Authentication of Extra Virgin Olive Oil from Sesame Oil Using FTIR Spectroscopy and Gas Chromatography

The presence of sesame oil in extra virgin olive oil has been investigated using Fourier transform infrared spectroscopy and gas chromatography. Frequencies of 1207–1018, 1517–1222, and 3050–2927 cm^?1 were chosen for quantification of sesame oil in extra virgin olive oil. Using Fourier transform infrared normal spectra coupled with a partial least square model, the root mean standard error of calibration and root mean standard error of prediction obtained were relatively low, i.e., 0.331 and 1.01% (vol/vol), respectively. Using fatty acid profiles as determined by gas chromatography, the levels of palmitic and oleic acids were decreased linearly with R² of 0.969 and 0.934, meanwhile the levels of stearic and linoleic acids were increased with R² of 0.930 and 0.959, respectively, with the increasing levels of sesame oil. From level 10% sesame oil (vol/vol), all these fatty acids are significantly different (p < 0.05).     

红外光谱结合偏最小二乘法快速检测油茶籽油脂肪酸组成的模型建立与评价北大核心CSCD

为建立快速检测油茶籽油中脂肪酸组成的方法,利用傅里叶红外光谱仪扫描86个油茶籽油样品,红外光谱数据分别通过Savitzky-Golay平滑(SG)、多元散射校正(MSC)、标准正态变换(SNV)、一阶导数(FD)和二阶导数(SD)等5种方法进行降噪处理,然后以气相色谱测定的脂肪酸组成作为标准值,采用全波长偏最小二乘法(PLS)、区间偏最小二乘法(iPLS)和联合区间偏最小二乘法(siPLS)分别构建油茶籽油中主要脂肪酸(油酸、棕榈酸、亚油酸)的定量回归模型。结果表明:油酸、棕榈酸、亚油酸的红外光谱数据预处理分别以SG、SNV、SD较好;siPLS通过有效波段的选择可去掉更多的噪声,比PLS和iPLS建立的模型精确度高,油酸、棕榈酸、亚油酸的校正集和预测集的相关系数(R)分别为0.9479和0.8539、0.9008和0.9101、0.9793和0.9505。红外光谱结合siPLS更适用于油茶籽油脂肪酸组成的快速测定。     

Use of Fourier Transform Infrared Spectroscopy in Combination with Partial Least Square for Authentication of Black Seed Oil

Fourier transform infrared spectroscopy in combination with multivariate calibration of partial least square is intended for quantitative analysis of black seed oil in binary mixture with sunflower oil and walnut oil, as well as in ternary mixture with sunflower oil and walnut oil. The spectra of black seed oil, sunflower oil, walnut oil, and their mixture with certain concentration were scanned using attenuated total reflectance at mid infrared region of 4000–650 cm^?1. For quantitatve analysis, Fourier transform infrared spectral treatment (normal or derivatives) with the highest values of coefficient of determination (R²) and the lowest values of root mean square error of calibration was selected as optimal calibration model. Partial least square at whole mid infrared region of 4000–650 cm^?1 is well suited for quantitative analysis of black seed oil either in binary mixture or ternary mixture with walnut oil and sunflower oil. Furthermore, using absorbancies at frequency region of 3009–721 cm^?1, principal component analysis is succesfully used for classification of black seed oil and that mixed with sunflower oil and walnut oil. The developed method is rapid, no sample preparation needed, and is not involving the use of chemical reagents and solvents.     

Use of Fourier Transform Infrared (FTIR) Spectroscopy and Chemometrics for Analysis of Lard Adulteration in “Rambak” Crackers

“Rambak” crackers are one of the traditional foods consumed among Indonesian people made from various kinds of animal skin. The present study highlights the analysis of lard obtained from extraction of “rambak” crackers using Fourier transform infrared (FTIR) spectroscopy in combination with chemometrics of partial least square and principle component analysis. FTIR spectroscopy at wavenumber regions of 1200–1000 cm^–1 was successfully used for quantification and classification of lard in “rambak” crackers. The relationship between actual value of lard and Fourier transform infrared predicted value has R² value of 0.946 with low errors in calibration and validation models. Furthermore, the chemometrics principle component analysis can be successfully used for determination of pig skin through analysis of lard in commercial “rambak” crackers. The developed method (FTIR spectroscopy coupled with chemometrics) is rapid and reliable for quantification and classification of lard in “rambak” crackers.     

