Rapid detection of fat adulteration in bakery products using Raman and near-infrared spectroscopies

Adulteration is frequently encountered in the food industry and can be identified using currently available techniques. Infrared and Raman spectroscopic procedures are the most attractive techniques regarding fats and oils. The objective of this study was to determine the adulteration of the fat source (margarine or butter) in bakery products using Raman and near-infrared (NIR) spectroscopies. Margarine and butter samples were purchased at local markets in Turkey and examined using Raman and NIR devices. A mixture (50 % margarine : 50 % butter) of fat samples was examined as well. The NIR and Raman spectral output data of all the fat samples were processed using principal component analysis (PCA). Good classification was obtained for margarine, butter and the 1:1 adulterated mixture. The chosen bakery product (cake) was produced using the same fat samples according to the method of the American Association of Cereal Chemists. Then, the fat fraction was extracted from the cakes with n-hexane. Extracted fat samples from the cakes were examined as before. PCA was applied to Raman and NIR spectral data to achieve the separation of fat sources in the cakes. PCA was also validated in each of the two stages. Significant decomposition was observed in the Raman study in contrast to the NIR study. A chemometric comparison was also applied to processed (baked) fat samples in cakes and purchased samples by PCA to assess the effects of heat treatment on sample spectra. Raman spectroscopy with multivariate analyses such as PCA can be used to detect the adulteration of the fat source in bakery products in a faster and more suitable way than the other methods.     

Determination of Chinese honey adulterated with high fructose corn syrup by near infrared spectroscopy

The use of fibre optic diffuse reflectance near infrared spectroscopy (NIR) in combination with chemometric techniques has been investigated to discriminate authenticity of honey. NIR spectra of unadulterated honey and adulterated honey samples with high fructose corn syrup were registered within 10,000–4000 cm⁻¹ spectral region. Discriminant partial least squares (DPLS) models were constructed to distinguish between unadulterated honey and adulterated honey samples and main bands responsible for the discrimination of samples are in the range of 6000–10,000 cm⁻¹. For these models, the correct classification rate for calibration samples were above 90%. Hundred percentage of unadulterated honey and 95% of adulterated honey samples from test set were correctly classified after appropriate preprocessing of first derivative, 13 smoothing points, followed by mean centering pre-treatment and eight model factors, respectively. Our results showed that NIR spectroscopy data with chemometrics techniques can be applied to rapid detecting honey adulteration with high fructose corn syrup.     

The potential of NIR spectroscopy for the detection of the adulteration of orange juice

Near-infrared spectroscopy was used to investigate the adulteration of 65 authentic concentrated orange juice samples obtained from Brazil and Israel. These samples were adulterated with 100 g kg^?1 additions (ie 100 g added to 900 g) of (1) orange pulpwash, (2) grapefruit juice, and (3) a synthetic sugar/acid mixture and with 50 g kg^?1 additions (ie 50 g added to 950 g) of (4) orange pulpwash, and (5) grapefruit juice. All samples were scanned on the NIR systems 6500 spectrophotometer over the 1100-2498 nm wavelength range. Principal component analysis was used to reduce each spectrum to 20 principal components. Factorial discriminant analysis was used to distinguish between the different sample groups. Using orange juice and orange juice adulterated at the 100 g kg^?1 level, accurate classification rates of 94–95% were obtained. To classify samples adulterated at the 50 g kg^?1 level, the calibration development sample set had to be augmented by the inclusion of samples adulterated at this lower level—after this augmentation, an accurate classification rate of 94% was obtained. The results demonstrated that the application of principal component and factorial discriminate analysis to NIR reflectance spectra can detect the adulteration of orange juice with an average accuracy of 90%. Furthermore, not one adulterated sample was predicted as being an authentic orange juice throughout the entire test regime.     

