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1.
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.  相似文献   

2.
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.  相似文献   

3.
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.  相似文献   

4.
The potential of intrinsic fluorescence spectroscopy was investigated for differentiating between processed grains (flours, pasta, and semolinas) of different wheat cereal products. A total of 59 samples (e.g., 20 complete Kamut, semicomplete Kamut, and soft wheat flours, 28 pasta, and 11 semolinas manufactured from complete Kamut, semicomplete Kamut, and hard wheat flours) were analyzed by front-face fluorescence spectroscopy. Tryptophan fluorescence spectra were scanned between 305 and 400 nm on samples following excitation at 290 nm. The principal component analysis (PCA) performed on flour spectra clearly differentiated complete Kamut and semicomplete Kamut samples from those produced from complete and semicomplete soft wheat flours. The PCA performed on pasta spectra discriminated samples manufactured from complete Kamut and complete hard wheat flours from those made with semicomplete Kamut and semicomplete hard wheat flours. The best discrimination was obtained from tryptophan spectra recorded on semolinas since the four groups were well discriminated. Correct classification amounting to 61.9% was obtained for pasta spectra. A better classification was obtained for flour and semolina spectra since correct classification amounted to 86.7% and 87.9%, respectively. Front-face fluorescence spectroscopy has the potential to be a rapid, low-cost, and efficient method for the authentication of cereal products.  相似文献   

5.
为研究反复冻融对水产品品质的影响,通过理化方法检测了不同冻融次数处理对大黄鱼解冻损失、pH 值、色泽、硫代巴比妥酸值、羰基含量等指标的影响,并采用前表面荧光光谱结合主成分分析（principal component analysis,PCA）和 Fisher 线性判别分析法（Fisher linear discriminant analysis,FLDA）对不同冻融次数的大黄鱼进行区分。结果显示随着冻融次数增加,大黄鱼的解冻损失显著增加（P<0.05）;pH 值呈先上升后下降的趋势;L*（亮度）值、b*（黄度）值均有不同程度的增加（P<0.05）,a*（红度）值下降（P<0.05）;羰基含量和硫代巴比妥酸反应物值（thiobarbituric acid reactive substances,TBARS）增加（P<0.05）,反复冻融导致大黄鱼的品质下降。色氨酸和烟酰胺腺嘌呤二核苷酸磷酸（nicotinamide adenine dinucleotide,NADH）的荧光光谱分别结合 PCA 和 FLDA 对不同冻融处理组进行分析,结果表明 FLDA 识别效果优于 PCA。通过 FLDA 建立了新鲜大黄鱼与冻融大黄鱼荧光光谱判别模型,发现色氨酸原始判别的准确率和交叉验证的准确率分别为68.3%和66.7%,NADH 原始判别的准确率和交叉验证的准确率均达到100%。由此可见,利用 NADH荧光光谱结合化学计量分析可以鉴别不同冻融处理的大黄鱼。研究结果为水产品新鲜度的快速评价提供参考。  相似文献   

6.
The free amino acid content of 61 honey samples from Estonia has been determined by HPLC-UV with precolumn derivatization with diethyl ethoxymethylenemalonate. Analyzed samples were seven types of unifloral honeys and polyfloral honeys. The main amino acids found in Estonian honeys were proline and phenylalanine. The resulting data have been analyzed by t test and principal component analysis (PCA). t Test revealed that some amino acids (alpha-alanine, beta-alanine, asparagine, gamma-aminobutyric acid, glutamine, glycine, histidine, ornithine, phenylalanine, proline, serine, and tryptophan) are more potent for assigning honey botanical origin than others. PCA enabled differentiation of some honey types by their botanical origin. In the space of the two first principal components, heather honeys form a cluster that is clearly separable from, for example, polyfloral honeys. It is concluded that analysis of the free amino acid profile may serve as a useful tool to assess the botanical origin of Estonian honeys.  相似文献   

7.
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.  相似文献   

8.
The importance of geographical origin determination is an increasing and pressing requirement for all foods. Honey is one of the largest studied foods due to its nutritional and medicinal properties in a correct diet. In this paper, a total of 41 honey samples (polyfloral and acacia) from different countries have been analyzed in terms of (1)H NMR spectroscopy coupled with multivariate statistical methods. Unsupervised principal component analysis resulted as an efficient tool in distinguishing (1)H NMR spectra of polyfloral and acacia honey samples and for geographical characterization of the latter ones. Hierarchical projection to latent structures discriminant analysis was successfully applied for the discrimination among polyfloral honey samples of different geographical origins. (13)C NMR spectroscopy was applied to honey samples with the aim to investigate possible sugar isoforms differentiation. Our preliminary data indicated a different isoforms ratio between betaFP and betaFF only for polyfloral Argentinean samples, while Hungarian samples showed resonance shifts for some carbons of alphaFF, betaFP, betaFF, and alphaGP isoforms for both varieties. These data confirmed the potentiality of (13)C spectroscopy in food characterization, especially in sugar-based foods.  相似文献   