Fourier Transform Infrared Spectroscopy Combined with Multivariate Calibrations for the Authentication of Avocado Oil

Avocado oil is one of the functional oils having high quality and high price in the market. This oil shows many benefits for the human health and is applied in many cosmetic products. The authentication of avocado oil becomes very important due to the possible adulteration of avocado oil with other lower priced oils, such as palm oil and canola oil. In this study, Fourier transform infrared spectroscopy using attenuated total reflectance in combination with chemometrics techniques of partial least squares and principal component regression is implemented to construct the quantification and classification models of palm oil and canola oil in avocado oil. Partial least squares at the wavenumbers region of 1260–900 cm^–1 revealed the best calibration models, having the highest coefficient of determination (R² = 0.999) and the lowest root mean square error of calibration, 0.80%, and comparatively low root mean square error of prediction, 0.79%, for analysis of avocado oil in the mixture with palm oil. Meanwhile, the highest R², root mean square error of calibration, and root mean square error of prediction values obtained for avocado oil in the mixture with canola oil at frequency region of 3025–2850 and 1260–900 cm^–1 were 0.9995, 0.83, and 0.64%, respectively.     

Rapid detection and quantification of soya bean oil and common sugar in bovine milk using attenuated total reflectance–fourier transform infrared spectroscopy

Attenuated total reflectance–Fourier transform infrared spectroscopy, along with chemometrics, were used to detect and quantify soya bean oil (SO) and sugar (CS) adulteration in milk. Bovine milk was artificially adulterated with SO (0.2–2.0%; v/v) and CS (1–10%; w/v) separately. Spectra revealed significant differences in specific wavenumber regions (SO: 1450–1250 cm^?1; CS: 1200–900 cm^?1). Soya bean oil adulteration was best predicted in wavenumber range of 1262–1164 cm^?1, using partial least square regression (coefficient of determination (R²: 0.90 and 0.88 for calibration and validation, respectively). Common sugar adulteration was best predicted in wavenumber range of 1010–910 cm^?1 (R²: 0.99 for calibration and validation) using partial least square.     

FTIR技术结合区间偏最小二乘法快速测定油脂中反式脂肪酸

研究了傅里叶红外光谱技术结合区间偏最小二乘法(iPLS)快速分析食用油中低含量(0.1%~5%)反式脂肪酸的分析方法。通过系统地比较衰减全反射红外光谱法(ATR-FTIF)及衰减透射红外光谱法(TR-FTIR)光谱的模型效果,优化建模区间。研究结果表明,ATR-FTIR、TR-FTIR-PLS回归模型均能有效测定油脂中低浓度反式脂肪酸的含量,但TR-FTIR法灵敏度优于ATR-FTIR法。iPLS区间选择结果显示,以1 000~940 cm~(-1)波段透射光谱建模,相关系数R~2为0.998 8,标准集的RMSEC 0.016 6,验证集RMSEP为0.008 75,预测相对标准偏差2.92%,预测值与实际值高度相关,Y_(预测)=1.00X_(实际)-0.003 44,R~2=0.998 7。12组外部验证试验相对标准偏差为4.80%,说明预测精确度较高、模型稳定性好,有潜力替代传统气相色谱法用于油脂中低含量反式脂肪酸快速定量测定。     

Fourier transform infrared (FTIR) spectroscopy for analysis of extra virgin olive oil adulterated with palm oil

Fourier transform infrared (FTIR) spectroscopy has been developed for analysis of extra virgin olive oil (EVOO) adulterated with palm oil (PO). Measurements were made on pure EVOO and that adulterated with varying concentrations of PO (1.0–50.0% wt./wt. in EVOO). Two multivariate calibrations, namely partial least square (PLS) and principle component regression (PCR) were optimized for constructing the calibration models, either for normal spectra or its first and second derivatives. The discriminant analysis (DA) was used for classification analysis between EVOO and that adulterated with PO and the other vegetable oils (palm oil, corn oil, canola oil, and sunflower oil). Frequencies at fingerprint region, especially at 1500–1000 cm^?1, were exploited for both quantification and classification. Either PLS or PCR at first derivative spectra revealed the best calibration models for predicting the concentration of adulterated EVOO samples, with coefficient of determination (R²) of 0.999 and root mean standard error of cross validation (RMSECV) of 0.285 and 0.373, respectively. DA was able to classify pure and adulterated samples on the basis of their FTIR spectra with no misclassified group obtained. In addition, DA was also effective enough to classify EVOO samples as the distinct group from the evaluated other vegetable oils.     