基于近红外光谱的大鲵肉粉掺伪鉴别及纯度检测

利用近红外光谱技术进行大鲵肉粉的掺伪鉴别及纯度检测。分别采集大鲵纯肉粉、掺入江团鱼肉粉、草鱼肉粉和土豆淀粉的掺伪大鲵肉粉（各40 个样本,4 类共160 个样本）的近红外光谱图。原始光谱经光谱预处理后,利用偏最小二乘-判别分析（partial least square-discriminant analysis,PLS-DA）法分别建立2分类（纯样和掺伪样）和4分类（纯样、掺江团鱼样、掺草鱼样和掺淀粉样）的定性判别模型,利用偏最小二乘回归（partial least squares regression,PLSR）分析法分别建立3 类掺伪大鲵肉粉的纯度定量校正模型。结果表明,PLS-DA定性模型中,经一阶导数＋多元散射校正光谱预处理后,所建2分类和4分类模型性能均为最佳,校正集和预测集的预测准确率均为100%;PLSR定量模型中,大鲵肉粉掺江团鱼肉粉、大鲵肉粉掺草鱼肉粉和大鲵肉粉掺土豆淀粉模型的校正集相关系数（Rc2）分别为0.990 6、0.986 4和0.993 3,校正集的均方根误差分别为1.14%、1.39%和0.88%;测试集的相关系数（Rp2）分别为0.994 4、0.992 4和0.990 8,测试集的均方根误差分别为0.83%、0.89%和1.22%。运用近红外光谱技术结合化学计量学方法能够对大鲵肉粉进行掺伪鉴别及纯度检测。     

Applications of FT-NIRS combined with PLS multivariate methods for the detection & quantification of saccharin adulteration in commercial fruit juices

Detection of adulteration in carbohydrate-rich foods like fruit juices is particularly difficult because of the variety of the commercial sweeteners available that match the concentration profiles of the major carbohydrates in the foods. In present study, a new sensitive and robust assay using Fourier Transform Near-Infrared Spectroscopy (FT-NIRS) combined with partial least square (PLS) multivariate methods has been developed for detection and quantification of saccharin adulteration in different commercial fruit juice samples. For this investigation, six different commercially available fruit juice samples were intentionally adulterated with saccharin at the following percentage levels: 0%, 0.10%, 0.30%, 0.50%, 0.70%, 0.90%, 1.10%, 1.30%, 1.50%, 1.70% and 2.00% (weight/volume). Altogether, 198 samples were used including 18 pure juice samples (unadulterated) and 180 juice samples adulterated with saccharin. PLS multivariate methods including partial least-squares discriminant analysis (PLS-DA) and partial least-squares regressions (PLSR) were applied to the obtained spectral data to build models. The PLS-DA model was employed to differentiate between pure fruit juice samples and those adulterated with saccharin. The R² value obtained for the PLS-DA model was 97.90% with an RMSE error of 0.67%. Similarly, a PLS regression model was also developed to quantify the amount of saccharin adulterant in juice samples. The R² value obtained for the PLSR model was 97.04% with RMSECV error of 0.88%. The employed model was then cross-validated by using a test set which included 30% of the total adulterated juice samples. The excellent performance of the model was proved by the low root mean squared error of prediction value of 0.92% and the high correlation factor of 0.97. This newly developed method is robust, nondestructive, highly sensitive and economical.     

Discrimination and classification of adulterants in maple syrup with the use of infrared spectroscopic techniques

Food adulteration is a profit‐making business for some unscrupulous manufacturers. Maple syrup is a soft target for adulterators owing to its simplicity of chemical composition. The use of infrared spectroscopic techniques such as Fourier transform infrared (FTIR) and near‐infrared (NIR) as a tool to detect adulterants such as cane and beet invert syrups as well as cane and beet sugar solutions in maple syrup was investigated. The FTIR spectra of adulterated samples were characterised and the regions of 800–1200 cm^?1 (carbohydrates) and 1200–1800 and 2800–3200 cm^?1 (carbohydrates, carboxylic acids and amino acids) were used for detection. The NIR spectral region between 1100 and 1660 nm was used for analysis. Linear discriminant analysis (LDA) and canonical variate analysis (CVA) were used for discriminant analysis, while partial least squares (PLS) and principal component regression (PCR) were used for quantitative analysis. FTIR was more accurate in predicting adulteration using the two different regions (R² > 0.93 and 0.98) compared with NIR (R² > 0.93). Classification and quantification of adulterants in maple syrup show that both NIR and FTIR can be used for detecting adulterants such as pure beet and cane sugar solutions, but FTIR was superior to NIR in detecting invert syrups. © 2002 Society of Chemical Industry     

