Charcoal column/thin layer chromatographic method for high fructose corn sirup and spectrophotometric method for hydroxymethylfurfural in honey: collaborative studies.

A new spectrophotometric method is described for determining hydroxymethylfurfural in honey in which interfering background absorption of honey is corrected for by use of a bisulfite-treated sample as blank. Two procedures for detecting high-fructose corn sirup (HFCS) in honey were also tested. In one, charcoal column pretreatment is used to concentrate trace oligosaccharides, followed by thin layer chromatography to differentiate those of HFCS from those of honey. The other method depends on measurement of the isomaltose/maltose ratio by gas-liquid chromatography. The charcoal/thin layer chromatographic method for HFCS has been adopted official first action. The bisulfite method for hydroxymethylfurfural has been adopted interim first action.     

A new methodology based on GC-MS to detect honey adulteration with commercial syrups

Honey adulterations can be carried out by addition of inexpensive sugar syrups, such as high fructose corn syrup (HFCS) and inverted syrup (IS). Carbohydrate composition of 20 honey samples (16 nectar and 4 honeydew honeys) and 6 syrups has been studied by GC and GC-MS in order to detect differences between both sample groups. The presence of difructose anhydrides (DFAs) in these syrups is described for the first time in this paper; their proportions were dependent on the syrup type considered. As these compounds were not detected in any of the 20 honey samples analyzed, their presence in honey is proposed as a marker of adulteration. Detection of honey adulteration with HFCS and IS requires a previous enrichment step to remove major sugars (monosaccharides) and to preconcentrate DFAs. A new methodology based on yeast (Saccharomyces cerevisiae) treatment has been developed to allow the detection of DFAs in adulterated honeys in concentrations as low as 5% (w/w).     

Sensitive thin layer chromatographic detection of high fructose corn sirup and other adulterants in honey.

A highly sensitive procedure has been developed to detect the undeclared addition of high fructose corn sirup (HFCS) to honey. Carbohydrates must be separated first to achieve the requisite degree of sensitivity; charcoal-Celite chromatography was used to isolate a fraction containing oligo- and polysaccharides. The fraction was then concentrated and examined by thin layer chromatography on silica gel. Pure honeys yielded only 1 or 2 blue-grey or blue-brown spots at Rf values greater than 0.35; a series of spots or blue streaks extending from the origin characterized adulterated samples. The method detects HFCS and conventional honey adulterants at levels as low as 10% or less of the total mixture. In addition, the procedure detects the presence in honey of all starch-derived sugar sirups tested thus far, regardless of the plant source.     

Detection of sugar adulterants in apple juice using fourier transform infrared spectroscopy and chemometrics

Fourier transform infrared spectroscopy and attenuated total reflection sampling have been used to detect adulteration of single strength apple juice samples. The sample set comprised 224 authentic apple juices and 480 adulterated samples. Adulterants used included partially inverted cane syrup (PICS), beet sucrose (BS), high fructose corn syrup (HFCS), and a synthetic solution of fructose, glucose, and sucrose (FGS). Adulteration was carried out on individual apple juice samples at levels of 10, 20, 30, and 40% w/w. Spectral data were compressed by principal component analysis and analyzed using k-nearest neighbors and partial least squares regression techniques. Prediction results for the best classification models achieved an overall (authentic plus adulterated) correct classification rate of 96.5, 93.9, 92.2, and 82.4% for PICS, BS, HFCS, and FGS adulterants, respectively. This method shows promise as a rapid screening technique for the detection of a broad range of potential adulterants in apple juice.     

Initial study of honey adulteration by sugar solutions using midinfrared (MIR) spectroscopy and chemometrics

Fourier transform infrared (FTIR) spectroscopy and attenuated total reflection (ATR) sampling have been used to detect adulteration of honey samples. The sample set comprised 320 spectra of authentic (n = 99) and adulterated (n = 221) honeys. Adulterants used were solutions containing both d-fructose and d-glucose prepared in the following respective weight ratios: 0.7:1.0, 1.2:1.0 (typical of honey composition), and 2.3:1.0. Each adulterant solution was added to individual honeys at levels of 7, 14, and 21% w/w. Spectral data were compressed and analyzed using k-nearest neighbors (kNN) and partial least squares (PLS) regression techniques. A number of data pretreatments were explored. Best classification models were achieved with PLS regression on first derivative spectra giving an overall correct classification rate of 93%, with 99% of samples adulterated at levels of 14% w/w or greater correctly identified. This method shows promise as a rapid screening technique for detection of this type of honey adulteration.     

