中红外光谱技术对乙醇汽油乙醇含量的检测

乙醇汽油是一种新型清洁燃料,燃料乙醇在乙醇汽油中的含量会影响发动机的性能。为了确保发动机的工作可靠性,需要对乙醇汽油中的乙醇含量进行快速精准检测。本文使用中红外光谱技术对采集到的乙醇汽油的光谱数据进行定量分析。首先对原始光谱数据使用多元散射校正、基线校正、一阶导数、二阶导数等预处理方法进行预处理。然后利用ELM、LSSVM、PLS对乙醇汽油中的乙醇含量建立预测模型,通过比较3种建模方法对乙醇含量的预测能力发现,PLS方法的精度比其余两种方法更高。模型决定因子R2为0.958,预测均方误差RMSEP为1.479%(V/V,体积比)。中红外光谱技术对乙醇汽油乙醇含量的快速准确检测提供了新的思路。     

Determination of dimethoate and phosmet pesticides with surface-enhanced Raman spectroscopy

ABSTRACT

Surface-enhanced Raman spectroscopy spectra of dimethoate and phosmet pesticides were recorded using a Klarite substrate. Significant enhancements were achieved with dimethoate over a concentration range of 0.5–10 µg mL^?1 and phosmet over a concentration range of 0.1–10 µg mL^?1. The best prediction model for dimethoate pesticide was achieved with a correlation coefficient of 0.940 and a root mean square error of prediction of 0.864 µg mL^?1, with the first derivative and standard normalized variate data preprocessing, and the best prediction model of phosmet pesticide was achieved with a correlation coefficient of 0.949 and a root mean square error of prediction of 0.741 µg mL^?1 with the first derivative data preprocessing. Our study shows that pesticides, including dimethoate and phosmet, could be quantitatively measured at as low as 0.5 µg mL^?1 level using surface-enhanced Raman technology coupled with a Klarite substrate and the results indicated that surface-enhanced Raman spectroscopy with a Klarite substrate has potential for the analysis of dimethoate and phosmet residues.     

Identification of waste cooking oil and vegetable oil via Raman spectroscopy

This paper made a qualitative identification of ordinary vegetable oil and waste cooking oil based on Raman spectroscopy. Raman spectra of 73 samples of four varieties oil were acquired through the portable Raman spectrometer. Then, a partial least squares discriminant analysis (PLS‐DA) model and a discrimination model based on characteristic wave band ratio were established. A classification variable model of olive oil, peanut oil, corn oil and waste cooking oil that was established through the PLS‐DA model could identify waste cooking oil accurately from vegetable oils. The identification model established based on selection of waveband characteristics and intensity ratio of different Raman spectrum characteristic peaks could distinguish vegetable oils from waste cooking oil accurately. Research results demonstrated that both ratio method and PLS‐DA could identify waste cooking oil samples accurately. The identification model based on characteristic waveband ratio is simpler than PLS‐DA model. It is widely applicable to identification of waste cooking oil. Copyright © 2016 John Wiley & Sons, Ltd.     

基于近红外光谱和偏最小二乘法的慈竹纤维素结晶度预测研究

将近红外光谱技术和化学计量学相结合分析慈竹纤维素结晶度。通过区间偏最小二乘法(iPLS)、联合区间偏最小二乘法(siPLS)和反向区间偏最小二乘法(biPLS)优化建模区域,建立经多元散射校正后光谱的结晶度分析模型,并与全光谱范围350～2 500nm建立的偏最小二乘(PLS)模型进行比较。结果表明,三种改进偏最小二乘法建立的结晶度模型预测效果均优于PLS模型,并且当采用联合区间偏最小二乘法将全光谱进行30个子区间划分,选择三个子区间[8 12 19]组合时,建立的siPLS模型预测效果最好,相关系数(r)达到0.88,预测标准差(RMSEP)为0.0117。因此,采用联合区间偏最小二乘法可以有效选择建模光谱区域,提高模型预测能力,实现慈竹纤维素结晶度的快速预测。     

