瓦斯浓度红外检测噪声消除方法研究

将数学方法与实验方法相结合,通过测量信号和参考信号的比值,消除由于光源的不稳定及其他因素引起的检测误差、外界环境引起的误差等,并利用数学方法推导得到计算方程。实验结果验证此类方法的可行性,可有效消除井下复杂恶劣环境带来的一系列噪声干扰,包括检测系统固有的性能误差和外部环境引起的干扰误差。     

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.     

Empirical predictions of plant material C and N mineralization patterns from near infrared spectroscopy, stepwise chemical digestion and C/N ratios

Prediction of carbon (C) and nitrogen (N) mineralization patterns of plant litter is desirable for both agronomic and environmental reasons. Near infrared reflectance (NIR) spectroscopy has recently been introduced in decomposition studies to characterize biochemical composition. The purpose of the current study was to use empirical techniques to predict C and N mineralization patterns of a wide range of plant materials incubated under controlled temperature and moisture conditions. We hypothesized that the richness of information in the NIR spectra would considerably improve predictions compared to traditional stepwise chemical digestion (SCD) or C/N ratios. Initially, we fitted a number of empirical functions to the observed C and N mineralization patterns. The best functions fitted with R²=0.990 and 0.949 to C and N, respectively. The fractions of C and N mineralized at different points in time were then either predicted directly with regression functions or indirectly by prediction of the parameters of the empirical functions fitted to incubation data. In both cases, partial least squares (PLS) regressions were used and predictions were validated by cross-validations. We found that the NIR spectra (best R²=0.925) were able to predict C mineralization patterns marginally better than the SCD fractions (best R²=0.911), but considerably better than the C/N ratios (best R²=0.851). In contrast, N mineralization was better predicted by SCD fractions (best R²=0.533) than the C/N ratio (best R²=0.497), which was better than NIR predictions (best R²=0.446). Although the predictions with the NIR spectra were only slightly better for C and worse for N mineralization compared to SCD fractions, NIR spectroscopy still holds advantages, as it is a much less laborious and cheaper analytical method. Furthermore, exploration of the applications of NIR spectroscopy in decomposition studies has only just begun, and offers new ways to gain insights into the decomposition process.     

近红外光谱技术及其在农产品品质分析中的应用

近红外光谱技术是一种高效、快速的现代分析技术,已在很多领域得到广泛应用。文章对近红外光谱分析的技术原理、技术方法、技术特点作了简要介绍,并对其在农产品品质分析中的应用现状和应用前景进行了综述。     

Application of Wavelet Transform in the Prediction of Navel Orange Vitamin C Content by Near-Infrared Spectroscopy

This study was to search for an approach for rapid measurement of orange vitamin C （Vc） content. By using different decomposing levels of Daubechies 3 wavelet transform, the near-infrared spectra signals obtained from intact fruits of 100 navel orange samples were denoised, and the results of the predicted Vc contents for the corresponding samples determined by the reconstructed spectra after denoising were validated by means of PLS-CV （partial least squared-cross validation）. It was shown that the prediction effects verified by PLS-CV analysis varied when different wavelet transform decomposing levels were employed. At the wavelet decomposing level 4, the best prediction effect was obtained, with the correlation coefficient R between the prediction and true values being 0.9574 and the expected variance RMSECV being as low as 3.9 mg 100 g^-1. Furthermore, the 11 different approaches for the pretreatment of the near-infrared spectrum were compared. It was found that the calibration model established by PLS using spectra pretreated by wavelet transform denoising provided the best prediction for Vc content, exhibiting the highest correlation between the prediction and true values by cross validation. In conclusion, the near infrared spectral model denoised by means of wavelet transform can be used for accurate, rapid, and nondestructive quantitative analysis on navel orange Vc content.     

基于近红外光谱技术的大米掺伪定量判别

针对现在市场上常见的两种大米掺伪现象,利用近红外光谱技术结合化学计量学方法分别建立了大米中掺入低档米和掺入矿物油的定量分析模型。制配不同掺伪比例的大米样品,采集其近红外光谱,并选用标准正态变量变换、最大最小归一化、平滑和一阶导数4种方法对原始光谱进行预处理,分别结合偏最小二乘法建立PLS定量分析模型。通过对比建模结果选出的最优预处理方法是最大最小归一化,建立的掺低档米模型的校正集和预测集相关系数分别为0.9698和0.9845,均方根误差分别为8.66和6.46;掺矿物油米模型的校正集和预测集相关系数分别为0.9739和0.9888,均方根误差分别为0.106和0.0698。模型的预测精度和稳定性均很好,实现了对两种掺伪大米快速、准确的定量判别,为大米的品质监控提供了一种新的方法思路。     

近红外漫反射光谱法用于不同人参种子的鉴别

目的建立近红外漫反射光谱法鉴别不同种类的人参及西洋参种子。方法利用近红外光谱(NIRS)法对不同产地和种类的人参及西洋参种子样品进行聚类分析并建立了鉴别分析模型。结果聚类分析结果良好,所建立的鉴别分析模型能有效地判别人参及西洋参种子的类别。结论近红外光谱法能够实现人参及西洋参种子的快速无损分析,为区分不同种类的人参及西洋参种子提供了一个客观的评价方法。     

水稻蛋白质含量NIR模型适配范围的研究

【目的】比较不同类型样品建立水稻蛋白质近红外模型的效果和适配范围。【方法】通过对178份来自“II-32B/岳早籼6号”的重组自交系和496份水稻品种的近红外反射光谱的比较分析，选择其中59个株系和76份品种作为建模样品，采用偏最小二乘法建立基于品种、重组自交系和混合样品的3个蛋白质含量回归模型。【结果】经模型内部交叉验证和对模型外部重组自交系和品种样品的验证结果的比较分析，发现基于分离群体的模型因蛋白质含量范围较窄，样品来源较单一，适应范围仅局限于本群体内样品蛋白质含量预测，而品种和混合模型对群体和品种样品都表现出良好的适应能力，交叉验证决定系数大于0.90，外部验证决定系数大于0.89, 本试验可为近红外建模的样本集选择提供良好的指导意义。【结论】不同类型样品对建模效果有显著影响，品种模型和混合模型的适配范围显著大于群体模型，研究结果不能支持用背景变异较小的样品建立较高精度回归模型的设想。     

结构铁的还原对绿脱石红外光谱的影响

伴随Fe^2 浓度增加和还原反应的进行，绿脱石红外光谱中位于3570，1030和821cm^-1吸收峰向低波数位移，其相应的振动方式分别为0-H伸缩振动、Si-Ob（基部氧）面内振动和FeO-H变形的振动，还原反应对Si-Oa（顶部氧）相互作用在1110cm^-1处产生的扇峰没有影响。结构铁的还原导致848cm^-1的吸收峰向较高能量方向转移，但在结构羟基完全重氢化后，Fe-OH振动在840cm^-1产生的吸收峰仍然保持不变，由于四面体和八面体薄层中间的压力减弱，Si-Ob振动能量随还原反应而降低。