后的SOM估算模型,利用决定系数(coefficient of determination,R^2)、均方根误差(root mean square error,RMSE)和RPD(the ratio of prediction to deviation)3个指标比较PLSR、EPO-PLSR、OSC-PLSR模型的性能。【结果】土壤Vis-NIR光谱受SM的影响十分明显,随着SM的增加,土壤光谱反射率呈非线性降低趋势。OSC处理前的湿土光谱数据主成分得分散点相对分散,与干土光谱数据主成分得分空间的位置不重叠,不同SM梯度之间的光谱相关系数变化较大;OSC处理后的湿土光谱数据主成分得分空间的位置基本与干土光谱数据相重合,各样本光谱数据之间相似性很高,不同SM梯度之间的光谱相关系数变化较小。9个SM梯度的EPO-PLSR模型的验证平均R^2_(pre)、RPD分别为0.69、1.7。9个SM梯度的OSC-PLSR模型的验证平均R^2_(pre)、RPD分别为0.72、1.89,校正后的OSC-PLSR模型受SM的较小,有效提升SOM估算模型的精度和鲁棒性。【结论】OSC能够消除SM变化对土壤Vis-NIR光谱的影响,可为将来田间原位实时监测SOM信息提供一定的理论支撑。展开更多
使用高光谱仪ASD Field Spec于吐丝期采集不同氮素处理的夏玉米叶片光谱,并进行对数变换处理;通过对"绿峰"(450～680nm)和"近红外反射平台"(760～1000nm)谱段光谱数据进行多尺度小波分解,获取第二层离散近似小波系...使用高光谱仪ASD Field Spec于吐丝期采集不同氮素处理的夏玉米叶片光谱,并进行对数变换处理;通过对"绿峰"(450～680nm)和"近红外反射平台"(760～1000nm)谱段光谱数据进行多尺度小波分解,获取第二层离散近似小波系数向量;采用主成分分析,从第二层离散近似小波系数向量中提取特征作为输入参数,建立对叶片氮素含量的广义回归神经网络估算模型.结果表明:对数变换显著地增强了"绿峰"和"近红外反射平台"谱段夏玉米叶片光谱对不同氮素处理的响应差异;从第二层离散近似小波系数向量中提取的小波主成分能够反映夏玉米叶片光谱在不同氮素处理下的整体变化趋势;以小波主成分作为输入参数的广义回归神经网络能够较为准确地预测夏玉米叶片氮素含量,并且具有一定的推广能力.展开更多
基金The authors acknowledged that this work was financially supported by Major Scientific and Technological Project of Henan Province(Grant No.161100110600)Key Scientific and Technological Project of Henan Province(No.212102310491,No.182102310060)+3 种基金China Postdoctoral Science Foundation(No.2018M632767)Henan Postdoctoral Science Foundation(No.001801021)Youth Talents Support Project of Henan Province(No.2018HYTP008)and Bainong Outstanding Talents Project of Henan Institute of Science and Technology(No.BNYC2018-2-27).
文摘Pseudomonas spp.and Enterobacteriaceae are dominant spoilage bacteria in chicken during cold storage(0°C-4°C).In this study,high resolution spectra in the range of 900-1700 nm were acquired and preprocessed using Savitzky-Golay convolution smoothing(SGCS),standard normal variate(SNV)and multiplicative scatter correction(MSC),respectively,and then mined using partial least squares(PLS)algorithm to relate to the total counts of Pseudomonas spp.and Enterobacteriaceae(PEC)of fresh chicken breasts to predict PEC rapidly.The results showed that with full 900-1700 nm range wavelength,MSC-PLS model built with MSC spectra performed better than PLS models with other spectra(RAW-PLS,SGCS-PLS,SNV-PLS),with correlation coefficient(RP)of 0.954,root mean square error of prediction(RMSEP)of 0.396 log10 CFU/g and residual predictive deviation(RPD)of 3.33 in prediction set.Based on the 12 optimal wavelengths(902.2 nm,905.5 nm,923.6 nm,938.4 nm,946.7 nm,1025.7 nm,1124.4 nm,1211.6 nm,1269.2 nm,1653.7 nm,1691.8 nm and 1693.4 nm)selected from MSC spectra by successive projections algorithm(SPA),SPA-MSC-PLS model had RP of 0.954,RMSEP of 0.397 log10 CFU/g and RPD of 3.32,similar to MSC-PLS model.The overall study indicated that NIR spectra combined with PLS algorithm could be used to detect the PEC of chicken flesh in a rapid and non-destructive way.