9.
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.  相似文献   

10.
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.  相似文献   

11.
为研究前表面荧光光谱法在水产品品质评价方面的应用,利用前表面荧光对不同冷藏时间的大黄鱼肌肉进行扫描,对色氨酸和烟酰胺腺嘌呤二核苷酸(NADH)的荧光光谱数据进行主成分分析(PCA)和Fisher线性判别分析(FLDA),并运用偏最小二乘回归(PLSR)建立了大黄鱼鱼肉荧光光谱数据和冷藏时间的预测模型。结果表明,用PCA方法提取色氨酸和NADH荧光光谱的有效信息,所建模型可区分不同冷藏时间(0~8 d)的大黄鱼样品,且色氨酸作为内源荧光探针的分析效果更好;用FLDA方法分析色氨酸和NADH荧光光谱,留一法(leave-one-out)交叉验证的判别正确率分别为100%和98%,对不同冷藏时间的大黄鱼区分效果优于PCA方法;PLSR模型中色氨酸和NADH荧光光谱的校正集和预测集的相关系数均大于0.9,交互验证均方根误差(RMSECV)分别约为1.13、0.41,校正集均方根误差(RMSEC)/预测集均方根误差(RMSEP)分别约为0.53、0.99,通过NADH荧光光谱建立的PLSR模型预测能力较好。前表面荧光光谱法结合化学计量学技术能够对不同冷藏时间的大黄鱼进行有效区分。本研究结果为前表面荧光光谱技术在大黄鱼冷藏保鲜中对冷藏时间的预测提供了一定的理论依据。  相似文献   

12.
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.  相似文献   

13.
高光谱遥感技术已广泛应用于植被类型制图。然而,稀疏植被冠层覆盖和土壤背景影响仍然是干旱区植被类型遥感分类的主要挑战,单独利用遥感数据光谱或纹理特征难以获得可靠的分类精度和稳定性。广义正态分布优化算法（Generalized Normal Distribution Optimization,GNDO）的特征优选结果在质量和稳定性方面相较传统优化算法具有优势,但目前还未应用于高光谱波段选取研究。为探索结合ZY-1 02D光谱与纹理特征进行干旱区植被类型遥感分类的可行性,验证GNDO方法应用于高光谱波段选取的有效性,同时探讨不同数量训练像元条件下,各特征选取方法的选择结果差异和对植被类型分类精度的影响,该研究以青海省都兰县宗加镇为例,在随机选取各分类类别不同数量训练像元（30、50、100、150、200）基础上,分别利用遗传算法（Genetic Algorithm,GA）、粒子群优化算法（Particle Swarm Optimization,PSO）、灰狼优化算法（Grey Wolf Optimization,GWO）以及GNDO算法进行高光谱波段选取并对比结果,同时利用灰度共生矩阵（Gray-Level Co-occurrence Matrix,GLCM）方法提取纹理特征,将提取的光谱特征和纹理特征组合成30组分类数据集,利用随机森林（Random Forest,RF）方法完成植被类型自动分类,对比不同分类数据集的分类精度。结果显示：蓝波段（400～450 nm）、红边波段（700～750 nm）和红波段（600～650 nm）对区分植被类型最敏感;基于光谱特征的分类数据集中,使用200个训练像元和GNDO方法进行特征优选获取的分类数据集（GNDO200）获得了最高的总体分类精度（80.44%）;随着训练像元的增加,各分类数据集总体分类精度整体均呈上升趋势,不同的特征选择方法的分类精度对训练像元数量表现出不同的依赖程度;图像纹理特征的加入,明显提升了植被分类精度,将使用200个训练像元和GWO方法进行波段优选的结果与纹理特征结合的分类数据集（GWO200+TEX）获得了最高的总体分类精度（82.86%）。该研究验证了ZY1-02D国产高光谱卫星数据光谱纹理特征结合进行干旱区植被类型划分的潜力,证实了GNDO方法对高光谱波段选取的有效性,为高光谱植被类型制图中光谱、纹理特征选取提供了一种思路。  相似文献   

14.
HPLC-DAD-MS/MS chromatograms of thistle (Galactites tomentosa Moench) unifloral honeys, previously selected by sensory evaluation and melissopalynological analysis, showed high levels of two compounds. One was characterized as phenyllactic acid, a common acid found in honeys, but the other compound was very unusual for honeys. This compound was extracted from honey with ethyl acetate and purified by SPE using C(18), SiOH, and NH(2) phases. Its structure was elucidated on the basis of extensive 1D and 2D NMR experiments as well as HPLC-MS/MS and Q-TOF analysis, and it was identified as lumichrome (7,8-dimethylalloxazine). Lumichrome is known to be the main product of degradation obtained in acid medium from riboflavin (vitamin B(2)), and this is the first report of the presence of lumichrome in honeys. Analysis of the G. tomentosa raw honey and flowers extracts confirmed the floral origin of this compound. The average amount of lumichrome in thistle honey was 29.4 ± 14.9 mg/kg, while phenyllactic acid was 418.6 ± 168.9 mg/kg. Lumichrome, along with the unusual high level of phenyllactic acid, could be used as a marker for the botanical classification of unifloral thistle (G. tomentosa) honey.  相似文献   

