气相色谱结合化学计量学区分大米贮藏时间与产地

香气是衡量大米质量的一个主要因素,对大米的食用品质有重要影响。该文以顶空固相微萃取(SPME)技术为基础,采用气相色谱法分别分析了不同贮藏时间和不同产地大米样本的挥发性成分,通过主成分分析法(PCA)和偏最小二乘判别分析法(PLS-DA)对大米样本进行分类和判别分析。PCA及PLS投影图显示不同储藏时间的大米明显聚为4类,通过留一交叉验证法(LOO)计算PLS预报的准确率为96%,相对标准误差为8.2%。同时,PCA投影图中可将4个不同产地的大米样本进行区分,分类效果显著;所建PLSDA模型可靠,不同产地大米样本均能被准确识别,正确率为100%。以顶空固相微萃取/气相色谱检测大米中挥发性成分,利用主成分分析法和偏最小二乘判别分析法鉴别大米新鲜程度和产地具有可行性。     

基于离散傅立叶变换的支持向量机光谱定量分析法

研究了火电厂电煤煤粉的近红外光谱特征,提取了前3个主成分和前6个离散傅立叶变换(DFT)系数,结合主成分得分、马氏距离和偏最小二乘(PLS)交互验证方法剔除异常样本,并建立偏最小二乘回归(PLSR)、栅格支持向量机回归(G-SVR)、遗传算法支持向量机回归(GA-SVR)和粒子群算法支持向量机回归(PSO-SVR)等定量分析模型。结果表明,利用DFT系数作为PSO-SVR模型的输入变量,当其进化代数为300,种群规模为20,模型参数c1、c2为1.5,1.7时,性能最优,其中校正集相关系数(RC)为0.990,测试集相关系数(RP)为0.954,定标标准差(SEC)为0.366,测试标准差(SEP)为0.128。该方法准确可靠,已成功应用于近红外在线电煤发热量监测系统,并可推广用于其它较为复杂的近红外在线分析系统。     

基于氢核磁共振技术和化学计量学方法鉴别蜂蜜品种

利用氢核磁共振(~1H NMR)技术结合化学计量学方法对不同品种的蜂蜜进行鉴别。采集33个洋槐蜜、48个油菜蜜、63个荔枝蜜的核磁指纹图谱,对数据进行不同方式的预处理后,采用有监督的偏最小二乘判别分析(PLS-DA)和正交偏最小二乘判别分析(OPLS-DA)建立判别模型。结果表明,不同的数据预处理方式对模型解释能力和预测能力的影响较大,自标度化(UV)模式更适于蜂蜜核磁数据的分析。建立的OPLS-DA模型可有效地分离判别3种蜂蜜,所建模型对3种蜂蜜的判别解释能力达95.8%,对未知样本的预测能力为90.5%。因此,利用~1H NMR结合OPLS-DA方法可有效地实现不同品种蜂蜜的快速鉴别。     

基于最小二乘支持向量机的色谱指纹图谱预测银杏叶总抗氧化活性

在色谱图基线校正和色谱峰匹配基础上,提出以40个银杏叶提取物HPLC指纹图谱的色谱图轮廓作为输入,相应的提取物总抗氧化活性作为输出,建立最小二乘支持向量机回归模型,并对包含10个样本的测试集进行了预测.最小二乘支持向量机的测试集预测误差均方根(RMSEP)为0.0230,预测结果优于目前普遍使用的误差反向传播神经网络和偏最小二乘回归.与采用色谱峰面积为分析变量的模型预测结果比较表明:采用消除干扰后的色谱图全谱轮廓保留了样本的全部信息,预测结果更好     

