Near-Infrared Spectroscopy Coupled with Chemometrics Tools: A Rapid and Non-Destructive Alternative on Soil Evaluation

Near-infrared (NIR) spectroscopy is a rapid, non-destructive and accurate technique for analyzing a wide variety of samples, thus, the growing interest of using this technique in soil science. The objective of this study was to evaluate the potential of NIR spectroscopy to predict organic carbon (OC), total nitrogen (TN), available phosphorus (P) and available potassium (K) in the soil. NIR spectra from 20 cm³ of soil samples were acquired on the range of 750 to 2500 nm in diffuse reflectance mode, resolution of 16 cm^?1 and 64 scans. Eight models of calibration/validation were constructed. Calibration and validation models showed that the predictive potential of NIR varied with the specific soil property (OC, TN, P and K) under evaluation and according to the methodology employed in the model construction (cross-validation or test set). Good prediction models were obtained for OC and TN content based on the statistical parameters. Test set methodology was able to predict soil OC, TN, P, and K better than cross-validation methodology.     

近红外透射光谱法快速测定瓶装醋中挥发酸含量的研究

采用近红外光谱直接对瓶装醋进行透射检测。结果发现,玻璃包装对入射光源强度影响很大。采用室内外置光源解决此问题,在850～2000nm的波长范围内,采用偏最小二乘方法（PLs）对醋中的挥发酸含量进行了定量预测。结果表明,原始光谱结合PLs方法可以很好地预测瓶装醋中的挥发酸含量,校正样本和预测样本的相关系数分别为0．98、0．97;校正样本和预测样本的标准偏差分别为o．776、0．878。此外,对挥发酸含量起主要作用的波段主要集中在900nm和1100nm左右。     

Use of Near Infrared Spectroscopy to Measure Mechanical Properties of Solid Wood

The visible and near infrared （NIR） （350-2500 nm） spectra and the MOE of 438 small clear wood samples from Chinese fir, eucalyptus and poplar 72 were measured. Using partial least-square （PLS） modeling, the NIR spectra could be used to predict MOE and MOR of the clear-wood samples from Chinese fir and eucalyptus solid wood. NIR spectra could only be used to Predict MOE but not MOR of poplar clear-wood samples. With the exception of MoR of poplar clear-wood samples, the correlations between NIR and the mechanical properties are very strong, and the calibration and test correlation coefficients are both above 0.80.     

应用NIR及主成分回归预测落叶松密度的研究

运用近红外光谱主成分回归法对落叶松样品密度进行研究，校正集的相关系数（R）为0．86，校正集标准误差（SEC）为0．01，预测集的相关系数（R）为0．89，预测集标准误差（SEP）为0．02，对未参与建模的12个未知样品进行密度预测，相关系数达0．95。研究表明，近红外光谱能够快速、准确地对落叶松样品密度进行预测，这为快速检测落叶松木材材性提供了一种新方法。     

多组分相关分析技术在近红外检测中的应用研究

采用信号分析理论中的相关分析技术,将三组分的涤纶-棉-毛样品与二组分的棉-毛样品进行相关分析,提取出三组分样品中的棉-毛信息,并对毛的含量进行定量分析,在31个样品中选22个样品建立了基于偏最小二乘(PLS)方法的NIR(Near Infrared,NIR)校正模型,对余下的9个样品进行了预测分析。结果表明,借助相关分析算法,毛含量的预测相对均方差和相关系数分别从3.5575、0.9855提高到2.7835、0.9876。试验证明,多组分相关分析技术可以在复杂混合样品中提取出待测组分的近红外光谱信息,为建立较高精度的校正模型创造了条件。     

