基于衰减全反射式太赫兹时域光谱技术的食用油光谱特性研究

食用油是人类营养和能量的重要来源,为人体提供必需的脂肪酸,研究食用油在太赫兹波段光学特性,对食用油成分分析及品质评价具有重要价值。衰减全反射式太赫兹时域光谱技术是一种新型的太赫兹时域光谱技术,通过样品与倏逝波的相互作用,获取样品的太赫兹光谱。与透射式或反射式太赫兹时域光谱技术相比,该技术能有效地避免测量食用油等液体样品时样品池对光学参数的影响,并能获得样品的精确光学参数。分别利用透射式太赫兹时域光谱技术和衰减全反射式太赫兹时域光谱技术测量了大豆油的吸收光谱。结果表明,与透射式太赫兹时域光谱技术相比,衰减全反射式太赫兹时域光谱技术能更有效地提取大豆油的吸收系数、吸收峰分布等光学特性。进一步利用衰减全反射式太赫兹时域光谱技术研究了大豆油、核桃油、葡萄籽油在太赫兹波段的光学特性,获得了三种食用油在1~1.8 THz范围内的折射率谱和吸收光谱。利用密度泛函理论计算了食用油中四种主要成分(软脂酸、硬脂酸、油酸和亚油酸)在太赫兹波段的振动、转动模式,理论计算结果同实验测量结果吻合较好。研究表明,在太赫兹波段食用油的吸收峰与所含脂肪酸分子种类与含量有关,其主要来源为脂肪酸分子的低频振动和转动。研究成果对食用油成分定性定量分析及品质检测等具有指导意义。     

基于FTIR的芝麻油真伪鉴别和掺伪定量分析模型

把低价油掺入到高价油是食用油脂中的常见掺伪现象,芝麻油由于品质好价格高,市场上时有假冒伪劣产品,因此应用FTIR并结合化学计量学,建立了芝麻油的真伪和掺伪的快速分析方法。首先分析了芝麻油与大豆油、葵花籽油在4 000～650 cm-1范围的FTIR谱图,由于食用植物油都是不同脂肪酸甘油三酯的混合物,其谱图极为相似,很难发现芝麻油与其他油脂的明显差异。但是不同食用油的脂肪酸组成不同,其1 800～650 cm-1红外指纹特征区也有所不同,因此可以选择该区域,对红外光谱数据用化学计量学方法进行分类识别。通过建立主成分分析(PCA)和簇类独立软模式识别(SIMCA)模型,进行了芝麻油的真伪鉴别,该模型聚类效果较为理想,识别正确率达到了100％;采用标准正态化校正(SNV)和偏最小二乘法(PLS),经过PCA分析计算,芝麻油中掺入大豆油、葵花籽油的掺伪检测限均为10%;利用FTIR和PLS,建立了芝麻油掺的定量分析模型,该模型预测值与实际值有着良好的对应关系,预测相对误差为-6.87％～8.07％之间,说明定量模型可行。本方法能够实现芝麻油的快速真伪鉴别和掺伪定量分析,其优点是模型一旦建立,分析简便、快速,可以满足大量样品的日常监测。     

一阶导数光谱法测定食品植物油中桐油掺混的研究

A method of first derivative spectrophotometry for qualitative and quantitative analysis of tung oil adulterated in vegetable oils, including peanut oil, bean oil, rape seed oil, tea seed oil, palm oil and mixed vegetable oil, was established. The spectrum of tung oil features three valleys at 291.3, 278.3 and 266.4nm, and three peaks at 284.1, 271.5 and 260.7nm. At 291.3nm, the coefficient (deltaE(1%)1cm/deltalambda) was -1.03 x 10(3). When the concentration of tung oil adulterated in vegetable oils was downed to 0.1%, the above specialities still remain and the changes in wavelengths were not more than 0.7nm. The detection limit of the concentration of tung oil adulterated in vegetable oils was lower than 0.1%.     

Comparison of ultrasound-assisted extraction with conventional extraction methods of oil and polyphenols from grape (Vitis vinifera L.) seeds

Ultrasound-assisted extraction (US) carried out at 20 KHz, 150 W for 30 min gave grape seed oil yield (14% w/w) similar to Soxhlet extraction (S) for 6 h. No significant differences for the major fatty acids was observed in oils extracted by S and US at 150 W. Instead, K₂₃₂ and K₂₆₈ of US- oils resulted lower than S-oil. From grape seeds differently defatted (S and US), polyphenols and their fractions were extracted by maceration for 12 h and by ultrasound-assisted extraction for 15 min. Sonication time was optimized after kinetics study on polyphenols extraction. Grape seed extracts obtained from seeds defatted by ultrasound (US) and then extracted by maceration resulted the highest in polyphenol concentration (105.20 mg GAE/g flour) and antioxidant activity (109 Eq αToc/g flour).     