Discrimination and classification of adulterants in maple syrup with the use of infrared spectroscopic techniques

Food adulteration is a profit‐making business for some unscrupulous manufacturers. Maple syrup is a soft target of adulterators owing to its simplicity of chemical composition. In this study the use of Fourier transform infrared (FTIR) spectroscopy and near‐infrared (NIR) spectroscopy to detect adulterants such as cane and beet invert syrups as well as cane and beet sugar solutions in maple syrup was investigated. The FTIR spectrum of adulterated samples was characterised and the regions 800–1200 cm^?1 (carbohydrates) and 1200–1800 and 2800–3200 cm^?1 (carbohydrates, carboxylic acids and amino acids) were used for detection. The region between 1100 and 1660 nm in the NIR spectrum was used for analysis. Linear discriminant analysis (LDA) and canonical variate analysis (CVA) were used for discriminant analysis, while partial least squares (PLS) and principal component regression (PCR) were used for quantitative analysis. FTIR was more accurate in predicting adulteration using two different regions (R² > 0.93 and >0.98) compared with NIR (R² > 0.93). Classification and quantification of adulterants in maple syrup show that NIR and FTIR can be used for detecting adulterants such as pure beet and cane sugar solutions, but FTIR was superior to NIR in detecting invert syrups. © 2003 Society of Chemical Industry     

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.     

Determination of Milk Fat Adulteration with Vegetable Oils and Animal Fats by Gas Chromatographic Analysis

This study assessed the potential application of gas chromatography (GC) in detecting milk fat (MF) adulteration with vegetable oils and animal fats and of characterizing samples by fat source. One hundred percent pure MF was adulterated with different vegetable oils and animal fats at various concentrations (0%, 10%, 30%, 50%, 70%, and 90%). GC was used to obtain the fatty acid (FA) profiles, triacylglycerol (TG) contents, and cholesterol contents. The pure MF and the adulterated MF samples were discriminated based on the total concentrations of saturated FAs and on the 2 major FAs (oleic acid [C18:1n9c] and linoleic acid [C18:2n6c], TGs [C52 and C54], and cholesterol contents using statistical analysis to compared difference. These bio‐markers enabled the detection of as low as 10% adulteration of non‐MF into 100% pure MF. The study demonstrated the high potential of GC to rapidly detect MF adulteration with vegetable and animal fats, and discriminate among commercial butter and milk products according to the fat source. These data can be potentially useful in detecting foreign fats in these butter products. Furthermore, it is important to consider that several individual samples should be analyzed before coming to a conclusion about MF authenticity.     

Application of mid-infrared spectroscopy with multivariate analysis and soft independent modeling of class analogies (SIMCA) for the detection of adulterants in minced beef

Chemometric MID-FTIR methods were developed to detect and quantify the adulteration of mince meat with horse meat, fat beef trimmings, and textured soy protein. Also, a SIMCA (Soft Independent Modeling Class Analogy) method was developed to discriminate between adulterated and unadulterated samples. Pure mince meat and adulterants (horse meat, fat beef trimmings and textured soy protein) were characterized based upon their protein, fat, water and ash content. In order to build the calibration models for each adulterant, mixtures of mince meat and adulterant were prepared in the range 2–90% (w/w). Chemometric analyses were obtained for each adulterant using multivariate analysis. A Partial Least Square (PLS) algorithm was tested to model each system (mince meat + adulterant) and the chemical composition of the mixture. The results showed that the infrared spectra of the samples were sensitive to their chemical composition. Good correlations between absorbance in the MID-FTIR and the percentage of adulteration were obtained in the region 1800–900 cm^− 1. Values of R² greater than 0.99, standard errors of calibration (SEC) in the range to 0.0001–1.278 and standard errors of prediction (SEP estimated) between 0.001 and 1.391 for the adulterant and chemical parameters were obtained. The SIMCA model showed 100% classification of adulterated meat samples from unadulterated ones. Chemometric MID-FTIR models represent an attractive option for meat quality screening without sample pretreatments which can identify the adulterant and quantify the percentage of adulteration and the chemical composition of the sample.     