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。     

仪器噪声在近红外光谱偏最小二乘模型内的传播效应

在实际多元校正应用中有很多因素会影响偏最小二乘(PLS)模型的预测效果,作为光谱数据本源的仪器噪声是其中的重要影响因素。以往的研究工作多使用各种滤波器或平滑方法来降低仪器噪声的影响,然而对于仪器噪声如何影响偏最小二乘的建模过程和模型预测能力鲜有报道。该文阐述并论证了仪器噪声怎样通过第一个隐变量的计算被引入模型中,经过对偏最小二乘计算过程的理论推导,论述了噪声的引入对偏最小二乘权重向量、载荷向量计算具有累积效应,并随着后续隐变量的计算不断在模型中传递,从而对偏最小二乘模型产生影响。同时对偏最小二乘模型的预测误差进行理论分解,将其划分为无噪理想模型本身的误差和由噪声传播导致的误差。结果表明,仪器噪声不仅会降低偏最小二乘模型的预测性能,还会影响偏最小二乘模型的最优复杂度选择。     

8种名优绿茶香气品质与其成分间协同定量组效关系研究

本文首先利用电子鼻(E-nose)结合偏最小二乘判别分析(PLSDA)成功判别了8种不同种类和等级的名优绿茶的香气品质.为进一步解释香气质量差异,借助局部极小值背景漂移校正、多尺度高斯平滑以及色谱保留时间校正法对名优绿茶的气相色谱-质谱联用(GC-MS)指纹图谱进行预处理,再利用移动窗口偏最小二乘回归(MWPLSR)将预处理后的指纹图谱与香气品质得分构建谱效关系模型,筛选出21种潜在特征香气物质,最后利用变量加权最小二乘支持向量机(PSO-VWLS-SVM)将特征香气物质的含量与不同绿茶香气质量得分相关联,根据各特征香气物质的贡献率,成功揭示了名优绿茶香气物质与香气品质之间的协同量-组效关系.本文提出的方法为绿茶特征香气品质标志物的筛查和其量效-组效关系研究提供了一种新的策略方法.     

基于LVs-LS-SVM近红外光谱检测柴油运动粘度的方法研究

为了研究不同光谱输入方法对柴油运动粘度预测模型的影响,本文对预处理后的柴油光谱进行主成分分析(PCA)得到的前6个主成分(PCs)、建立偏最小二乘(PLS)回归曲线选取有效波长(EWs)和运用偏最小二乘回归(PLSR)建模得到的14个潜变量(LVs),将三者分别输入至最小二乘支持向量机(LS-SVM),对结果进行比较分析表明:LVs-LS-SVM建模得到的结果为柴油运动粘度(R_(Pre)~2=0.839,RMSEP=0.317,RPD=1.54),优于EWs-LS-SVM和PCs-LS-SVM模型,为柴油参数测定便携式仪器的开发奠定基础。     

近红外光谱方法预测生物柴油主要成分

采用近红外光谱快速测定法对生物柴油的成分(脂肪酸甲酯、单甘酯、二甘酯、三甘酯和甘油)进行了研究.采用气相色谱方法获得其成分的基础数据,通过偏最小二乘方法与近红外光谱数据进行回归运算,分别建立以文冠果油生物柴油为例的单原料油校正模型及多种原料油生物柴油的混合校正模型,并以花椒油生物柴油为例考察了校正模型的适用性.结果表明: 通过偏最小二乘方法可以建立适合多种原料油生物柴油的通用校正模型.对于新型生物柴油,向校正集中添加10个以上样本,扩充校正模型后,便可较为准确地测定这类新生物柴油样本的成分含量.此方法分析速度快、成本低、操作便捷、重复性好,适合于生物柴油生产过程的中间控制分析.     