基于近红外光谱技术的蜂蜜掺假识别

为了实现蜂蜜掺假的快速识别,应用近红外光谱结合模式识别方法对蜂蜜掺假现象进行了识别分析。该研究收集了中国不同品种、不同地域的典型天然蜂蜜样品,根据目前市场上常见的蜂蜜掺假手段,掺假物质及相对含量情况配制了掺假蜂蜜样品,利用傅立叶近红外光谱仪采集其透反射近红外光谱,分别采用偏最小二乘判别分析（PLS-DA）,独立软模式法（SIMCA）,误差反向传播神经网络（BP-ANN）和最小二乘支持向量机（LS-SVM）等模式识别方法,进行蜂蜜掺假识别研究。研究结果表明：利用这4种方法在蜂蜜中掺入果葡糖浆和果葡糖水的情况下均能很好地识别出掺假蜂蜜样品,其中对于掺入果葡糖浆的掺假情况,校正集的正确判别率均达到95%以上,验证集的正确判别率均达到87%以上,对于掺入果葡糖水的掺假蜂蜜校正集的正确判别率均达到93%以上,验证集的正确判别率均达到84%以上。通过比较4种不同的识别算法,发现采用LS-SVM时,对两种掺假情况下校正集和验证集的正确判别率均达到了100%,表明基于近红外光谱的蜂蜜掺假快速准确识别是可行的。     

Honey protein as internal standard for stable carbon isotope ratio detection of adulteration of honey

Using the difference in stable carbon isotope ratio between a honey and its protein fraction permits objective evaluation of possible adulteration of honey with small amounts (7-20%) as well as larger amounts of corn or cane sugar. The present uncertainty in interpretation of results from pure honey with delta 13C values outside the generally accepted limits for pure honey of -27.5% to -23.5% is eliminated; likewise TLC testing to resolve questionable samples with delta 13C values between -23.5 and -21.5% is not needed. Fifty certified samples of pure honey were used to establish criteria for purity, and 38 other samples with delta 13C values in the "questionable" or "adulterated" range for the AOAC official method were tested. A difference of 1.0% or more between honey and protein fractions is proposed to indicate adulteration.     

Liquid chromatography coupled to isotope ratio mass spectrometry: a new perspective on honey adulteration detection

A new procedure to determine individual sugar (sucrose, glucose, and fructose) 13C isotope ratios, using liquid chromatography-isotope ratio mass spectrometry (HPLC-IRMS), has been developed to improve isotopic methods devoted to the study of honey authenticity. For this purpose 79 commercial honey samples from various origins were analyzed. Values of delta13Choney ranged from -14.2 to -27.2", and delta13Cprotein ranged from -23.6 to -26.9". A very strong correlation is observed between the individual sugar 13C ratios, which are altered in the event of sugar addition, even at low levels. The use of Deltadelta13C [fruct-glu], Deltadelta13C [fruct-suc], and Deltadelta13C [gluc-suc] systematic differences as an authenticity criterion permits the sugar addition [C3, beet sugar; or C4, cane sugar, cane syrup, isoglucose syrup, and high-fructose corn syrup (HFCS)] to be reliably detected (DL = 1-10%). The new procedure has advantages over existing methods in terms of analysis time and sensitivity. In addition, it is the first isotopic method developed that allows beet sugar addition detection.     

Application of Fourier transform midinfrared spectroscopy to the discrimination between Irish artisanal honey and such honey adulterated with various sugar syrups

A collection of authentic artisanal Irish honeys (n = 580) and certain of these honeys adulterated by fully inverted beet syrup (n = 280), high-fructose corn syrup (n = 160), partial invert cane syrup (n = 120), dextrose syrup (n = 160), and beet sucrose (n = 120) was assembled. All samples were adjusted to 70 degrees Bx and scanned in the midinfrared region (800-4000 cm(-1)) by attenuated total reflectance sample accessory. By use of soft independent modeling of class analogy (SIMCA) and partial least-squares (PLS) classification, authentic honey and honey adulterated by beet sucrose, dextrose syrups, and partial invert corn syrup could be identified with correct classification rates of 96.2%, 97.5%, 95.8%, and 91.7%, respectively. This combination of spectroscopic technique and chemometric methods was not able to unambiguously detect adulteration by high-fructose corn syrup or fully inverted beet syrup.     