Feasibility of estimating copper content using visible and near-infrared spectra of bedrock,weathered rock and soil - a case study of the Baogutu stock II,Xinjiang, China

Geochemical anomalies are important symbols for prospecting. To determine a new method for identifying geochemical anomalies rapidly, this study examines the possibility of using visible and near-infrared spectra of bedrock, weathered rock, and soil to assess copper content in the Baogutu stock II, Xinjiang, China. Partial least squares regression was employed to study the relationship between visible and near-infrared spectra and the ore-forming element contents of collected bedrock samples, weathered rock samples, and soil samples. More specifically, our study aims to achieve three objectives. First, to explore the correlations between the contents of different elements and the correlations between the visible and near-infrared spectra and the ore-forming element contents for the three sample types. Second, to evaluate the performance of the reflectance-based partial least squares regression model. The models for iron in bedrock samples and copper in soil samples were chosen for estimating copper content. Third, to assess the effectiveness of data transformation methods in the partial least squares regression model. In our case study, the square root-based models for iron in bedrock samples and copper in soil samples with coefficients of determination for prediction are 0.614 and 0.409 respectively, maximized the model performance. Therefore, it is evident that visible and near-infrared spectra can be an alternative method for estimating ore-forming element contents based on bedrock, weathered rock, and soil samples.     

Adulteration of sesame oil with corn oil detected by use of two-dimensional infrared correlation spectroscopy and multivariate calibration

An innovative methodology was developed to detect adulteration of sesame oil with corn oil based on two-dimensional mid-infrared correlation spectroscopy with multivariate calibration. Forty pure sesame oils and 40 adulterated sesame oils with corn oil were prepared and the infrared absorption spectra were measured at room temperature, respectively. The synchronous two-dimensional mid-infrared correlation spectra were calculated to develop multivariate calibration models for adulteration of sesame oil with corn oil. The results showed the higher classification accuracy of 96.3% for the prediction set using two-dimensional mid-infrared correlation spectra and N-way partial least square discriminant analysis, versus 88.9% using traditional one-dimensional mid-infrared spectra and partial least squares discriminant analysis. Also, the multivariate calibration models were developed for quantitative analysis of sesame oil adulteration with corn oil. The root mean square error of prediction was 0.98% v/v using two-dimensional mid-infrared correlation spectra and N-PLS, and 1.15% v/v using traditional one-dimensional mid-infrared spectra and PLS. The results of our analyses indicated that the proposed method could provide better predictive results than traditional one-dimensional mid-infrared spectra and multivariate calibration.     

基于近红外光谱法的沉积物间隙水化学组分快速测量

通过对巢湖柱状沉积物剖面间.隙水样品的近红外光谱(NIR)分析,采用OSC+WC和WC+OSC光谱预处理方法,结合偏最小二乘法(PLS)回归,建立了分层沉积物间隙水中TN,NH3-N,PO33-,TOC和SiO3的校正模型,该模型的TN,NH3-N,PO33-,TOC和SiO-3的预测值与实测值的校正相关系数分别为0....     

Determination of calcium and magnesium in the Solanaceae plant by near infrared spectroscopy combined with interval combination optimization algorithm

Determination of the content of inorganic nutrients was done as part of a project to improve crop yield by use of fertilizers. Near-infrared spectroscopy coupled with interval combination optimization was used to determine the inorganic nutrients of calcium and magnesium in the Solanaceae plant. Interval combination optimization was employed to choose effective intervals with the merit of assigning smaller weights to less significant variables. Compared with full spectrum partial least squares regression, interval combination optimization improved the predictive accuracy and precision for both calcium and magnesium. The result was a high predictive ability for calcium, with a root mean squared error prediction of 0.159%, and for magnesium, with a root mean squared error prediction of 0.064%. In this study, the superiority of interval combination optimization over full spectrum partial least squares was shown in the aspects of effective variables and accurate results. Near-infrared spectroscopy combined with interval combination optimization was efficient for the determination of inorganic nutrients, calcium and magnesium, in the Solanaceae plant.     

