基于离子迁移谱的葡萄籽油掺伪鉴别研究

建立了基于离子迁移谱(IMS)技术的葡萄籽油掺伪鉴别新方法。经优化,选取进样口温度170℃,迁移管温度60℃。正己烷50倍稀释油样后进样检测,分析时间20 s。为了建立高效的葡萄籽油掺伪鉴别模型,本研究采用递归支持向量机(R-SVM)方法对葡萄籽油和掺伪葡萄籽油的IMS谱图进行分类,建立葡萄籽油和掺伪葡萄籽油分类判别模型,采用十折交互检验对建立的模型进行评价,结果显示模型判别正确率为91.2%。本方法操作简单,分析速度快,为食用油真伪鉴别提供了一种新方法。     

基于干涉图解析的被动式遥感FTIR分析

针对被动式遥感傅里叶变换红外光谱(FTIR)在实际应用中存在受环境干扰较大,检测信号较弱的问题,利用背景和样品干涉图的衰减速度不同,建立了基于干涉图对被动式遥感FTIR谱图进行分析的方法. 选择有限脉冲响应(FIR)滤波器提取信号,带宽选择为20 cm-1,干涉图长度为100点,距离中心爆发点(centerburst)第50点为干涉图起始点,对样品苯的被动式遥感FTIR信号进行了有效提取. 然后采用偏最小二乘法(PLS)建立模式识别模型,对26个未知样品的干涉图数据进行预测,总识别率为96%. 该方法的建立,减弱了背景对遥感测试的影响,强化了被动式FTIR的分析信号,同时与化学计量学方法结合实现了被动式遥感FTIR对污染物的自动检测.     

汽油样品类型的模式识别研究与应用

研究了应用化学计量学方法解决汽油单体烃的气相色谱分析中单体烃定性库的自动选择问题。通过提取汽油单体烃谱图中的29个组分及其含量信息作为特征值,利用主成分分析法对不同工艺得到的催化裂化汽油、焦化汽油、直馏汽油、重整汽油和烷基化汽油进行分类,结合相似分析方法(即SIMCA方法)建立了各类汽油样本的类模型,借助这些类模型可以实现对未知样本的类型判别。所提出的识别方法可方便快速地判别待分析样品所属的汽油类别,并据此推荐适合该样品的定性模型库,从而实现汽油单体烃的快速、自动分析。     

粒子群最小二乘支持向量机结合偏最小二乘法用于芝麻油质量的鉴别

结合粒子群最小二乘支持向量机(PSO-LSSVM)与偏最小二乘法(PLS)提出一种基于气相色谱技术的新方法,对芝麻油进行真伪鉴别,并对掺伪品中掺假比例进行定量分析。采用主成分分析法(PCA)对857个样本的脂肪酸色谱数据进行分析,优选主成分作为最小二乘支持向量机(LSSVM)的输入向量。利用粒子群算法(PSO)优化LSSVM,构建芝麻油掺伪鉴别的两级分类模型,同时运用PLS建立掺伪芝麻油中掺伪油脂的定量校正模型,两级分类模型的准确率分别达到了100%和98.7%,定量分析模型的平均预测标准偏差(RMSEP)为3.91%。结果表明,本方法的鉴别准确性和模型泛化能力均优于经典的BP神经网络和支持向量机(SVM),可用于食用油脂加工和流通环节的质量控制,为食用油质量的准确鉴定提供了一条有效途径。     

纹党参与白条党参红外光谱的SIMCA聚类鉴别方法研究

以纹党参和白条党参的红外光谱为聚类分析的对象,研究了红外光谱结合SIMCA聚类分析法对纹党参和白条党参进行识别与分类的可行性.选取400 ～2 000 cm~(-1)范围内的光谱,通过基线补偿(Offset)和散射校正(MSC)等预处理后,采用SIMCA聚类分析法建立识别模型.结果表明,所建模型对纹党参和白条党参的识别率分别达92%和96%,拒绝率均为100%.用盲样对所建模型进行了测试,测试结果全部正确.该法可实现对纹党参和白条党参的快速鉴别.     