15.
We report on the development of a novel alternative method for the assessment of floral origin in honey samples based on the study of honey proteins using immunoblot assays. The main goal of our work was to evaluate the use of honey proteins as chemical markers of the floral origin of honey. Considering that honeybee proteins should be common to all types of honey, we decided to verify the usefulness of pollen proteins as floral origin markers in honey. We used polyclonal anti-pollen antibodies raised in rabbits by repeated immunization of Sunflower (Elianthus annuus) and Eucalyptus (Eucalyptus sp.) pollen extracts. The IgG fraction was purified by immunoaffinity. These antibodies were verified with nitrocellulose blotted pollen and unifloral honey protein extracts. The antibodies anti-Sunflower pollen, bound to the 36 and 33 kDa proteins of Sunflower unifloral honey and to honey containing Sunflower pollen; and the antibodies anti-Eucalyptus sp. pollen bound to the 38 kDa proteins of Eucalyptus sp. unifloral honey in immunoblot assays. Satisfactory results were obtained in differentiating between the types of pollen analyzed and between Sunflower honey and Eucalyptus honey with less cross reactivity with other types of honey from different origin and also with good sensitivity in the detection. This immunoblot method opens an interesting field for the development of new antibodies from different plants, which could serve as an alternative or complementary method to the usual melissopalynological analysis to assess honey floral origin.  相似文献   

16.
Near-infrared (NIR) transflectance spectra of Listeria innocua FH, Lactococcus lactis, Pseudomonas fluorescens, Pseudomonas mendocina, and Pseudomonas putida suspensions were collected and investigated for their potential use in the identification and classification of bacteria. Unmodified spectral data were transformed (first and second derivative) using the Savitzsky-Golay algorithm. Principal component analysis (PCA), partial least-squares discriminant analysis (PLS2-DA), and soft independent modeling of class analogy (SIMCA) were used in the analysis. Using either full cross-validation or separate calibration and prediction data sets, PLS2 regression classified the five bacterial suspensions with 100% accuracy at species level. At Pseudomonas genus level, PLS2 regression classified the three Pseudomonas species with 100% accuracy. In the case of SIMCA, prediction of an unknown sample set produced correct classification rates of 100% except for L. innocua FH (77%). At genus level, SIMCA produced correct classification rates of 96.7, 100, and 100% for P. fluorescens, P. mendocina, and P. putida, respectively. This successful investigation suggests that NIR spectroscopy can become a useful, rapid, and noninvasive tool for bacterial identification.  相似文献   

17.
Modification of an existing single kernel wheat characterization system allowed collection of visible and near-infrared (NIR) reflectance spectra (450–1,688 nm) at a rate of 1 kernel/4 sec. The spectral information was used to classify red and white wheats in an attempt to remove subjectivity from class determinations. Calibration, validation, and prediction results showed that calibrations using partial least squares regression and derived from the full wavelength profile correctly classed more kernels than either the visible region (450–700 nm) or the NIR region (700–1,688 nm). Most results showed >99% correct classification for single kernels when using the visible and NIR regions. Averaging of single kernel classifications resulted in 100% correct classification of bulk samples.  相似文献   

18.
Heat damage is a serious problem frequently associated with wet harvests because of improper storage of damp grain or artificial drying of moist grain at high temperatures. Heat damage causes protein denaturation and reduces processing quality. The current visual method for assessing heat damage is subjective and based on color change. Denatured protein related to heat damage does not always cause a color change in kernels. The objective of this research was to evaluate the use of nearinfrared (NIR) reflectance spectroscopy to identify heat-damaged wheat kernels. A diode-array NIR spectrometer, which measured reflectance spectra (log (1/R)) from 400 to 1,700 nm, was used to differentiate single kernels of heat-damaged and undamaged wheats. Results showed that light scattering was the major contributor to the spectral characteristics of heat-damaged kernels. For partial least squares (PLS) models, the NIR wavelength region of 750–1,700 nm provided the highest classification accuracy (100%) for both cross-validation of the calibration sample set and prediction of the test sample set. The visible wavelength region (400–750 nm) gave the lowest classification accuracy. For two-wavelength models, the average of correct classification for the classification sample set was >97%. The average of correct classification for the test sample set was generally >96% using two-wavelength models. Although the classification accuracies of two-wavelength models were lower than those of the PLS models, they may meet the requirements for industry and grain inspection applications.  相似文献   

19.
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.  相似文献   

20.
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.  相似文献   

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