土壤硝态氮含量原位检测系统设计

针对现阶段土壤硝态氮测量成本较高、无法长期原位测量等问题,该研究提出了一种使用钛烧结滤芯收集土壤溶液,通过近红外光谱法检测土壤溶液中的硝酸根浓度进而得到土壤中硝态氮含量的方法,并设计了相应的检测装置。通过试验对比陶土头与钛烧结滤芯在不同土壤条件下的土壤溶液收集效果,选用钛烧结滤芯作为土壤溶液采集器收集土壤溶液,以近红外LED作为测量光源,采集人工配置土壤溶液的光谱数据,利用BP神经网络进行预训练建立硝态氮含量预测模型。建立的硝态氮含量预测模型其训练集皮尔逊相关系数、测试集皮尔逊相关系数、预测均方根误差分别达到0.997、0.995、3.43。实地测量土壤溶液并与硝酸根离子电极以及土壤养分速测仪进行对比,最大相对偏差为5.9%,可满足实际测量准确性要求。该套检测设备在深度为10～40 cm、含水率为15%以上的土壤中有较好的土壤溶液采集效果;检测装置的长期测量标准差为0.006,动态响应时间为1.4 s,具有良好的检测特性。试验结果表明,使用溶液吸光度数据建立的硝态氮预测模型具有较好的预测效果,可以应用于土壤溶液硝态氮原位监测,为长期自动测量土壤硝态氮及水肥一体化系统的搭建提供了一种可行的方案。     

近红外光谱在转基因农产品检测中的研究进展

简述了近年来近红外光谱分析技术在农产品和食品定量分析中的应用,重点介绍了近红外光谱分析在转基因植株筛选以及转基因和非转基因农产品分类和识别等方面的研究现状,比较和分析了近红外光谱与传统的转基因农产品检测方法的特点,并展望了近红外光谱分析技术在转基因农产品检测研究中的发展前景。     

Near‐infrared spectroscopy can predict the composition of organic matter in soil and litter

The usefulness and limitations of near‐infrared reflectance spectroscopy (NIRS) for the assessment of several soil characteristics are still not sufficiently explored. The objective of this study was to evaluate the ability of visible and near‐infrared reflectance (VIS‐NIR) spectroscopy to predict the composition of organic matter in soils and litter. Reflectance spectra of the VIS‐NIR region (400–2500 nm) were recorded for 56 soil and litter samples from agricultural and forest sites. Spectra were used to predict general and biological characteristics of the samples as well as the C composition which was measured by ¹³C‐CPMAS‐NMR spectroscopy. A modified partial least‐square method and cross‐validation were used to develop equations for the different constituents over the whole spectrum (1st to 3rd derivation). Near‐infrared spectroscopy predicted well the C : N ratios, the percentages of O‐alkyl C and alkyl C, the ratio of alkyl C to O‐alkyl C, and the sum of phenolic oxidation products: the ratios of standard deviation of the laboratory results to standard error of cross‐validation (RSC) were greater than 2, the regression coefficients (a) of a linear regression (measured against predicted values) ranged from 0.9 to 1.1, and the correlation coefficients (r) were greater than 0.9. Satisfactorily (0.8 ≤ a ≤ 1.2, r ≥ 0.8, and 1.4 ≤ RSC ≤ 2.0) assessed were the contents of C, N, and production of DOC, the percentages of carbonyl C and aromatic C and the ratio of alkyl C to aromatic C. However, the N‐mineralization rate and the microbial biomass were predicted unsatisfactorily (RSC < 1.4). The good and satisfactory predictions reported above indicate a marked usefulness of NIRS in the assessment of biological and chemical characteristics of soils and litter.     

便携式近红外水果分析仪研究进展

主要介绍了便携式近红外水果分析仪的检测流程以及系统组成,对比分析了几种国内外商品化的便携式近红外水果检测仪,总结了其国内外发展研究近况,并展望了便携式近红外水果检测仪的发展趋势。便携式近红外水果分析仪检测目标由产后管理检测逐步发展为产中管理监测,有力地保证了产后水果产品的质量。     

基于近红外光谱岭回归的甜椒氮素检测试验

针对设施作物营养水平无损检测技术,着重描述了基于近红外光谱数据岭回归分析的甜椒氮素检测试验研究过程.利用近红外反射光谱成像技术对目标作物进行叶片尺度的光谱图像采集,应用计算机图像分析软件进行光谱数字图像处理、提取光谱数据,经过统计分析对数据完成筛选作为变量,结合化学分析试验结果建立作物营养检测模型,检验模型得出结论.为了解决自变量间存在的多重共线性造成模型难以建立的问题,在数据处理阶段,采用了在农业探测领域内并不多见的岭回归分析方法,利用其特殊的有偏估计算法,拟合建立回归方程.同时,由于岭回归分析可以用于进一步筛选特征波段,最终得到的是基于三特征波段近红外光谱反射率数据的甜椒叶片氮营养检测模型.经过模型检验,模型的调整R2为0.843,RMSE为0.105.