基于拉曼光谱和最小二乘支持向量机的橄榄油掺伪检测方法研究

为实现橄榄油中掺伪油类型的识别和掺伪量预测,对掺入葵花籽油、大豆油、玉米油的橄榄油共117个样品进行拉曼光谱检测,并用基于多重迭代优化的最小二乘支持向量机模型对掺入油的类型进行识别,综合识别率为97%。同时分别采用最小二乘支持向量机、人工神经网络模型、偏最小二乘回归建立橄榄油中葵花籽油、大豆油、玉米油含量的拉曼光谱定标模型,结果显示最小二乘支持向量机具有最优的预测效果,其预测均方根误差(RMSEP)在0.007 4～0.014 2之间。拉曼光谱结合最小二乘支持向量机可为橄榄油掺伪检测提供一种精确、快速、简便、无损的方法。     

Quantitative detection of adulterated olive oil by Raman spectroscopy and chemometrics

Commercially available extra virgin olive oils are often adulterated with some other cheaper edible oils with similar chemical compositions. A set of extra virgin olive oil samples adulterated with soybean oil, corn oil and sunflower seed oil were characterized by Raman spectra in the region 1000–1800 cm⁻¹. Based on the intensity of the Raman spectra with vibrational bands normalized by the band at 1441 cm⁻¹ (CH₂), external standard method (ESM) was employed for the quantitative analysis, which was compared with the results achieved by support vector machine (SVM) methods. By plotting the adulterant content of extra virgin olive oil versus its corresponding band intensity in the Raman spectrum at 1265 cm⁻¹, the calibration curve was obtained. Coefficient of determination (R²) of each curve was 0.9956, 0.9915 and 0.9905 for extra virgin olive oil samples adulterated with soybean oil, corn oil and sunflower seed oil, respectively. The mean absolute relative errors were calculated as 7.41, 7.78 and 9.45%, respectively, with ESM, while they were 5.10, 6.96 and 4.55, in the SVM model, respectively. The prediction accuracy shows that the ESM based on Raman spectroscopy is a promising technique for the authentication of extra virgin olive oil. The method also has the advantages of simplicity, time savings and non‐requirement of sample preprocessing; especially, a portable Raman system is suitable for on‐site testing and quality control in field applications. Copyright © 2011 John Wiley & Sons, Ltd.     

Endogenous lipase catalyzed transesterification of olive oil fats. The formation of isomeric and oligomeric triacyleglycerols

The role of lipase to catalyze hydrolysis and transesterification of triacylglycerols (TAGs) was evaluated in model systems as well as in virgin olive oil. Tandem mass spectrometry was applied in the identification of modified TAGs, ionized by electrospray, formed during the incubation of selected TAGs with mono and di carboxylic acids. The oligomerization of TAGs was observed in authentic olive oil samples and verified in model systems under catalysis exerted by lipase, whose presence in olive oil was already documented. The hydrolytic pathways taken under enzymatic treatment is balanced by the formation of TAG oligomers that should not alter the nutritional value of the aliment. Copyright ? 2012 John Wiley & Sons, Ltd.     

三指标值法快速筛查不合格植物油

通过测定南方地区常用的花生油、玉米油、菜籽油、大豆油、葵花油、茶籽油、橄榄油等植物油以及地沟油和过期植物油的A3005(代表不饱和度)、A₉₈₅(代表共轭脂肪酸含量)、A₉₆₀+A₉₈₅(代表反式脂肪酸含量)三个指标值,得到了合格植物油三个指标值设定范围。在此基础上,建立快速筛查不合格植物油(过期、添加低价油、添加地沟油)的方法,有效地提高了植物油的监控效率。利用该法筛查出的若干疑不合格油,通过脂肪酸构成法和11, 12, 13, 17脂肪酸含量判定法等,均证实它们是掺杂油或过期油,几种检测方法的结合应用,可进一步推断植物油不合格的原因。     

