Soft independent modeling of class analogy (SIMCA) was successful in classifying a large library of 758 commercially available, non-blended samples of acetate, cotton, polyester, rayon, silk and wool 89% - 98% of the ...Soft independent modeling of class analogy (SIMCA) was successful in classifying a large library of 758 commercially available, non-blended samples of acetate, cotton, polyester, rayon, silk and wool 89% - 98% of the time at the 95% confidence level (p = 0.05 significance level). In the present study, cotton and silk had a 62% and 24% chance, respectively, of being classified with their own group and also with rayon. SIMCA correctly identified a counterfeit “silk” sample as polyester. When coupled with diffuse NIR reflectance spectroscopy and a large sample library, SIMCA shows considerable promise as a quick, non-destructive, multivariate method for fiber identification. A major advantage is simplicity. No sample pretreatment of any kind was required, and no adjust-ments were made for fiber origin, manufacturing process residues, topical finishes, weave pattern, or dye content. Increasing the sample library should make the models more robust and improve identification rates over those reported in this paper.展开更多
The near infrared (NIR) spectroscopy technique has been applied in many fields because of its advantages of simple preparation, fast response, and non-destructiveness. We investigated the potential of NIR spectroscopy...The near infrared (NIR) spectroscopy technique has been applied in many fields because of its advantages of simple preparation, fast response, and non-destructiveness. We investigated the potential of NIR spectroscopy in diffuse reflectance mode for determining the soluble solid content (SSC) and acidity (pH) of intact loquats. Two cultivars of loquats (Dahongpao and Jiajiaozhong) harvested from two orchards (Tangxi and Chun'an, Zhejiang, China) were used for the measurement of NIR spectra between 800 and 2500 nm. A total of 400 loquats (100 samples of each cultivar from each orchard) were used in this study. Re- lationships between NIR spectra and SSC and acidity of loquats were evaluated using partial least square (PLS) method. Spectra preprocessing options included the first and second derivatives, multiple scatter correction (MSC), and the standard normal variate (SNV). Three separate spectral windows identified as full NIR (800～2500 nm), short NIR (800～1100 nm), and long NIR (1100～2500 nm) were studied in factorial combination with the preprocessing options. The models gave relatively good predic- tions of the SSC of loquats, with root mean square error of prediction (RMSEP) values of 1.21, 1.00, 0.965, and 1.16 °Brix for Tangxi-Dahongpao, Tangxi-Jiajiaozhong, Chun'an-Dahongpao, and Chun'an-Jiajiaozhong, respectively. The acidity prediction was not satisfactory, with the RMSEP of 0.382, 0.194, 0.388, and 0.361 for the above four loquats, respectively. The results indicate that NIR diffuse reflectance spectroscopy can be used to predict the SSC and acidity of loquat fruit.展开更多
