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 %.     

Characteristics of California Navel Orange Juice and Pulpwasn

California navel orange juice, two-stage pulpwash and concentrated orange juice obtained from a commercial processing plant during the 1979–80 processing season were analyzed by chemical, physical and microbiological methods. Statistically significant differences (P < 0.01) were found between single-strength orange juice and pulpwash in the concentration of nitrate, sulfate, phenolics, carbohydrates, protein, pectin, UV/VIS absorption characteristics, minerals and carotenes. Seasonal variations were found in carbohydrates, organic acids, minerals, microbiological data, UV/VIS absorption characteristics, carotenoids, protein and phenolics. This report outlines methods of analysis, summarizes analytical results and discusses application of these findings to the detection of pulpwash adulteration of orange juice.     

Robust new NIRS coupled with multivariate methods for the detection and quantification of tallow adulteration in clarified butter samples

Cows’ butterfat may be adulterated with animal fat materials like tallow which causes increased serum cholesterol and triglycerides levels upon consumption. There is no reliable technique to detect and quantify tallow adulteration in butter samples in a feasible way. In this study a highly sensitive near-infrared (NIR) spectroscopy combined with chemometric methods was developed to detect as well as quantify the level of tallow adulterant in clarified butter samples. For this investigation the pure clarified butter samples were intentionally adulterated with tallow at the following percentage levels: 1%, 3%, 5%, 7%, 9%, 11%, 13%, 15%, 17% and 20% (wt/wt). Altogether 99 clarified butter samples were used including nine pure samples (un-adulterated clarified butter) and 90 clarified butter samples adulterated with tallow. Each sample was analysed by using NIR spectroscopy in the reflection mode in the range 10,000–4000 cm^?1, at 2 cm^?1 resolution and using the transflectance sample accessory which provided a total path length of 0.5 mm. Chemometric models including principal components analysis (PCA), partial least-squares discriminant analysis (PLSDA), and partial least-squares regressions (PLSR) were applied for statistical treatment of the obtained NIR spectral data. The PLSDA model was employed to differentiate pure butter samples from those adulterated with tallow. The employed model was then externally cross-validated by using a test set which included 30% of the total butter samples. The excellent performance of the model was proved by the low RMSEP value of 1.537% and the high correlation factor of 0.95. This newly developed method is robust, non-destructive, highly sensitive, and economical with very minor sample preparation and good ability to quantify less than 1.5% of tallow adulteration in clarified butter samples.     

Statistical Evaluation of Data for Detecting Adulteration of California Navel Orange Juice

Compositional data from California early-, mid-, and late-season navel orange juice, concentrate and two-stage pulpwash were analyzed by various statistical techniques (ratios, regression, chi-square, Linear combination, and discriminant analysis) for detecting adulteration. These techniques were evaluated according to applicability for detecting three types of adulteration: (1) addition of sugar and/or citric acid, (2) addition of excess or unauthorized pulpwash, and (3) addition of other constituents intended to mask the dilution. Most parameters were significantly affected by time of harvest following commercial maturity. Two-stage pulpwash showed small but significant differences from corresponding single strength juice. Product differences were of about the same magnitude as the differences due to harvest date.     

Use of pectinesterase for detection of hydrocolloids addition in natural orange juice

In natural orange juice, pectin is the major constituent responsible for cloud stability. In adulterated and imitation orange juices, for technical reasons, cloud stability is effected by different natural or synthetic hydrocolloids. Pectinesterase, an enzyme specific for pectin hydrolysis, was used to detect substitution of natural pectin of orange juice by other hydrocolloids. Natural orange juice, imitation orange juice and mixtures of them were used in this study. In pasteurized orange juice samples, pectinesterase was added at 20 mg/100 ml of juice. Mixtures were heated at 50°C in a water bath for 24 h and the percentage of clarification, as well as the amount of precipitate formed after centrifugation were measured. A linear correlation was found to exist between natural orange juice percentage in the natural–imitation orange juice mixtures and precipitate formed. A second order relation was found to exist between the percentage of the natural orange juice in the natural–imitation orange juice mixtures and the degree of clarification of orange juice. The method was used successfully for the detection of addition of extraneous hydrocolloids in orange juice for adulteration purposes.     