最小二乘支持向量机结合红外光谱法测定润滑油酸值

在润滑油酸值进行红外光谱法测定中,采用最小二乘支持向量机(LS-SVM)建立了酸值的定量预测模型。用Kennard-Stone方法将30个样本划分为训练集(24个样本)和验证集(6个样本),进行定量预测,并与偏最小二乘法和径向基函数神经网络所建模型的预测进行比较。结果表明:LS-SVM所建模型的预测标准偏差(SEP)最小,仅为0.002;预测值的相对误差为1.3%~5.3%。由此认为LS-SVM所建模型的训练和预测结果均优于其余两种方法所建模型。对5个未知样品的分析结果表明:LS-SVM模型的预测值与化学法实测值之间的相对误差(1.2%~3.1%)也较少。     

Effect of pH on lipid oxidation of red onion skin extracts treated with washed tilapia (Oreochromis niloticus) muscle model systems

The aim of the study was to investigate the effect of pH on the lipid oxidation of red onion skin extracts (ROSEs) treated with washed tilapia muscle model systems (WTMS). Minced and buffered washed samples were prepared at pH 6.3 and 6.8. The WTMS were treated with2 different concentrations of red onion skin prior to storage for 5 days. Lipid oxidation was investigated via peroxide values (PVs), thiobarbituric acid reactive substances (TBARS), and the formation of volatile compounds. Fatty acid profiles of the samples were also identified. The ROSEs were able to significantly suppress the PV (~71%) and TBARS (~42%) formation. Hexanal and octanal formations in the WTMS were relatively less in the ROSE-treated samples. The WTMS samples prepared at pH 6.3 were more vulnerable to lipid oxidation than those prepared at pH 6.8. Red onion skin polyphenols may increase the lag phase of lipid oxidation, depending on pH levels, resulting in the shelf life extension of raw fish.     

基于超高效液相色谱-四极杆-静电场轨道离子阱串联质谱的冷链贮藏过程中滩羊肉的脂质组学研究

该文建立了超高效液相色谱-四极杆-静电场轨道离子阱串联质谱(UHPLC-Q-Orbitrap MS/MS)结合脂质组学分析滩羊肉在冷链贮藏过程中脂质变化规律和脂质分子碎裂机理的方法。样品经异丙醇提取后,采用质谱全扫描模式和二级扫描模式对目标物质进行定性。共鉴定出48个变化显著性脂质,包括8个脂肪酰基肉碱、23个磷脂酰胆碱(PC)、3个溶血性磷脂酰胆碱(LPC)、13个磷脂酰乙醇胺(PE)、1个溶血性磷脂酰乙醇胺(LPE)。含量差异表现为部分PC、PE和脂肪酰基肉碱在前12 d短暂性升高,12 d后开始降低,而LPE和LPC在整个冷链贮藏期间表现为上升趋势。PC、PE和脂肪酰基肉碱短暂性的积累易导致大量的脂质氧化反应,进一步阐明最佳冷链时间为12 d。该方法适用于复杂基质中脂质分子的分离与定量,为肉及肉类产品在冷链贮藏过程中的脂质变化规律研究及质量控制提供了依据。     

Preparation of a selective receptor for carbofuran for the development of a simple optical spot test for its rapid detection using stabilized in air lipid films with incorporated receptor

The present technique describes the preparation of a selective receptor for carbofuran and the development of a simple sensitive spot optical test for the rapid one-shot detection of carbofuran using stabilized lipid films supported on a methacrylate polymer on a glass fiber filter with incorporated artificial receptor. The selective receptor was synthesized by a chemical reaction using a resorcin[4]arene receptor by transforming all the –OH groups into phosphoryl groups. The lipid films without this receptor provided fluorescence under a UV lamp. The use of the receptor in these films quenched this fluorescence and the colour became similar to that of the filters without the lipid films. A drop of aqueous solution of carbofuran provided a “switching on” of the fluorescence which allows the rapid detection of this insecticide at the levels of 10⁻⁹ M concentrations. The effect of potent interferences included a wide range of compounds. The results showed no interferences from these compounds in concentration levels usually found in real samples. The effect of interference of proteins and lipids was also examined. The reproducibility of the method was checked in about 100 samples and all of them were found to provide similar results. The device was tested/evaluated in real samples of fruits, vegetables and dairy products. Note that the colours of the filters remain stable for periods of more than 2 months.     