基于偏最小二乘回归的类模型方法用于中药牛黄的真伪鉴别

针对独立软模式类簇法(SIMCA)在确定主成分数和决策区间时遇到的困难,提出了一种基于PLSR的类模型方法——PLS类模型方法(PLSCM)。通过把类描述问题转化为常见的PLSR问题,采用成熟的蒙特卡罗交互验证法确定模型的隐变量数和决策区间。采用本方法对不同牛黄样品的近红外光谱数据(波长范围4000～9000 cm-1)进行分析,可成功鉴别牛黄的真伪。本方法的可操作性和鉴别准确率均优于经典的SIMCA方法。对于原始光谱数据,PLSCM的训练和预测准确率均为100%,对于经SNV处理的数据,训练和预测准确率分别为99%和100%。     

偏最小二乘法在红外光谱识别茶叶中的应用

采用漫反射傅立叶变换红外光谱(FTIR)法结合主成分分析(PCA)、偏最小二乘法(PLS)、簇类的独立软模式(SIMCA)识别法对十三种茶叶进行了分类判别研究。研究结果表明,通过多元散射校正(MSC)对原始光谱进行预处理,可以提高模式识别技术的分类判别效果。在此基础上,选取1 900~900 cm-1波长范围内的茶叶红外光谱建立识别模型,三种方法都得到了满意的分类判别效果。在对检验集中全部130个样本的判别中,PCA仅有两类样本无法判别,SIMCA的识别率和拒绝率都在90%以上,而PLS的识别效果最佳,全部样本都得到了正确的归类。这一研究结果表明傅立叶变换红外光谱法与化学计量学方法相结合可以实现茶叶品种的快速鉴别,这为茶叶的客观评审提供了一种新思路。     

水溶性封闭异氰酸酯单体的解封动力学

采用热失重分析(TGA)法研究了水溶性封闭型异佛尔酮二异氰酸酯(IPDI)的热分解过程, 利用傅里叶变换红外光谱法(FTIR)考察了谱图中40 与140 ℃两种温度下的异氰酸酯特征峰. TGA与FTIR的结果表明失重阶段即对应封闭异氰酸酯的解封闭反应. 用Friedman-Reich-Levi (FRL)和Flynn-Wall-Ozawa (FWO)两种动力学模型研究了解封反应的表观活化能E, 所得平均表观活化能分别为125.0和124.5 kJ·mol-1. 采用双等双步法对解封过程进行表观机理函数判断, 结果符合Jander方程, 反应机理为三维扩散, 结合FWO方程确定了反应级数n和指前因子对数lnA的范围.     

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。     

非正常食用油的在线甲基化-气相色谱测定及模式识别分析研究

建立了食用油中脂肪酸组成的在线水解甲基化-气相色谱测定方法,分析了20余种常用食用油与非正常食用油样品。将1μL(3 mg/mL)油脂样品与2μL衍生化试剂四甲基氢氧化铵(TMAH,25%甲醇溶液)加入裂解器,在350℃下,油脂加水分解瞬间衍生化成相应的脂肪酸甲酯。基于气相色谱图上分离鉴定到的10个共有峰的相对强度,建立了食用油的气相色谱指纹图谱。结合化学模式识别即主成分分析和系统聚类分析对合格食用油和非正常食用油样品的色谱图进行了识别分析。结果表明,建立的指纹图谱结合模式识别技术可以较好的区分合格食用油与非正常食用油样品。     

GC-IMS技术结合化学计量学方法在食用植物油分类中的应用

建立了一种快速、无损分析食用植物油中挥发性有机物质的顶空进样/气相色谱-离子迁移谱(GC-IMS)联用方法。以芝麻油、菜籽油、山茶油共56个样品为研究对象,量取2 mL待测油样于标准样品瓶中,并用磁帽密封,直接进行GC-IMS分析检测。结果表明,基于GC-IMS三维谱中对应挥发性有机物质的特征峰强度可以有效表征不同类植物油的样品信息,选取对应三维谱中40个特征峰的强度作为变量,进行主成分(PCA)信息降维后,采用k最近邻(kNN)算法建立植物油种类的判别模型,训练集的识别率达到100%,预测集中仅有1个山茶油样品被误判成芝麻油样品,预测集的识别率达到94.44%。GC-IMS联用分析技术简单、快速、无损,可用于食用植物油等其他食品、农产品种类的快速分类识别。     

Rapid screening of mixed edible oils and gutter oils by matrix-assisted laser desorption/ionization mass spectrometry

Authentication of edible oils is a long-term issue in food safety, and becomes particularly important with the emergence and wide spread of gutter oils in recent years. Due to the very high analytical demand and diversity of gutter oils, a high throughput analytical method and a versatile strategy for authentication of mixed edible oils and gutter oils are highly desirable. In this study, an improved matrix-assisted laser desorption/ionization mass spectrometry (MALDI-MS) method has been developed for direct analysis of edible oils. This method involved on-target sample loading, automatic data acquisition and simple data processing. MALDI-MS spectra with high quality and high reproducibility have been obtained using this method, and a preliminary spectral database of edible oils has been set up. The authenticity of an edible oil sample can be determined by comparing its MALDI-MS spectrum and principal component analysis (PCA) results with those of its labeled oil in the database. This method is simple and the whole process only takes several minutes for analysis of one oil sample. We demonstrated that the method was sensitive to change in oil compositions and can be used for measuring compositions of mixed oils. The capability of the method for determining mislabeling enables it for rapid screening of gutter oils since fraudulent mislabeling is a common feature of gutter oils.     

