Usefulness of near‐infrared spectroscopy for the prediction of chemical and biological soil properties in different long‐term experiments

The prediction accuracy of visible and near‐infrared (Vis‐NIR) spectroscopy for soil chemical and biological parameters has been variable and the reasons for this are not completely understood. Objectives were (1) to explore the predictability of a series of chemical and biological properties for three different soil populations and—based on these heterogeneous data sets—(2) to analyze possible predictive mechanisms statistically. A number of 422 samples from three arable soils in Germany (a sandy Haplic Cambisol and two silty Haplic Luvisols) of different long‐term experiments were sampled, their chemical and biological properties determined and their reflectance spectra in the Vis‐NIR region recorded after shock‐freezing followed by freeze‐drying. Cross‐validation was carried out for the entire population as well as for each population from the respective sites. For the entire population, excellent prediction accuracies were found for the contents of soil organic C (SOC) and total P. The contents of total N and microbial biomass C and pH were predicted with good accuracy. However, prediction accuracy for the other properties was less: content of total S was predicted approximately quantitatively, whereas Vis‐NIR spectroscopy could only differentiate between high and low values for the contents of microbial N, ergosterol, and the ratio of ergosterol to microbial biomass C. Contents of microbial biomass P and S, basal respiration, and qCO₂ could not be predicted. Prediction accuracies were greatest for the entire population and the Luvisol at Garte, followed by the Luvisol at Hohes Feld, whereas the accuracy for the sandy Cambisol was poor. The poor accuracy for the sandy Cambisol may have been due to only smaller correlations between the measured properties and the SOC content compared to the Luvisols or due to a general poor prediction performance for sandy soils. Another reason for the poor accuracy may have been the smaller range of contents in the sandy soil. Overall, the data indicated that the accuracy of predictions of soil properties depends largely on the population investigated. For the entire population, the usefulness of Vis‐NIR for the number of chemical and biological soil properties was evident by markedly greater correlation coefficients (measured against Vis‐NIR predicted) compared to the Pearson correlation coefficients of the measured properties against the SOC content. However, the cross‐validation results are valid only for the closed population used in this study.     

Comparison of an infrared anaesthetic agent analyser (Datex‐Ohmeda) with refractometry for measurement of isoflurane,sevoflurane and desflurane concentrations

ObjectiveTo assess agreement between infrared (IR) analysers and a refractometer for measurements of isoflurane, sevoflurane and desflurane concentrations and to demonstrate the effect of customized calibration of IR analysers.Study designIn vitro experiment.SubjectsSix IR anaesthetic monitors (Datex-Ohmeda) and a single portable refractometer (Riken).MethodsBoth devices were calibrated following the manufacturer’s recommendations. Gas samples were collected at common gas outlets of anaesthesia machines. A range of agent concentrations was produced by stepwise changes in dial settings: isoflurane (0–5% in 0.5% increments), sevoflurane (0–8% in 1% increments), or desflurane (0–18% in 2% increments). Oxygen flow was 2 L minute^?1. The orders of testing IR analysers, agents and dial settings were randomized. Duplicate measurements were performed at each setting. The entire procedure was repeated 24 hours later. Bland–Altman analysis was performed. Measurements on day-1 were used to yield calibration equations (IR measurements as dependent and refractometry measurements as independent variables), which were used to modify the IR measurements on day-2.ResultsBias ± limits of agreement for isoflurane, sevoflurane and desflurane were 0.2 ± 0.3, 0.1 ± 0.4 and 0.7 ± 0.9 volume%, respectively. There were significant linear relationships between differences and means for all agents. The IR analysers became less accurate at higher gas concentrations. After customized calibration, the bias became almost zero and the limits of agreement became narrower.Conclusions and clinical relevanceIf similar IR analysers are used in research studies, they need to be calibrated against a reference method using the agent in question at multiple calibration points overlapping the range of interest.     

