The use of Fourier transform mid infrared (FT-MIR) spectroscopy for detection and quantification of adulteration in virgin coconut oil

Currently, the authentication of virgin coconut oil (VCO) has become very important due to the possible adulteration of VCO with cheaper plant oils such as corn (CO) and sunflower (SFO) oils. Methods involving Fourier transform mid infrared (FT-MIR) spectroscopy combined with chemometrics techniques (partial least square (PLS) and discriminant analysis (DA)) were developed for quantification and classification of CO and SFO in VCO. MIR spectra of oil samples were recorded at frequency regions of 4000–650 cm⁻¹ on horizontal attenuated total reflectance (HATR) attachment of FTIR. DA can successfully classify VCO and that adulterated with CO and SFO using 10 principal components. Furthermore, PLS model correlates the actual and FTIR estimated values of oil adulterants (CO and SFO) with coefficient of determination (R²) of 0.999.     

Potential Use of FTIR-ATR Spectroscopic Method for Determination of Virgin Coconut Oil and Extra Virgin Olive Oil in Ternary Mixture Systems

The aim of this study was to investigate the feasibility of Fourier-transform infrared (FTIR) spectroscopy combined with multivariate calibrations of partial least square (PLS) and principle component regression (PCR) for analysis of virgin coconut oil (VCO) in the ternary mixture with palm oil (PO) and olive oil, and for analysis of extra virgin olive oil (EVOO) mixed with soybean oil (SO) and corn oil (CO). The spectra of individual oils and their blends with certain concentrations were scanned using horizontal attenuated total reflectance accessory at mid-infrared region of 4,000–650 cm⁻¹. The optimal frequency regions selected for calibration models were based on its ability to give the highest values of coefficient of determination (R ²) and the lowest values of root mean standard error of calibration (RMSEC). PLS was slightly better for quantitative analysis of VCO and EVOO compared with PCR. VCO in ternary mixtures is successfully determined at frequency region of 1,200–1,000 using second derivative FTIR spectra with R ² and RMSEC values of 0.999 and 0.200, respectively. Meanwhile, EVOO is best determined at 1,200–1,000 using first derivative FTIR spectra with R ² and RMSEC values of 0.999 and 0.975, respectively. The results showed that FTIR spectroscopy offers accurate and reliable technique for quantitative analysis of VCO and EVOO in ternary systems. In addition, the developed method can be used for the monitoring of VCO and EVOO adulteration with cheaper oils like PO in VCO as well as SO and CO in EVOO.     

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.     

FTIR Spectroscopy Combined with Chemometric for Analysis of Sesame Oil Adulterated with Corn Oil

Sesame oil is an edible vegetable oil derived from the sesame seed that has been used as a flavor enhancer in Southeast Asian cuisine. This highly valuable oil can be subjected to adulterations with lower price oils in order to gain economical profit. Among 10 vegetable oils evaluated using fatty acid profiles with principal component analysis, corn oil has the closest similarity in fatty acids combined together with sesame oil; therefore, corn oil is a potential adulterant in sesame oil. FTIR spectra at 1072?935 cm^?1 was chosen for quantitative analysis with acceptable values of coefficient determination (R²), root mean square errors of calibration and prediction. These combined methods using first derivative FTIR spectra in partial least square showed well quantified corn oil in sesame oil with R² (0.992), root mean square errors of calibration (0.53% v/v) and root mean square errors of prediction (1.31% v/v) values. Moreover, the Coomans plot based on Mahalanobis distance were able to discriminate between sesame oil with adulterated oils such as corn oil, grape seed oil, and rice bran oil.     

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.     

Quantitative determination of the iodine values of unsaturated plant oils using infrared and Raman spectroscopy methods

Thirteen edible oils: sunflower, avocado, hemp, high-linolenic flax, low-linolenic flax, safflower, walnut, roasted sesame, rice, corn, rapeseed, pumpkin seed, and hazel were studied in this work. Their fatty acid composition, iodine, acidic, peroxide, and saponification values were determined. Infrared and Raman spectra of the oils were recorded in the range 400–3200 cm^?1. The integral intensities of the bands at about 1655 and 2852 cm^?1 corresponding to ν(C=C) and ν(CH₂) vibrations were evaluated and used to construct a relationship between the spectroscopic data and the iodine value. The following linear dependencies were obtained: I_ν(C=C)/I_ν(CH2) = 7.449 × 10^?4 × iodine value – 0.0339 and I_ν(C=C)/I_ν(CH2) = 9.299 × 10^?4 × iodine value – 0.023 for the infrared and Raman spectra with a correlation coefficient 0.988 and 0.976, respectively. These calibration lines can be used to determine the iodine value for oils with unknown unsaturation level, and may be applied for margarines and other fatty materials.     