Quantification of saccharides in multiple floral honeys using fourier transform infrared microattenuated total reflectance spectroscopy

Fourier transform infrared (FTIR) spectroscopy with microattenuated total reflectance (mATR) sampling accessory and chemometrics (partial least squares and principal component regression) was used for the simultaneous determination of saccharides such as fructose, glucose, sucrose, and maltose in honey. Two calibration models were developed. The first model used a set of 42 standard mixtures of fructose, glucose, sucrose, and maltose prepared over the range of concentrations normally present in honey, whereas the second model used a set of 45 honey samples from various floral and regional sources. The developed models were validated with different data sets and verified by high-performance liquid chromatography (HPLC) measurements. The R (2) values between the FTIR-mATR predicted and HPLC results of the different sugars were between 0.971 and 0.993, demonstrating the predictive ability and accuracy of the procedure.     

LC-MS/MS analysis of neonicotinoid insecticides in honey: methodology and residue findings in Austrian honeys

An analytical method for the simultaneous determination of residues of eight neonicotinoid insecticides and two metabolites in honey using LC-MS/MS was developed and validated. Two approaches of sample preparation were investigated, with the final method involving acetonitrile extraction and subsequent cleanup by dispersive solid-phase extraction (QuEChERS type). Validation was based on quintuplicate analysis at three fortification levels and showed satisfactory recoveries (60-114%) and high precision (RSDs between 2.7 and 12.8%). Low limits of detection and quantification could be achieved for all analytes ranging from 0.6 to 5 μg/kg and from 2 to 10 μg/kg, respectively. Investigations of Austrian honey samples revealed the presence of acetamiprid, thiacloprid, and thiamethoxam residues in honey; however, no sample exceeded the maximum residue limits. On average, flower honey samples contained neonicotinoid residues in higher quantities compared to forest honey samples.     

NMR characterization of saccharides in Italian honeys of different floral sources

The saccharide profiles of 5 different botanical species in 86 Italian honey samples were investigated by 1H and 1H-13C NMR spectroscopy. Nineteen saccharides were identified in the aqueous extracts, namely, fructose, glucose, gentiobiose, isomaltose, kojibiose, maltose, maltulose, melibiose, nigerose, palatinose, sucrose, turanose, erlose, isomaltotriose, kestose, maltotriose, melezitose, raffinose, and maltotetraose. PCA performed on NMR spectral regions, in particular between 4.400 and 5.700 ppm and the fructose signal at 4.050 ppm, revealed a partial sample grouping. The score contribution plots derived from PCA performed using the mean values for the buckets of the anomeric region for each floral source allowed the identification of saccharides characterizing different honeys. OPLS-DA models were further evaluated to confirm the previous findings. OPLS-DA models were also built to highlight differences between polyfloral and high mountain polyfloral honeys and between high mountain polyfloral and rhododendron honeys, both collected at high altitude; S-plots highlighted the characteristic saccharides.     

Determination of chemical composition of commercial honey by near-infrared spectroscopy.

The feasibility of using near-infrared spectroscopy to determine chemical composition of commercial honey was examined. The influences of various sample presentation methods and regression models on the performance of calibration equations were also studied. Transmittance spectra with 1 mm optical path length produced the best calibration for all constituents examined. The regression model of modified partial least squares (mPLS) was selected for the calibration of all honey constituents except moisture, for which the optimal calibration was developed with PLS. Validation of the established calibration equations with independent samples showed that the spectroscopic technique could accurately determine the contents of moisture, fructose, glucose, sucrose, and maltose with squared correlation coefficients (R(2)) of 1.0, 0.97, 0.91, 0.86, and 0.93 between the predicted values and the reference values. The prediction accuracy for free acid, lactone, and hydroxymethylfurfural (HMF) contents in honey was poor and unreliable. The study indicates that near-infrared spectroscopy can be used for rapid determination of major components in commercial honey.     