The Assessment of Changes in the Fatty Acid Profile and Dietary Indicators Depending on the Storage Conditions of Goose Meat

The deterioration of food quality due to lipid oxidation is a serious problem in the food sector. Oxidation reactions adversely affect the physicochemical properties of food, worsening its quality. Lipid oxidation products are formed during the production, processing, and storage of food products. In the human diet, the sources of lipid oxidation products are all fat-containing products, including goose meat with a high content of polyunsaturated fatty acids. This study aims at comparing the fatty acid profile of goose breast muscle lipids depending on the storage conditions: type of atmosphere, temperature, and storage time. Three-way variance analysis was used to evaluate changes in the fatty acids profile occurring in goose meat. The health aspect of fatty acid oxidation of goose meat is also discussed. In general, the fatty acid composition changed significantly during storage in the meat packed in the high-oxygen modified atmosphere at different temperatures (1 °C and 4 °C). Higher temperature led to a higher degree of lipid oxidation and nutrient loss. During the storage of samples in vacuum, no changes in the fatty acid content and dietary indices were found, regardless of the storage temperature, which indicates that the anaerobic atmosphere ensured the oxidative stability of goose meat during 11 days of refrigerated storage.     

Development of analytical procedures to study changes in the composition of meat phospholipids caused by induced oxidation

Lipid peroxidation affects quality of meat products. The aim of this study was to develop a model system and analytical procedures for evaluating the oxidation level of meat samples, by studying the changes in meat phospholipids (PL) composition and the compounds generated by induced oxidation. Different techniques (liquid-, dry column-, accelerated solvent extraction) were investigated to identify a suitable lipid extraction system for extracting PL from bovine meat and to induce lipid oxidation by using tert-butyl hydroperoxide, 2,2'-azobis(2-amidinopropane) dihydrochloride (ABAP) or Fe(2+) and Cu(2+) salts. Accelerated solvent extraction (ASE) gave results not significantly different from the other extraction methods, but offered the advantage of being a rapid and solvent-saving procedure. The method using a silica column proved to be valid in eluting and separating the components of the phospholipidic fraction and the PL standard mixture. The analytical techniques used to analyse oxidation products of PL included GC-FID, HPLC with corona charged aerosol detector (CAD), MDA determination and the spectrophotometric measurement of peroxide levels (PxL). By means of CAD, PL were quantified and their concentration in the lipid extract was 0.98%+/-0.17 (w/w+/-SD, n=10). The oxidation method induced by ABAP proved to be fast and did not produce any artifacts. Three oxidation times were monitored (0, 90 and 180 min). The oxidation levels after 180 min correlated with a significant increase in the peroxide levels PxL (+71%), MDA (+29%) and aldehydes (+75%), whereas a decrease or even total disappearance of some unsaturated fatty acids was observed. The results obtained demonstrate that the model used in this work is useful for studying oxidation of meat phospholipids. Also, the use of the innovative detector CAD proved to be a good complementary technique in the investigation of lipids.     

Chromatographic method for the determination of aflatoxin M1 in cheese, yogurt, and dairy beverages

The aim of this work was to develop and validate a method to determine aflatoxin M1 (AFM1) in cheese, yogurt, and dairy beverages. The method consisted of aqueous methanol extraction, immunoaffinity column purification and isolation, RPLC separation, and fluorescence detection. The four types of cheese samples were classified according to moisture and fat content. The mean recoveries were 71% for cheese at spiked levels from 100 to 517 ng/kg, and 76% for yogurt and dairy beverages spiked at levels from 66 to 260 ng/kg. The mean RSDs were 5.9% for cheese, and 10% for yogurt and dairy beverages. The LOD was 3 ng/kg and the LOQ was 10 ng/kg for all test commodities. To test the applicability of the developed method, a small survey of the presence of AFM1 in cheese, yogurt, and dairy beverages purchased in Ribeir?o Preto-SP, Brazil, was conducted. AFM1 was detected (> 3 ng/kg) in all samples. Twenty cheese samples (83%) were contaminated with AFM1 in the range of 13-304 ng/kg. In yogurt and dairy beverages, the contamination was lower (13-22 ng/kg) in five samples (42%). The results indicated that the method is adequate for the determination of AFM1 in these four types of cheese, as well as in yogurt and dairy beverages.     