东北黑土不同组分有机碳的近红外光谱测定

不同颗粒组分的土壤有机碳(soil organic carbon,SOC)具有不同的化学组成且对不同农艺措施响应不同,因此了解其信息有助于深入理解SOC对土壤肥力的贡献。本研究旨在评价近红外光谱(near Infraredspectroscopy,NIRS)预测黑土不同颗粒组分SOC(水稳性团聚体结合碳、颗粒态有机碳及不同大小粒级有机碳)的潜力。土壤样品(n=136)采集于东北典型黑土带上,利用偏最小二乘法建立定量模型(n=100),并用独立样本对模型进行检验(n=36)。结果表明:NIRS可以在一定程度上预测水稳性团聚体结合碳含量(R2=0.69-0.82,RPD=1.2-1.8);对矿质结合态SOC(<53μm)(R2=0.97,RPD=5.4)及细粒级SOC(<20μm)(R2=0.93,RPD=3.8)预测结果较好,对颗粒态有机碳(>53μm)和粗粒级SOC(>20μm)预测结果不理想。NIRS在简化黑土不同颗粒组分SOC的测定,特别是矿质结合态(<53μm)SOC,具有很好的应用前景。     

近红外漫反射光谱法检测小麦粉过氧化苯甲酰

国家已禁止向小麦粉中添加过氧化苯甲酰(BPO),快速检测小麦粉中BPO添加量对于保障食品安全非常重要.BPO在添入小麦粉中后部分会被还原成为苯甲酸,使得对其原始添加量的检测非常繁琐.本文探讨了利用近红外漫反射光谱法进行小麦粉中BPO添加当量检测的可行性.通过向无添加剂小麦粉中添加不同量的过氧化苯甲酰粉末,制备小麦粉样本...     

激光诱导击穿光谱结合偏最小二乘法定量分析脐橙中Cd含量

基于激光诱导击穿光谱(LIBS)技术对赣南脐橙中Cd元素进行定量分析. 利用LIBS获取样品中Cd元素的特征谱线信息, 并结合原子吸收分光光度计测量样品中Cd元素的真实含量.采用五点平滑法和中心化法对样品光谱数据进行预处理, 基于偏最小二乘法(PLS)对其中的39个样品建立Cd元素的定量分析模型, 在该模型的基础上预测另外13个样品的Cd含量, 并对PLS模型进行对比验证. PLS模型中拟合曲线的相关系数为0.9806, 12个样品的验证结果的相对误差为10.94%.研究结果表明, 激光诱导击穿光谱技术能够准确的检测农产品中重金属含量, 为农产品的安全检测提供技术方法. 关键词： 激光诱导击穿光谱 Cd 定量分析 偏最小二乘法     

氯化钠近红外光谱检测技术研究

氯化钠(NaCl)近红外光谱分析在生物医学上有着重要的意义。钠离子(Na+)是人体血液中电解质的主要成分,而电解质有助于维持身体的酸碱平衡。采用近红外光谱技术测量氯化钠浓度,在分析钠离子近红外光谱检测机理的基础上,选定波长建立了NaCl浓度线性回归预测模型,同时为了减小温度对水吸收的扰动,使用选定光谱区建立偏最小二乘(PLS)非线性回归模型。结果表明所建立的非线性校正模型决定系数(R2)=99.82%,交叉验证均方误差(RMSECV)=14.5,剩余预测偏差(RPD)=23.7。完全满足日常生化检测精度要求,该技术可以应用于医院实验室钠离子浓度定量分析。     

