同步-异步二维中红外相关谱检测掺假芝麻油

将同步-异步二维中红外相关谱和多维偏最小二乘判别法相结合定性分析掺假芝麻油。分别配置40个纯芝麻油和含有玉米油不同体积分数(3%~60%)的掺假芝麻油样品40个。室温下,分别采集所有样品的常规一维中红外光谱(650~4 000cm-1)。在研究纯芝麻油和掺假芝麻油的一维中红外光谱的基础上,以芝麻油中掺假的玉米油浓度为外扰,进行相关计算,得到同步和异步二维中红外相关谱矩阵,并对其进行标准化。分别提取标准化的同步和异步二维中红外相关谱主对角线上部分和下部分元素进行融合,得到同步-异步二维中红外相关谱矩阵。在此基础上,分别基于同步-异步二维中红外相关谱矩阵、同步二维中红外相关谱矩阵和异步二维中红外相关谱矩阵建立了三个定性分析掺假芝麻油的多维偏最小二乘判别模型对预测集未知样品进行预测,其识别正确率分别为100%,96.2%和96.2%。结果表明:相对于同步和异步二维中红外相关谱,同步-异步二维中红外相关谱不仅包含了完整的掺假油特征信息,而且剔除了冗余信息,因此能取得更好的判别结果。     

基于互信息方法的燃油品质光谱分析模型构建与简化

近红外漫反射光谱和紫外吸收光谱分别用于燃油的辛烷值和单芳香族化合物含量的测定,偏最小二乘回归(partial least squares regression,PLSR)用于光谱多元校正模型的构建。基于互信息(mutual infor-mation,MI)理论的变量筛选方法用于模型优化以提高模型的预测精度,降低模型的复杂度。结果表明,MI-PLSR可以有效的提高燃油品质模型的预测精度,简化分析模型。辛烷值的预测均方根误差(root meansquare error of prediction,RMSEP)由0.288减小为0.111,预测相关系数R从0.985提高到0.998,建模变量由401减小为112;单芳香族化合物含量的RMSEP从0.753减小为0.478,R由0.996提高为0.998,建模变量由572缩减为37。说明振动光谱结合MI-PLSR方法可用于燃油品质检测,具有高效率低成本的特点。     

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.     

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.     

应用近红外漫反射光谱对猪肉肉糜进行定性定量检测研究

利用傅里叶变换近红外漫反射光谱结合不同数学建模算法对不同部位取样的猪肉肉糜进行定性判别建模,并建立猪肉肉糜品质指标脂肪、蛋白质和水分含量的定量检测模型。结果表明:不同部位取样猪肉肉糜判别分析PLSDA模型性能良好,最优模型校正集判别正确率为100%,预测集判别正确率为96%;比较两种方法结合,不同光谱预处理建立各品质指标的定量模型,LS-SVM模型性能优于PLSR模型,脂肪和水分含量最佳预测模型校正及预测相关系数r均高于0.9,蛋白质含量最优模型校正及预测相关系数r,RMSEC,RMSEP和RMSECV分别为0.722,0.593,1.595,1.550和1.888,模型精度需进一步提高。研究表明利用傅里叶变换近红外漫反射光谱快速判别不同部位猪肉肉糜的方法是可行的,脂肪和水分含量定量分析模型从预测精度、稳定性及适应性考虑均具一定的通用性,具有良好的市场应用前景。     

Rapid prediction of fatty acid composition of vegetable oil by Raman spectroscopy coupled with least squares support vector machines

The fatty acid composition of vegetable oil plays a significant role in a nutrition‐balanced diet, which makes this industry more quality conscious. A set of store‐purchased vegetable oils and their binary mixtures were characterized by Raman spectra in a region of 800–2000 cm⁻¹. The obtained Raman spectral data were pretreated, and intensities of eight characteristic peaks were extracted as the eigenvalues of an entire spectrum. A prediction model of fatty acid content based on least squares support vector machines (LS‐SVM) were established for multivariate analysis between the Raman spectral eigenvalues and the fatty acid composition measured by gas chromatography (GC) method. The performance of the model was evaluated by comparing the predicted values to the reference values from GC analysis. The correlation coefficient for the prediction of oleic acid, linoleic acid and α‐linolenic acid was 0.9972, 0.9982 and 0.9854, respectively. Raman spectroscopy based on LS‐SVM can be a promising technique for predicting the fatty acid composition of vegetable oil with the advantages of being simple and time‐effective while not requiring any sample preprocessing. In particular, a portable Raman system is suitable for on‐site detection in practical applications. Copyright © 2013 John Wiley & Sons, Ltd.