Feasibility study of quantifying and discriminating soybean oil adulteration in camellia oils by attenuated total reflectance MIR and fiber optic diffuse reflectance NIR

Camellia oil is often the target for adulteration or mislabeling in China because of it is a high priced product with high nutritional and medical values. In this study, the use of attenuated total reflectance infrared spectroscopy (MIR-ATR) and fiber optic diffuse reflectance near infrared spectroscopy (FODR-NIR) as rapid and cost-efficient classification and quantification techniques for the authentication of camellia oils have been preliminarily investigated. MIR spectra in the range of 4000–650 cm⁻¹ and NIR spectra in the range of 10,000–4000 cm⁻¹ were recorded for pure camellia oils and camellia oil samples adulterated with varying concentrations of soybean oil (5–25% adulterations in the weight of camellia oil). Identifications is successfully made base on the slightly difference in raw spectra in the MIR ranges of 1132–885 cm⁻¹ and NIR ranges of 6200–5400 cm⁻¹ between the pure camellia oil and those adulterated with soybean oil with soft independent modeling of class analogy (SIMCA) pattern recognition technique. Such differences reflect the compositional difference between the two oils with oleic acid being the main ingredient in camellia oil and linoleic acid in the soybean oil. Furthermore, a partial least squares (PLS) model was established to predict the concentration of the adulterant. Models constructed using first derivative by combination of standard normal variate (SNV), variance scaling (VS), mean centering (MC) and Norris derivative (ND) smoothing pretreatments yielded the best prediction results With MIR techniques. The R value for PLS model is 0.994.The root mean standard error of the calibration set (RMSEC) is 0.645, the root mean standard error of prediction set (RMSEP) and the root mean standard error of cross validation (RMSECV) are 0.667 and 0.85, respectively. While with NIR techniques, NIR data without derivative gave the best quantification results. The R value for NIR PLS model is 0.992. The RMSEC, RMSEP and RMSECV are 0.70, 1.78 and 1.79, respectively. Overall, either of the spectral method is easy to perform and expedient, avoiding problems associated with sample handling and pretreatment than the conventional technique.     

Multispectral image analysis approach to detect adulteration of beef and pork in raw meats

The aim of this study was to investigate the potential of multispectral imaging supported by multivariate data analysis for the detection of minced beef fraudulently substituted with pork and vice versa. Multispectral images in 18 different wavelengths of 220 meat samples in total from four independent experiments (55 samples per experiment) were acquired for this work. The appropriate amount of beef and pork-minced meat was mixed in order to achieve nine different proportions of adulteration and two categories of pure pork and beef. After an image processing step, data from the first three experiments were used for partial least squares-discriminant analysis (PLS-DA) and linear discriminant analysis (LDA) so as to discriminate among all adulteration classes, as well as among adulterated, pure beef and pure pork samples. Results showed very good discrimination between pure and adulterated samples, for PLS-DA and LDA, yielding 98.48% overall correct classification. Additionally, 98.48% and 96.97% of the samples were classified within a ± 10% category of adulteration for LDA and PLS-DA respectively. Lastly, the models were further validated using the data of the fourth experiment for independent testing, where all pure and adulterated samples were classified correctly in the case of PLS-DA, while LDA was proved to be less accurate.     

