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.     

基于同步荧光光谱的杜仲籽油掺假检测研究

建立基于同步荧光光谱的杜仲籽油掺假判别分析模型及检测方法。以杜仲籽油和7种常见植物油为研究对象,采集激发波长范围为250～700 nm,波长间隔为60 nm的同步荧光光谱,分析杜仲籽油和常见食用油的荧光光谱特性,利用光谱峰面积建立掺假判别模型并对其进行验证。结果表明:杜仲籽油与其他7种植物油的荧光特性存在显著差异;分别利用600～700 nm和300～500nm波长范围同步荧光光谱进行主成分分析,其对杜仲籽油掺假识别准确率高达100%;利用峰面积与掺假比例建立定量判别分析模型,检测限分别为1%和0. 48%。该方法可实现对杜仲籽油掺假的定性和定量分析,且具有较高的灵敏度、简便和快速等特点。     

基于傅里叶变换红外光谱的大豆原油掺伪定量 分析模型研究

目的应用傅里叶变换红外光谱(FTIR)结合最小偏二乘法(PLS)建立大豆原油-棕榈油二元掺伪体系的定量分析模型。方法以42个大豆原油、21个精炼油、88个掺伪油的FIIR谱图为模型样本,预处理方法选用标准正态变量(SNV),在此基础上应用主成分分析(PCA)提取特征变量,随机选取60个掺伪油样组成校正集,28个掺伪油样组成验证集,以PLS方法建立大豆原油的掺伪定量模型。结果 PCA可将大豆原油及精炼油分成独立的2类。经PCA分析,大豆原油中掺入棕榈油的掺伪检测限为5%。PLS校正模型的判定系数R2为0.9926,校正误差均方根RMSEC为1.8121。预测模型的R2为0.9823,交叉验证误差均方根RMSECV为2.8189。同时得到的预测结果的偏差在1.3909%~3.1019%之间,差异不显著,说明此模型可行。结论 FTIR-PLS模型能够实现大豆原油的掺伪定量分析,分析速度快,能够满足大豆原油入库要求,是一种可行的大豆原油掺伪分析方法。     

近红外光谱结合偏最小二乘法用于纯茶油中掺杂菜籽油和大豆油的定量分析

采用偏最小二乘法(PLS)建立了油茶籽油中掺杂菜籽油和大豆油的近红外光谱定量检测模型。配制不同比例(0～100%)的油茶籽油和菜籽油、油茶籽油和大豆油混合样品共256个,采集样品在10000～4000cm-1范围内的近红外透反射光谱,模型采用交互验证和外部检验来考察所建立模型的可靠性,不需进行任何光谱预处理,所建立的PLS模型相关系数为0.9997,训练集的交叉验证均方根误差(RMSECV)为0.504,预测集的预测均方根误差(RMSEP)为0.66。应用建立的模型对未知样品进行预测,并对预测值和真实值进行比较,在掺杂油含量为2.5%～100%之间范围内准确可靠,研究结果表明,采用近红外光谱技术可以实现纯茶油中菜籽油和大豆油掺杂量检测。     

油脂种类对软冰淇淋品质影响研究

对反映软冰淇淋品质的各指标进行测试,以考察棕榈油、椰子油和黄油对软冰淇淋品质的影响,结果表明,除棕榈油抗溶性不如黄油外,棕榈油和黄油对软冰淇淋品质影响接近。椰子油除了硬度与两者接近外,对软冰淇淋其他指标的影响均明显有别与棕榈油和黄油。椰子油膨化率最高,黄油抗溶性最好。     

Monitoring of Adulteration and Purity in Coconut Oil Using Raman Spectroscopy and Multivariate Curve Resolution

