基于植物油可见吸收光谱的相关系数鉴别特级初榨橄榄油

目前市场上的橄榄油品牌很多,质量参差不齐,亟需完善橄榄油的等级分类检测和特级初榨橄榄油的鉴别方法。可见吸收光谱光谱法可在不直接接触样品的情况下对样品进行无添加试剂的探测,因此为实现特级初榨橄榄油的鉴别,采用可见吸收光谱法对不同种类植物油进行了光谱测量。实验结果发现特级初榨橄榄油在500~780 nm波段内具有4个明显的吸收峰,而其他种类植物油在此波段内吸光度较弱或无吸收峰,且同种植物油不同品牌之间的光谱特征极其相似。采用相关系数比对不同种类植物油可见吸收光谱,分别计算了四个不同波长范围内植物油的可见吸收光谱的相关系数,实验发现不同波长范围内的植物油可见光谱相关系数差别较大。在520～700 nm范围内,特级初榨橄榄油间的光谱相关系数在0.999 6以上,特级初榨橄榄油与其他种类植物油的光谱相关系数均低于0.267 8,特级初榨橄榄油与其他等级橄榄油的光谱相关系数在0.194 6～0.835 8之间。研究结果表明可见吸收光谱相关系数法是一种快速非接触式鉴别特级初榨橄榄油的可行性方法。建立了一种特级初榨橄榄油快速鉴别方法,即可见吸收光谱相关系数法。该方法在特级初榨橄榄油的实际鉴别中具有一定的应用价值。     

基于拉曼-紫外可见融合光谱技术的进口橄榄油质量等级可视化快速鉴别方法研究

橄榄油因其高营养等特点,成为植物油中日常消费量逐渐增大的主要品类。橄榄油按照加工工艺分为初榨、精炼和混合等不同质量等级。由于不同等级橄榄油价格差异较大,导致橄榄油市场存在以次充好等问题。同时,涉及等级鉴定的指标繁杂,对应的理化检测方法大部分涉及大型实验室设备,检测成本高、效率低且工作量繁重。我国是橄榄油的主要进口国,采用产品标准中逐项指标确认后判定的模式,无法满足目前急速增长的进口产品快速通关要求。该研究聚焦进口橄榄油在口岸监管现场的快速质量评价需求,开发了多光谱信息同时采集和降维融合成像的方法,将紫外-可见光谱与拉曼光谱进行特征数据融合,构建拉曼-紫外可见2D谱图,通过二维成像进行指纹特征判断,构建特级初榨橄榄油、精炼橄榄油以及果渣油的标准2D融合成像源图,作为等级区分标准对照二维谱,进行橄榄油等级可视化判定;结合空间角度值转化算法对橄榄油进行等级定性评判,通过角度值计算得到特级初榨橄榄油与精炼橄榄油的夹角范围在0.794 7～1.094 7之间,与油橄榄果渣油其值在1.157 0～1.319 8之间,而特级初榨橄榄油之间角度值均小于0.1,由此可进行不同橄榄油的等级判定;采用角度决...     

基于近红外光谱的橄榄油品质鉴别方法研究

目前市面上销售的橄榄油主要分为特级初榨橄榄油和普通初榨橄榄油两类,为了鉴别两种不同品质的橄榄油,提出了一种应用siPLS-IRIV-PCA算法的橄榄油品质鉴别的新方法。基于橄榄油的近红外光谱数据,应用联合区间偏最小二乘法(siPLS)对橄榄油的近红外光谱进行了波长区间优选,使用交叉验证均方根误差(RMSECV)评估模型的性能并选择最优波长区间,通过迭代保留信息变量(IRIV)算法从最优波长区间中选择特征波长,根据选择的特征波长构建主成分分析(PCA)模型。对90组特级初榨橄榄油和90组普通橄榄油样本进行了判别鉴定。PCA将1 427个波长变量作为输入变量,前两个主成分贡献率为51.891 8%和26.473 2%;siPLS-PCA将408个波长变量作为输入变量,前两个主成分贡献率为56.039 1%和36.235 5%;siPLS-IRIV-PCA将6个波长变量作为输入变量,前两个主成分贡献率为66.347 6%和32.304 3%。结果表明,与PCA和siPLS-PCA鉴别方法相比,siPLS-IRIV-PCA具有最佳的鉴别性能。     