基于近红外光谱的杂交水稻种子发芽率测试研究

现阶段水稻种子发芽率测试仍然按照传统的农作物种子发芽技术规定进行发芽试验,此方法存在试验周期长、成本高、专业性要求高等缺点,本研究提出一种基于近红外光谱技术的快速、无损测试杂交水稻种子发芽率的新方法。采用人工老化方法在温度45 ℃、湿度100%的条件下分别老化处理2个品种杂交水稻种子0,24,48,72,96,120,144 h;用近红外光谱仪分别采集2个品种不同老化时间段杂交水稻种子光谱数据共280份,随机分成校正集(168份)和检验集(112份);测试不同老化时间段的水稻种子发芽率;以偏最小二乘算法(PLS)建立了回归模型,分析不同光谱波段和比较不同光谱预处理方法对模型精度的影响。2个品种的水稻种子光谱数据采用全波段和标准化＋正交信号校正预处理时模型最优,模型校正集决定系数(R_C)与验证集相关系数(R_P)分别为0.965和0.931,校正标准误差(SEC)与预测标准误差(SEP)分别为1.929和2.899,验证集预测值与真实值之间的相对误差在4.2%以内。研究结果表明利用近红外光谱分析技术进行杂交水稻种子发芽率的快速无损检测是可行的。     

基于Vis/NIR光谱技术的酿酒葡萄成熟期间SSC预测研究

酿酒葡萄成熟度是确定葡萄采收期的重要品质指标,针对酿酒葡萄大田中成熟度检测难度大的问题,利用可见/近红外（Vis/NIR）光谱技术和化学计量学,研究了酿酒葡萄可溶性固形物含量（SSC）与光谱数据之间的内在联系。采用USB2000+光谱仪获取5种酿酒葡萄及其叶片在不同成熟时期的Vis/NIR光谱数据,通过OMNIC 8.0软件提取光谱数据,将化学值与光谱吸收率值通过TQ Analyst8.0软件建立模型。选取信噪比高的450~1 000 nm波段,利用PCA剔除异常光谱数据,将一阶导数（FD）、Savitzky-Golay卷积平滑（S-G）、多元散射校正（MSC）、标准正态变换（SNV）分别组合共4种方法用于光谱数据预处理。利用偏最小二乘（PLS）法分别建立了5种葡萄基于酿酒葡萄光谱数据的SSC预测模型,建立了5种葡萄基于冠层叶片光谱数据的SSC预测模型,对比了不同方式预处理后的建模效果,并选择最优预处理方式建模。最后用外部样本分别验证了SSC预测模型。结果表明,采用S-G平滑+FD+MSC的预处理方法时大多数预测模型性能达到最好。5种葡萄浆果校正集和验证集的R分别达到0.93和0.86以上,最高均方根误差分别为0.30和0.48,5种葡萄冠层叶片校正集和验证集的R分别达到0.73和0.65以上,最大均方根误差分别为0.95和0.75。5种葡萄浆果外部试验样本预测值与真实值间的平均RE最高为0.43%。基于酿酒葡萄浆果光谱的SSC预测模型具备良好的预测能力,优于基于酿酒葡萄冠层叶片光谱的SSC预测模型,SSC预测模型能够为酿酒葡萄成熟度评价研究提供理论参考。Vis/NIR光谱技术适用于在酿酒葡萄大田中快速、无损检测SSC。     

基于三维荧光光谱-平行因子分析的海上溢油识别技术研究

在世界范围内溢油事件频繁发生,溢油的组成成分会影响人类身体健康和生态系统。因此,迫切地需要一种可以快速识别溢油种类的方法。针对溢油污染物现场快速鉴别的需求,利用平行因子分析技术建立了基于三维荧光光谱的原油、燃料油识别方法。首先,利用Delannay三角形内插值法对实验选的6种原油(Roncador原油、巴士拉原油、俄罗斯原油、沙特原油(重质)、上扎库姆原油、海二站原油)和三种燃料油(380CST燃料油、5-7号燃料油、岚山燃料油)的三维荧光光谱去散射,去散射后的三维光谱数据进行归一化处理;之后,对三维荧光光谱进行平行因子解析,确定七个荧光组分为最佳荧光组分,进而得到由7个荧光成分组成的样品荧光特征谱,将风化第3,15和45天的样品及未风化样品的第一平行样的荧光特征谱进行贝叶斯方法(Bayes)判别分析和聚类分析,确定油品荧光特征谱的分析能力和18条荧光标准谱库(12条原油标准谱和6条燃料油标准谱);最后,利用非负最小二乘多元线性回归建立溢油荧光识别方法,对第0,7和30天风化的样品和未风化样品的另一平行样进行识别。实验结果表明,除对风化及未风化的俄罗斯原油识别外,该方法对其余风化和未风化的五种原油和三种燃料油识别正确率均为100.0%,整体识别原油正确率为87.5%,燃料油正确率为100.0%。     

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.     

