近红外光谱结合化学计量学方法检测蜂蜜产地

为了实现蜂蜜产地的快速判别,应用近红外光谱结合化学计量学方法对蜂蜜产地进行了判别分析。kennard-Stone法划分训练集和预测集。光谱用一阶导数加自归一化预处理后,再用小波变换（WT）进行压缩和滤噪。结合滤波后光谱信息,分别用径向基神经网络（RBFNN）和偏最小二乘-线性判别分析（PLS-LDA）建立了苹果蜜产地和油菜蜜产地的判别模型。对不同小波基和分解尺度进行了讨论。对苹果蜜,WT-RBFNN模型和WT-PLS-LDA模型都是小波基为db1、分解尺度为2时的预测精度较好,都为96.2%。对油菜蜜：WT-RBFNN模型在小波基为db4和分解尺度为1时,预测精度较好,为85.7%;WT-PLS-LDA模型在小波基为db9、分解尺度也为1时,预测精度较好,为90.5%。研究表明：WT结合线性的PLS-LDA建模比WT结合非线性的RBFNN建模更适于蜂蜜产地判别;近红外光谱技术具有快速判别蜂蜜产地的潜力。     

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.     

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.     

Rapid quantitative assessment of the adulteration of virgin olive oils with hazelnut oils using Raman spectroscopy and chemometrics

The authentication of extra virgin olive oil and its adulteration with lower-priced oils are serious problems in the olive oil industry. In addition to the obvious effect on producer profits, adulteration can also cause severe health and safety problems. A number of techniques, including chromatographic and spectroscopic methods, have recently been employed to assess the purity of olive oils. In this study Raman spectroscopy together with multivariate and evolutionary computational-based methods have been employed to assess the ability of Raman spectroscopy to discriminate between chemically very closely related oils. Additionally, the levels of hazelnut oils used to adulterate extra virgin olive oil were successfully quantified using partial least squares and genetic programming.     

Detection of hazelnut oil adulteration using FT-IR spectroscopy

Fourier transform infrared spectroscopy (FT-IR) was used to detect the adulteration of hazelnut oil with different types of oils and to detect the adulteration of extra-virgin olive oil with hazelnut oil. Spectra of hazelnut oil, seven other types of oils, extra-virgin olive oil, and the adulterated oils were collected with a FT-IR equipped with a ZnSe-ATR accessory and a MCTA detector. Discriminant analysis and partial least-squares analysis were used to analyze the data. Classification of hazelnut oil, olive oil, and the other types of oils was achieved successfully with FT-IR. The detection level for sunflower oil adulteration of hazelnut oil was 2%, and the correlation coefficient for the PLS model was 0.99. Adulteration of virgin olive oil with hazelnut oil could be detected only at levels of 25% and higher.     

Rapid determination of the attenuation limit of beer using middle-infrared (MIR) spectroscopy and a multivariate model

A new approach for the determination of the attenuation limit of beer samples using the specific fingerprint region of middle-infrared (MIR) spectroscopy in combination with multiple regression by partial least-squares (PLS) was developed using an attenuated total reflectance (ATR) module. A specific spectral region between 1200 and 800 cm(-1) was identified as highly informative for the quantification of the limit of attenuation. The absorptions in this region are induced by vibrational bands of ethanol (1080, 1040, and 880 cm(-1)) and dissolved extract, in majority maltotriose (1160-1140 and 1040-980 cm(-1)). The multivariate calibration results in a root mean squared error of calibration (RMSEC) of 0.40% and a validation procedure with independent samples results in a root mean squared error of validation (RMSEV) of 0.50%. A repeatability test, concerning the precision of the developed MIR method as well as the reference method, was analyzed using Student's t test. The test has shown no significant difference between the two random samples.     

Classification of smoke tainted wines using mid-infrared spectroscopy and chemometrics

In this study, the suitability of mid-infrared (MIR) spectroscopy, combined with principal component analysis (PCA) and linear discriminant analysis (LDA), was evaluated as a rapid analytical technique to identify smoke tainted wines. Control (i.e., unsmoked) and smoke-affected wines (260 in total) from experimental and commercial sources were analyzed by MIR spectroscopy and chemometrics. The concentrations of guaiacol and 4-methylguaiacol were also determined using gas chromatography-mass spectrometry (GC-MS), as markers of smoke taint. LDA models correctly classified 61% of control wines and 70% of smoke-affected wines. Classification rates were found to be influenced by the extent of smoke taint (based on GC-MS and informal sensory assessment), as well as qualitative differences in wine composition due to grape variety and oak maturation. Overall, the potential application of MIR spectroscopy combined with chemometrics as a rapid analytical technique for screening smoke-affected wines was demonstrated.     

