Application of cryoprobe 1H nuclear magnetic resonance spectroscopy and multivariate analysis for the verification of corsican honey

Proton nuclear magnetic resonance spectroscopy ((1)H NMR) and multivariate analysis techniques have been used to classify honey into two groups by geographical origin. Honey from Corsica (Miel de Corse) was used as an example of a protected designation of origin product. Mathematical models were constructed to determine the feasibility of distinguishing between honey from Corsica and that from other geographical locations in Europe, using (1)H NMR spectroscopy. Honey from 10 different regions within five countries was analyzed. (1)H NMR spectra were used as input variables for projection to latent structures (PLS) followed by linear discriminant analysis (LDA) and genetic programming (GP). Models were generated using three methods, PLS-LDA, two-stage GP, and a combination of PLS and GP (PLS-GP). The PLS-GP model used variables selected by PLS for subsequent GP calculations. All models were generated using Venetian blind cross-validation. Overall classification rates for the discrimination of Corsican and non-Corsican honey of 75.8, 94.5, and 96.2% were determined using PLS-LDA, two-stage GP, and PLS-GP, respectively. The variables utilized by PLS-GP were related to their (1)H NMR chemical shifts, and this led to the identification of trigonelline in honey for the first time.     

Preliminary studies for the differentiation of apple juice samples by chemometric analysis of solid-phase microextraction-gas chromatographic data

A combination of gas chromatography (GC) and chemometrics was evaluated for its ability to differentiate between apple juice samples on the basis of apple variety and applied heat treatment. The heat treatment involved exposure of 15 mL juice samples for 30 s in a 900 W domestic microwave oven. The chromatographic results were subjected to two chemometric procedures: (1) partial least squares (PLS) regression and (2) linear discriminant analysis (LDA) applied to principal component (PC) scores. The percent correct classification of samples were obtained from PLS and LDA in terms of separation on the basis of apple variety and applied heat treatment. PLS gave the highest level of correct classification of the apple juice samples according to both variety and heat treatment, 92.5% correct classification in each case. When LDA was performed on the PC scores obtained from GC analysis, 87.5% and 80% of samples were correctly classified according to apple variety used and applied heat treatment, respectively.     

Chemical authentication of extra virgin olive oil varieties by supervised chemometric procedures.

This work has focused on discriminating extra virgin olive oils from Sabina (Lazio, Italy) by olive fruit variety (cultivar). A set of oils from five of the most widespread cultivars (Carboncella, Frantoio, Leccino, Moraiolo, and Pendolino) in this geographical area was analyzed for chemical composition using only the Official Analytical Methods, recognized for the quality control and commercial classification of this product. The obtained data set was converted into a computer-compatible format, and principal component analysis (PCA) and a method based on the Fisher F ratio were used to reduce the number of variables without a significant loss of chemical information. Then, to differentiate these samples, two supervised chemometric procedures were applied to process the experimental data: linear discriminant analysis (LDA) and artificial neural network (ANN) using the back-propagation algorithm. It was found that both of these techniques were able to generalize and correctly predict all of the samples in the test set. However, these results were obtained using 10 variables for LDA and 6 (the major fatty acid percentages, determined by a single gas chromatogram) for ANN, which, in this case, appears to provide a better prediction ability and a simpler chemical analysis. Finally, it is pointed out that, to achieve the correct authentication of all samples, the selected training set must be representative of the whole data set.     

Characterization of irradiated starches by using FT-Raman and FTIR spectroscopy

Fourier transform infrared (FTIR) and Fourier transform Raman (FT-Raman) methods were used for rapid characterization and classification of selected irradiated starch samples. Biochemical changes due to irradiation were detected using the two vibrational spectroscopic techniques, and canonical variate analysis (CVA) was applied to the spectral data for discriminating starch samples based on the extent of irradiation. The O-H (3000-3600 cm(-1)) stretch, C-H (2800-3000 cm(-1)) stretch, the skeletal mode vibration of the glycosidic linkage (900-950 cm(-1)) in both Raman and infrared spectra, and the infrared band of water adsorbed in the amorphous parts of starches (1550-1750 cm(-1)) were employed in classification analysis of irradiated starches. Spectral data related to water adsorbed in the noncrystalline regions of starches provided a better classification of irradiated starches with 5 partial least-squares (PLS) factors in the multivariate model.     

Predicting the age and type of tuocha tea by fourier transform infrared spectroscopy and chemometric data analysis

Fourier transform infrared (FTIR) spectroscopy combined with chemometric multivariate methods was proposed to discriminate the type (unfermented and fermented) and predict the age of tuocha tea. Transmittance FTIR spectra ranging from 400 to 4000 cm(-1) of 80 fermented and 98 unfermented tea samples from Yunnan province of China were measured. Sample preparation involved finely grinding tea samples and formation of thin KBr disks (under 120 kg/cm(2) for 5 min). For data analysis, partial least-squares (PLS) discriminant analysis (PLSDA) was applied to discriminate unfermented and fermented teas. The sensitivity and specificity of PLSDA with first-derivative spectra were 93 and 96%, respectively. Multivariate calibration models were developed to predict the age of fermented and unfermented teas. Different options of data preprocessing and calibration models were investigated. Whereas linear PLS based on standard normal variate (SNV) spectra was adequate for modeling the age of unfermented tea samples (RMSEP = 1.47 months), a nonlinear back-propagation-artificial neutral network was required for calibrating the age of fermented tea (RMSEP = 1.67 months with second-derivative spectra). For type discrimination and calibration of tea age, SNV and derivative preprocessing played an important role in reducing the spectral variations caused by scattering effects and baseline shifts.     