基于光谱技术的Bipls算法结合CARS算法的苹果可溶性固形物含量检测

可溶性固形物含量是判断苹果内部品质的重要参考属性之一。利用高光谱技术获取苹果感兴趣区域的反射光谱,以S-G平滑(Savitzky-Golay smoothing)和直接正交信号校正(Direct orthogonal signal correction, DOSC)算法对光谱数据进行梯度预处理后,用后向区间偏最小二乘法(Bipls)优选出3,5,6,7,8,9,13,14,15,16,17,18,19,20,21,23等16个子区间,共计177个波长。结合竞争自适应重加权采样算法(CARS)再作进一步筛选,提取出449.6,512.9,544.8,547.2,594.3,596.8,928.2 nm等7个特征波长,利用偏最小二乘算法(PLS)建立基于特征波长的可溶性固形物含量检测模型,所得模型评价为R_c=0.906 2,RMSEC为0.482 2,R_p=0.871 6,RMSEP为0.614 0。该算法模型预测性能同Bipls和Bipls-SPA模型相比更为优异,证明了Bipls结合CARS算法在提高苹果可溶性固体物含量检测精度方面的有效性。     

蜂蜜真伪的近红外光谱鉴别研究

如何有效鉴别蜂蜜真伪是目前我国蜂产品质量控制的难题之一。提出了一种用傅里叶变换近红外光谱结合判别偏最小二乘法(DPLS)快速鉴别蜂蜜真伪的新方法。首先采集了71个商品蜜样品的近红外光谱数据,其中包括27个纯蜂蜜和44个掺假蜂蜜,然后5次随机划分建模集样本和验证集样本,并对建模样本进行不同光谱预处理,选择并优化不同波段范围和主成分数,用DPLS法建立了5组蜂蜜样本的真伪鉴别模型,外部交叉验证法预测模型。通过对结果进行比较分析,5组校正模型中真蜂蜜和掺假蜂蜜的总体识别准确率分别为91.49%,94.68%,92.98%,93.86%,94.87%;预测样本的识别准确率为86.96%～93.75%,其中模型2,3,4中掺假蜜的识别准确率达100%。研究结果表明,该方法可作为鉴别商品蜜真伪的一种快速筛选技术,在我国蜂蜜质量监控中具有重要意义。     

Near-infrared spectroscopy combined with equidistant combination partial least squares applied to multi-index analysis of corn

Development of small, dedicated, reagentless, and low-cost spectrometer has broad application prospects in large-scale agriculture. An appropriate wavelength selection method is a key, albeit difficult, technical aspect. A novel wavelength selection method, named equidistant combination partial least squares (EC-PLS), was applied for wavenumber selection for near-infrared analysis of crude protein, moisture, and crude fat in corn. Based on the EC-PLS, a model set that includes various models equivalent to the optimal model was proposed to select independent and joint-analyses models. The independent analysis models for crude protein, moisture, and crude fat contained only 16, 12, and 22 wavenumbers, whereas the joint-analyses model for the three indicators contained only 27 wavenumbers.Random validation samples excluded from the modeling process were used to validate the four selected models. For the independent analysis models, the validation root mean square errors (V_SEP), validation correlation coefficients (V_R_P), and relative validation root mean square errors (V_RSEP) of prediction were 0.271%, 0.946, and 2.8% for crude protein, 0.275%, 0.936, and 2.6% for moisture, and 0.183%, 0.924, and 4.5% for crude fat, respectively. For the joint-analyses model, the V_SEP, V_R_P, and V_RSEP were 0.302%, 0.934, and 3.2% for crude protein, 0.280%, 0.935, and 2.7% for moisture, and 0.228%, 0.910, and 5.6% for crude fat, respectively. The results indicated good validation effects and low complexity. Thus, the established models were simple and efficient.The proposed wavenumber selection method provided also valuable reference for designing small dedicated spectrometer for corn. Moreover, the methodological framework and optimization algorithm are universal, such that they can be applied to other fields.     