Targeted and non-targeted detection of lemon juice adulteration by LC-MS and chemometrics

Economically motivated adulteration (EMA) of lemon juice was detected by LC-MS and principal component analysis (PCA). Twenty-two batches of freshly squeezed lemon juice were adulterated by adding an aqueous solution containing 5% citric acid and 6% sucrose to pure lemon juice to obtain 30%, 60% and 100% lemon juice samples. Their total titratable acidities, °Brix and pH values were measured, and then all the lemon juice samples were subject to LC-MS analysis. Concentrations of hesperidin and eriocitrin, major phenolic components of lemon juice, were quantified. The PCA score plots for LC-MS datasets were used to preview the classification of pure and adulterated lemon juice samples. Results showed a large inherent variability in the chemical properties among 22 batches of 100% lemon juice samples. Measurement or quantitation of one or several chemical properties (targeted detection) was not effective in detecting lemon juice adulteration. However, by using the LC-MS datasets, including both chromatographic and mass spectrometric information, 100% lemon juice samples were successfully differentiated from adulterated samples containing 30% lemon juice in the PCA score plot. LC-MS coupled with chemometric analysis can be a complement to existing methods for detecting juice adulteration.     

Characteristics of Israeli Citrus Peel and Citrus Juice

Data on the composition of Israeli orange juice, grapefruit juice, orange peel and grapefruit peel were analyzed. Significant differences were found mainly between the juice and peel in the content of isocitric acid, ash, minerals, total pectin, total flavonoids, chlorides, phosphates, chloramine-T number and arginine. The significance of these findings in detecting juice adulteration with peel extract solids is discussed. The chemical and amino acids characteristics of the citrus products analyzed may be used to establish guidelines required for detecting citrus juice adulteration.     

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     

Combined use of HMF and furosine to assess fresh honey quality

BACKGROUND: The quality of honey can be affected by practices such as adulteration, inadequate storage or the application of severe heat treatments. Because hydroxymehylfurfural (HMF) is an indicator of honey freshness and furosine (ε‐2‐furoylmethyl lysine) has proved to be a useful chemical indicator of the progress of the Maillard reaction in foods, the aim of this work was to assess their usefulness as indicators of fresh honey quality. The effect of heat treatment, storage and adulteration with different types of syrups on HMF and furosine content has been studied. RESULTS: In fresh honey, HMF and furosine values ranged from 0.9 to 14.6 mg kg^?1 of product and from 3.06 to 12.06 g kg^?1 of protein, respectively. Heating of honey samples with different pH (3.76 and 5.14) produced slight increases in HMF content and negligible changes were detected in furosine values. The storage of fresh honey for 2 years caused a high increase in the HMF level, reaching values above EU limits. However, furosine showed a different behavior depending on the type of honey sample. Adulteration assays using different syrups produced an increase in HMF and a decrease of furosine values by dilution effect. HMF content of adulterated honey samples with syrup of known origin did not exceed EU limits. CONCLUSION: These results show the influence of long periods of storage or adulteration, using different percentages of corn or invert sugar syrups, on HMF and furosine content of fresh honey. This seems to indicate that the combination of HMF and furosine may be useful for evaluating the quality of fresh honey. Copyright © 2009 Society of Chemical Industry     

Detection of orange juice adulteration by tangelo juice using multivariate analysis of polymethoxylated flavones and carotenoids

Reverse phase HPLC has been applied to quantify levels of polymethoxylated flavones and carotenoids in orange and tangelo juices. Lower levels of sinensetin and tetramethyl‐o‐scutellarein and higher levels of heptamethoxyflavone and tangeretin relative to nobiletin indicated the addition of tangelo to orange juice. β‐Cryptoxanthin and its esters, identified by positive ion electrospray mass spectrometry, were present in larger amounts relative to β‐carotene in tangelo than in orange juice. Using canonical discriminant analysis, the addition of 100 g kg^?1 tangelo to orange juice can be detected. © 2002 Society of Chemical Industry     

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.     

Non-destructive determination of fat,moisture and protein in salmon fillets by use of near-infrared diffuse spectroscopy

Near infrared (NIR) diffuse spectroscopy was used to determine the fat, moisture and protein contents in whole and ground farmed atlantic salmon fillets. A remote fibre-optic probe was used for NIR measurements on 50 whole salmon fillets. The constituent ranges were: 91-205 g kg^?1 fat, 599-709g kg^?1 moisture and 186-209 g kg^?1 protein. Principal component regression resulted in the following prediction errors for ground salmon fillets, expressed as root mean square error of cross validation: 6.6 g kg-1 fat, 3.8 g kg^?1 moisture and 2.0 g kg^?1 protein. The corresponding prediction errors for non-destructive measurements on whole salmon fillets were 10.8 g kg^?1 fat, 8.5 g kg^?1 moisture and 3.7 g kg^?1 protein. Regression models using the 760-1100 m range gave lower prediction errors than models using the 1100-2500 mm or 760-2500 nm ranges. The results show that fibre-optic probe NIR instruments are suited to determine fat and moisture in whole salmon fillets non-destructively.     

