小麦种子发芽率近红外定量分析模型的优化（英文）

The backward interval partial least squares(Bi PLS)and the synergy interval partial least squares(Si PLS)were applied to select the characteristic spectral regions representing the germination rate of 84 wheat seeds and build the near infrared(NIR)quantitative analysis model of wheat seed germination rate.Results from comparison showed that the models built by two variable selection methods had better predictive ability than full-spectral partial least squares(PLS)model.The optimal model was obtained by Si PLS with the calibration and prediction correlation coefficient(R)at 0.902 and 0.967 respectively,and ratio of performance to standard deviate(RPD)at 3.75.Based on this,the physical chemistry significance of characteristic spectral regions was analyzed.The characteristic spectral of wheat seed germination rate contained characteristic peaks of water,protein,starch,fiber,which were the internal nutrients of the seed that influence the germination ability,thus explaining the mechanism of measuring wheat seed germination rate using NIR to a certain extent.     

基于dbiPLS-SPA变量筛选的固态发酵湿度近红外光谱检测

为了提高基于近红外光谱技术的固态发酵关键过程参数——湿度快速检测的精度和稳定性,研究采用动态反向区间偏最小二乘（dbiPLS）法结合连续投影算法（SPA）进行最佳光谱子区间和特征组合变量的筛选,通过交互验证法确定偏最小二乘（PLS）模型的主成分因子数,并以预测均方根误差（RMSEP）和相关系数（Rp）作为模型的评价标准。试验结果显示,最佳dbiPLS-SPA模型筛选的组合变量个数为8,其RMSEP和Rp分别为1.1795%（质量分数）和0.9430。试验结果表明,dbiPLS-SPA是一个有效的波长组合变量筛选方法,可简化模型结构、增强模型精度和稳健性。     

iPLS-SPA变量选择方法在螺旋藻粉无损检测中的应用

该文研究了基于可见-近红外光谱技术的螺旋藻粉类别无损检测方法。采用簇类独立软模式法（SIMCA）建立可见-近红外光谱模型。全波段光谱所建立的模型得到了93.33%的预测集正确率。文章提出了基于间隔偏最小二乘法（iPLS）和连续投影算法（SPA）的组合光谱变量选择方法进行有效波长的选择。该方法从全波段675个变量中选择了5个最优的有效波段,并且得到了96.67%的预测集正确率。和基于全波段光谱、可见光波段光谱和近红外波段光谱进行SPA运算相比,基于iPLS的SPA运算可以有效减少计算时间。研究表明可见-近红外光谱可以用于对螺旋藻粉类别进行无损检测,同时iPLS-SPA是一个有效的光谱变量选择方法。     

基于近红外漫反射光谱的云芝提取物多糖含量快速检测研究

为探索快速无损测定云芝提取物中多糖含量的方法,通过采集粉末状云芝提取物近红外光谱,经预处理和波段选择,结合间隔偏最小二乘法(iPLS)和反向区间偏最小二乘法(Bi-PLS),建立并优化云芝提取物多糖含量检测模型。结果表明,光谱区间为9 365.92~8 918.76 cm~(-1)和5 341.48~4 894.32cm~(-1),二阶导数(SD)预处理后,建立的反向区间偏最小二乘法模型更优,其校正决定系数(R_(cal))、校正均方根差(RMSECV)、验证决定系数(R_(val))和验证均方根差(RMSEP)可分别达到0.9089、0.00781、0.9879和0.00292。该模型可以更有效地优选建模所需波段,降低模型复杂度,降低多糖含量的检测成本,提高检测效率,实现云芝提取物多糖含量的快速、无损检测。     

Prediction chemical composition and alveograph parameters on wheat by near-infrared transmittance spectroscopy

Moisture, protein, wet gluten, dry gluten, and alveograph parameters (W, P, and P/L) of whole wheat grown in different countries around the world were analyzed using near infrared (NIR) transmittance spectroscopy. Modified partial least squares on NIR spectra (850-1048.2 nm) were developed for each constituent or physical property. The best models were obtained for protein, moisture, wet gluten, and dry gluten with r(2) = 0.99, 0.99, 0.95, and 0.96, respectively. Initial alveograph NIR models proposed for all wheat samples did not perform well. However, when wheat samples were divided in two groups depending on W (deformation energy) values, NIR models were highly improved, showing enough prediction accuracy for screening wheat at the receiving stage at mills or elevators.     