近红外光谱-BP神经网络-PLS法用于橄榄油掺杂分析

橄榄油兼有食用和保健的作用,价值与价格远远高于其他食用油,所以橄榄油中以劣充好的现象十分普遍。可采用近红外光谱法测定初榨橄榄油中掺杂芝麻油、大豆油和葵花籽油的光谱数据,运用改进的BP算法——Levenberg-Marquardt方法,建立PCA-BP人工神经网络方法对其进行定性判别。同时采用偏最小二乘法(PLS)建立了初榨橄榄油中芝麻油、大豆油、葵花籽油含量的近红外光谱定标模型,用交互验证法进行验证。结果表明,BP人工神经网络有很好的定性鉴别能力,PLS建立的芝麻油、大豆油、葵花籽油定标模型的相关系数分别为98.77,99.37,99.44,交叉验证的均方根误差分别为1.3,1.1,1.04。该方法无损、快速、简便,为橄榄油掺杂的检测提供了一种新的途径。     

激光诱导荧光初榨橄榄油掺杂定量分析

利用激光诱导荧光技术开展了初榨橄榄油掺杂定量分析的研究。利用波长为450nm的激光激发不同掺杂浓度的掺杂橄榄油样品产生荧光并进行荧光光谱采集。将采集到的光谱利用线性判别法(linear discriminant analysis,LDA)结合k-近邻方法(k-Nearest Neighbor,kNN)建立掺杂橄榄油掺杂浓度预测的模型。通过交叉验证,该模型预测的橄榄油掺杂浓度的均方根误差为3.74%。按照掺杂浓度的不同将样品分为4组进行分类识别,分类正确率达88%。结果表明,利用激光诱导荧光原理结合LDA-kNN能够实现掺杂橄榄油掺杂浓度的定量分析,该方法可以用于掺杂橄榄油快速初筛。     

基于高光谱技术检测全蛋粉掺假的研究

传统食品掺假分析多集中于检测特定已知或者怀疑可能存在的掺假物，然而由于掺假形式的多样性以及新的掺假物不断出现，使得传统检测方法具有局限性。目前，全蛋粉作为鲜蛋理想替代品掺假现象十分严重，然而不管是国内还是国外，其掺假检测都鲜有研究。因此，为了探索一种快速检测全蛋粉掺假的方法，研究尝试使用最近快速发展起来的具有绿色、无损等优点的高光谱技术来检测全蛋粉掺假的可行性。从不同地区收集不同品牌的鸡蛋全蛋粉，按不同比例分别掺入淀粉、大豆分离蛋白、麦芽糊精以及三种掺假物的混合物进行试验样品的制备。样品进行光谱采集后，采用ENVI软件选取感兴趣区域(ROI)后提取出平均光谱。根据获得的光谱数据建立全波段下支持向量机(SVM)模型进行掺假的判别并采用偏最小二乘回归(PLSR)模型建立全波段与掺假浓度之间的关系。结果显示，采用径向基核函数所建立的SVM模型，其分类的正确率达到90%以上，基于PLSR建立掺假模型实际值与预测值相关系数R2_P均高于0.90。为了简化模型，采用回归系数法(RC)及连续投影法(SPA)提取特征波长，根据特征波长下的光谱数据建立RC-PLSR和SPA-PLSR模型，结果显示，经简化的模型依然具有良好的性能，说明使用高光谱技术来检测全蛋粉掺假是可行且高效的。     

近红外光谱法定性定量检测蜂蜜中掺入甜菜糖浆的可行性研究

为进一步检验近红外光谱技术(NIRS)快速检测蜂蜜掺假的能力,利用近红外光谱结合化学计量学方法对蜂蜜中掺入甜菜糖浆进行了定性和定量检测。偏最小二乘-判别分析法(PLS-DA)对真假蜂蜜预测集的判别总正确率为90.2%;不同判别方法对掺假量等级预测集的判别总正确率都低于33.3%;PLS回归只对同一蜂蜜样本掺假的定量分析结果满意：预测集真实值与预测值的相关系数(r)为0.982 9,预测均方差(RMSEP)为1.394 2,而对不同植物来源和同一植物来源的不同样本的掺假量的定量分析结果不满意。研究表明,蜂蜜中掺入甜菜糖浆后,NIRS可实现真假蜂蜜的快速鉴别,而不能实现掺假量等级的鉴别及掺假量的定量分析。     

