一种基于低场核磁共振技术的葵花籽品质快速鉴定方法

利用低场核磁共振结合化学计量学方法对葵花籽的品种进行区分,并对不同品种的葵花籽的含油量进行测定,以建立一种快速鉴定葵花籽品质的方法。将Carr-Purcell-Meiboom-Gill(CPMG)序列下的弛豫衰减数据通过主成分分析(PCA)和偏最小二乘回归(PLSR)方法处理发现:不同品种的葵花籽处于主成分得分图的不同区域,这说明主成分分析法能够实现对葵花籽品种的有效区分;所建立的PLSR模型具有高的相关系数和低的均方根误差,并且对未知葵花籽样品含油量预测的相对误差小。因此,低场核磁共振与化学计量学相结合的方法能够实现对葵花籽品质的快速、无损鉴定。     

LF-NMR结合化学模式识别鉴别油脂种类及餐饮废弃油脂

应用主成分分析(Principal component analysis,PCA)和聚类分析法(Cluster analysis,CA)对9种(27个)常见食用植物油及100个餐饮废油的低场核磁共振(Low-field nuclear magnetic resonance,LF-NMR)(T2)弛豫特性数据进行分析。结果表明:在正常食用油种类区分方面,主成分分析的效果较优,9种食用油在主成分分布图上按种类正确分组,边界清晰。而在正常食用油与餐饮废油的区分方面,聚类分析效果较优,引入30个待测样本后,聚类分析(127个样品,欧式距离=5)的正确率为94.49%,分析误判率为5.51%,分组效果良好。LF-NMR结合化学模式识别可实现对油脂种类及餐饮废弃油脂的鉴别。     

基于低场核磁共振技术的小鼠体成分无损分析方法开发

本研究利用传统的全身化学分析方法,包括干燥法、索氏提取法、凯氏定氮法、灼烧法测定的小鼠样品的体成分含量作为参考值,结合低场核磁共振技术及偏最小二乘回归法(PLSR)和主成分回归法(PCR),分别建立了样品体液、脂肪、瘦肉含量的预测模型。结果显示,PLSR预测模型的校正集相关系数(R_(cal)~2)和交互验证集相关系数(R_(cv)~2)均大于0.98,且标准误差值较低,说明该模型的稳定性较好,通过该模型可以准确预测小鼠体液、脂肪和瘦肉含量。对未参与建模的小鼠进行体液、脂肪和瘦肉含量的测定,其相对误差均小于1.5%,表现出较好的应用潜力。     

应用激光拉曼光谱法检查核桃油的掺假

应用激光拉曼光谱法对掺入其它植物油(如大豆油、花生油、玉米油、棕榈油或棉籽油)的核桃油进行检测。从上述6种纯植物油的拉曼光谱图中取6个主峰的相对拉曼光谱强度为变量进行主成分分析,从所得的二维散点图可知核桃油与其它5种油均分处于图的不同区域;对掺杂不同比例(质量分数)其它油品的核桃油,在其散点图中也可见两者分处不同区域,据此可对掺杂其它油品的核桃油作出定性鉴别。利用波数为1 256cm-1处拉曼光谱相对强度与掺杂油品的含量(w%)之间的线性关系及其所建立的线性回归方程,对掺杂的样品进行了定量分析。在纯核桃油中分别掺入质量分数从8%~95%的棕榈油制备了10份试样,按上述方法进行定量分析,结果表明预测值与已知值的相对误差在1.01%~2.35%之间。     

中国有望推行牛乳掺假快速检测方法

中国科协在不久前召开的新闻发布会上宣布,一种快速检测牛乳掺假的方法已经研发出来,广泛推行后将对保障我国乳品安全发挥重要作用。为了寻找一种适合企业和牧场用于监控牛乳品质的快速、价廉、简便、无损的方法,浙江工商大学研究小组利用低场核磁共振仪研究了纯牛乳和各种形式（掺水、掺食盐、掺尿素、掺豆浆、掺复原乳）的掺假乳的弛豫特性（差别）,并用主成分分析法处理样品的检测数据。     

DSC方法在特级初榨橄榄油掺假鉴别中的应用

采用差示扫描量热法(DSC)对进口特级初榨橄榄油中葵花籽油的掺假鉴别进行了系统研究。由橄榄油入手考察了升降温循环实验条件下油品的重复性及数据可靠性,以此为基础提出采用程序降温的方法研究油品的结晶特性。统计了研究体系内的8种特级初榨橄榄油、6种其他食用油以及5种比例的模拟掺假油的结晶峰温度值,建立了回归方程。结果表明:进口特级初榨橄榄油在-60~-46℃区间内具有尖锐的结晶峰;随着掺入葵花籽油比例的升高,模拟掺假油的结晶温度逐渐向低温区移动,结晶峰形由尖锐逐渐变平坦;由结晶起始温度和结晶峰值温度分别相对于掺假油体积分数建立的回归方程具有很好的相关性,可以快速准确地鉴别特级初榨橄榄油。     