Classification and quantitation of finishing oils by near infrared spectroscopy

One of the steps in the manufacturing of synthetic fibres involves using finishing oils to ensure proper lubricity and adherence between fibres, and also the absence of static electricity. Choosing an appropriate oil and dosage are essential with a view to ensuring effective subsequent processing and use. The aim of this work was to develop a fast method for determining the different finishing oil content in acrylic fibres by use of near infrared spectroscopy (NIRS) in conjunction with partial least-squares regression (PLSR). The high similarity between the NIR spectra of finishing oils led us to assume that a single calibration model might allow determine the oil content. However, the inability to quantify accurately different finishing oils by using a sole calibration model, constrain to the prior classification of the fibres coated with the different finishing oils. Two different pattern recognition methods were used: supervised independent modeling of class analogy (SIMCA) and artificial neural networks (ANNs). However, the low contribution of the finishing oil to the NIR spectrum for the fibre sample, the high similarity between the NIR spectra for the different oils and the substantial contribution of the linear density of the acrylic fibre to the spectrum precluded correct classification by SIMCA; on the other hand, ANNs provided good results. By constructing appropriate PLSR models for the different types of finishing oils, these can be accurately determined in acrylic fibres.     

Comprehensive Review on Application of FTIR Spectroscopy Coupled with Chemometrics for Authentication Analysis of Fats and Oils in the Food Products

Currently, the authentication analysis of edible fats and oils is an emerging issue not only by producers but also by food industries, regulators, and consumers. The adulteration of high quality and expensive edible fats and oils as well as food products containing fats and oils with lower ones are typically motivated by economic reasons. Some analytical methods have been used for authentication analysis of food products, but some of them are complex in sampling preparation and involving sophisticated instruments. Therefore, simple and reliable methods are proposed and developed for these authentication purposes. This review highlighted the comprehensive reports on the application of infrared spectroscopy combined with chemometrics for authentication of fats and oils. New findings of this review included (1) FTIR spectroscopy combined with chemometrics, which has been used to authenticate fats and oils; (2) due to as fingerprint analytical tools, FTIR spectra have emerged as the most reported analytical techniques applied for authentication analysis of fats and oils; (3) the use of chemometrics as analytical data treatment is a must to extract the information from FTIR spectra to be understandable data. Next, the combination of FTIR spectroscopy with chemometrics must be proposed, developed, and standardized for authentication and assuring the quality of fats and oils.     

Preliminary study on application of mid infrared spectroscopy for the evaluation of the virgin olive oil "freshness"

The freshness of virgin olive oils (VOO) from typical cultivars of Garda regions was evaluated by attenuated total reflectance (ATR) and Fourier transform infrared (FTIR) spectroscopy, in combination with multivariate analysis. The olive oil freshness decreased during storage mainly because of oxidation processes. In this research, 91 virgin olive oils were packaged in glass bottles and stored either in the light or in the dark at room temperature for different periods. The oils were analysed, before and after storage, using both chemical methods and spectroscopic technique.Classification strategies investigated were partial least square discriminant analysis (PLS-DA), linear discriminant analysis (LDA), and soft independent modelling of class analogy (SIMCA).The results show that ATR-MIR spectroscopy is an interesting technique compared with traditional chemical index in classifying olive oil samples stored in different conditions. In fact, the FTIR PCA results allowed a better discrimination among fresh and oxidized oils, than samples separation obtained by PCA applied to chemical data. Moreover, the results obtained by the different classification techniques (PLS-DA, LDA, SIMCA) evidenced the ability of FTIR spectra to evaluate the olive oil freshness. FTIR spectroscopy results are in agreement with classical methods. The spectroscopic technique could be applied for the prediction of VOOs freshness giving information related to chemical modifications. The great advantages of this technique, compared to chemical analysis, are related to rapidity, non-destructive characteristics and low cost per sample. In conclusion, ATR-MIR represents a reliable, cheap and fast classification tool able to assess the freshness of virgin olive oils.     