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.     

Application of FTIR Spectroscopy for Monitoring the Stabilities of Selected Vegetable Oils During Thermal Oxidation

Fourier transform infrared spectroscopy has been developed for rapid monitoring of the oxidative stabilities of selected vegetable oils, namely corn oil, rice bran oil, soybean oil, and sunflower oil during thermal treatment at 160°C for 120 h. There were several absorbance changes between non-oxidized and oxidized vegetable oils during thermal oxidation. Peak intensities at 3470, 1655, and 967 cm^?1 were increased; meanwhile peak intensities at 3008 and 722 cm^?1 were decreased. The R² values for the correlation between the absorbance changes at 3008 cm^?1 and the specific absorptivities of conjugated dienes for corn oil, rice bran oil, soybean oil, and sunflower oil were 0.938, 0.845, 0.978, and 0.824, respectively. The absorbance changes of Fourier transform infrared spectra at 3008 cm^?1 were also correlated with the specific absorptivities of conjugated trienes and p-anisidine values with the acceptable R² values. Compared with the conventional technique, the use of Fourier transform infrared spectroscopy for measurement the thermal oxidative stability has some advantages, i.e., it is a rapid technique and no sample preparation. In addition, Fourier transform infrared spectroscopy reduces or eliminates solvents and chemical reagents that are hazardous to human health or to the environment; therefore, Fourier transform infrared spectroscopy can support the campaign of “green analytical chemistry.”     

Authentication of yerba mate according to the country of origin by using Fourier transform infrared (FTIR) associated with chemometrics

This study deals with the development of a method for classification of yerba mate (Ilex paraguariensis) using attenuated total-reflectance Fourier transform infrared (ATR-FTIR) and multivariate analysis. Fifty-four brands of yerba mate from southern South America were analysed in order to classify the commercialised yerba mate according to the respective country of yerba mate processing. The yerba mate was ground in a cryogenic mill, and the reflectance was directly measured in the region ranging from 4000 to 650 cm^?1. Different pre-processing algorithms and three methods of multivariate analysis were investigated, including principal component analysis (PCA), partial least square discriminant analysis (PLS-DA) and support vector machine discriminant analysis (SVM-DA). The yerba mate classification was 100% correct when the reflectance spectra were pre-treated (derived at first order, normalised by standard normal variation, smoothed and mean centred) and analysed using the SVM-DA method.     

A chemometric approach for the quantification of free fatty acids in cottonseed oil by Fourier transform infrared spectroscopy

The aim of this study was to quantify free fatty acid in cottonseed oil (Gossypium) variety by a chemometric approach using Fourier transform infrared spectroscopy. Calibration standards were prepared by gravimetrical mixing of oleic acid (0.1–40 g/100 g) in neutralized cottonseed oil containing <0.1% free fatty acids. Fourier transform infrared technique coupled with partial least square and principle component regression models were used to develop calibrations in the specific absorption region of carbonyl between 1690–1727 cm^?1. On the basis of regression coefficient and evaluated free fatty acids results with comparison to titration method, partial least square was found to be more accurate than principle component regression calibration model. All the analyzed cottonseed oil varieties showed high content of free fatty acids in the range of 17.1–38.5%. The results of the present study indicated that Fourier transform infrared method in combination with partial least square or principle component regression could be used as a greener alternative to the standard titration method.     

Rapid Quantitative Determination of Walnut Oil Adulteration with Sunflower Oil Using Fluorescence Spectroscopy

Adulteration of walnut oil with sunflower oil is a major issue for the walnut oil industry. In this paper, the potential use of total synchronous fluorescence (TSyF) spectra to differentiate walnut oil from sunflower oil and synchronous fluorescence spectra combined with multivariate analysis to assess the adulteration of walnut oil is demonstrated. TSyF spectra were acquired by varying the excitation wavelength in the region 250–700 nm and the wavelength interval (Δλ) in the region from 10 to 100 nm. TSyF contour plots for walnut oil, in contrast to sunflower oil, show an extra fluorescence region in the excitation wavelength lower than 280 nm. Fifty-one oil mixtures were prepared by adulterating walnut oil with sunflower oil at varying levels (0–100 %). The partial least-squares regression model was used for the quantification of adulteration using wavelength intervals of 20, 40, 60 and 80 nm. This technique is useful for the detection of sunflower oil in walnut oil at levels down to 0.3 % (v/v) in just 2.5 min using an 80-nm wavelength interval.     

