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.     

Geographical classification of honey samples by near-infrared spectroscopy: a feasibility study

The potential of near-infrared (NIR) spectroscopy to determine the geographical origin of honey samples was evaluated. In total, 167 unfiltered honey samples (88 Irish, 54 Mexican, and 25 Spanish) and 125 filtered honey samples (25 Irish, 25 Argentinean, 50 Czech, and 25 Hungarian) were collected. Spectra were recorded in transflectance mode. Following preliminary examination by principal component analysis (PCA), modeling methods applied to the spectral data set were partial least-squares (PLS) regression and soft independent modeling of class analogy (SIMCA); various pretreatments were investigated. For unfiltered honey, best SIMCA models gave correct classification rates of 95.5, 94.4, and 96% for the Irish, Mexican, and Spanish samples, respectively; PLS2 discriminant analysis produced a 100% correct classification for each of these honey classes. In the case of filtered honey, best SIMCA models produced correct classification rates of 91.7, 100, 100, and 96% for the Argentinean, Czech, Hungarian, and Irish samples, respectively, using the standard normal variate (SNV) data pretreatment. PLS2 discriminant analysis produced 96, 100, 100, and 100% correct classifications for the Argentinean, Czech, Hungarian, and Irish honey samples, respectively, using a second-derivative data pretreatment. Overall, while both SIMCA and PLS gave encouraging results, better correct classification rates were found using PLS regression.     

Authentication of the botanical and geographical origin of honey by front-face fluorescence spectroscopy

Front-face fluorescence spectroscopy, directly applied on honey samples, was used for the authentication of 11 unifloral and polyfloral honey types (n = 371 samples) previously classified using traditional methods such as chemical, pollen, and sensory analysis. Excitation spectra (220-400 nm) were recorded with the emission measured at 420 nm. In addition, emission spectra were recorded between 290 and 500 nm (excitation at 270 nm) as well as between 330 and 550 nm (excitation at 310 nm). A total of four different spectral data sets were considered for data analysis. Chemometric evaluation of the spectra included principal component analysis and linear discriminant analysis; the error rates of the discriminant models were calculated by using Bayes' theorem. They ranged from <0.1% (polyfloral and chestnut honeys) to 9.9% (fir honeydew honey) by using single spectral data sets and from <0.1% (metcalfa honeydew, polyfloral, and chestnut honeys) to 7.5% (lime honey) by combining two data sets. This study indicates that front-face fluorescence spectroscopy is a promising technique for the authentication of the botanical origin of honey and may also be useful for the determination of the geographical origin within the same unifloral honey type.     

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).     

Authentication of the botanical origin of honey by near-infrared spectroscopy

Fourier transform near-infrared spectroscopy (FT-NIR) was evaluated for the authentication of eight unifloral and polyfloral honey types (n = 364 samples) previously classified using traditional methods such as chemical, pollen, and sensory analysis. Chemometric evaluation of the spectra was carried out by applying principal component analysis and linear discriminant analysis. The corresponding error rates were calculated according to Bayes' theorem. NIR spectroscopy enabled a reliable discrimination of acacia, chestnut, and fir honeydew honey from the other unifloral and polyfloral honey types studied. The error rates ranged from <0.1 to 6.3% depending on the honey type. NIR proved also to be useful for the classification of blossom and honeydew honeys. The results demonstrate that near-infrared spectrometry is a valuable, rapid, and nondestructive tool for the authentication of the above-mentioned honeys, but not for all varieties studied.     

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。     

Authentication of the botanical and geographical origin of honey by mid-infrared spectroscopy

The potential of Fourier transform mid-infrared spectroscopy (FT-MIR) using an attenuated total reflectance (ATR) cell was evaluated for the authentication of 11 unifloral (acacia, alpine rose, chestnut, dandelion, heather, lime, rape, fir honeydew, metcalfa honeydew, oak honeydew) and polyfloral honey types (n = 411 samples) previously classified with traditional methods such as chemical, pollen, and sensory analysis. Chemometric evaluation of the spectra was carried out by applying principal component analysis and linear discriminant analysis, the error rates of the discriminant models being calculated by using Bayes' theorem. The error rates ranged from <0.1% (polyfloral and heather honeys as well as honeydew honeys from metcalfa, oak, and fir) to 8.3% (alpine rose honey) in both jackknife classification and validation, depending on the honey type considered. This study indicates that ATR-MIR spectroscopy is a valuable tool for the authentication of the botanical origin and quality control and may also be useful for the determination of the geographical origin of honey.     