基于主成分回归分析和偏最小二乘回归法的樱桃糖度检测的研究

选用烟台大樱桃为研究对象,采用便携式光谱仪对樱桃糖度进行检测,利用极差标准归一化方法和小波滤波,对其可见-近红外光谱数据进行预处理,分别运用主成分回归分析(PCR)法和偏最小二乘回归(PLSR)法建立了樱桃糖度定量分析模型,并对两种模型进行了比较。实验结果表明:在600~1 100nm波段范围内对樱桃糖度进行检测是可行的,并且PLSR模型的性能优于PCR模型。     

人工神经网络用于直接化学电离质谱分析食用油品质的研究

无需任何样品预处理,采用表面解吸常压化学电离质谱(DAPCI-MS)技术直接对涂覆在载玻片表面的食用油样品和地沟油样品进行检测,快速获得了不同油类样品的质谱信号;并运用改进的反向传输(BP)人工神经网络对DAPCI-MS所得到的油类样品质谱数据进行有监督的分类识别,建立多分组预测模型。结果表明:DAPCI-MS能够承受食用油中复杂基体的影响,可对油类样品进行直接快速质谱分析;误差反转(BP)神经网络具有良好的分类判别能力,对食用油样品质谱数据识别效果比较理想,能够在对地沟油和非地沟油样品进行有效区分的同时,实现对不同品种的食用油的分离及分类判别。本方法分析速度快,信息提取准确,识别精度高,对快速质谱技术结合神经网络在该领域的应用以及食用油品质的快速鉴定具有重要的借鉴意义。     

漫反射光谱结合化学计量学方法无损分析彩绘文物颜料

为了对彩绘文物颜料进行无损判别分析,该文采集了12种彩绘常用颜料参考样品的漫反射光谱,根据光谱曲线外形将参考样品分为红黄色系及蓝绿色系两类。使用主成分分析(PCA)分别对两个色系参考样品的光谱数据集进行降维,抽提出最有代表性的3个主成分,利用成分得分散点图确认了颜料间的类间差异,并通过成分矩阵探讨了对该差异贡献率最高的光谱波长区间。在此基础上,采用线性判别分析(LDA)对PCA的分析结果进行建模,拟合判别函数并将其应用于2件颐和园仁寿殿上架彩画文物样品颜料的分析。基于样品判别得分散点图与各类别参考样品间的距离,判定文物使用的颜料分别为铁红和群青。研究表明,该文构建的判别函数能够准确区分颜料的种类,可用于彩绘文物样品的无损分析鉴别。文物样品的污染或老化可能会影响分析,需事先进行表面清理。     

电喷雾萃取电离质谱法用于掺假白酒的快速分析

采用自行研制的电喷雾萃取电离源(EESI)与LTQ-XL质谱仪耦合,并结合主成分分析(PCA)和聚类分析(CA),建立了一种能在无需样品预处理的条件下直接、快速、准确鉴别茅台等八种掺假白酒(混掺水和工业酒精,且通过酒精计调控掺假酒的酒精度与真酒保持一致)的新方法. 同时,利用串联质谱能够对目标组分进行准确鉴定. 研究结果表明,EESI-LTQ-MS检测单个样品的时间小于1 min,且重现性好,PCA区分正确率高达96.5%. 通过设置未知样分析进一步验证了该方法的可行性. 此外,还结合单光子电离飞行时间质谱法(SPI-TOF-MS)对检测谱图进行对比分析. 阐述了EESI和SPI两种电离技术在挥发性有机物分析上具有各自的优势,且两种检测手段具有互补的特点. 为市场上酒类饮品真假的快速鉴别及品质鉴定建立了一个综合的分析方法,对于快速筛选伪劣酒类产品具有非常重要的应用价值.     

Simultaneous Determination of Oil and Water in Soybean by LF-NMR Relaxometry and Chemometrics

A fast, non-destructive and eco-friendly method was developed to simultaneously determine the oil and water contents of soybean based on low field nuclear magnetic resonance(LF-NMR) relaxometry combined with chemometrics, such as partial least squares regression(PLSR). The Carr-Purcell-Meiboom-Gill(CPMG) magnetization decay data of ten soybean samples were acquired by LF-NMR and directly applied to the PLSR analysis. Calibration models were established via PLSR with full cross-validation based on the reference values obtained by the Soxhlet extraction method for measuring oil and oven-drying method for measuring water. The results indicate that the calibration models are satisfactory for both oil and water determinations; the root mean squared errors of cross-validation(RMSECV) for oil and water are 0.2285% and 0.0178%, respectively. Furthermore, the oil and water contents in unknown soybean samples were predicted by the PLSR models and the results were compared with the reference values. The relative errors of the predicted oil and water contents were in ranges of 1.25%-4.96% and 0.44%-2.49%, respectively. These results demonstrate that the combination of LF-NMR relaxometry with chemometrics shows great potential for the simultaneous determination of contents of oil and water in soybean with high accuracy.     