DIFFERENTIAL SCANNING CALORIMETRIC ANALYSIS FOR DETERMINATION OF SOME ANIMAL FATS AS ADULTERANTS IN PALM OLEIN

A study was conducted to assess the use of differential scanning calorimetry (DSC) for monitoring the presence of common animal fats such as genuine lard (GLD), chicken fat (CF) and beef tallow (BT) as adulterants in palm olein. Pure palm olein samples spiked separately with GLD, CF and BT in various levels (1 to 20%) were analyzed using DSC to obtain the cooling profiles. High performance liquid chromatographic (HPLC) analyses were also performed to obtain compositional changes in triacylglycerols (TAG) of these samples. Qualitative differences in the cooling-thermograms of adulterated samples are proposed as a basis for identification of adulteration peaks and differentiating the unadulterated sample of palm olein from those adulterated with the above mentioned animal fats. As the adulteration increased from 3 to 20%, the peaks corresponding to GLD and CF were found to appear in the low-temperature region below -42.75C whereas that corresponding to BT adulteration appeared in the high-temperature region above 8.5C. Despite their close proximity in position, GLD and CF adulteration peaks were shown to be distinguishable by making use of the subtractive procedures facilitated by the DSC computer software.     

Application of Near-Infrared Spectroscopy for the Detection of Metanil Yellow in Turmeric Powder

Turmeric (Curcumina Longa) is a globally traded commodity which is subjected to economically motivated chemically unsafe adulteration, namely metanil yellow. In this work, we report a simplistic and convenient approach to find the adulteration of turmeric with metanil yellow by near-infrared (NIR) spectroscopy coupled with chemometrics. Pure turmeric sample was prepared in the laboratory and spiked with different concentrations of metanil yellow. The reflectance spectra of 248 pure turmeric, metanil yellow, and adulterated samples (1–25%) (w/w) were collected using NIR spectroscopy. The calibration models based on NIR spectra of 144 samples were built for two different regression models, principal component analysis (PCR), and partial least square (PLSR) methods. Another 72 samples were used for external validation. The coefficient of determination (R ²) and root mean square error of calibration for validation and prediction were found to be 0.96–0.99, 0.44–0.91, respectively, for most of the results depending upon different pre-processing techniques and mathematical models used. The original reflectance spectra, the 1st derivative plot, the plot of PLSR regression coefficient (β), and the first three principal component loadings revealed metanil-related absorption regions. To verify the robustness of the models, the figures of merit (FOM) of the models were calculated with the help of net analyte signal (NAS) theory. Overall, it was found that PLSR yielded superior results as compared to the PCR technique. These methods can be applied to other spices also to detect the adulteration rapidly and without any prior sample preparations and with low cost.     

Detection of adulterated commercial Spanish beeswax

The physical and chemical parameters (melting point and saponification number), and the fraction of hydrocarbons, monoesters, acids and alcohols have been determined in 90 samples of Spanish commercial beeswax from Apis mellifera L. The adulteration with paraffins of different melting point, cow tallow, stearic acid, and carnauba wax were determined by HTGC-FID/MS detection, and the research was focussed mainly on paraffins and microcrystallines waxes. In general, the added adulterant can be identified by the presence of non-naturally beeswax components, and by the differences of values of selected components between pure and adulterated beeswax. The detection limits were determined using pure and adulterated beeswax with different amounts of added waxes (5%, 10%, 20% and 30%). Percentages higher than 1-5% of each adulterant can be detected in the mixtures. Paraffin waxes were confirmed in 33 of the 90 samples analysed at concentrations between 5% and 30%.     

Detection of adulterants such as sweeteners materials in honey using near-infrared spectroscopy and chemometrics

Near-infrared (NIR) spectroscopy combined with chemometrics methods has been used to detect adulteration of honey samples. The sample set contained 135 spectra of authentic (n = 68) and adulterated (n = 67) honey samples. Spectral data were compressed using wavelet transformation (WT) and principal component analysis (PCA), respectively. In this paper, five classification modeling methods including least square support vector machine (LS-SVM), support vector machine (SVM), back propagation artificial neural network (BP-ANN), linear discriminant analysis (LDA), and K-nearest neighbors (KNN) were adopted to correctly classify pure and adulterated honey samples. WT proved more effective than PCA, as a means for variables selection. Best classification models were achieved with LS-SVM. A total accuracy of 95.1% and the area under the receiver operating characteristic curves (AUC) of 0.952 for test set were obtained by LS-SVM. The results showed that WT-LS-SVM can be as a rapid screening technique for detection of this type of honey adulteration with good accuracy and better generalization.     