Detection and quantification of apple adulteration in diluted and sulfited strawberry and raspberry purées using visible and near-infrared spectroscopy

Adulteration of sulfited strawberry and raspberry purées by apple is a commercial problem. Strawberry (n = 31) and raspberry (n = 30) purées were prepared from Irish-grown fruit and adulterated at levels of 10-75% w/w using cooking apples. Visible and near-infrared transflectance spectra were recorded using a 0.1 mm sample thickness. Classification and quantification models were developed using raw and scatter-corrected and/or derivatized spectral data. Classification as pure strawberry or raspberry was attempted using soft independent modeling of class analogy. The best models used spectral data in the wavelength ranges 400-1098 nm (strawberry) and 750-1098 nm (raspberry) and produced total correct classification rates of 75% (strawberry) and 95% (raspberry). Quantification of apple content was performed using partial least-squares regression. Lowest predictive errors obtained were 11.3% (raspberry) and 9.0% (strawberry). These results were obtained using spectral data in the wavelength ranges 400-1880 and 1100-1880 nm, respectively. These results suggest minimum detection levels of apple in soft fruit purées of approximately 25 and 20% w/w for raspberry and strawberry, respectively.     

Gas-liquid chromatographic test for honey adulteration by high fructose corn sirup.

A gas-liquid chromatographic (GLC) method has been developed to detect the addition to honey of high fructose corn sirup (HFCS). Samples are derivatized directly with trimethylsilane, cholestane is added as an internal standard, and the levels of maltose (includes other minor disaccharides) and isomaltose are determined after chromatography on OV-17. Domestic and imported honey samples (115) contained 2.00% maltose and 0.71% isomaltose. HFCS samples (21) contained 1.50% maltose, and 2.09% isomaltose. A discriminatory equation was developed (D = 2.73 - 5. 35 (Isomaltose/maltose)) and, when applied to the data for these samples and 37 adulterated samples, 81.4% of authentic honey samples and 78.4% of samples known to be adulterated with HFCS were correctly classified.     

Classification of Italian honeys by 2D HR-NMR

The importance of honey has been recently increased because of its nutrient and therapeutic effects, but the adulteration of honey in terms of botanical origin has increased, too. The floral origin of honeys is usually determined using melisso-palynological analysis and organoleptic characteristics, but the application of these techniques requires some expertise. A number of papers have confirmed the possibility of characterizing honey samples by selected chemical parameters. In this study high-resolution nuclear magnetic resonance (HR-NMR) and multivariate statistical analysis methods were used to identify and classify honeys of five different floral sources. The 71 honey samples (robinia, chestnut, citrus, eucalyptus, polyfloral) were analyzed by HR-NMR using both 1H NMR and heteronuclear multiple bond correlation spectroscopy (HMBC). Spectral data were analyzed by application of unsupervised and supervised pattern recognition and multivariate statistical techniques such as principal component analysis (PCA) and general discriminant analysis (GDA). The use of 1H-(13)C HMBC coupled with appropriate statistical analysis seems to be an efficient technique for the classification of honeys.     

Detecting and quantifying sunflower oil adulteration in extra virgin olive oils from the eastern mediterranean by visible and near-infrared spectroscopy

One hundred and thirty-eight oil samples have been analyzed by visible and near-infrared transflectance spectroscopy. These comprised 46 pure extra virgin olive oils and the same oils adulterated with 1% (w/w) and 5% (w/w) sunflower oil. A number of multivariate mathematical approaches were investigated to detect and quantify the sunflower oil adulterant. These included hierarchical cluster analysis, soft independent modeling of class analogy (SIMCA method), and partial least squares regression (PLS). A number of wavelength ranges and data pretreatments were explored. The accuracy of these mathematical models was compared, and the most successful models were identified. Complete classification accuracy was achieved using 1st derivative spectral data in the 400-2498 nm range. Prediction of adulterant content was possible with a standard error equal to 0.8% using 1st derivative data between 1100 and 2498 nm. Spectral features and chemical literature were studied to isolate the structural basis for these models.     

Authentication of the botanical origin of honey by front-face fluorescence spectroscopy. A preliminary study

The potential of front-face fluorescence spectroscopy for the authentication of unifloral and polyfloral honey types (n = 57 samples) previously classified using traditional methods such as chemical, pollen, and sensory analysis was evaluated. Emission spectra were recorded between 280 and 480 nm (excit: 250 nm), 305 and 500 nm (excit: 290 nm), and 380 and 600 nm (excit: 373 nm) directly on honey samples. In addition, excitation spectra (290-440 nm) were recorded with the emission measured at 450 nm. A total of four different spectral data sets were considered for data analysis. After normalization of the spectra, chemometric evaluation of the spectral data was carried out using principal component analysis (PCA) and linear discriminant analysis (LDA). The rate of correct classification ranged from 36% to 100% by using single spectral data sets (250, 290, 373, 450 nm) and from 73% to 100% by combining these four data sets. For alpine polyfloral honey and the unifloral varieties investigated (acacia, alpine rose, honeydew, chestnut, and rape), correct classification ranged from 96% to 100%. This preliminary study indicates that front-face fluorescence spectroscopy is a promising technique for the authentication of the botanical origin of honey. It is nondestructive, rapid, easy to use, and inexpensive. The use of additional excitation wavelengths between 320 and 440 nm could increase the correct classification of the less characteristic fluorescent varieties.     