Discrimination of irradiated starch gels using FT-Raman spectroscopy and chemometrics

The effects of gamma-irradiation on starch gels were characterized at the molecular level by Fourier transform (FT) Raman spectroscopy. Starches from five different sources were gelatinized and irradiated at 3, 5, and 10 kGy using a Co60 gamma-irradiator. Gamma-irradiation effects on starch gels were noted by the C-H stretch (2800-3000 cm(-1)) and O-H stretch (3000-3600 cm(-1)) and bend (1600-1800 cm(-1)) regions of the FT-Raman spectra. FT-Raman molecular fingerprints obtained through spectral analyses were used for discrimination of the gels based on the extent of irradiation by means of two different pattern-recognition techniques: canonical variate analysis (CVA) and soft modeling of class analogy (SIMCA). A complete discrimination of irradiated starches was attained using a hybrid partial least-squares (PLS) and CVA model, using the spectral variations in the C-H stretch and O-H stretch and bend regions of FT-Raman spectra. Using the same spectral regions, SIMCA predicted 84% of samples correctly.     

Quality evaluation of sugar beet (Beta vulgaris) by near-infrared spectroscopy

The legal method (polarimetric measurement) for the determination of sucrose content and the wet chemical analysis for the quality control of sugar beet uses lead acetate. Because heavy metals are pollutants, the law could forbid their use in the future. Therefore, near-infrared spectroscopy (NIRS) was evaluated as a procedure to replace these methods. However, there are alternatives to lead clarification, such as the use of aluminum salts, which have been applied at many sugar companies. The real advantage of NIRS is in speed and ease of analysis. The aim of this study was to determine simultaneously the concentration of several components which define the industrial quality of beets. The first objective was the determination of sucrose content, which determines the sugar beet price. The standard error of prediction (SEP) was low: 0.11 g of sucrose/100 g of fresh beet. NIRS was also able to determine other beet quality parameters: brix, marc, glucose, nitrogen, sodium, potassium, sugar in molasses (i.e. sucrose in molasses), and juice purity. The results concerning brix, marc, sugar in molasses, and juice purity were satisfactory. NIRS accuracy was lower for the other parameters. Nevertheless, RPD (ratio standard deviation of concentration/SEP) and RER (ratio concentration range/SEP ratio) show that NIRS might be used for the sample screening on nitrogen, potassium, sodium, and glucose content.     

Dose detection of radiated rice by infrared spectroscopy and chemometrics

Infrared spectroscopy based on sensitive wavelengths (SWs) and chemometrics was proposed to discriminate the nine different radiation doses (0, 250, 500, 750, 1000, 1500, 2000, 2500, and 3000 Gy) of rice. Samples ( n = 16 each dose) were selected randomly for the calibration set, and the remaining 36 samples ( n = 4 each dose) were selected for the prediction set. Partial least-squares (PLS) analysis and least-squares-support vector machine (LS-SVM) were implemented for calibration models. PLS analysis was implemented for calibration models with different wavelength bands including near-infrared (NIR) regions and mid-infrared (MIR) regions. The best PLS models were achieved in the MIR (400-4000 cm (-1)) region. Furthermore, different latent variables (5-9 LVs) were used as inputs of LS-SVM to develop the LV-LS-SVM models with a grid search technique and radial basis function (RBF) kernel. The optimal models were achieved with six LVs, and they outperformed PLS models. Moreover, independent component analysis (ICA) was executed to select several SWs based on loading weights. The optimal LS-SVM model was achieved with SWs (756, 895, 1140, and 2980 cm (-1)) selected by ICA and had better performance than PLS and LV-LS-SVM with the parameters of correlation coefficient ( r), root-mean-square error of prediction, and bias of 0.996, 80.260, and 5.172 x 10 (-4), respectively. The overall results indicted that the ICA was an effective way for the selection of SWs, and infrared spectroscopy combined with LS-SVM models had the capability to predict the different radiation doses of rice.     

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.     