Fast quantitative detection of sesame oil adulteration by near-infrared spectroscopy and chemometric models

Adulteration of foods has been known to exist for a long time and various analytical tests have been reported to address this problem. Among them, authenticity of sesame oil has attracted much attention. Near-infrared (NIR) spectral quantitative detection models of sesame oil adulterated with other oils are constructed by chemometric methods, i.e., competitive adaptive reweighted sampling (CARS), elastic component regression (ECR) and partial least squares (PLS). Sixty samples adulterated with different proportions of five kinds of other oils of lower price were scanned by a Fourier-transform-NIR spectrometer and the NIR spectra were collected in 4500–10000 cm⁻¹ region by transmission mode. All samples were divided into the training set and an independent test set. Model population analysis has also been carried out and confirms the importance of selecting representative samples. The experimental results indicate that the PLS model using only 10 variables from CARS and the ECR model show similar performance and both are superior to the full-spectrum PLS model. CARS focuses on selecting variables and ECR focuses on optimizing the parameters, implying that both roads lead to the same destination. It seems that NIR technique combined with CARS or ECR is feasible for rapidly detecting sesame oil adulterated with other vegetable oils.     

A New Method for Olive Oil Screening Using Multivariate Analysis of Proton NMR Spectra

A new NMR-based method for the discrimination of olive oils of any grade from seed oils and mixtures thereof was developed with the aim of allowing the verification of olive oil authenticity. Ten seed oils and seven monovarietal and blended extra virgin olive oils were utilized to develop a principal component analysis (PCA) based analysis of ¹H NMR spectra to rapidly and accurately determine the authenticity of olive oils. Another twenty-eight olive oils were utilized to test the principal component analysis (PCA) based analysis. Detection of seed oil adulteration levels as low as 5% v/v has been shown using simple one-dimensional proton spectra obtained using a 400 MHz NMR spectrometer equipped with a room temperature inverse probe. The combination of simple sample preparation, rapid sample analysis, novel processing parameters, and easily interpreted results, makes this method an easily accessible tool for olive oil fraud detection by substitution or dilution compared to other methods already published.     

Prediction of the acid value,peroxide value and the percentage of some fatty acids in edible oils during long heating time by chemometrics analysis of FTIR-ATR spectra

Edible oils are used in the preparation of foods as a part of their recipe or for frying. So to ensure of food safety, checking the quality of the oils before and after usage is an important subject in food control laboratories. In this study, edible oils from four different sources (canola, corn, sunflower and frying) were heated for 36 h at 170 °C and sampling was done every 6 h. The free fatty acid, peroxide value and the content of some fatty acids (C16:0, C18:0, C18:1, C18:2, C18:3) of the oil samples were determined by standard methods. Then, the ATR-FTIR spectra of the samples were collected. The partial least squares (PLS) regression combined with genetic algorithm was performed on the spectroscopic data to obtain the appropriate predictive models for the simultaneous estimation of acid value, peroxide value and the percentage of five kinds of fatty acids. The effect of some preprocessing methods on these models was also investigated. Preprocessing of data by orthogonal signal correction (OSC) resulted in the best predictive models for all oil properties. The correlation coefficients of calibration set (>0.99) and validation set (>0.86 and in most case >0.94) of the OSC–PLS model suggested suitable predictive modeling for all studied parameters in the oil samples. This method could be suggested as a rapid, economical and environmental friendly technique for simultaneous determination of seven noted parameters in the edible oils.     

基于近红外光谱分析技术的食品包装塑料的定性分析

该文基于近红外漫反射光谱分析技术对食品包装材料聚乙烯、聚丙烯进行定性判别试验研究,选取不同波段范围、采用不同光谱预处理方法,使用主成分分析法(Principal component analysis,PCA)结合SIMCA、贝叶斯判别、K-近邻3种模式识别方法建立定性预测模型,并根据正确识别率比较了各模型预测性能。结果表明：使用SIMCA方法、贝叶斯判别、K-近邻3种方法建立的定性校正模型均在1 050~1 550 nm波长范围内效果较好;采用矢量归一化、标准正态变量变换、中心化、滑动均值滤波、多项式平滑滤波、一阶微分6种光谱预处理方法和上述3种模式识别方法对塑料样品近红外光谱进行了数据处理,其中在1 050~1 550 nm范围内,主成分因子数为3,采用原始光谱建立的K-近邻定性校正模型较优,对样品校正集和预测集的正确识别率均为100%。可为食品包装材料聚乙烯、聚丙烯的快速鉴别研究提供参考。