以香草素为内标之气相层析光谱法定量牛樟木精油指标成分松油醇的快速分析方法

牛樟精油具有独特浓郁的香气,其中以α-松油醇(α-terpineol)为牛樟精油主要成分。由于樟树精油制品可依种类、变种、亚种等不同而有不同成分,以松油醇最具独特性,可作为牛樟木精油品质重要指标成分,应用以区别牛樟树与冇樟以及香樟等木材精油的方法,因此建立市售牛樟木精油指标成分α-松油醇含量的简便快速又准确的检验方法便有其必要性。该研究拟以市售精油液体样品,不经任何前处理,加入适当之内标准溶液溶解后,直接注入气相层析光谱仪中,配合适当的分离管柱及气相层析条件,以期建立简便快速又准确的牛樟木精油指标成分α-松油醇之定量方法。毛细管柱气相层析具有高解析度及高灵敏度等优点,仍为现代最重要分析技术之一,因此研究建立了以香草素为内标准定量牛樟木精油指标成分α-松油醇(α-terpineol)之气相色谱层析法的快速分析方法。牛樟木精油液体样品,加入适当量之香草素内标准溶液混合溶解后,即可直接注入配有广口径之毛细管柱(megabore column)气相色谱仪中分析,相当简便,每分析一个样品仅需约30 min。结果显示松油醇之最低定量浓度（limit of quantitation, LOQ）为1 μg·mL-1左右。在添加回收试验中添加松油醇1.0及10.0 mg 于市售冇樟精油及牛樟精油中,其回收率在98%~103%,变异系数均在10.8%以下,显示该方法的精密度相当高。以该研究建立的方法分析15件市售牛樟精油中松油醇含量,结果显示,市售牛樟木精油的松油醇含量最高约51.6%及最低为21.3%左右,此结果显示以定量松油醇作为市售牛樟精油品质指标是一快速、准确且可行的方法。     

薰衣草精油的衰减全反射红外光谱辨别分析

为建立薰衣草精油品种品质的快速辨别分析模型,采用衰减全反射红外光谱法测定三个品种共96个薰衣草精油样品,对原始光谱数据求二阶导数,通过方差计算,确定1 750～900 cm-1波长段为判别分析用数据。分析结果表明,主成分分析(PCA)基本能实现精油品种区分,前三个主成分主要代表着酯、醇和萜类物质。使用68个样品的校正集建立正交偏最小二乘判别分析(OPLS-DA)模型,三个品种薰衣草精油的回归曲线测定系数分别为0.959 2, 0.976 4, 0.958 8,验证集中三个品种精油预测均方根误差(RMSEP)分别为0.142 9, 0.127 3, 0.124 9,OPLS-DA法建立的模型对校正集和验证集的判别率和预测率都达到100%,模型对薰衣草精油品种品质有很好的识别能力。为薰衣草精油品种品质提供一个快速、直观的方法。     

Visible Raman spectroscopy for the discrimination of olive oils from different vegetable oils and the detection of adulteration

We have investigated the potential of Raman spectroscopy with excitation in the visible spectral range (VIS Raman) as a tool for the classification of different vegetable oils and the quantification of adulteration of virgin olive oil as an example. For the classification, principal component analysis (PCA) was applied, where 96% of the spectral variation was characterized by the first two components. A significant similarity between sunflower oil and extra‐virgin olive oil was found using this approach. Therefore, sunflower oil is a potential candidate for adulteration in most commercially available olive oils. Beside the classification of the different vegetable oils, we have successfully applied Raman spectroscopy in combination with partial least‐squares (PLS) regression analysis for very fast monitoring of adulteration of extra‐virgin olive oil with sunflower oil. Different mixtures of extra‐virgin olive oil with three different sunflower oil types were prepared between 5 and 100% (v/v) in 5% increments of sunflower oil. While in the present context the adulteration usually refers to the addition of reasonable amounts of the adulterant (given the similarity with the basic product), we show that the technique proposed can also be used for trace analysis of the adulterant. Without using techniques like surface‐enhanced Raman scattering (SERS), a quantitative detection limit down to 500 ppm (0.05%) could be achieved, a limit irrelevant for adulteration in commercial terms but significant for trace analysis. The qualitative detection limit even was at considerably lower concentration values. Based on PCA, a clear discrimination between pure extra‐virgin olive oil and olive oil adulterated with sunflower oil was achieved. The adulterant content was successfully determined using PLS regression with a high correlation coefficient and small root mean‐square error for both prediction and validation. Copyright © 2009 John Wiley & Sons, Ltd.     