Differentiation of carbohydrate gums and mixtures using fourier transform infrared spectroscopy and chemometrics

Guar gum, a nonionic galactomannan, is used as an economical thickener and stabilizer in the food industry and is often combined with xanthan, locust bean gum (LBG), or carboxymethylcellulose (CMC) to promote synergistic changes in viscosity or gelling behavior via intermolecular interactions; however, the adulteration of LBG with guar gum is a well-known industrial problem. The ability to identify the purity of gums and concentrations of individual gums in mixtures would be advantageous for quality control in the food industry. Fourier transform infrared spectroscopy (FTIR) methods are rapid and require minimum sample preparation. The objectives of this study were to evaluate the ability of FTIR techniques to (1) differentiate LBG with a variety of mannose/galactose (M/G) ratios, (2) differentiate guar, LBG, tara, and fenugreek gums, (3) differentiate pure guar gum from guar gum mixed with LBG, xanthan gum, or CMC, (4) quantify LBG, xanthan gum, and CMC in guar gum, and (5) quantify guar gum in LBG. Two FTIR methods were used: diffuse reflectance (DRIFT) on powdered gum samples added to KBr at 5%, w/w, and attenuated total reflectance (ATR) on 1%, w/w, gum solutions. Spectra were collected and then analyzed by multivariate statistical procedures (chemometrics). The DRIFT method provided better discrimination and quantitative results than the ATR method. Canonical variate analysis (CVA) of DRIFT spectra (1200-700 cm(-1)) was able to classify LBG with various M/G ratios, pure galactomannans, and pure versus mixtures of gums with 100% accuracy. Quantification of an individual gum in gum mixtures (0.5-15%, w/w) was possible using partial least-squares (PLS) analysis of DRIFT spectra with R2 > 0.93 and using this approach for quantifying guar gum added to LBG resulted in an R2 > 0.99, RMSEC = 0.29, and RMSEP = 3.31. Therefore, the DRIFT FTIR method could be a useful analytical tool for quality control of select gums and gum mixtures used in the food industry.     

Authentication of organic feed by near-infrared spectroscopy combined with chemometrics: a feasibility study

Organic products tend to retail at a higher price than their conventional counterparts, which makes them susceptible to fraud. In this study we evaluate the application of near-infrared spectroscopy (NIRS) as a rapid, cost-effective method to verify the organic identity of feed for laying hens. For this purpose a total of 36 organic and 60 conventional feed samples from The Netherlands were measured by NIRS. A binary classification model (organic vs conventional feed) was developed using partial least squares discriminant analysis. Models were developed using five different data preprocessing techniques, which were externally validated by a stratified random resampling strategy using 1000 realizations. Spectral regions related to the protein and fat content were among the most important ones for the classification model. The models based on data preprocessed using direct orthogonal signal correction (DOSC), standard normal variate (SNV), and first and second derivatives provided the most successful results in terms of median sensitivity (0.91 in external validation) and median specificity (1.00 for external validation of SNV models and 0.94 for DOSC and first and second derivative models). A previously developed model, which was based on fatty acid fingerprinting of the same set of feed samples, provided a higher sensitivity (1.00). This shows that the NIRS-based approach provides a rapid and low-cost screening tool, whereas the fatty acid fingerprinting model can be used for further confirmation of the organic identity of feed samples for laying hens. These methods provide additional assurance to the administrative controls currently conducted in the organic feed sector.     

Dietary supplement oil classification and detection of adulteration using Fourier transform infrared spectroscopy

Fourier transform infrared spectroscopy (FT-IR) methods and common chemometric techniques [including discriminant analysis (DA), Mahalanobis distances, and Cooman plots] were used to classify various types of dietary supplement oils (DSO) and less expensive, common food oils. Rapid FT-IR methods were then developed to detect adulteration of DSO with select common food oils. Spectra of 14 types of DSO and 5 types of common food oils were collected with an FT-IR equipped with a ZnSe attenuated total reflectance cell and a mercury cadmium telluride A detector. Classification of DSO and some common food oils was achieved successfully using FT-IR and chemometrics. Select DSO were adulterated (2-20% v/v) with the common food oils that had the closest Mahalanobis distance to them in a Cooman plot based on the DA analysis, and data were also analyzed using a partial least-squares (PLS) method. The detection limit for the adulteration of DSO was 2% v/v. Standard curves to determine the adulterant concentration in DSO were also obtained using PLS with correlation coefficients of >0.9. The approach of using FT-IR in combination with chemometric analyses was successful in classifying oils and detecting adulteration of DSO.     