The detection and quantification of adulteration in olive oil by near-infrared spectroscopy and chemometrics.

A new procedure has been developed for the classification and quantification of the adulteration of pure olive oil by soya oil, sun flower oil, corn oil, walnut oil and hazelnut oil. The study was based on a chemometric analysis of the near-infrared (NIR) spectra of olive-oil mixtures containing different adulterants. The adulteration of olive oil was carefully carried out gravimetrically in a 4 mm quartz cuvette, starting with pure olive oil in the cuvette first. NIR spectra of the 525 adulterated mixtures were measured in the region of 12,000-4000 cm(-1). The spectra were subjected batch wise to multiplicative signal correction (MSC) before calculating the principal component (PCA) models. The MSC-corrected data were subjected to Savitzky-Golay smoothing and a mean normalization procedure before developing partial least-squares calibration (PLS) models. The results revealed that the models predicted the adulterants, corn oil, sun flower oil, soya oil, walnut oil and hazelnut oil involved in olive oil with error limits +/-0.57, +/-1.32, +/-0.96, +/-0.56 and +/-0.57% weight/weight, respectively. Furthermore, the PCA developed models were able to classify unknown adulterated olive oil mixtures with almost 100% certainty. Quantification of the adulterants was carried out using their respective PLS models within the same error limits as mentioned above.     

Sequential (step-by-step) detection,identification and quantitation of extra virgin olive oil adulteration by chemometric treatment of chromatographic profiles

An analytical method for the sequential detection, identification and quantitation of extra virgin olive oil adulteration with four edible vegetable oils--sunflower, corn, peanut and coconut oils--is proposed. The only data required for this method are the results obtained from an analysis of the lipid fraction by gas chromatography-mass spectrometry. A total number of 566 samples (pure oils and samples of adulterated olive oil) were used to develop the chemometric models, which were designed to accomplish, step-by-step, the three aims of the method: to detect whether an olive oil sample is adulterated, to identify the type of adulterant used in the fraud, and to determine how much aldulterant is in the sample. Qualitative analysis was carried out via two chemometric approaches--soft independent modelling of class analogy (SIMCA) and K nearest neighbours (KNN)--both approaches exhibited prediction abilities that were always higher than 91% for adulterant detection and 88% for type of adulterant identification. Quantitative analysis was based on partial least squares regression (PLSR), which yielded R2 values of >0.90 for calibration and validation sets and thus made it possible to determine adulteration with excellent precision according to the Shenk criteria.     

Composition Profiling and Authenticity Assessment of Camellia Oil Using High Field and Low Field 1H NMR

Camellia oil (CA), mainly produced in southern China, has always been called Oriental olive oil (OL) due to its similar physicochemical properties to OL. The high nutritional value and high selling price of CA make mixing it with other low-quality oils prevalent, in order to make huge profits. In this paper, the transverse relaxation time (T₂) distribution of different brands of CA and OL, and the variation in transverse relaxation parameters when adulterated with corn oil (CO), were assessed via low field nuclear magnetic resonance (LF-NMR) imagery. The nutritional compositions of CA and OL and their quality indices were obtained via high field NMR (HF-NMR) spectroscopy. The results show that the fatty acid evaluation indices values, including for squalene, oleic acid, linolenic acid and iodine, were higher in CA than in OL, indicating the nutritional value of CA. The adulterated CA with a content of CO more than 20% can be correctly identified by principal component analysis or partial least squares discriminant analysis, and the blended oils could be successfully classified by orthogonal partial least squares discriminant analysis, with an accuracy of 100% when the adulteration ratio was above 30%. These results indicate the practicability of LF-NMR in the rapid screening of food authenticity.     