文摘Soil texture is an indicator of soil physical structure which delivers many ecological functions of soils such as thermal regime, plant growth, and soil quality. However, traditional methods for soil texture measurement are time-consuming and labor-intensive. This study attempts to explore an indirect method for rapid estimating the texture of three subgroups of purple soils (i.e. calcareous, neutral, and acidic). 190 topsoil (0 - 10 cm) samples were collected from sloping croplands in Tongnan and Beibei Districts of Chongqing Municipality in China. Vis-NIR spectrum was measured and processed, and stepwise multiple linear regression (SMLR), partial least squares regression (PLSR), and back propagation neural network (BPNN) models were constructed to inform the soil texture. The clay fractions ranged from 4.40% to 27.12% while sand fractions ranged from 0.34% to 36.57%, hereby soil samples encompass three textural classes (i.e. silt, silt loam, and silty clay loam). For the original spectrum, the texture of calcareous and neutral purple soils was not significantly correlated with spectral reflectance and linear models (SMLR and PLSR) exhibited low prediction accuracy. The correlation coefficients and the goodness-of-fits between soil texture and the transformed spectra of all soil groups increased by continuum-removal (CR), first-order differential (R'), and second-order differential (R") transformations. Among them, the R" had the best performance in terms of improving the correlation coefficients and the goodness-of-fits. For the calcareous purple soil, the SMLR exceeds PLSR and BPNN with a higher coefficient of determination (R<sup>2</sup>) and the ratio of performance to inter-quartile distance (RPIQ) values and lower root mean square error of validation (RMSEV), but for the neutral and acidic purple soils, the PLSR model has a better prediction accuracy. In summary, the linear methods (SMLR and PLSR) are more reliable in estimating the texture of the three purple soil groups when using Vis-NIR spectroscopy inversion.
文摘【目的】快速、准确地监测土壤有机质对于精准农业的发展具有重要意义。可见光-近红外(visible and near-infrared,Vis-NIR)光谱技术在土壤属性估算、数字化土壤制图等方面应用较为广泛,然而,在田间进行光谱测量,易受土壤含水量(soil moisture,SM)、温度、土壤表面状况等因素的影响,导致光谱信息中包含大量干扰信息,其中,SM变化是影响光谱观测结果最为显著的因素之一。此研究的目的是探讨OSC算法消除其影响,提升Vis-NIR光谱定量估算土壤有机质(soil organic matter,SOM)的精度。【方法】以江汉平原公安县和潜江市为研究区域,采集217份耕层(0—20 cm)土壤样本,进行风干、研磨、过筛等处理,采用重铬酸钾-外加热法测定SOM;将总体样本划分为3个互不重叠的样本集:建模集S^0(122个样本)、训练集S^1(60个样本)、验证集S^2(35个样本);设计SM梯度试验(梯度间隔为4%),在实验室内获取S^1和S^2样本集的9个梯度SM(0%—32%)的土壤光谱数据;分析SM对土壤Vis-NIR光谱反射率的影响,采用外部参数正交化算法(external parameter orthogonalization,EPO)、正交信号校正算法(orthogonal signal correction,OSC)消除SM对土壤光谱的干扰;利用主成分分析(principal component analysis,PCA)的前两个主成分得分和光谱相关系数两种方法检验消除SM干扰前、后的效果;基于偏最小二乘回归(partial least squares regression,PLSR)方法建立EPO和OSC处理前、后的SOM估算模型,利用决定系数(coefficient of determination,R^2)、均方根误差(root mean square error,RMSE)和RPD(the ratio of prediction to deviation)3个指标比较PLSR、EPO-PLSR、OSC-PLSR模型的性能。【结果】土壤Vis-NIR光谱受SM的影响十分明显,随着SM的增加,土壤光谱反射率呈非线性降低趋势。OSC处理前的湿土光谱数据主成分得分散点相对分散,与干土光谱数据主成分得分空间的位置不重叠,不同SM梯度之间的光谱相关系数变化较大;OSC处理后的湿土光谱数据主成分得分空间的位置基本与干土光谱数据相重合,各样本光谱数据之间相似性很高,不同SM梯度之间的光谱相关系数变化较小。9个SM梯度的EPO-PLSR模型的验证平均R^2_(pre)、RPD分别为0.69、1.7。9个SM梯度的OSC-PLSR模型的验证平均R^2_(pre)、RPD分别为0.72、1.89,校正后的OSC-PLSR模型受SM的较小,有效提升SOM估算模型的精度和鲁棒性。【结论】OSC能够消除SM变化对土壤Vis-NIR光谱的影响,可为将来田间原位实时监测SOM信息提供一定的理论支撑。
文摘使用高光谱仪ASD Field Spec于吐丝期采集不同氮素处理的夏玉米叶片光谱,并进行对数变换处理;通过对"绿峰"(450～680nm)和"近红外反射平台"(760～1000nm)谱段光谱数据进行多尺度小波分解,获取第二层离散近似小波系数向量;采用主成分分析,从第二层离散近似小波系数向量中提取特征作为输入参数,建立对叶片氮素含量的广义回归神经网络估算模型.结果表明:对数变换显著地增强了"绿峰"和"近红外反射平台"谱段夏玉米叶片光谱对不同氮素处理的响应差异;从第二层离散近似小波系数向量中提取的小波主成分能够反映夏玉米叶片光谱在不同氮素处理下的整体变化趋势;以小波主成分作为输入参数的广义回归神经网络能够较为准确地预测夏玉米叶片氮素含量,并且具有一定的推广能力.
