Electrospray ionization mass spectrometry and partial least squares discriminant analysis applied to the quality control of olive oil

Direct infusion electrospray ionization mass spectrometry in the positive ion mode [ESI(+)‐MS] is used to obtain fingerprints of aqueous–methanolic extracts of two types of olive oils, extra virgin (EV) and ordinary (OR), as well as of samples of EV olive oil adulterated by the addition of OR olive oil and other edible oils: corn (CO), sunflower (SF), soybean (SO) and canola (CA). The MS data is treated by the partial least squares discriminant analysis (PLS‐DA) protocol aiming at discriminating the above‐mentioned classes formed by the genuine olive oils, EV (1) and OR (2), as well as the EV adulterated samples, i.e. EV/SO (3), EV/CO (4), EV/SF (5), EV/CA (6) and EV/OR (7). The PLS‐DA model employed is built with 190 and 70 samples for the training and test sets, respectively. For all classes (1–7), EV and OR olive oils as well as the adulterated samples (in a proportion varying from 0.5 to 20.0% w/w) are properly classified. The developed methodology required no ions identification and demonstrated to be fast, as each measurement lasted about 3 min including the extraction step and MS analysis, and reliable, because high sensitivities (rate of true positives) and specificities (rate of true negatives) were achieved. Finally, it can be envisaged that this approach has potential to be applied in quality control of EV olive oils. Copyright © 2013 John Wiley & Sons, Ltd.     

一种用于中药纯化过程的近红外光谱分析新方法

针对中药大孔吸附树脂纯化过程缺乏在线检测分析手段等问题，提出基于光纤 近红外透射光谱的过程分析新方法．以黄连提取物为例，采用HPLC分析测定值作参 比，用偏最小二乘算法建立了近红外透射光谱校正模型，并成功地用于预测黄连生 物碱在大孔树脂纯化过程中的洗脱曲线．本方法实时、快速，可同时测定洗脱物中 盐酸小檗碱、盐酸巴马亭、盐酸药相碱和黄连总生物碱的浓度．预测精度满足工业 过程分析要求，为提高中药质量分析水平提供了新的途径．     

导数光谱多组分标定法对阿司匹林、扑热息痛及水杨酸的同时测定

采用导数光谱多组分标定法,根据中心合成设计建立标定集和预测集,将PLS和PCR应用于阿司匹林、扑热息痛及水杨酸混合物的零阶和一阶导数光谱,从而实现了混合物中各组分的同时测定.实验结果表明,阿司匹林、扑热息痛及水杨酸的吸收光谱严重重叠;由于各组分零交叉点重合,零交叉点比光谱-导数分光光度法无法同时测量混合物中各组分的含量...     

Short-wave near-infrared spectroscopy analysis of major compounds in milk powder and wavelength assignment

In this study, short-wave near-infrared (NIR) spectroscopy at 800–1050 nm region was investigated for the analysis of main compounds in milk powder. Through quantitative analysis, the feasibility is further demonstrated for the simultaneous measurement of fat, proteins and carbohydrate in milk powder. Two models, partial least-squares and least-squares support vector machine, were compared and utilized for regression coefficients and loading weights. The affect of standard normal variate spectral pretreatment to model performance was evaluated. Based on the resulted coefficients and loading weights, interesting wavelength regions of nutrition in milk powder are screened and the assignment of all specific wavelengths is firstly proposed in the details associated with chemical base. Instead of the whole short-wave NIR spectral data, these assigned wavelengths which can be reliably exploited were used for the content determination. Compared with other spectroscopy technique, assigned short-wave NIR spectral wavelengths did a good work. Determination coefficients for prediction are 0.981, 0.984, and 0.982, respectively for three components. The proposed wavelength assignment in the short-wave NIR region could be used for the component contents determination of milk powder, and could be as a guidance to interpret the spectra of milk powder.     

利用可见近红外光谱多指标综合预测生鲜牛肉储存期

建立了牛肉基于TVB-N、菌落总数、pH值和肉色参数L*多个指标的储存期预测模型,利用可见近红外光谱(Vis/NIR)技术结合区间偏最小二乘(iPLS)和遗传算法(GA)建立了各个指标的PLS预测模型,实现了多指标综合无损快速预测4 ℃下牛肉的储存期。用iPLS和iPLS-GA提取有效波长变量建立PLS预测模型,以预测相关系数和预测标准差作为模型评价标准,结果表明用iPLS-GA选择变量建立的各个指标的PLS预测模型均优于全波段和iPLS组合的PLS模型。由多个指标的预测值和储存期的预测模型,对校正集和预测集样品储存期进行预测,其预测相关系数和标准差分别是0.903, 0.897和1.88, 2.24。说明利用光谱技术结合得出的储存期预测模型可以实现多指标综合预测牛肉储存期,为无损快速检测牛肉储存期或货架期提供了一种新方法。      