近红外光谱和化学计量学方法用于橄榄油品质分析与掺杂量检测

基于橄榄油的近红外光谱数据,用判别分析(Discriminant analysis)方法把20个样品成功地分为特级初榨橄榄油和普通橄榄油两类,正确率为100％。同时测定了纯橄榄油中分别掺入菜籽油、玉米油、花生油、山茶油、葵花籽油、罂粟油的混合油的近红外光谱,掺杂油体积百分数范围为0～100%。选择最佳的光谱波段组合用偏最小二乘(PLS)法分别建立定量分析模型,预测相对误差范围在-5.67%～5.61%之间。研究结果表明,基于化学计量学方法和近红外光谱数据可为橄榄油的品质鉴定和掺杂量检测提供了一种简便、快捷、准确的方法。     

基于便携式拉曼光谱的进口散装橄榄油品质现场快速无损鉴别方法

随着食品全球产业链的整合和大众生活水平提高,进口植物油在日常饮食中占比逐步增加,具有丰富营养价值的橄榄油在植物油产品中备受关注。在进口散装橄榄油的跨境运输和通关过程中,由于环境、温度和时间等因素的影响,分仓储运的橄榄油中不饱和脂肪酸可能发生氧化,以及初榨橄榄油中果肉碎渣沉淀在多次换仓时进行累积,导致橄榄油不同分仓和同一仓位不同位置的植物油品质出现较大差异,给橄榄油口岸现场的抽样监管和质量评价带来较大困扰。针对散装橄榄油现场快速品质评价的需求,在偏最小二乘法的基础上,将拉曼响应强度转换为向量空间角度值,建立橄榄油品质指标分析预测模型,针对不同抽样点样本进行橄榄油品质的快速现场预判,确保散装橄榄油在进出口环节的精准监管。首先采用传统方法分别测定经过220,240和260 ℃温度下,加热不同时长的橄榄油的酸价、过氧化值和亚麻酸的实测值,同时采用便携式拉曼光谱仪检测对应油样的拉曼光谱,通过平滑滤波求导等手段对光谱数据进行预处理,采用偏最小二乘法及角度度量法,对橄榄油的酸价、过氧化值、亚麻酸三种指标进行建模分析,两种方法建立的指标模型相关系数均达到0.99以上,其中角度度量法的相对误差范围不超过-5.43%。在进口散装橄榄油中随机抽取七个不同的样品进行验证,角度度量法建立的三种模型预测结果均方根误差分别为0.025 8,0.222 8和17.064 1,相对误差范围在-4.71%~5.98%之间,结果显示角度度量法建立的模型更准确,具有更好的预测性及稳定性。该方法可应用于进口散装橄榄油品质的现场快速品质鉴别,提升口岸现场监管环节质量评价的精准性,为进出口散装橄榄油质量综合评价提供技术保障。     

基于最小二乘支持向量机的橄榄油掺杂拉曼快速鉴别方法

提出了一种基于最小二乘支持向量机(LS-SVM)的橄榄油掺杂拉曼快速鉴别方法。首先,收集若干己知类别的橄榄油样作为训练样本,获取其拉曼谱图,并对其谱图进行预处理和波段选择,进而构建LSSVM分类器;对于未知类别的油样,获取其拉曼谱图,并进行相应的预处理和波段选择,由LSSVM分类器获得鉴别结果。实验以7种已知的特级初榨橄榄油为基础,分别掺入4种其它植物油(大豆油、菜籽油、玉米油、葵花籽油),获得112个掺杂油样。将全部样本随机分成训练集和测试集,对测试集样本的预测实验结果表明,本文方法能有效鉴别橄榄油掺杂,且掺杂量最低检测限为5%。与其它分类方法相比,LSSVM分类法具有最佳的分类性能。该方法快速、简便,为橄榄油掺杂鉴别提供了一种全新的方法。     