This study evaluates the use of Raman spectroscopy with a multivariate curve resolution–alternating least squares (MCR-ALS) analysis to monitor the adulteration and purity of coconut oil. Sunflower, soybean, canola, sesame, corn, castor bean, peanut, palm kernel, babassu, mineral, and Vaseline oils have been used as adulterants in this work. Control charts were developed to evaluate the purity of an oil sample using the scores from the MCR-ALS analysis of a data set containing pure and adulterated oils. These control charts were able to detect the adulteration of coconut oil in a range of 2–30% with all the oils tested. Additionally, quantification models were developed using MCR-ALS with correlation constraints for coconut oil adulterated with sunflower, canola, Vaseline, babassu, and palm kernel oils. The models presented satisfactory results, which had absolute errors below 5%, for samples adulterated with sunflower, canola, and Vaseline oils. The babassu and palm kernel adulterants could also be quantified with a superior margin of error. The results indicated that using Raman spectroscopy with MCR is a clean and non-destructive method for assessing coconut oil purity that can be used without removing a sample from its bottle.     

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.     

Detection of adulteration of extra-virgin olive oil by chemometric analysis of mid-infrared spectral data

This study focuses on the detection and quantification of extra-virgin olive oil adulteration with different edible oils using mid-infrared (IR) spectroscopy with chemometrics. Mid-IR spectra were manipulated with wavelet compression previous to principal component analysis (PCA). Detection limit of adulteration was determined as 5% for corn–sunflower binary mixture, cottonseed and rapeseed oils. For quantification of adulteration, mid-IR spectral data were manipulated with orthogonal signal correction (OSC) and wavelet compression before partial least square (PLS) analysis. The results revealed that models predict the adulterants, corn–sunflower binary mixture, cottonseed and rapeseed oils, in olive oil with error limits of 1.04, 1.4 and 1.32, respectively. Furthermore, the data were analysed with a general PCA model and PLS discriminant analysis (PLS-DA) to observe the efficiency of the model to detect adulteration regardless of the type of adulterant oil. In this case, detection limit for adulteration is determined as 10%.     

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.     

6种食品专用油脂的风味特征研究

旨在为食品专用油脂的风味提升提供参考,采用顶空固相微萃取-气相色谱-质谱联用(HS-SPME-GC-MS)技术和感官评定分析黄油、椰子油、全氢化棕榈仁油、全氢化棕榈仁油硬脂、部分氢化大豆油和全氢化大豆油6种代表性食品专用油脂的挥发性成分和感官特征,并结合相对气味活度值(ROAV)及其聚类分析确定关键风味化合物。结果显示：椰子油的挥发性成分最为丰富,共鉴定出13个关键风味化合物,主要为内酯类、醛类化合物,椰子香和奶油香风味浓郁;黄油共鉴定出8个关键风味化合物,主要为内酯类、酮类和醛类化合物,具有果香、奶油香和青香;部分氢化大豆油共鉴定出6个关键风味化合物,主要为醛类和酮类化合物,主要呈果香、蜜蜡香、脂肪气味和奶油香;全氢化棕榈仁油、全氢化大豆油和全氢化棕榈仁油硬脂检出的关键风味化合物种类较少,分别为5个、2个和4个,主要为醛类和甲酯类/酸类化合物,主要呈果香风味和脂肪气味;感官评价结果和关键风味化合物分析结果存在一定联系。综上,6种食品专用油脂中,黄油、椰子油和部分氢化大豆油风味化合物较为丰富,具有突出的感官特征。     

MONITORING THE ADULTERATION OF VIRGIN COCONUT OIL BY SELECTED VEGETABLE OILS USING DIFFERENTIAL SCANNING CALORIMETRY

The crystallization and melting enthalpy of virgin coconut oil adulterated with palm kernel oil (PKO) and soybean oil (SBO) were studied by using differential scanning calorimetry. Virgin coconut oil was spiked separately with PKO and SBO from 2% to 40% (w/w) of adulterant oils. Fatty acids of all oils were determined to complement the differential scanning calorimetry data. The heating curve of SBO-adulterated samples showed the adulteration peak appearing at the lower temperature region at 10% adulteration level. Regression analyses using stepwise multiple linear regression were used to predict the percentage adulterant with R ² of 0.9490. PKO-adulterated oils did not show any adulteration peak but demonstrated a gradual decrease in the peak height of the major exothermic peak.