近红外光谱在快速测定蛹虫草有效成分含量中的应用

采用偏最小二乘法(PLS)建立测定蛹虫草菌丝体中多糖、蛋白、虫草酸和腺苷含量的近红外光谱定量分析模型.选择了原始光谱建立蛹虫草菌丝体中腺苷和多糖含量的PLS定量分析模型,选择卷积光滑预处理后的光谱建立蛹虫草菌丝体中蛋白和虫草酸含量的PIS定量分析模型.测定蛹虫草菌丝体中多糖、蛋白、虫草酸和腺苷含量的最优PLS定量分析模型的交叉验证均方根误差(RMSECV)分别为0.0091.0.0222,0.0088和0.6520,预测均方根误差(RMSEP)分别为0.0079,0.0196,0.0087和0.5780.结果表明,测定蛹虫草菌丝体四种有效成分含量的定量分析模型稳健性好和预测精度高,在中药有效成分定量分析方面有很大的应用前景.     

紫外-可见分光光度法测定玉米不同部位籽粒早期发育的生理指标

为测定玉米不同部位籽粒早期发育的差异及氮素对籽粒发育的影响,实验用紫外-可见分光光度法对不同施氮水平下(0,120,180,240 kg·hm-2)玉米顶部和中下部籽粒在授粉后5～20 d的生理指标进行了检测.结果表明,施氮可明显促进籽粒可溶性总糖、蔗糖、淀粉含量的增加,促进蔗糖分解转化及淀粉合成关键酶活性的增强.当施氮量为180 kg·hm-2时,顶部籽粒的体积、干重及可溶性总糖、蔗糖、淀粉含量在授粉后20 d明显高于其他处理,酸性蔗糖转化酶AI、中性蔗糖转化酶NI、蔗糖合成酶SS、腺苷二磷酸葡萄糖焦磷酸化酶ADPGase及淀粉合成酶活性在授粉后5～20 d处于较高水平,使顶部籽粒的蔗糖利用能力及淀粉合成能力得到一定程度的改善,从而促进顶部籽粒发育,减少败育,提高产量.     

An intelligent background‐correction algorithm for highly fluorescent samples in Raman spectroscopy

Fluorescent background is a major problem in recoding the Raman spectra of many samples, which swamps or obscures the Raman signals. The background should be suppressed in order to perform further qualitative or quantitative analysis of the spectra. For this purpose, an intelligent background‐correction algorithm is developed, which simulates manual background‐correction procedure intelligently. It basically consists of three aspects: (1) accurate peak position detection in the Raman spectrum by continuous wavelet transform (CWT) with the Mexican Hat wavelet as the mother wavelet; (2) peak‐width estimation by signal‐to‐noise ratio (SNR) enhancing derivative calculation based on CWT but with the Haar wavelet as the mother wavelet; and (3) background fitting using penalized least squares with binary masks. This algorithm does not require any preprocessing step for transforming the spectrum into the wavelet space and can suppress the fluorescent background of Raman spectra intelligently and validly. The algorithm is implemented in R language and available as open source software ( http://code.google.com/p/baselinewavelet ). Copyright © 2009 John Wiley & Sons, Ltd.     

基于小波核偏最小二乘回归方法的混沌系统建模研究

基于核学习的强大非线性映射能力,结合用于回归建模的线性偏最小二乘(PLS)算法,提出一种小波核偏最小二乘(WKPLS)回归方法. 该方法基于支持向量机使用的经典核函数技巧,将输入映射到高维非线性的特征空间,在特征空间中,构造线性的PLS回归模型. PLS方法利用输入与输出变量之间的协方差信息提取潜在特征,而可允许的小波核函数具有近似正交以及适用于信号局部分析的特性. 因此,结合它们优点的WKPLS方法显示了更好的非线性建模性能. 将WKPLS方法应用在非线性混沌动力系统建模上,并与基于高斯核的核偏最小二乘 关键词： 小波核 偏最小二乘回归 混沌系统 建模