Carbon and nitrogen natural stable isotopes in Slovene honey: adulteration and botanical and geographical aspects

Isotope parameters (δ(13)C(honey), δ(13)C(protein), δ(15)N) were determined for 271 honey samples of 7 types (black locust, multifloral, lime, chestnut, forest, spruce, and fir honeys) from 4 natural geographical regions of Slovenia. Carbon and nitrogen stable isotope ratios were measured to elucidate the applicability of this method in the identification of the botanical and geographical origin of honey and in honey adulteration. Only 2.2% of the samples were adulterated according to the internal standard carbon isotope ratio analysis method. Botanical origin did not have any major influence on the honey isotope profiles; only black locust honey showed higher δ(13)C values. Some differences were seen across different production years, indicating that the influence of season should be further tested. Statistical and multivariate analyses demonstrated differences among honeys of various geographical origins. Those from the Alpine region had low δ(13)C (-26.0‰) and δ(15)N values (1.1‰); those from the Mediterranean region, high δ(13)C (-24.6‰) and medium δ(15)N values (2.2‰); those from the Pannonian region, medium δ(13)C (-25.6‰) and high δ(15)N value (3.0‰); and those from the Dinaric region, medium δ(13)C (-25.7‰) and low δ(15)N values (1.4‰).     

Analysis of sugars in Chinese rice wine by Fourier transform near-infrared spectroscopy with partial least-squares regression

The feasibility of rapid analysis for oligosaccharides, including isomaltose, isomaltotriose, maltose, and panose, in Chinese rice wine by Fourier transform near-infrared (FT-NIR) spectroscopy together with partial least-squares regression (PLSR) was studied in this work. Forty samples of five brewing years (1996, 1998, 2001, 2003, and 2005) were analyzed by NIR transmission spectroscopy with seven optical path lengths (0.5, 1, 1.5, 2, 2.5, 3, and 5 mm) between 800 and 2500 nm. Calibration models were established by PLSR with full cross-validation and using high-performance anion-exchange chromatography coupled with pulsed amperometric detection as a reference method. The optimal models were obtained through wavelength selection, in which the correlation coefficients of calibration (r(cal)) for the four sugars were 0.911, 0.938, 0.925, and 0.966, and the root-mean-square errors of calibrations were 0.157, 0.147, 0.358, and 0.355 g/L, respectively. The validation accuracy of the four models, with correlation coefficients of cross-validation (r(cv)) being 0.718, 0.793, 0.681, and 0.873, were not very satisfactory. This might be due to the low concentrations of the four sugars in Chinese rice wine and the influence of some components having structures similar to those of the four sugars. The results obtained in this study indicated that the NIR spectroscopy technique offers screening capability for isomaltose, isomaltotriose, maltose, and panose in Chinese rice wine. Further studies with a larger Chinese rice wine sample should be done to improve the specificity, prediction accuracy, and robustness of the models.     

Simultaneous determination of dextrose, sucrose, maltose, and lactose in sausage products by liquid chromatography

A liquid chromatographic (LC) method for the simultaneous determination of dextrose, sucrose, maltose, and lactose in sausage products has been developed. Dextrose, sucrose, maltose, and lactose are extracted from comminuted meat products with 52% ethanol. After filtration, the extracts are purified by passing them through a C18 Sep-Pak cartridge and 2 ion exchange resin Econo-columns in series. After concentration and filtration, extracts are analyzed by LC using a normal phase amino column and a differential refractometer detector. Homogeneously ground samples of cooked and fresh sausages are fortified with dextrose, sucrose, maltose, and lactose at 4 different concentrations. Average recovery for dextrose, sucrose, maltose, and lactose at all 4 levels of fortification was greater than 80% with a coefficient of variation less than 10%.     