Detection of adulteration in cooked meat products by mid-infrared spectroscopy

Mid-infrared spectroscopy was used to discriminate between pure beef and beef containing 20% w/w of a range of potential adulterants (heart, tripe, kidney, and liver). Spectra were acquired from raw samples and from samples cooked using two different cooking regimes. Chemometric methods (principal component analysis, partial least squares regression, and linear discriminant analysis) applied to the spectra showed that discrimination between the pure and adulterated sample types was possible, irrespective of cooking regime. The cross-validated classification success rate obtained was approximately 97%. Discrimination between all five sample types (pure beef and beef containing one of each of the four adulterants) at each level of cook was also possible, but became more difficult as the cooking level increased.     

Quantitative analysis and detection of adulteration in crab meat using visible and near-infrared spectroscopy

Visible and near-infrared spectroscopy (VIS/NIR) has been used to detect economic adulteration of crab meat samples. Atlantic blue and blue swimmer crab meat samples were adulterated with surimi-based imitation crab meat in 10% increments. Waveform evaluation revealed that the main features seen in the spectral data arise from water absorptions with a decrease in sample absorbance with increasing adulteration level. Prediction and quantitative analysis was done using raw data, a 15-point smoothing average, a first derivative, a second derivative, and 150 wavelength spectral data gathered from a correlogram. Regression analysis included partial least squares (PLS) and principal component analysis (PCR). Both models were able to perform similarly in predicting crab meat adulteration. The best model for both PLS and PCR used the first derivative spectral data gathered from the correlogram, with a standard error of prediction (SEP) of 0.252 and 0.244, respectively. The results suggest that VIS/NIR technology can be successfully used to detect adulteration in crab meat samples adulterated with surimi-based imitation crab meat.     

Detection and quantification of species authenticity and adulteration in crabmeat using visible and near-infrared spectroscopy

Seafood processing often removes morphological properties of seafood species that enable the consumer to distinguish one type of organism from another. For this reason, species substitution is the most common form of economic adulteration in the seafood industry. Visible and near-infrared spectroscopy (Vis/NIR) has been used to detect and quantify species authenticity and adulteration in crabmeat samples. Atlantic blue crabmeat was adulterated with blue swimmer crabmeat in 10% increments. Water absorption bands dominated the main features in the crabmeat spectra, with a decrease in sample absorbance with increasing adulteration percentage. Several data pretreatments, i.e., moving average, combing, first and second derivatives, and multiplicative scatter correction, in addition to the raw data, were investigated for prediction and quantitative data analysis using partial least-squares. In addition, quantitative analysis was done using the full spectrum and a sequential approach in which 50 wavelengths were added sequentially to determine a new model and find an optimal solution. The results suggest that Vis/NIR spectroscopy is a suitable technology that can be applied to detect and quantify species authenticity and adulteration in crabmeat.     

Heterospectral two-dimensional correlation spectroscopy of mid-infrared and Fourier self-deconvolved near-infrared spectra of sugar solutions

The mid- and near-infrared (mid-IR and NIR) spectra of aqueous solutions of glucose and fructose, fructose and galactose, and glucose and galactose were recorded and analyzed by heterospectral two-dimensional correlation spectroscopy (H2D-CS) to determine characteristic NIR wavelengths for each sugar. Fourier self-deconvolution (FSD) was applied to the NIR spectra prior to H2D-CS analysis to help resolve the strongly overlapping sugar absorptions. Examination of the H2D-CS data gave characteristic absorption wavelengths for glucose, fructose, and galactose. The wavelengths identified by H2D-CS were then used to develop multiple linear regression (MLR) calibrations for the quantitative analysis of mixtures of the three sugars in solution. This approach gave comparable results to MLR calibrations based on wavelengths selected by examination of the first- and second-derivative spectra of solutions of the individual sugars.     

Major components of honey analysis by near-infrared transflectance spectroscopy

NIR transflectance spectroscopy was used to analyze fructose, glucose, and moisture in honey. A total of 161 honey samples were collected during 1992 (46), 1995 (58), and 1996 (57). Samples were analyzed by instrumental, enzymatic (fructose and glucose), and refractometric (moisture) methods. Initially, different calibrations were performed for each of the 3 years of sampling. Good predictions were obtained for all three components with equations of the particular year. But good predictions were not always obtained when the equations calculated one year were applied to samples from another year. To perform a lasting calibration, unique calibration (121 samples) and validation (40 samples) sets were built; honeys of the 3 years were included in both sets. Good statistics (bias, standard error of validation (SEV), and R(2)) were obtained for all three components of the validation set. No statistically significant differences (p = 0.05) were found between instrumental and reference methods.     

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.