橄榄油的聚集荧光猝灭及吸收光谱特性的研究

素有“液体黄金”之称的橄榄油已成为健康食用油的代名词,不仅身价陡增,而且在非产地市场也已成为一种畅销油。在橄榄油检测技术中光谱法与其他技术相比具有快速、无损、无样品处理等优势而备受关注,而不同的光谱检测方法在检测的物质成分上各有侧重,例如红外光谱法侧重于脂肪酸含量的检测、拉曼光谱法侧重于分子的检测、荧光光谱法侧重于光敏物质的检测以及吸收光谱法侧重于光敏物质和不饱和脂肪酸的检测等。荧光及吸收光谱对光敏物质反应极其灵敏,而橄榄油富含叶绿素等光敏物质,因此荧光及吸收光谱成为一种鉴别橄榄油的有效技术手段。叶绿素是一种含有环卟啉结构的有机分子,该类分子结构具有吸光特性,且不同的叶绿素吸收光谱各异,其中绿色植物的叶绿素a含量最多。为深入研究叶绿素的吸收光谱及荧光特性在橄榄油鉴别中的应用,将特级初榨橄榄油中掺入不同比例的玉米油,已达到间接调控橄榄油中叶绿素含量的目的,测量不同掺伪比例橄榄油的荧光及吸收光谱并研究与叶绿素浓度的相关性,以此来研究叶绿素浓度与掺伪量对橄榄油吸收光谱及荧光特性的影响。取10份同批次的特级初榨橄榄油,将其中9份按照等比例稀释,并对10份样品按照掺伪量依次排序;依次采集这10份样品的荧光及吸收光谱,比较叶绿素浓度与掺伪量的相关性及对这两种光谱技术在橄榄油鉴别中的影响。随着叶绿素浓度的上升,荧光强度由弱变强,并在某一时刻后会出现荧光强度急剧减弱的现象,即聚集荧光猝灭。这种现象主要是由于叶绿素的环卟啉分子结构引起的分子间π-π作用,使未被激发的低能分子与高能分子堆叠在一起,能量的辐射跃迁（荧光）也转变为分子间的能量转移（热能交换）。对于吸收光谱,随着叶绿素浓度的上升,吸收光谱的强度也逐渐增强。橄榄油中叶绿素吸收的能量主要去向包括镁电子辐射跃迁产生荧光以及分子间热能交换两部分,而橄榄油的吸收光谱并未出现类似于聚集荧光猝灭的现象,且吸收光谱强度与掺伪浓度间存在近似线性相关的关系。结果表明：当聚集荧光猝灭出现时,叶绿素吸收的能量仍然与浓度呈线性相关,此时高、低能分子堆叠引起的热能交换效率提高。     

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.     

Use of Raman spectroscopy for analyzing edible vegetable oils

Vegetable oils provide high nutritional value in the human diet. Specifically, extra virgin olive oil is one of the main ingredients of the Mediterranean diet, which is among the healthiest of eating practices. This article reviews the use of Raman spectroscopy for analyzing edible vegetable oils including olive oil. Although the spectra for edible vegetable oils are similar, they exhibit some differences which, however small, enable their discrimination. Thus, Raman spectra allow one to determine the degree of unsaturation of oils. This property is correlated with the iodine value but much faster and simpler to obtain. The degree of unsaturation can be used to classify and authenticate oils, which is especially useful with high-quality oils. In fact, adulteration with mixtures of more inexpensive oils can be easily detected by Raman spectroscopy. This technique additionally allows some minor components present in unsaponifiable matter to be identified. Fats in general and vegetable oils in particular, are prone to oxidation. Thus, double bonds in them are oxidized to form triglycerides. Vegetable oils are widely used for frying and Raman spectroscopy allows for their oxidative stability against heating at the usual frying temperatures to be assessed.     

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.     

Analysis of Argan Oil Adulteration Using Infrared Spectroscopy

The determination of argan oil adulteration by other vegetable oils is a real analytical challenge. The authentication of argan oil needs fast and simple analytical techniques for quality control and testing. This study focuses on the detection and quantification of argan oil adulteration with different edible oils, using midinfrared spectroscopy with chemometrics. Chemometric treatment of MIR spectra has been assessed for the classification and quantification of argan oil adulteration with sunflower or soybean oils. The potential of MID spectroscopy combined with partial least squares regression (PLS) as a rapid analytical technique for the quantitative determination of adulterants in argan oil has been demonstrated. A PLS model has been established to predict the concentration of soybean and sunflower oil as adulterants in the calibration range between 0% and 30% (w/w) in argan oil with good prediction performances in the external validation.