Preliminary chemometric study on the use of honey as an environmental marker in Galicia (northwestern Spain)

Thirteen metal elements were determined in 40 honey samples from Galicia with different environmental origins: rural, urban, and industrial areas. The data set of the honey metallic profiles was studied with a double purpose: first, to make a preliminary evaluation of honey as an environmental indicator in Galicia with the aim of monitoring pollution and, second, to compare the different capabilities of diverse pattern recognition prediction procedures for modeling the environmental surrounding of the hive. A certain level of similarity for urban and industrial samples was obtained using principal component analysis and cluster analysis, whereas significant differences for urban and industrial honeys were found in relation to rural honey samples. Different classification rules to associate metal content of honeys with their environmental surrounding were obtained by chemometric pattern recognition procedures. In general, the classification methods developed by neural networks provided better results than the traditional pattern recognition procedures. The metal profiles of honey seem to provide sufficient information to enable categorization criteria for classifying samples according to their environmental surrounding. Thus, honey could be a potential pollution indicator for the Galician area.     

Determination of polarimetric parameters of honey by near-infrared transflectance spectroscopy.

NIR transflectance spectroscopy was used to determine polarimetric parameters (direct polarization, polarization after inversion, specific rotation in dry matter, and polarization due to nonmonosaccharides) and sucrose in honey. In total, 156 honey samples were collected during 1992 (45 samples), 1995 (56 samples), and 1996 (55 samples). Samples were analyzed by NIR spectroscopy and polarimetric methods. Calibration (118 samples) and validation (38 samples) sets were made up; honeys from the three years were included in both sets. Calibrations were performed by modified partial least-squares regression and scatter correction by standard normal variation and detrend methods. For direct polarization, polarization after inversion, specific rotation in dry matter, and polarization due to nonmonosaccharides, good statistics (bias, SEV, and R(2)) were obtained for the validation set, and no statistically (p = 0.05) significant differences were found between instrumental and polarimetric methods for these parameters. Statistical data for sucrose were not as good as those of the other parameters. Therefore, NIR spectroscopy is not an effective method for quantitative analysis of sucrose in these honey samples. However, NIR spectroscopy may be an acceptable method for semiquantitative evaluation of sucrose for honeys, such as those in our study, containing up to 3% of sucrose. Further work is necessary to validate the uncertainty at higher levels.     

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.     

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.     

羊肉纯度电子舌快速检测方法

为实现掺假羊肉的快速、客观评价,利用电子舌对混入不同比例鸡肉的掺假羊肉糜进行检测及定性和定量分析。3种浸提溶液分别浸提,样品量均对电子舌传感器的响应影响极显著;以数据点重复性和聚类效果为依据,采用主成分分析方法确定了电子舌检测羊肉糜样品的较佳条件为0.1 mol/L KCl溶液浸提15 g肉糜样品。在此较佳条件下,对混入不同比例鸡肉的掺假羊肉进行检测,结果表明：采用主成分分析和典则判别分析,前2个主成分累积贡献率均超过80%,电子舌均能很好地区分混入不同比例鸡肉的羊肉糜样品;采用多元线性回归分析和偏最小二乘回归分析建立的定量预测模型能有效预测混入的鸡肉比例（R2>0.99,RMSE<3%）。试验表明：电子舌在羊肉掺入鸡肉的鉴别中具有可行性,研究结果可为羊肉掺假鉴别提供参考。     

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.     

Multivariate correlation between color and mineral composition of honeys and by their botanical origin

The mineral content and color characteristics of 77 honey samples were analyzed. Eighteen minerals were quantified for each honey. Multiple linear regression (MLR) was used to establish equations relating the colorimetric CIELAB coordinates to the mineral data. The results obtained shown that lightness (L) was significantly correlated with S, Ca, Fe, As, Pb, and Cd for the dark honey types (avocado, heather, chestnut, and honeydew). For the light and brown honey types (citrus, rosemary, lavender, eucalyptus, and thyme), C(ab) and b showed the lower correlation with the mineral content of the honeys; their regression functions involve a few independent variables (Mg and Al for b and only Al for C(ab)). Furthermore, by means of application of linear discriminant analysis to the mineral content, it was possible to obtain a model that classifies the honeys by their lightness. The prediction ability of the built model, determined with the test set method, was 85%.     

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‰).