Use of ATR-FTIR spectroscopy coupled with chemometrics for the authentication of avocado oil in ternary mixtures with sunflower and soybean oils

Avocado oil is a high-value and nutraceutical oil whose authentication is very important since the addition of low-cost oils could lower its beneficial properties. Mid-FTIR spectroscopy combined with chemometrics was used to detect and quantify adulteration of avocado oil with sunflower and soybean oils in a ternary mixture. Thirty-seven laboratory-prepared adulterated samples and 20 pure avocado oil samples were evaluated. The adulterated oil amount ranged from 2% to 50% (w/w) in avocado oil. A soft independent modelling class analogy (SIMCA) model was developed to discriminate between pure and adulterated samples. The model showed recognition and rejection rate of 100% and proper classification in external validation. A partial least square (PLS) algorithm was used to estimate the percentage of adulteration. The PLS model showed values of R² > 0.9961, standard errors of calibration (SEC) in the range of 0.3963–0.7881, standard errors of prediction (SEP estimated) between 0.6483 and 0.9707, and good prediction performances in external validation. The results showed that mid-FTIR spectroscopy could be an accurate and reliable technique for qualitative and quantitative analysis of avocado oil in ternary mixtures.     

基于衰减全反射傅里叶变换红外光谱的餐饮废油掺假检测

为了建立基于衰减全反射傅里叶变换红外光谱(ATR-FTIR)的餐饮废油掺假检测方法,以常见食用油和餐饮废油为原料,收集8个餐饮废油和25个食用油样品,制备30个掺假油样品,共63个油样进行红外光谱扫描。随机取48个油样作为校正集样品,15个油样作为验证集样品,建立餐饮废油定性分析模型,并对定性模型进行验证;从30个掺假油样品中,随机取20个油样作为校正集样品,10个油样作为验证集样品,建立餐饮废油定量分析模型,并对定量模型进行验证。结果表明:在红外光谱范围为1 550~650 cm-1条件下,采用原始光谱结合判别分析建立定性分析模型,其识别率可达100%;采用偏最小二乘法(PLS)建立定量分析模型,在掺假比例1%~10%时,模型预测值与实际掺假比例呈良好的线性关系,相关系数(R)为0.982 2,标准偏差(SD)为0.47。表明基于ATR-FTIR的餐饮废油掺假检测是可行的。     

First Break Forward Interval PLS (FB-FiPLS) Procedure as Potential Tool in Analysis of FTIR Data for Fast and Robust Quantitative Determination of Food Adulteration

Ternary mixtures of sugar solutions containing maple syrup were studied quantitatively using Fourier transform infrared (FTIR) attenuated total reflectance (ATR) technique coupled with partial least squares regression (PLS) and selection of spectral variables. Two ternary mixtures were analyzed; first ternary mixture contained maple syrup, white sugar solution, and fully inverted sugar solution; second ternary mixture comprised maple syrup, white, and brown sugar solutions. In this paper, a procedure for selection of spectral variables with PLS, called first break forward interval PLS (FB-FiPLS), is tested on maple syrup adulteration. The method achieved almost exactly the same performance as synergy interval PLS (SiPLS) but with much shorter computational time. The upper limit of number of latent variables (LVs), which is the critical factor for both interval PLS methods, was determined using repeated double cross-validation on whole spectral region of calibration set for each analyzed component in each analyzed ternary mixture set. FB-FiPLS procedure for selection of spectral variables, using only root mean square error of cross validation (RMSECV) values for whole optimization of spectral variables, is fast and robust. After spectral variables and LVs for each particular model had been selected with minimum RMSECV of FB-FiPLS procedure, final results in terms of RMSECV and RMSEP for FB-FiPLS were in most cases statistically significantly better than PLS on whole spectral region and on selected spectral regions. Predictions of each component in analyzed ternary mixture set is promising (R ²(training set)?>?0.98, R ²(test set)?>?0.97), especially for fully inverted sugar solution (RMSEP?=?0.142 % w/w).     