Application of IR Spectroscopy for the Estimation of the Relative Content of Unsaturated Fats in Vegetable Oils

Journal of Surface Investigation: X-ray, Synchrotron and Neutron Techniques - The IR spectra of five samples of sunflower seed oils and five samples of cold-pressed olive oil of various brands are...     

Adulteration of sesame oil with corn oil detected by use of two-dimensional infrared correlation spectroscopy and multivariate calibration

An innovative methodology was developed to detect adulteration of sesame oil with corn oil based on two-dimensional mid-infrared correlation spectroscopy with multivariate calibration. Forty pure sesame oils and 40 adulterated sesame oils with corn oil were prepared and the infrared absorption spectra were measured at room temperature, respectively. The synchronous two-dimensional mid-infrared correlation spectra were calculated to develop multivariate calibration models for adulteration of sesame oil with corn oil. The results showed the higher classification accuracy of 96.3% for the prediction set using two-dimensional mid-infrared correlation spectra and N-way partial least square discriminant analysis, versus 88.9% using traditional one-dimensional mid-infrared spectra and partial least squares discriminant analysis. Also, the multivariate calibration models were developed for quantitative analysis of sesame oil adulteration with corn oil. The root mean square error of prediction was 0.98% v/v using two-dimensional mid-infrared correlation spectra and N-PLS, and 1.15% v/v using traditional one-dimensional mid-infrared spectra and PLS. The results of our analyses indicated that the proposed method could provide better predictive results than traditional one-dimensional mid-infrared spectra and multivariate calibration.     

荧光光谱法快速鉴别花生油、芝麻油和调和油

比较分析了不同品种、同品种不同厂家、不同批次的市售花生油、芝麻油、调和油的分子荧光光谱差异特征,结合系统聚类分析法进行品种鉴别。结果表明:3种食用植物油的荧光光谱图具有各自不同特征,同一品种不同厂家的谱图存在一定的差异,同品种同厂家不同批次的也有微小差异。提取荧光谱图特征信息,利用系统聚类和三维聚类识别模式,从宏观上简便、直观、快速地鉴别3种食用植物油的品种。     

基于荧光光谱及矩阵分析的植物油鉴别技术

利用FS920荧光光谱仪测量42个油样(包括36个纯植物油样,3个调和油样和3个混合油样)的荧光光谱,并对其数据矩阵(EEMs)进行归一化处理,确定了植物油特征激发波长及矩阵分析模型。综合分析植物油在特定范围内(激发波长为250～550 nm,发射波长为260～750 nm)的等高线光谱图和特征发射谱线图,将植物油划分为三类;将矩阵分析模型应用于纯植物油鉴别,分类正确率100%;为验证矩阵分析的定量判别能力,对三种混合油样进行分析,得到接近实际配比的分析结果;对市售三种调和油样本进行分析,得出调和油以大豆和菜籽油为基底的结论。通过对植物油荧光光谱的图谱特征和其矩阵模型的分析,证实荧光光谱技术和矩阵分析法对植物油进行分析和种类鉴别的有效性。     

薏仁种类的近红外光谱技术快速鉴别

薏仁是一种药食两用资源,对其品质快速鉴别的需求也越来越多,近红外光谱技术(near infrared spectroscopy,NIRS)作为一种快速、 无损且环保的方法正适合这一需求。 以不同产地和品种薏仁的近红外光谱为基础,结合化学计量学方法对薏仁种类进行鉴别。 对原光谱用无监督学习算法主成分分析(principal component analysis,PCA)和有监督学习算法学习向量量化(learning vector quantization,LVQ)神经网络、 支持向量机(support vector machine,SVM)进行定性判别分析。 由于不同地区和不同品种的薏仁营养物质组成复杂且含量相近,所选两类薏仁的特征变量很相似,因而PCA得分图重叠严重,很难区分;而LVQ神经网络和SVM都能得到满意结果,LVQ神经网络的预测正确率为90.91%,SVM在经过惩罚参数和核函数参数优选后,分类准确率能达到100%。 结果表明：近红外光谱技术结合化学计量学方法可作为一种快速、 无损、 可靠的方法用于薏仁种类的鉴别,并为市场规范提供技术参考。