为了实现霉变稻谷脂肪酸含量无损、快速检测,该文研究应用可见/近红外光谱技术检测霉变稻谷的脂肪酸含量。考虑到直接选用霉变稻谷可见/近红外光谱数据构建脂肪酸含量预测模型存在建模费时、预测失准等问题,研究提出了霉变稻谷脂肪酸含...为了实现霉变稻谷脂肪酸含量无损、快速检测,该文研究应用可见/近红外光谱技术检测霉变稻谷的脂肪酸含量。考虑到直接选用霉变稻谷可见/近红外光谱数据构建脂肪酸含量预测模型存在建模费时、预测失准等问题,研究提出了霉变稻谷脂肪酸含量的可见/近红外优化校正模型。研究中通过光谱-理化值共生距离（sample set partitioning based on joint xy distance,SPXY）算法结合偏最小二乘法初步分析了不同校正集样本预测霉变稻谷脂肪酸含量的差异;利用连续投影算法（SPA）提取了反映霉变稻谷脂肪酸含量变化的特征波段;采用偏最小二乘法（partial least square,PLS）和多元线性回归法（multivariable linear regression,MLR）分别建立了基于特征波段光谱反射值的霉变稻谷脂肪酸含量预测模型,并对比分析了采用SPXY样本集划分的模型预测效果。结果表明：采用SPXY法筛选出的65个校正集样本分布与初始校正集相近,脂肪酸含量变化范围为18.55~127.26 mg,其标准差为32.39;SPA算法最终从256个全光谱波段中优选出9个特征波段,实现了光谱数据的压缩;分别建立的SPXY-SPA-PLSR模型和SPXY-SPA-MLR模型预测霉变稻谷脂肪酸含量相关系数RP为0.922 1和0.915 9,预测均方根误差RMSEP为13.889 3和14.261 0;SPXY筛选校正集所构建模型预测精度与初始校正集所建模型相当,但校正集样本数量减少为初始校正集的41%,运算时长缩短为初始样本集的32%,提高了模型的校正速度。展开更多
针对目前近红外光谱分析中模型传递现有方法的局限性,文章介绍了一种简便的近红外光谱标准化方法,并构造了一种新的光谱标准化误差指标(spectra standard error,SSE)作为评价传递结果的指标。SSE为J2和J1的比值,这里,J2表示同一样本在...针对目前近红外光谱分析中模型传递现有方法的局限性,文章介绍了一种简便的近红外光谱标准化方法,并构造了一种新的光谱标准化误差指标(spectra standard error,SSE)作为评价传递结果的指标。SSE为J2和J1的比值,这里,J2表示同一样本在不同仪器上测得的谱线的距离,J1表示目标机的不同样本相对中心谱线的平均距离。文章首先对不同光谱仪所测得的吸光度谱图进行多项式卷积平滑处理以去除基线,接着采用标准归一法以实现谱图的标准化,并采用多项式卷积滤波以去除噪声。为使SSE达到最小,在处理过程中可进行波长范围和卷积窗口宽度的优化。经过上述处理后的标准化谱图可用于光谱建模分析。该方法不需要预先获得大量样本,也不需要将同一样本在不同光谱仪上测得的谱图进行比较。针对一批汽油样本的试验结果表明,借助于此方法可使SSE从1.418下降至0.167,谱图标准化效果令人满意。展开更多
The objective of this paper is to present a review of different calibration and classification methods for functional data in the context of chemometric applications. In chemometric, it is usual to measure certain par...The objective of this paper is to present a review of different calibration and classification methods for functional data in the context of chemometric applications. In chemometric, it is usual to measure certain parameters in terms of a set of spectrometric curves that are observed in a finite set of points (functional data). Although the predictor variable is clearly functional, this problem is usually solved by using multivariate calibration techniques that consider it as a finite set of variables associated with the observed points (wavelengths or times). But these explicative variables are highly correlated and it is therefore more informative to reconstruct first the true functional form of the predictor curves. Although it has been published in several articles related to the implementation of functional data analysis techniques in chemometric, their power to solve real problems is not yet well known. Because of this the extension of multivariate calibration techniques (linear regression, principal component regression and partial least squares) and classification methods (linear discriminant analysis and logistic regression) to the functional domain and some relevant chemometric applications are reviewed in this paper.展开更多
文摘Soft independent modeling of class analogy (SIMCA) was successful in classifying a large library of 758 commercially available, non-blended samples of acetate, cotton, polyester, rayon, silk and wool 89% - 98% of the time at the 95% confidence level (p = 0.05 significance level). In the present study, cotton and silk had a 62% and 24% chance, respectively, of being classified with their own group and also with rayon. SIMCA correctly identified a counterfeit “silk” sample as polyester. When coupled with diffuse NIR reflectance spectroscopy and a large sample library, SIMCA shows considerable promise as a quick, non-destructive, multivariate method for fiber identification. A major advantage is simplicity. No sample pretreatment of any kind was required, and no adjust-ments were made for fiber origin, manufacturing process residues, topical finishes, weave pattern, or dye content. Increasing the sample library should make the models more robust and improve identification rates over those reported in this paper.