Rapid Differentiation of Listeria monocytogenes Epidemic Clones III and IV and Their Intact Compared with Heat‐Killed Populations Using Fourier Transform Infrared Spectroscopy and Chemometrics

The objectives of this study were to determine if Fourier transform infrared (FT‐IR) spectroscopy and multivariate statistical analysis (chemometrics) could be used to rapidly differentiate epidemic clones (ECs) of Listeria monocytogenes, as well as their intact compared with heat‐killed populations. FT‐IR spectra were collected from dried thin smears on infrared slides prepared from aliquots of 10 μL of each L. monocytogenes ECs (ECIII: J1‐101 and R2‐499; ECIV: J1‐129 and J1‐220), and also from intact and heat‐killed cell populations of each EC strain using 250 scans at a resolution of 4 cm^?1 in the mid‐infrared region in a reflectance mode. Chemometric analysis of spectra involved the application of the multivariate discriminant method for canonical variate analysis (CVA) and linear discriminant analysis (LDA). CVA of the spectra in the wavelength region 4000 to 600 cm^?1 separated the EC strains while LDA resulted in a 100% accurate classification of all spectra in the data set. Further, CVA separated intact and heat‐killed cells of each EC strain and there was 100% accuracy in the classification of all spectra when LDA was applied. FT‐IR spectral wavenumbers 1650 to 1390 cm^?1 were used to separate heat‐killed and intact populations of L. monocytogenes. The FT‐IR spectroscopy method allowed discrimination between strains that belong to the same EC. FT‐IR is a highly discriminatory and reproducible method that can be used for the rapid subtyping of L. monocytogenes, as well as for the detection of live compared with dead populations of the organism.     

Prediction of Polyphenol Fraction in Virgin Olive Oil Using Mid-Infrared Attenuated Total Reflectance Attenuated Total Reflectance Accessory–Mid-Infrared Coupled With Partial Least Squares Regression

This study focuses on the quantification of virgin olive oil phenolic compound while using mid-infrared spectroscopy with chemometrics. For that, 100 samples of virgin olive oils in phenolic compounds which varied between 1.04 and 10.33 g/L were picked up in Morocco’s regions and subjected to infrared analysis. The aim was to develop a calibration model for the prediction of phenolic compounds in olive oil by using Fourier transform-infrared spectroscopy. The spectral transmissions of all samples were obtained in the spectral range of 600–4000 cm^–1. The values obtained for correlation coefficient and root mean square errors of prediction were 0.99 and 0.11, respectively. These results showed the capability of the Fourier transform-infrared spectroscopy and the chemometric in developing accurate models to predict the phenolic compounds in virgin olive oil.     

基于近中红外光谱分析的大黄鱼产地区分技术

为实现4种产地来源的大黄鱼(福建养殖大黄鱼、温州养殖大黄鱼、舟山养殖大黄鱼和舟山野生大黄鱼)的准确快速区分,本研究应用傅里叶变换红外光谱仪对4种大黄鱼样本在4000～650 cm-1^的红外吸收指纹图谱进行测定,基于特征波段下的光谱吸收差异并结合PCA、CA和LDA、SVM模型对大黄鱼样品进行产地区分。结果如下：采用测定波段4000～650 cm-1^的全光谱采集信息经过SG预处理后建立的SVM模型效果最优,对4种大黄鱼样本的测试集准确率为83.3%。进一步对福建养殖大黄鱼和野生大黄鱼的产地区分方法优化后,选取特征波段3690～2800 cm-1⁺¹⁸⁰⁰～650 cm-1^{的光谱信息}经过1st ^Der、2nd ^Der和SNV 3种方式预处理后建立LDA判别模型,光谱训练集与预测集的准确率均达到100%;3690～2800 cm-1⁺¹⁸⁰⁰～650 cm-1^{的光谱信息}经SNV、MSC预处理后的PCA效果最佳,2种大黄鱼样本间彼此间距远、无重叠,且前两个主成分累计贡献率均在90%以上;经SNV预处理后的CA分析中除21号野生大黄鱼被误聚到福建养殖大黄鱼类中,其余2种产地大黄鱼样本各自聚为一类。研究表明基于近中红外光谱测定并结合化学计量学处理的方法能够对大黄鱼产地进行较准确地快速区分,从而为大黄鱼溯源鉴别、维护消费者权益和保护区域大黄鱼产业等方面提供技术支撑。     

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。     

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).     

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.