Relationship Between NIR Spectra and RVA Parameters During Wheat Germination

The process of germination in six different wheat cultivars was monitored using NIR spectroscopy and the Rapid Visco Analyser (RVA) method. Near‐infrared spectra provided insight into both chemical and physical changes that occur in the seed, in particular mobilization processes involving carbohydrates. RVA curves also contain physical and chemical information and can be interpreted as physicochemical spectra. The process of germination was followed sensitively through the RVA curves and some rheological parameters (peak viscosity, trough, breakdown, final viscosity, and setback) were highly correlated (R = 0.95–0.98) with predicted values calculated from NIR spectra. Viscosity data calculated from RVA curves collected at 200–480 sec showed the most characteristic changes during the early heat treatment stage of the pasting procedure. Strong intercorrelations were found between viscosity data and NIR spectra from the beginning of the swelling and gelatinization processes in germinating seed. The NIR and RVA methods were sensitive tools for the rapid investigation of the germination process, which is important both from a physiological and technological point of view.     

利用近红外光谱与PCA-SVM识别热损伤番茄种子

为了研究近红外光谱技术用于热损伤种子快速无损识别的可行性,该文以120粒番茄种子为研究对象,其中60粒番茄种子通过高温加热处理的方式成为热损伤种子组,其他60粒番茄种子为正常种子组,利用实验室自主搭建的近红外光谱检测系统获取单粒番茄种子在980~1 700 nm范围内的光谱,分别采用偏最小二乘判别法(partial least squares discriminant analysis,PLS-DA)和支持向量机(support vector machines,SVM)建立了番茄种子热损伤的定性分析模型。试验结果表明:2种判别模型的验证集总正确率均大于96%,均可用于热损伤种子的判别。其中,基于主成分分析(principal component analysis,PCA)预处理的光谱数据构建的支持向量机模型的判别效果最好,其校正集和验证集的判别正确率均为100%,更适用于种子热损伤识别。因此,应用近红外光谱技术可快速无损识别热损伤番茄种子,为种子检验提供了一种新的方法。     

Soil carbon mapping using on-the-go near infrared spectroscopy, topography and aerial photographs

Efficient tools for accurate soil carbon (SC) mapping are imperative for large scale assessment of total SC stocks and their changes in time as well as for site-specific tailoring of agricultural management practices. On-the-go near infrared (NIR) reflectance spectroscopy has been used recently in aid to the conventional, laborious and expensive soil analyses, since NIR measurements are taken in-situ quickly and non-destructively. However, NIR spectrum data need to be effectively calibrated with conventionally measured SC. Our objectives are to compare calibration approaches, including pre-processing transformations (Savitzky-Golay derivatives, standard normal variate and mean centering) and multivariate statistical methods (principal component regression, partial least squares, partial least squares leaving one-outlier-out) for using NIR spectra data as SC predictor, to evaluate NIR reliability in predicting SC under low carbon contents typical for Midwest Alfisols; and finally to compare predictions of SC by using three sources of auxiliary information (NIR spectral data, visible-NIR reflectance obtained from aerial photographs and topographical features). No improvements in calibration accuracy were observed when using pre-processing transformations. Partial least squares (RMSE = 1.90) tended to perform better than principal component regression (RMSE = 1.96); especially when spectral-NIR outliers are dropped (RMSE = 1.68). Our results suggested that visible-NIR data from aerial photographs used along with topographical attributes outperformed on-the-go spectral NIR data. Topographical data improved prediction in the models with aerial photograph visible-NIR data; however no improvement was noticed when used with spectral-NIR data. Though, NIR spectral data is frequently used as a proxy for SC prediction, we notice that this auxiliary information is not well suited under all scenarios. Particularly, when SC levels are low and the range of SC data is narrow, as in this study, NIR was only moderately successful in predicting SC.     

特征波长筛选在近红外光谱测定梨硬度中的应用

为了提高应用近红外光谱分析技术快速测定梨硬度的精度和稳定性,该研究采用联合区间偏最小二乘和遗传算法（siPLS-GA）在校正模型中用来筛选特征光谱区域和波长,通过交互验证法确定模型的主成分因子数和筛选的波长,并以预测均方根误差（RMSEP）和相关系数（Rp）作为模型的评价标准。基于siPLS-GA的最优模型包含4个光谱区、96个变量和10个主成分因子。该模型结果显示：最佳预测模型相关系数（Rp）和RMSEP分别为0.9083和0.5573。研究结果表明,近红外光谱技术结合siPLS-GA建模用于无损、快速测定梨的硬度是可行的。     

不同磷肥水平的小麦冠层多光谱特征研究

利用便携式冠层光谱仪对小麦进行连续观测获取光谱数据。本文分析了小麦在不同P肥施用水平及生育期变化情况下冠层的光谱响应特征,运用t-检验等统计方法获得了小麦冠层光谱对不同P肥水平的敏感波段,并由此找到判断P肥施用是否合理的关键生育期。结果表明小麦冠层光谱的近红外波段(810~1100nm)对P素的相应关系优于可见光波段,870nm等近红外波段为小麦P素敏感波段;从拔节期到孕穗期前后为其P素丰缺状况光谱诊断的关键生育期;归一化植被指数(NDVI)也可与小麦产量建立很好的回归方程。