基金Project supported by the National Natural Science Foundation of China(No.30825027)the National Key Technology R&D Program of China(No.2006BAD11A12)
文摘The near infrared (NIR) spectroscopy technique has been applied in many fields because of its advantages of simple preparation, fast response, and non-destructiveness. We investigated the potential of NIR spectroscopy in diffuse reflectance mode for determining the soluble solid content (SSC) and acidity (pH) of intact loquats. Two cultivars of loquats (Dahongpao and Jiajiaozhong) harvested from two orchards (Tangxi and Chun'an, Zhejiang, China) were used for the measurement of NIR spectra between 800 and 2500 nm. A total of 400 loquats (100 samples of each cultivar from each orchard) were used in this study. Re- lationships between NIR spectra and SSC and acidity of loquats were evaluated using partial least square (PLS) method. Spectra preprocessing options included the first and second derivatives, multiple scatter correction (MSC), and the standard normal variate (SNV). Three separate spectral windows identified as full NIR (800～2500 nm), short NIR (800～1100 nm), and long NIR (1100～2500 nm) were studied in factorial combination with the preprocessing options. The models gave relatively good predic- tions of the SSC of loquats, with root mean square error of prediction (RMSEP) values of 1.21, 1.00, 0.965, and 1.16 °Brix for Tangxi-Dahongpao, Tangxi-Jiajiaozhong, Chun'an-Dahongpao, and Chun'an-Jiajiaozhong, respectively. The acidity prediction was not satisfactory, with the RMSEP of 0.382, 0.194, 0.388, and 0.361 for the above four loquats, respectively. The results indicate that NIR diffuse reflectance spectroscopy can be used to predict the SSC and acidity of loquat fruit.
文摘为了实现霉变稻谷脂肪酸含量无损、快速检测,该文研究应用可见/近红外光谱技术检测霉变稻谷的脂肪酸含量。考虑到直接选用霉变稻谷可见/近红外光谱数据构建脂肪酸含量预测模型存在建模费时、预测失准等问题,研究提出了霉变稻谷脂肪酸含量的可见/近红外优化校正模型。研究中通过光谱-理化值共生距离（sample set partitioning based on joint xy distance,SPXY）算法结合偏最小二乘法初步分析了不同校正集样本预测霉变稻谷脂肪酸含量的差异;利用连续投影算法（SPA）提取了反映霉变稻谷脂肪酸含量变化的特征波段;采用偏最小二乘法（partial least square,PLS）和多元线性回归法（multivariable linear regression,MLR）分别建立了基于特征波段光谱反射值的霉变稻谷脂肪酸含量预测模型,并对比分析了采用SPXY样本集划分的模型预测效果。结果表明：采用SPXY法筛选出的65个校正集样本分布与初始校正集相近,脂肪酸含量变化范围为18.55~127.26 mg,其标准差为32.39;SPA算法最终从256个全光谱波段中优选出9个特征波段,实现了光谱数据的压缩;分别建立的SPXY-SPA-PLSR模型和SPXY-SPA-MLR模型预测霉变稻谷脂肪酸含量相关系数RP为0.922 1和0.915 9,预测均方根误差RMSEP为13.889 3和14.261 0;SPXY筛选校正集所构建模型预测精度与初始校正集所建模型相当,但校正集样本数量减少为初始校正集的41%,运算时长缩短为初始样本集的32%,提高了模型的校正速度。
文摘针对目前近红外光谱分析中模型传递现有方法的局限性,文章介绍了一种简便的近红外光谱标准化方法,并构造了一种新的光谱标准化误差指标(spectra standard error,SSE)作为评价传递结果的指标。SSE为J2和J1的比值,这里,J2表示同一样本在不同仪器上测得的谱线的距离,J1表示目标机的不同样本相对中心谱线的平均距离。文章首先对不同光谱仪所测得的吸光度谱图进行多项式卷积平滑处理以去除基线,接着采用标准归一法以实现谱图的标准化,并采用多项式卷积滤波以去除噪声。为使SSE达到最小,在处理过程中可进行波长范围和卷积窗口宽度的优化。经过上述处理后的标准化谱图可用于光谱建模分析。该方法不需要预先获得大量样本,也不需要将同一样本在不同光谱仪上测得的谱图进行比较。针对一批汽油样本的试验结果表明,借助于此方法可使SSE从1.418下降至0.167,谱图标准化效果令人满意。
文摘The objective of this paper is to present a review of different calibration and classification methods for functional data in the context of chemometric applications. In chemometric, it is usual to measure certain parameters in terms of a set of spectrometric curves that are observed in a finite set of points (functional data). Although the predictor variable is clearly functional, this problem is usually solved by using multivariate calibration techniques that consider it as a finite set of variables associated with the observed points (wavelengths or times). But these explicative variables are highly correlated and it is therefore more informative to reconstruct first the true functional form of the predictor curves. Although it has been published in several articles related to the implementation of functional data analysis techniques in chemometric, their power to solve real problems is not yet well known. Because of this the extension of multivariate calibration techniques (linear regression, principal component regression and partial least squares) and classification methods (linear discriminant analysis and logistic regression) to the functional domain and some relevant chemometric applications are reviewed in this paper.