浮顶油罐降温储存高凝油品

介绍了改进后的浮顶式储罐加热装置的设计原理及结构,对其应用于间歇供热条件下储存高凝油品的安全性与可靠性进行了分析,并用模型罐进行了模拟试验。结果表明,新设计的加热装置可以使因间歇供热而凝结的浮顶式储油罐安全解凝,快速恢复作业。降温储存高凝油品是可行的。     

基于广泛靶向脂质组学与近红外技术的高低油酸花生鉴评

[目的]探索一种能鉴定花生品种与评价品质的新型检测方法,筛选高油酸花生特异性脂质生物标志物.[方法]采用超高效液相色谱-串联质谱(UPLC-MS/MS)技术,对2个油酸含量差异显著的花生品种,即高油酸品种开农1715(KN1715)和低油酸品种冀花7号(JH7H)进行农艺性状考察、近红外分析(Near infrared spectroscopy,NIRS)与广泛靶向脂质组学(Widely targeted lipidomics,WTL)研究.[结果]农艺性状测定结果表明,高油酸花生品种KN1715的主茎、第一侧枝长度比JH7H分别高11.72％和33.74％,差异显著;而单株荚果数、单株饱果率、百果重、百仁重、出仁率分别低46.23％、5.02％、5.35％、11.68％、17.17％,差异显著.品质性状检测结果表明,KN1715的油酸、硬脂酸、花生酸、蛋白质、氨基酸、缬氨酸、蛋氨酸、异亮氨酸、亮氨酸、苯丙氨酸、组氨酸、精氨酸、脯氨酸含量均显著高于JH7H,亚油酸、棕榈酸、山嵛酸、二十四烷酸含量则相反;广泛靶向脂质组代谢物分析表明,高、低油酸品种间存在较大的脂质代谢物差异,共筛选到16类295种脂质分子,进而通过对差异代谢物多元统计分析,最终筛选到3类31种核心差异脂质分子.结合核心差异脂质代谢物与品质指标,通过聚类与相关性分析,得到可视化表型与脂质代谢物特征图谱.[结论]该方法基于广泛靶向脂质组学与近红外技术,可以很好地实现从表型与脂质分子层面对花生品种进行品种与品质鉴评.     

太阳能集热与余热回收烤房的增温节能效果研究

为探索太阳能集热与余热回收烤房的增温节能效果,以气流上升式密集烤房为对照,对比研究了余热回收烤房、太阳能集热烤房以及太阳能集热+余热回收烤房3种改造烤房的增温能力、节能效果及烤后烟叶等级质量。结果表明:3种改造烤房集温室集热效果明显,表现为太阳能集热+余热回收烤房太阳能集热烤房余热回收烤房对照;余热回收烤房、太阳能集热烤房以及太阳能集热+余热回收烤房的节煤率较对照分别提高了8.7%~10.9%、12.9%~16.5%、21.7%~21.8%;改造烤房对烤后烟叶等级质量影响较小。可见,太阳能集热+余热回收烤房的增温节能效果表现最佳。     

基于近红外光谱技术的恩施玉露茶保存年份的快速无损鉴别

为快速、准确、无损鉴别恩施玉露茶的保存年份,扫描在良好条件下连续保存5a(2010-2014年)的100个恩施玉露茶,获得其近红外光谱,对光谱进行预处理,然后结合主成分分析法(PCA)和最小二乘支持向量机法(LS-SVM)建立绿茶保存年份的近红外光谱预测模型。结果表明,前3个主成分的累计贡献率为99.99%,验证集模型的决定系数(R2)为0.971 7,验证均方差(RMSEP)为0.255 0。初步实现了市售绿茶保存年份的快速鉴别,该方法也为其他茶类保存年份的判别提供参考。     