基于近红外光谱分析技术的栽培香菇产地快速鉴别

建立了一种基于近红外光谱分析技术的香菇产地鉴别方法。利用近红外光谱仪扫描不同主产地的香菇干样,获得样品的近红外漫反射光谱。利用偏最小二乘判别分析(PLSDA)分别建立了吉林、湖北、福建3个省份栽培香菇的产地判别模型,同时使用光谱预处理和波长筛选技术对判别模型进行优化,最后使用预测样品对模型进行验证。结果表明,使用原始光谱建立的模型能够初步实现对产地的判别,使用光谱预处理技术扣除光谱中的背景信息,同时利用波长筛选技术选择特定波长对模型进行优化后,可进一步提高预测正确率。该方法为香菇产地真实性溯源提供了一种新方法,对香菇产业发展具有重要的实际意义。     

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.     

Study on fluorometric discrimination of phytoplankton based on time-series vectors of wavelet transform

The feasibility of using time domain of wavelet transform as characteristics to establish a fluorometric discrimination method of phytoplankton was discussed. Twelve phytoplankton species belonging to nine genera of five divisions were studied. Five steps were introduced: firstly, the feasibility of utilizing 3D fluorescence spectra (3D-FS) to discriminate phytoplankton was discussed; the relative standard deviation (RSD) and included angle cosine (IAC) were used as the test criterion. 3D-FS had such potentials, for most RSD were <5% and most IAC were >0.990. Secondly, the 3D-FS were decomposed by db7 wavelet and time-series vectors (TSVs) were generated. Thirdly, the optimal characteristic spectra (OCS) were selected from the TSV by Bayesian linear discriminant analysis (BLDA). The ability of OCS to classify phytoplankton was tested, and the correct classification ratios (CCRs) at different levels were obtained. Most CCRs were 90–100% at the species level. They were >98% at the genus level, and >99% at the division level. Fourthly, the growth and light stability of the OCS were tested. Both stabilities were high with lower RSD (<3%) and higher IAC (>0.999) compared with 3D-FS. Fifthly, a “database of reference spectra” consisting of 46 reference spectra was established by hierarchical cluster analysis (HCA). Based on this, the discrimination method of phytoplankton species was established by nonnegative least squares (NNLSs). Most reference spectra were representative to phytoplankton species; and had moderate anti-noise ability: With noise ≤10%, the correct discrimination ratios (CDRs) were >98% at the genus level and >99% at the division level. 20% noise was a larger interference which made CDRs down to 85% at the genus level and to 99% at the division level. A fluorometric discrimination method of phytoplankton could be established based on TSV of wavelet transform.     

基于液相色谱-高分辨质谱的代谢组学技术用于麦卢卡蜂蜜的甄别

采用基于液相色谱-高分辨质谱的代谢组学技术对麦卢卡蜂蜜以及国内主要蜂蜜品种的代谢谱进行全面分析，实现了麦卢卡蜂蜜与其他蜂蜜的区分，建立了偏最小二乘法判别模型，对测试样品能够取得很好的鉴定效果。实验从麦卢卡蜂蜜中筛选出高度表达的3-苯乳酸、甲氧基苯乙酮、二氢麻醉椒苦素、芹菜素等黄酮和肉桂酸类等34种代谢标志物，所构建的标志物组合模型的受试者工作特征曲线下的面积达到了0.99。该文建立的代谢组学方法为麦卢卡蜂蜜的质量控制提供了新的思路。