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.     

拉曼光谱结合距离匹配法快速鉴别掺伪食用油

以农贸市场购买的散装问题油为掺伪物,采用大豆油和玉米油为简单背景制备掺伪样本65份,采用4类食用调和油为复杂背景制备掺伪样本40份,收集市售合格食用植物油样本27份。按样本数3∶1划分建模集和校验集,采用拉曼光谱和距离匹配法分别建立简单背景和复杂背景的食用油掺伪快速定性识别模型:在简单背景掺伪下采用全谱建模预测可得真样本识别率为85.7%,伪样本识别率为94.1%,总识别率为91.7%;在复杂背景掺伪下经谱区挑选优化建模预测可得真样本识别率为87.5%,伪样本识别率为100%,总识别率为94.4%。试验结果表明拉曼光谱结合距离匹配法能简单、有效、快速地检测食用植物油是否掺伪。     

近红外技术对不同动物来源肉掺假的检测

采用近红外光谱结合主成分分析法(PCA)、判别分析法,分别建立了牛肉和羊肉中掺杂其它动物肉的定性鉴别模型,根据鉴别准确率评价模型的预测性能。采用近红外光谱结合PCA、偏最小二乘法(PLS),建立了掺假物的定量检测模型,根据模型对预测集样品的预测均方差(RMSEP)以及预测值与实测值间的相关系数(r)验证模型的预测能力。结果,牛肉掺猪肉模型对训练集和预测集的鉴别准确率分别为97.86%和91.23%,羊肉掺猪肉模型对训练集和预测集的鉴别准确率分别为98.28%和92.98%,羊肉掺鸭肉模型对训练集和预测集的鉴别准确率分别为99.59%和93.97%,羊肉掺假模型对训练集和预测集的鉴别准确率分别为97.57%和90.76%。牛肉掺假定量模型对训练集的交互验证均方差(RMSECV)和预测集的RMSEP分别为3.87%和4.13%,r分别为0.9505和0.9134;羊肉掺假定量模型对训练集的RMSECV和预测集的RMSEP分别为4.48%和4.86%,r分别为0.9306和0.9082。表明近红外技术结合一定的化学计量学方法可实现不同动物来源肉掺假的鉴别,且能够对掺假物进行定量检测。      

Identification of Acacia Honey Adulteration with Rape Honey Using Liquid Chromatography–Electrochemical Detection and Chemometrics

The adulteration of honey is generally a concern of consumers and management departments of safety and quality. Adding low-price honey to high-price honey is often seen in the market. In this study, a reliable and simple method of liquid chromatography–electrochemical detection (LC-ECD) was presented to detect the adulteration of acacia honey which was added with rape honey at different levels (5–50 %, w/w). Chromatographic separation was carried out with a reversed phase column, and the mobile phase was methanol/2 % (v/v) aqueous acetic acid. Fingerprints of authentic honeys showed that the contents of chlorogenic acid were higher in acacia honey (1.738 mg kg^?1), while those of ellagic acid were much lower (0.274 mg kg^?1) in rape honey, so the chlorogenic acid and ellagic acid could be considered as possible markers of acacia and rape honeys, respectively. Samples were classified by cluster analysis and principal component analysis (PCA) according to the contents of phenolic acids. The results of PCA showed that chlorogenic acid and ellagic acid were the major variables, and no adulterated sample was identified as authentic honey. The results of cluster analysis (CA) indicated that the samples were appropriately divided into three main clusters, and adulterated samples were identified. Therefore, acacia honey adulteration with rape honey could be undoubtedly detected by LC-ECD combined with chemometric methods down to the level of 5 %.