近红外漫反射光谱技术快速无损识别灵芝和云芝提取物研究

为了满足食用菌提取物实际生产监管需要,本研究采用近红外漫反射光谱技术对来自不同地区的灵芝和云芝提取物样品进行定性识别研究。在800~2 750nm波段范围,采集灵芝和云芝提取物的漫反射光谱,应用主成分聚类分析和偏最小二乘判别法分别建立识别模型,用146个样品进行建模和48个外部样品集进行验证。结果表明:采用主成分聚类判别分析法,灵芝和云芝提取物的判别界线清晰,正确率达到88.54%;采用偏最小二乘判别法,建立的鉴别分类模型能较好地对灵芝和云芝提取物进行鉴别,校正集和预测集样品的识别正确率均为100%。因此,近红外结合主成分聚类分析和偏最小二乘判别法识别灵芝和云芝提取物是可行的,同时研究结果为灵芝和云芝提取物的快速识别提供了理论依据和使用方法。     

Diagnosis of nitrogen status in Chinese cabbage (Brassica rapa chinensis) using the ratio of amide II to amide I in leaves based on mid‐infrared photoacoustic spectroscopy

In order to quickly diagnose the nitrogen status of Chinese cabbage (Brassica rapa chinensis), Fourier transform mid‐infrared photoacoustic spectroscopy, a nondestructive and rapid approach was used. Two clear stable peaks, in the range 1680–1650 cm^?1 associated with amide I and in the range of 1530–1520 cm^?1 associated with amide II, were observed and responded positively to nitrogen treatments. The characteristic bands of amide I and amide II were also accurately separated by curve‐fitting via deconvolution using a Gauss expansion function. The ratio of the photoacoustic intensities of amide II to amide I was used for correlation analysis with nitrogen treatment, and the correlation coefficients (R², n = 59) at seedling and growth stage were 0.9769 (P ≤ 5%) and 0.9884 (P ≤ 5%), respectively. The ratio of the amide II and amide I peaks was found to be an indicator of nitrogen input during the seedling stage such that a ratio of less than 0.9 indicated a low nitrogen input, a ratio between 0.9 and 1.0 indicated a moderate nitrogen input, and a ratio of more than 1.0 implied an excessive N input.     

赣南脐橙叶片含水量和叶绿素含量的近红外检测

[目的]得到一种快速检测叶片含水量和叶绿素含量的定量估计模型。[方法]选取88片赣南脐橙叶片作为研究对象,运用近红外光谱技术检测叶片含水量和叶绿素含量2个指标。通过6种不同预处理方法(SG/MSC/1st D/2nd D/SNV/Baseline)在全波段4 000~12 000 cm-1范围内建立了叶片含水量和叶绿素含量的PLS、PCR和LS-SVM定量检测模型。[结果]试验得出,在预测叶片含水量的模型中,PLS、PCR和LS-SVM预测效果整体相差不大,其最优的预测模型是使用PLS建模,其中RP=0.985,RMSEP=0.023。在预测叶片叶绿素含量的模型中,3种方法预测效果相差不大,最优预测模型是MSC-LS-VSM,其中RP=0.933,RMSEP=0.23。[结论]研究表明,应用近红外光谱技术对赣南脐橙叶片含水量和叶绿素含量的快速检测具有可行性。     

变量筛选在茶叶咖啡碱近红外光谱定量分析模型中的应用

研究利用近红外光谱分析技术定量测定茶叶中咖啡碱的含量,目的是通过变量筛选简化模型并提高预测精度.试验中以135个来自大闽食品公司的茶叶作为研究对象,利用基于小波系数蒙特卡罗无信息变量消除法(WT-MC-UVE)进行变量筛选并结合偏最小二乘法(PLS)建立咖啡碱定量分析模型,选择交互验证均方根误差(RMSECV)和预测集均方根误差(RMSEP)以及预测相关系数(Rp)作为模型的评价指标.应用 WT-MC-UVE筛选的90个变量所建立的模型,交互验证均方根误差,预测卷均方根误差,预测相关系数分别为0.1248、0.1611和0.9574.结果表明,该方法有效可行.