A Fourier Transform Infrared Spectroscopy Method for Analysis of Palm Oil Adulterated with Lard in Pre-Fried French fries

Fourier transform infrared spectroscopy with attenuated total reflectance accessory was used to detect the presence of lard in French fries pre-fried in palm oil adulterated with lard. A Fourier transform infrared calibration model was obtained using partial least squares for prediction of lard in a blend mixture of lard and palm oil. The coefficient of determination (R²) of 0.9791 was obtained with 0.5% of detection limit. The error in calibration expressed with root mean square error of calibration was 0.979%. In addition, the error obtained during cross validation was 2.45%. A discriminant analysis test was able to distinguish between fries samples adulterated with lard and samples, which were pre-fried with palm oils. Fourier transform infrared spectroscopy is a fast and powerful technique for quantification of lard present in French fries.      

Rapid Discrimination and Determination of Polyunsaturated Fatty Acid Composition in Marine Oils by FTIR Spectroscopy and Multivariate Data Analysis

A rapid analytical approach for discrimination and quantitative determination of polyunsaturated fatty acid (PUFA) contents, particularly eicosapentaenoic acid (EPA) and docosahexaenoic acid (DHA), in a range of oils extracted from marine resources has been developed by using attenuated total reflection Fourier transform infrared spectroscopy and multivariate data analysis. The spectral data were collected without any sample preparation; thus, no chemical preparation was involved, but data were rather processed directly using the developed spectral analysis platform, making it fast, very cost effective, and suitable for routine use in various biotechnological and food research and related industries. Unsupervised pattern recognition techniques, including principal component analysis and unsupervised hierarchical cluster analysis, discriminated the marine oils into groups by correlating similarities and differences in their fatty acid (FA) compositions that corresponded well to the FA profiles obtained from traditional lipid analysis based on gas chromatography (GC). Furthermore, quantitative determination of unsaturated fatty acids, PUFAs, EPA and DHA, by partial least square regression analysis through which calibration models were optimized specifically for each targeted FA, was performed in both known marine oils and totally independent unknown n???3 oil samples obtained from an actual commercial product in order to provide prospective testing of the developed models towards actual applications. The resultant predicted FAs were achieved at a good accuracy compared to their reference GC values as evidenced through (1) low root mean square error of prediction, (2) good coefficient of determination close to 1 (i.e., R ²≥ 0.96), and (3) the residual predictive deviation values that indicated the predictive power at good and higher levels for all the target FAs.     

PRELIMINARY EVALUATION OF THE APPLICATION OF THE FTIR SPECTROSCOPY TO CONTROL THE GEOGRAPHIC ORIGIN AND QUALITY OF VIRGIN OLIVE OILS

ABSTRACT

A rapid Fourier transform infrared (FTIR) attenuated total reflectance spectroscopic method was applied to determine qualitative parameters such as free fatty acid (FFA) content and the peroxide value (POV) in virgin olive oils. Calibration models were constructed using partial least squares regression on a large number of virgin olive oil samples. The best results (R² = 0.955, root mean square error in cross validation [RMSECV] = 0.15) to evaluate FFA content expressed in oleic acid % (w/w) were obtained considering a calibration range from 0.2 to 9.2% of FFA relative to 190 samples. For POV determination, the result obtained, built on 80 olive oil samples with a calibration range from 11.1 to 49.7 meq O₂/kg of oil, was not satisfactory (R² = 0.855, RMSECV = 3.96). We also investigated the capability of FTIR spectroscopy, in combination with multivariate analysis, to distinguish virgin olive oils based on geographic origin. The spectra of 84 monovarietal virgin olive oil samples from eight Italian regions were collected and elaborated by principal component analysis (PCA), considering the fingerprint region. The results were satisfactory and could successfully discriminate the majority of samples coming from the Emilia Romagna, Sardinian and Sicilian regions. Moreover, the explained variance from this PCA was higher than 96%.

PRACTICAL APPLICATIONS

The verification of the declared origin or the determination of the origin of an unidentified virgin olive oil is a challenging problem. In this work, we have studied the applicability of Fourier transform infrared coupled with multivariate statistical analysis to discriminate the geographic origin of virgin olive oil samples from different Italian regions.