鱼粉中肉骨粉的可见-近红外光谱快速定性判别方法（简报）

为了能更加快速准确的定性判别鱼粉中是否掺有肉骨粉（MBM）,该文收集中国常用的鱼粉和肉骨粉,制备定标集样品201个,其中111个为掺有不同肉骨粉质量分数（1%～33%）的样品,90个为纯鱼粉,并独立制备113个验证集样品,其中74个为掺有不同肉骨粉质量分数（1%～33%）的样品,39个为纯鱼粉。在400～2 498 nm波长范围内进行光谱扫描,选择合适的光谱预处理方法和光谱范围,采用DPLS方法建立判别分析模型。建立的判别分析模型：数学预处理方法为2-8-6-1,散射校正方法为变量标准化处理（SNV）,光谱范围为全谱（408～ 2 492 nm）,定标模型的正确判断率为95.7%,外部验证正确判断率为95.6%,对于掺入量≥5%MBM时,正确判断率为100%。研究结果证明近红外反射光谱可以提供一种快速鉴别鱼粉中MBM的方法。     

FT-Raman spectroscopic simultaneous determination of fructose and glucose in honey

A new method for mass percentage determination of fructose and glucose based on FT-Raman spectroscopy is evaluated with a standard HPLC-based method. FT-Raman spectra manipulation is done via the spectrometer software, and a PLS (partial least squares) method is developed with the TQ Analyst software (Ver 1. 1a). The simultaneous quantitative determination uses an input range from 1700 to 700 cm(-1) without correction or baseline factors. The standards used in the PLS method are honey samples previously analyzed by HPLC to obtain their mass percentage concentrations in fructose and glucose. The returned results are statistically tested with those of the HPLC method. Both methods appear to score equally in terms of reproducibility. The honey content of the two sugars in total was found up to 40-74%. The honey samples content in fructose and glucose was determined by HPLC (24.1-42.9% and 16.2-33.1%, respectively) and FT-Raman (24.0-40.8% and 21.1-32.2%, respectively).     

Honey characterization and adulteration detection by pattern recognition applied on HPAEC-PAD profiles. 1. Honey floral species characterization

An improved COFRAC (COmité FRan?ais d'ACréditation) method for the analysis and evaluation of the quality of honey by high-performance anion-exchange chromatography of sugar profiles is proposed. With this method, both minor and major sugars are simultaneously analyzed and the technique is integrated in a new chemometric approach, which uses the entire chromatographic sugars profile of each analyzed sample to characterize honey floral species. Sixty-eight authentic honey samples (6 varieties) were analyzed by high-performance anion-exchange chromatography-pulsed amperometric detection. A new algorithm was developed to create automatically the corresponding normalized data matrix, ready-to-use in various chemometric procedures. This algorithm transforms the analytical profiles to produce the corresponding calibrated table of the surfaces or intensities according to retention times of peaks. The possibility of taking into account unknown peaks (those for which no standards are available) allows the maximum chemical information provided by the chromatograms to be retained. The parallel application of principal component analysis (PCA)/linear discriminant analysis (LDA) and artificial neural networks (ANN) shows a high capability in the classification of the analyzed samples (LDA, 93%; ANN, 100%) and a very good discrimination of honey groups. This work is the starting point of the elaboration of a new system designed for the automatic pattern recognition of food samples (first application on honey samples) from chromatographic analyses for food characterization and adulteration detection.     

Floral classification of honey using mid-infrared spectroscopy and surface acoustic wave based z-Nose Sensor

Fourier transform infrared spectroscopy (FTIR) and z-Nose were used as screening tools for the identification and classification of honey from different floral sources. Honey samples were scanned using microattenuated total reflectance spectroscopy in the region of 600-4000 cm(-1). Spectral data were analyzed by principal component analysis, canonical variate analysis, and artificial neural network for classification of the different honey samples from a range of floral sources. Classification accuracy near 100% was achieved for clover (South Dakota), buckwheat (Missouri), basswood (New York), wildflower (Pennsylvania), orange blossom (California), carrot (Louisiana), and alfalfa (California) honey. The same honey samples were also analyzed using a surface acoustic wave based z-Nose technology via a chromatogram and a spectral approach, corrected for time shift and baseline shifts. On the basis of the volatile components of honey, the seven different floral honeys previously mentioned were successfully discriminated using the z-Nose approach. Classification models for FTIR and z-Nose were successfully validated (near 100% correct classification) using 20 samples of unknown honey from various floral sources. The developed FTIR and z-Nose methods were able to detect the floral origin of the seven different honey samples within 2-3 min based on the developed calibrations.