PRACTICAL APPLICATIONS

An alternative method for detection of adulteration based on differential scanning calorimetry in virgin coconut oil is presented. Application of differential scanning calorimetry is rapid, does not require sample preparation and does not involve use of solvents or toxic chemicals.     

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.     

FTIR-ATR Spectroscopy Based Metabolite Fingerprinting as A Direct Determination of Butter Adulterated With Lard

Adulteration of butter with cheaper animal fats, such as lard, has become an issue in recent years. A simple and rapid analytical method of attenuated total reflectance in Fourier transform infrared spectroscopy was developed in order to determine the lard content in butter. The multivariate calibration of partial least square model for the prediction of adulterant was developed for quantitative measurement. The model yielded the highest regression with the correlation coefficient (R²) = 0.999, its lowest root mean square error estimation = 0.0947, and its root mean square error prediction = 0.0687, respectively. Cross validation testing evaluates the predictive power of the model. Partial least square model to be effective as their intercept of R²Y and Q²Y were 0.08 and –0.34, respectively.     

Physicochemical properties and antioxidant activity of a synthetic cocoa butter equivalent obtained through modification of mango seed oil

This research describes the interesterification of Malaysian mango seed oil (MSO) and palm oil mid‐faction (POMF) to develop a cocoa butter equivalent. Fat blends, formulated by binary blends of palm oil mid‐fraction and mango seed oil at different ratios ({100:0}, {60:40}, {50:50}, {40:60}, {0:100}), were subjected to enzymatic interesterification. The solid fat content revealed that all interesterified blends except 100% POMF {0:100} melted completely at body temperature. The interesterified {50:50} blend exhibited a slip melting point (30.35 °C) and saponification value (186.89) close to cocoa butter (P < 0.05). Thermal behaviour analysis by differential scanning calorimetry showed fusion and crystallisation behaviour similar to cocoa butter. Moreover, both the blend and cocoa butter scavenging abilities were based on the 2,2‐diphenyl‐1‐picrylhydrazyl assay, with the concentration required to reduce radical absorbance by 50% (IC₅₀) of 43.08% and 41.1%, respectively. Therefore, the MSO: POMF blend may have use as a health‐promoting food in human diets.     

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.      

A high performance liquid chromatography method for determination of furfural in crude palm oil

A modified steam distillation method was developed to extract furfural from crude palm oil (CPO). The collected distillates were analysed using high performance liquid chromatography (HPLC) coupled with an ultraviolet diode detector at 284 nm. The HPLC method allowed identification and quantification of furfural in CPO. The unique thermal extraction of CPO whereby the fresh fruit bunches (FFB) are first subjected to steam treatment, distinguishes itself from other solvent-extracted or cold-pressed vegetable oils. The presence of furfural was also determined in the fresh palm oil from FFB (without undergoing the normal extraction process), palm olein, palm stearin, olive oil, coconut oil, sunflower oil, soya oil and corn oil. The chromatograms of the extracts were compared to that of standard furfural. Furfural was only detected in CPO. The CPO consignments obtained from four mills were shown to contain 7.54 to 20.60 mg/kg furfural.     

基于荧光光谱法的植物油加热氧化规律的研究

为了探寻食用植物油加热后的氧化现象与荧光光谱之间的变化规律,采用了分子同步荧光法和LED固定波长激发的发射荧光光谱法,其中同步荧光光谱法的检测条件是激发波长190～800 nm、波长间隔10 nm,LED激发的发射荧光光谱法的检测条件是固定激发波长为425 nm,同时检测了5种食用植物油(一级大豆油、花生调和油、色拉油、芝麻油、棕榈油)不同加热时间下的两种荧光光谱,发现食用植物油随着加热时间的延长,其同步荧光光谱和固定波长激发的荧光光谱都呈规律性变化,同步荧光光谱的变化更具明显,加热后的分子同步荧光光谱在430～490 nm波长区域都产生了新的荧光峰,试验表明植物油的荧光分析可作为研究食用植物油加热氧化过程的一种手段,试验证明,通过分析同步荧光光谱的变化可以定性分析常用食用植物油的氧化程度,并可以区别出5种食用植物油的种类。     