     

最小二乘支持向量机和脂肪酸融合信息应用于花生油掺伪玉米油检测

将最小二乘支持向量机用于气相色谱分析实现对花生油掺伪玉米油的鉴别,基于油脂的全样和Sn-2位脂肪酸组成的不同,采用主成分分析消除融合数据中信息重叠的部分,利用粒子群优化最小二乘支持向量机的参数,对花生油的掺伪进行鉴别,识别率为100%;分别采用最小二乘支持向量机、偏最小二乘法和主成分回归对花生油中掺入玉米油含量进行预测,结果表明基于脂肪酸融合信息的最小二乘支持向量机的预测均方根误差和相关系数R2分别为3.452 1%和0.986 6,与偏最小二乘法和主成分回归法相比,最小二乘支持向量机具有更好的稳定性和预测精度,同时也为食用油的真伪鉴别及掺伪情况确定提供一种新方法。     

FT-NIR spectroscopy coupled with multivariate analysis for detection of starch adulteration in turmeric powder

This paper investigates the feasibility of Fourier transform near-infrared (FT-NIR) spectroscopy, a fast and easy method based on chemometric methods to detect corn starch illegally added to turmeric powder. In this work, the pure turmeric powders were blended with corn starch to generate different concentrations (1–30%)(w/w) of starch-adulterated turmeric samples. The reflectance spectra of total of 224 samples were taken by FT-NIR spectroscopy. The exploratory data analysis was done by principal component analysis (PCA). The starch related peaks were selected by variable importance in projection (VIP) method and were explored by examination of original reflectance spectra, 1^st derivative spectra, PCA loadings and β coefficients plot of the partial least square regression (PLSR) model. The coefficient of determination (R²) and root-mean-square error of partial least square regression (PLSR) models were found to be 0.91–0.99 and 0.23–1.3%, respectively, depending on the pre-processing techniques of spectral data. The figure of merit (FOM) of the model was found with the help of net analyte signal (NAS) theory.     

Quantitative determination of fatty acid compositions in micro-encapsulated fish-oil supplements using Fourier transform infrared (FTIR) spectroscopy

The research describes a rapid method for the determination of fatty acid (FA) contents in a micro-encapsulated fish-oil (μEFO) supplement by using attenuated total reflection Fourier transform infrared (ATR-FTIR) spectroscopic technique and partial least square regression (PLSR) analysis. Using the ATR-FTIR technique, the μEFO powder samples can be directly analysed without any pre-treatment required, and our developed PLSR strategic approach based on the acquired spectral data led to production of a good linear calibration with R(2)=0.99. In addition, the subsequent predictions acquired from an independent validation set for the target FA compositions (i.e., total oil, total omega-3 fatty acids, EPA and DHA) were highly accurate when compared to the actual values obtained from standard GC-based technique, with plots between predicted versus actual values resulting in excellent linear fitting (R(2)≥0.96) in all cases. The study therefore demonstrated not only the substantial advantage of the ATR-FTIR technique in terms of rapidness and cost effectiveness, but also its potential application as a rapid, potentially automated, online monitoring technique for the routine analysis of FA composition in industrial processes when used together with the multivariate data analysis modelling.     

棉籽油脂肪酸组成分析与评价

以收集于我国棉花主产区的82 份棉籽为实验材料,用全自动索氏浸提装置提取棉籽油,采用气相色谱-质谱联用仪和气相色谱仪对棉籽油脂肪酸组成进行定性和定量分析,探讨不同产地棉籽油脂肪酸组成的差异,并用主成分分析法对棉籽油的特征脂肪酸进行筛选。结果表明,棉籽仁出油率在18.84%～30.28%之间,平均出油率为24.95%。棉籽油中含有13 种脂肪酸,主要以亚油酸（51.99%～60.88%）、棕榈酸（18.30%～25.68%）和油酸（12.28%～18.50%）为主,其中不饱和脂肪酸占73.62%,多不饱和脂肪酸为亚油酸、亚麻酸占57.44%。除十七烷酸外,不同产地的棉籽油脂肪酸含量差异显著,并呈现明显的地域性。豆蔻酸、棕榈酸、棕榈油酸、硬脂酸、油酸、锦葵酸、亚油酸、苹婆酸、二氢苹婆酸和花生酸是棉籽油的特征脂肪酸。     

Determination of Total Sugar Content in Soy-Based Drinks Using Infrared Spectroscopy and Chemometrics

Partial least square (PLS) regression models were developed and compared in order to determine the total sugar content in soy-based drinks using an infrared spectroscopy technique known as attenuated total reflectance Fourier transform infrared (ATR-FTIR). On a spectrophotometer set for analyzing on the middle infrared region, spectral band of 1900 to 900 cm^?1, commercial samples of soy beverage were analyzed, as well as samples with crescent water additions of 5, 10, and 20% v/v. Reference data for total sugars were obtained using the Lane-Eynon method. To construct regression models, algorithms of interval partial least square (iPLS) and synergy of interval partial least square (siPLS) were applied using iToolbox package on Matlab 8.1 environment. Kennard-Stone algorithm was used to the selection of calibration and prediction sets. Two models have been the best obtained: the first was an iPLS with seven latent variables, which selected the spectral band of 1399–900 cm^?1 and presented root mean square error of cross-validation (RMSECV)?=?0.1678% (w/w). The second best model was siPLS with six latent variables, which selected spectral bands of 1025–1150 and 1151–1476 cm^?1 and presented RMSECV?=?0.1963% (w/w). The proposed method presents advantages such as a small-required amount of sample for spectrum achievement, no sample destruction, and a high analytical frequency.     