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.     

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.     

Survey of South African fruit juices using a fast screening HILIC-MS method

Adulteration of fruit juices – by the addition of sugar or other less expensive fruit juices as well as preservatives, artificial sweeteners and colours – was tested for by using a developed screening method. The method employs hydrophilic interaction liquid chromatography-mass spectrometry (HILIC-MS) using electrospray ionisation in the negative mode and ultraviolet light detection. Different fruit juices can be differentiated by the content of marker compounds like sorbitol, certain phenolic molecules and their saccharide profile. This method was used to test 46 fruit juice samples from the retail market as well as 12 control samples. The study focused on the main types of fruit juices consumed on the South African market including apple, orange, grape and blends of these juices with other fruits like mango, pear and guava. Overall, the 46 samples tested mostly agreed with label claims. One grape juice sample was adulterated, probably with apple juice. Natamycin above the legal limits was found in two samples. In addition, two samples contained natamycin and one sample benzoate without it being indicated on the label. The method is well suited as a quick screening method for fruit juice adulteration and if used routinely would reduce fruit juice adulteration without the cost of the current array of tests needed for authenticity testing.     

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.     

Classification of distilled alcoholic beverages and verification of adulteration by near infrared spectrometry

Near-infrared (NIR) spectrometry and chemometric methods of classification were used to classify and verify adulteration of 69 samples of alcoholic beverages (whiskey, brandy, rum and vodka). The characterization of the drinks was accomplished by chemometric models based on principal component analysis and soft independent modelling of class analogy elaborated for each different group. Alcoholic beverages adulterated with 5% and 10% (v/v) of water, ethanol or methanol were analyzed and used to verify the models capacity of prediction. Besides, actual samples with suspicions of adulteration were analyzed by gas chromatography and used to test the chemometric models which were able to predict the adulteration in the actual samples. The proposed method was successfully applied in the verification of alcoholic beverages adulteration with 100% of correct prediction at 95% of confidence level. The absence of reagents, low sample consumption, high sampling throughput and good predictive ability enables the developed methodology to be applied as a screening analysis to verify adulteration of the alcoholic beverage, that is, a prior step used to condition the sample to a deeper analysis only when a positive result for adulteration is obtained by the proposed method.     

氢核磁共振结合正交偏最小二乘法对油菜蜜中果葡糖浆掺假的判别分析

利用氢核磁共振(1H nuclear magnetic resonance,1H NMR)谱图结合正交偏最小二乘(orthogonal partial least squares,OPLS)法对油菜蜂蜜和果葡糖浆掺假蜂蜜进行判别分析。采集了303个油菜蜜样品和180个按照不同比例配制的果葡糖浆掺假蜂蜜样品的1H NMR谱图,并对油菜蜜主要糖类成分和部分低含量化合物进行了信号归属。采用OPLS对训练集数据进行分析,建立蜂蜜果葡糖浆掺假判别模型。通过排列实验法对模型进行可靠性检验。结果显示,油菜蜜和果葡糖浆掺假蜂蜜样品在OPLS得分图中能明显区分。训练集和测试集样品的总体判别正确率分别为98.40%和98.24%。因此,1H NMR与OPLS相结合可以实现油菜蜜中果葡糖浆掺假的快速鉴别。该方法是基于对蜂蜜成分的整体分析,避免了仅仅分析个别成分指标的检验方法中存在的缺陷,为蜂蜜质量监控提供了一种新思路。     

Tissue deposition and residue depletion of melamine in fattening pigs following oral administration

The adulteration of animal feed as well as milk products with melamine has led to concerns about the ability to establish appropriate withdrawal intervals to ensure food safety. Two experiments were conducted in this study. The first was to investigate the deposition and depletion of melamine in blood and tissues of pigs exposed to adulterated feed with high doses of melamine. A total of 500 or 1000 mg kg^–1 melamine was added to the diet for fattening pigs (initial BW = ±60.24 kg). Melamine residues were detected in tissues (brain, duodenum, liver, heart, muscle and kidney) by LC-MS/MS. Dose-dependent effects were found between melamine residual concentration and its dose in feed. Five days after the withdrawal of melamine from the diets, the residue concentration in tissues fell below 2.5 mg kg^–1. In the second experiment, blood samples were taken at different time points from fattening pigs (BW = 100 kg) fed with adulterated feed with 1000 mg kg^–1 of melamine for 42 days. Results from the pharmacokinetics analysis showed that it would take 83 h for the melamine level in plasma depleting to the safe level of 50 ng ml^–1 after an expose of 1000 mg kg^–1 melamine contaminated feed for 42 days.