近红外光谱快速检测食用植物油中酸价

目的 建立基于近红外光谱快速测定食用植物油中酸价的分析方法。方法 采用冷溶剂指示剂滴定法检测371个食用植物油样品的酸价,并采集样品的近红外光谱。经过标准正态变换结合一阶导数对近红外光谱进行数据预处理,选用竞争性自适应重加权采样算法选取重要变量,建立食用植物油酸价的偏最小二乘回归模型。结果 蒙特卡洛交互验证结果显示,食用植物油酸价预测模型的验证集决定系数Q2为0.9983,交互检验的均方根误差(root mean square error of cross validation, RMSECV)为0.0461,模型预测的独立测试集的酸价与实测值相关系数为0.9834,预测效果良好。结论 本研究建立的食用植物油酸价近红外光谱快速检测方法能够满足检测要求,为评价或跟踪食用油品质提供快速无损的技术思路。     

Application of temperature-controlled attenuated total reflectance-mid-infrared (ATR-MIR) spectroscopy for rapid estimation of butter adulteration

There is a continual need for development of rapid methods that meet or exceed the detection levels of currently available analytical methods for authentication of food products. The objective of this study was to evaluate temperature-controlled attenuated total reflectance-mid-infrared (ATR-MIR) spectroscopy combined with multivariate analysis as a simple and rapid method for the determination of butter adulteration as a dairy food system. Commercial samples of butter fat were adulterated with margarine fat at levels ranging from 0% to 100% (v/v). Partial least square regression (PLSR) models gave standard error of cross-validation (SECV) of <1.2% (v/v) and correlation coefficients (r) > 0.99. Excellent predicting capabilities were obtained using an external validation set consisting of butter adulterated with margarines at ratios of 2.5%, 13%, and 45%. We have demonstrated the feasibility of a temperature-controlled ATR-MIR spectroscopy technique that would allow for rapid analysis of dairy products.     

Use of MALDI-TOF MS technology to evaluate adulteration of small ruminant milk with raw bovine milk

Detection of adulteration of small ruminant milk is very important for health and commercial reasons. New analytical and cost-effective methods need to be developed to detect new adulteration practices. In this work, we aimed to explore the ability of the MALDI-TOF mass spectrometry to detect bovine milk in caprine and ovine milk using samples from 18 dairy farms. Different levels of adulteration (0.5, 1, 5, 10, 20, 40, 60, and 80%) were analyzed during the lactation period of goat and sheep (in May, from 60 to 90 d in milk, and in August, from 150 to 180 d in milk). Two different ranges of peptide-protein spectra (500–4,000 Da; 4–20 kDa) were used to establish a calibration model for predicting the concentration of adulterant using partial least squares and generalized linear model with lasso regularization. The low molecular weight part of the spectra together with the generalized linear model with lasso regularization regression model appeared to have greater potential for our aim of detection of adulteration of small ruminants' milk. The subsequent prediction model was able to predict the concentration of bovine milk in caprine milk with a root mean square error of 11.4 and 17.0% in ovine milk. The results offer compelling evidence that MALDI-TOF can detect the adulteration of small ruminants' milk. However, the method is severely limited by (1) the complexity of the milk proteome resulting from the adulteration technique, (2) the potential degradation of thermolabile proteins, and (3) the genetic variability of tested samples. Additionally, the root mean square error of prediction based only on one individual sample adulteration series can drop down to 6.34% for quantification of adulterated caprine milk and 6.28% for adulterated ovine milk for the full set of concentrations or down to 2.33 and 4.00%, respectively, if we restrict only to low concentrations of adulteration (0, 0.5, 1, 5, 10%).