傅里叶变换红外光谱法鉴别纯核桃油并定量检测掺伪含量

应用傅里叶变换红外光谱(FT—IR)法测定纯核桃油和分别混合大豆油、普洱茶籽油和葵花籽油的掺伪核桃油的红外光谱,结合主成分分析法(PCA)以及马氏距离判别法对核桃油的纯度进行判别,3个判别模型的准确率均达到100%;同时对验证集样品的类归属进行判别,判别准确率达均为100%。结合偏最小二乘法(PLS)定量检测核桃油纯度,建立的PLS校正集模型中核桃油的真实含量与FT—IR预测含量的相关系数R2分别为0.990 8、0.994 4和0.995 5,校正集均方根误差分别为0.032 7、0.023 5和0.019 6。试验结果证明,该方法可以作为核桃油质量监控的快速检测方法。     

Determination of C22:5 and C22:6 marine fatty acids in pork fat with Fourier transform mid-infrared spectroscopy

Fatty acids in samples (n=74) of pork adipose tissue were measured with a Fourier transform mid-infrared (FT-MIR) spectrometer and by gas chromatography. The measured absorption spectra provided information to estimate partial least squares regression models for fatty acid groups, the iodine value and several fatty acids. The iodine values were predicted with correlation coefficient R=0.996 and root mean square error of cross-validation RMSECV=0.658. The sum of the two marine fatty acids of main interest, C22:5n3+C22:6n3, were predicted with R=0.982 and RMSECV=0.062. The K nearest neighbours procedure successfully classified the samples in three classes, depending on their proportions of marine fatty acids. Application of fat and absorption measurements were rapid, requiring less than 5 min of labour per sample. The results reported in this paper demonstrate that FT-MIR measurements can serve as a rapid method to determine marine fatty acids in pork fat.     

漫反射红外光谱法结合PLS测定稻谷脂肪酸值研究

为了快速、简便、无污染地测定稻谷中脂肪酸含量,提出基于漫反射傅里叶变换红外光谱法(DRIFTS)和近红外漫反射光谱法(NIDRS)测定稻谷脂肪酸值快速检测方法。同时探讨了反向区间偏最小二乘法(BiPLS)对中红外光谱区域的优化效果。傅里叶变换原始光谱模型经7点平滑预处理及BiPLS优化,得到的最佳模型的R~2、RMSECV、RMSEP分别为0.998、3.65、3.69。近红外光谱经一阶导数和多元散射校正预处理后建立最佳模型,其R~2为0.97,RMSECV为3.43。在验证实验中,傅里叶变换红外光谱预测值与国标测定值的相对标准偏差为1.16%,近红外光谱预测值与国标测定值的相对标准偏差为1.70%。结果表明,DRIFTS能够更加准确检测脂肪酸值。     

Identification of pork in beef meatballs using Fourier transform infrared spectrophotometry and real-time polymerase chain reaction

A study on development of Fourier-transform infrared spectrophotometric method combined with principle component analysis as well as real-time polymerase chain reaction for determination of pork–beef mixture in meatballs has been performed. A lipid component extracted from pork and beef in meatballs is analyzed using Fourier-transform infrared spectroscopy, while DNA extracted from meatball was analyzed using real-time polymerase chain reaction. The correlation between actual and predicted concentration of lard using Fourier-transform infrared spectroscopy was performed by aid of partial least squares, while grouping of lard and beef fat components in meatball was carried out by Fourier-transform infrared spectra coupled with principle component analysis. The results showed that Fourier-transform infrared spectra at wavenumbers of 1000–1200 cm^?1 coupled with partial least square and principle component analysis are successfully used for quantification and classification of pork in beef meatballs. The relationship between actual value and predicted value of lard (lipid fraction obtained from meatballs containing pork) with Fourier-transform infrared spectrophotometric method revealed good correlation, with coefficient determination (R²) value of 0.997 and standard error of calibration of 0.04%. Principle component analysis is able to classify samples containing pork and beef meatballs. Fourier-transform infrared spectroscopy using normal spectra is fast technique for identification and quantification of lard extracted from pork in meatball. In addition, real-time polymerase chain reaction using Leptin Primer–AJ 865080 can be used for amplification of pork DNA specifically in meatballs containing pork.