基于高光谱成像技术的油菜叶片SPAD值检测

以油菜叶片为研究对象,利用高光谱成像技术,成功建立了叶绿素相对值SPAD值的预测模型。共采集了160个油菜叶片样本在380～1030 nm范围内的高光谱图像。选择500～900 nm之间的平均光谱作为油菜叶片样本的光谱。利用蒙特卡罗最小二乘法(monte carlo partial least squares, MC-PLS)剔除了13个异常样本,基于剩余的147个样本光谱数据与SPAD测量值进行分析,采用了不同的方法建立了多种预测模型,包括：全光谱的偏最小二乘法(partial least squares, PLS)模型,连续投影算法(successive projections algorithm, SPA)选择特征波长的PLS预测模型,“红边”位置(λ_red)的简单经验估测模型,三种植被指数R₇₁₀/R₇₆₀,(R₇₅₀-R₇₀₅)/(R₇₅₀-R₇₀₅)和R₈₆₀/(R₅₅₀*R₇₀₈)分别建立的简单经验估测模型,以及基于这三种植被指数的PLS预测模型。建模结果显示,全光谱的PLS模型预测效果最为精确,其预测相关系数r_p为0.833 9,预测均方根误差RMSEP为1.52。而使用SPA算法选出的8个特征波长所建立的PLS模型其预测结果可达到与全光谱的PLS模型非常接近的水平,而且在保证一定精度的条件下减少了大量运算,节省了运算时间,大幅提高了建模的速度。而基于红边位置和选择的三种植被指数而建立的简单经验估计模型其预测结果虽与基于全光谱的PLS预测模型有一定差距,但模型简单、运算量小,适合用于对精度要求不高的场合,对后续的便携仪器设备开发有一定的指导作用。     

近红外光谱技术快速鉴别地沟油与食用植物油的研究

地沟油检测是我国食品安全最为关注的话题之一,它给人们的生活健康带来了极大的危害。国内现有的检测手段也仅停留在定性检测水平上,只能确定地沟油的有无,还难以进行定量检测。本实验利用近红外光谱技术与光纤传感技术相结合的新方法对勾兑混合油中地沟油的含量进行了定量分析。将煎炸老油与九三大豆油按照一定的体积比进行勾兑,共计50个样本,采集其近红外透射光谱,分别采用偏最小二乘法(PLS)和BP人工神经网络建立了煎炸老油含量的定量分析模型,校正集决定系数分别为0.908和0.934,验证集决定系数分别为0.961和0.952,均方估计残差(RMSEC)为0.184和0.136,预测均方根误差(RMSEP)都为0.111 6,符合应用要求,同时还结合主成分分析法(PCA)对煎炸老油与食用植物油进行了鉴别,识别准确率为100%。实验研究证明近红外光谱技术不仅可以准确快速的定性分析地沟油, 还能定量的检测地沟油的含量,在油脂的检测方面具有很大的应用前景。     

近红外光谱预测汽油辛烷值和辛烷值仪的研制

本文报道了用近红外光谱预测汽油马达法辛烷值,研究法辛烷值和抗爆指数的方法。用多重线性回归和偏最小二乘法回归建立辛烷值的预测模型。基于此模型,首次研制成功光学多道近红外辛烷值仪。     

PLS分析与RBF神经网络耦合环境模型

鉴于城市大气环境质量受到诸多复杂因素影响,且各因素间存在多重相关性,本文将偏最小二乘(PLS)分析与人工神经网络径向基网络(RBF)耦合,建立偏最小二乘径向基神经网络模型(PLSRBF),应用于贵阳大气环境质量的检验和预测。实例表明:PLSRBF模型可对原多自变量模型进行降维简化,并可有效提取解释变量信息,防止信息丢失,且具有较强的拟合能力。     

近红外光谱法快速测定淫羊藿中淫羊藿苷和朝藿定C

基于近红外光谱法对淫羊藿中有效成分淫羊藿苷和朝藿定C的含量进行快速同时定量分析,采用偏最小二乘法(PLS)建立近红外光谱信息与预测组分含量间的数学校正模型。结果表明通过标准正态变量转换(SNV)与一阶导数对光谱进行预处理,所建立的淫羊藿苷和朝藿定C的校正模型效果最佳。淫羊藿苷与朝藿定C校正集均方差(RM SEC)分别为0.217、0.718;相关系数分别为0.9825、0.9863;预测均方差(RM SEP)分别为0.155,0.297。该方法简单、准确,更适合于淫羊藿药材中主要成分的快速分析。