基于主成分和支持向量机浓度参量同步荧光光谱油种鉴别

基于浓度参量同步荧光光谱技术,对不同溢油类型不同油源原油样品集、引入外扰相似油源样品集进行光谱数据采集,获取其浓度同步荧光光谱矩阵Concentration-Synchronous-Matrix-Fluorescence(CSMF),利用主成分分析方法对两套不同层次的原油相关样品集进行了多类分类识别。结果表明:主成分载荷图可以很好地反映各个原油相关样品在油源上的相似程度,结合支持向量机可以实现不同溢油类型及不同油源原油的准确分类,对于引入风化和海水外扰相似油源溢油样品集,两类分类区分的结果远远高于多类分类识别的结果。通过详细的主成分分析讨论,为溢油油种鉴别提供了一种利用多类分类识别,逐步缩减嫌疑样本数量,最后通过两两分类实现溢油样品准确识别的新思路。     

Extra virgin (EV) and ordinary (ON) olive oils: distinction and detection of adulteration (EV with ON) as determined by direct infusion electrospray ionization mass spectrometry and chemometric approaches

Extra virgin (EV), the finest and most expensive among all the olive oil grades, is often adulterated by the cheapest and lowest quality ordinary (ON) olive oil. A new methodology is described herein that provides a simple, rapid, and accurate way not only to detect such type of adulteration, but also to distinguish between these olive oil grades (EV and ON). This approach is based on the application of direct infusion electrospray ionization mass spectrometry in the positive ion mode, ESI(+)‐MS, followed by the treatment of the MS data via exploratory statistical approaches, PCA (principal component analysis) and HCA (hierarchical clustering analysis). Ten distinct brands of each EV and ON olive oil, acquired at local stores, were analyzed by ESI(+)‐MS and the results from HCA and PCA clearly indicated the formation of two distinct groups related to these two categories. For the adulteration study, one brand of each olive oil grade (EV and ON) was selected. The counterfeit samples (a total of 20) were then prepared by adding assorted proportions, from 1 to 20% w/w, with increments of 1% w/w, of the ON to the EV olive oil. The PCA and HCA methodologies, applied to the ESI(+)‐MS data from the counterfeit (20) and authentic (10) EV samples, were able to readily detect adulteration, even at levels as low as 1% w/w. Copyright © 2010 John Wiley & Sons, Ltd.     

Development of a methodology based on headspace-gas chromatography-ion mobility spectrometry for the rapid detection and determination of patin fish oil adulterated with palm oil

The present research intends to develop a new method based on headspace-gas chromatography-ion mobility spectrometry (HS-GC-IMS) for the detection and determination of patin fish oil adulterated with different percentages of palm oil, because it is a cheaper vegetable oil. Five variables that affect headspace generation (incubation time and temperature, amount of sample, injection volume, and pre-heating time) have been optimized by means of a Box Behnken design in combination with Response Surface Methodology. Then, repeatability and intermediate precision have been studied where coefficients of variation lower than 10% were obtained. This new method has been applied to several samples of patin fish oil adulterated with palm oil at 20 different levels (5–50% palm oil content). The results have confirmed the suitability of the HS-GC-IMS for a rapid, easy, and reliable detection and discrimination of adulterated oil samples since a characteristic fingerprint that allows 100% successful discrimination between adulterated and unadulterated patin oil samples was achieved. Finally, a regression model has been developed to determine each sample’s adulteration level with an error lower than 10% and a coefficient of determination greater than 0.95.     

应用校正变换矩阵法识别掺伪食用油

根据食用油中脂肪酸、甾醇以及生育酚含量,应用校正转换矩阵法对花生油掺伪进行了定量检测。用所建立的校正模型对棕榈油、菜籽油和棉籽油掺入到花生油所得到的２７个二元和６个四元人工合成样品进行了验证,结果令人满意。对从市场上购得的５种花生油进行了测定,其中一种是由菜籽油假冒的花生油,另一种为掺入花生精油的棕榈油。     

Nanoparticle-based assay for the detection of virgin argan oil adulteration and its rapid quality evaluation

A new method, based on the formation of gold nanoparticles (AuNPs) and spectrophotometric analysis, is proposed to determine total phenolic acids in virgin argan oil samples. These compounds have reducibility due to the presence of the phenol group in their molecular structure, and a redox reaction occurs in the presence of HAuCl₄. The formation of AuNPs as a result of the redox reaction leading to colour changes can be visually observed, resulting in strong light signals that show absorption at 555 nm. As ferulic acid represents more than 95% of the total phenolic acid content of virgin argan oil, this compound was used as an adulteration marker to carry out the screening of samples for the evaluation of the authenticity of virgin argan oils. The analytical features of this screening method also allowed a low precision quantization of the quality of the product. Then, a reference HPLC-DAD/FD method was used to confirm the potential adulterated samples, as well as to provide a detailed quantitative analysis of the most representative phenolic compounds in the samples. The overall screening-confirmation strategy was validated by analysing pure virgin argan oil samples and argan oil samples adulterated with other commercial vegetable oils, demonstrating the reliability of the results. This approach is characterised by its simplicity, low cost, rapid information and responded to practical laboratories needs.