Geographic classification of spanish and Australian tempranillo red wines by visible and near-infrared spectroscopy combined with multivariate analysis

Visible (vis) and near-infrared (NIR) spectroscopy combined with multivariate analysis was used to classify the geographical origin of commercial Tempranillo wines from Australia and Spain. Wines (n = 63) were scanned in the vis and NIR regions (400-2500 nm) in a monochromator instrument in transmission. Principal component analysis (PCA), discriminant partial least-squares discriminant analysis (PLS-DA) and linear discriminant analysis (LDA) based on PCA scores were used to classify Tempranillo wines according to their geographical origin. Full cross-validation (leave-one-out) was used as validation method when PCA and LDA classification models were developed. PLS-DA models correctly classified 100% and 84.7% of the Australian and Spanish Tempranillo wine samples, respectively. LDA calibration models correctly classified 72% of the Australian wines and 85% of the Spanish wines. These results demonstrate the potential use of vis and NIR spectroscopy, combined with chemometrics as a rapid method to classify Tempranillo wines accordingly to their geographical origin.     

基于冠层光谱的锦橙叶片磷素营养监测研究

以盆栽蓬安100号锦橙施肥调控试验为基础,利用田间冠层光谱信息探索建立植株磷素营养监测技术与方法。通过采集蓬安100号锦橙95个单株样本的冠层光谱信息和室内检测分析叶片磷含量,随机选取76个作为建模样本,19个为检验样本,运用多种光谱预处理方法和偏最小二乘法(Partial least square method,PLS)及内部交叉验证方法建立校正模型与模型检验。结果表明,经多种光谱预处理方法的建模结果比较,冠层原始反射光谱经二阶求导和SNV处理后建立的蓬安100号锦橙叶片磷含量冠层光谱监测模型预测能力和稳健性最佳,其主成分数4个,能表达全波段63%的信息;校正模型相关系数为0.90,偏差Bias=2.45E-10,且RMSEC和RMSEP均最小。模型检验预测的决定系数R2=0.85。因此,利用二阶导数及标准归一化(Standard normal variate transformation,SNV)预处理的田间冠层光谱信息快速无损监测蓬安100号锦橙叶片磷含量具有一定的可行性。     

Determination of geographical origin of olive oils using 13C nuclear magnetic resonance spectroscopy. I - Classification of olive oils of the Puglia region with denomination of protected origin

13C nuclear magnetic resonance spectroscopy was used to classify olive oils from the three production areas of the Puglia region labeled with the "denomination of protected origin" (DPO) Terra di Bari, Colline di Brindisi, and Dauno. High resolution (13)C spectra of 173 olive oil samples were measured, and the intensity data of triacylglycerol resonances were processed by using linear discriminant analysis, which was carried out stepwise for variable selection. The olive oil samples from the DPOs Colline di Brindisi and Terra di Bari were 90% correctly classified, whereas only 74% of "Dauno" DPO oils were classified in the true group. The performance of the discriminant model was verified by applying the cross-validation procedure based on the "leave one out" formalism. The discriminant model was evaluated against a blind test set of olive oils from the three DPO areas. All the oils used for the purpose were correctly assigned to their respective groups, with the exception of the Dauno oil samples based on the Coratina cv. They were misclassified as Terra di Bari oils because of a common monovarietal composition.     

Investigation of the potential utility of single-bounce attenuated total reflectance Fourier transform infrared spectroscopy in the analysis of distilled liquors and wines

A new Fourier transform infrared (FTIR) spectroscopic method based on single-bounce attenuated total reflectance (SB-ATR) spectroscopy was developed for the analysis of distilled liquors and wines. For distilled liquors, a partial least-squares (PLS) calibration was developed for alcohol determination based on the SB-ATR/FTIR spectra of mixtures of ethanol and distilled water. An independent set of 12 different distilled liquor samples was predicted from the PLS calibration, and a standard deviation of the differences for accuracy (SDD(a)) between actual and predicted values of 0.142% (v/v) was obtained. The potential utility of SB-ATR/FTIR spectroscopy for the analysis of wines was initially evaluated based on a comparison with Fourier transform near-infrared (FT-NIR) spectroscopy and FTIR spectroscopy using a flow-through transmission cell. PLS calibrations for alcohol, total reducing sugars, total acidity and pH were developed using pre-analyzed wine samples (n = 28), and for SB-ATR/FTIR spectroscopy, the SDD(a) for the leave-one-out cross-validation statistics were of the order of 0.100% (v/v), 0.707 g L(-1), 0.189 g L(-1) (H2SO4), and 0.230, respectively. Overall, the SB-ATR/FTIR results were better than those obtained using FT-NIR spectroscopy and comparable to those obtained with transmission FTIR spectroscopy. A PLS calibration based on preanalyzed wine samples (n = 72) for the prediction of 11 different components and parameters in wines by SB-ATR/FTIR spectroscopy was subsequently developed and validated using an independent sample set (n = 77). Good coefficients of correlation between the reference and predicted values for the validation set were obtained for most of the components and parameters except citric acid, volatile acids, and total SO2. The results of this study demonstrate the suitability of SB-ATR/FTIR spectroscopy for the routine analysis of distilled liquors and wines.     

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.     

Determination of the geographic origin of onions between three main production areas in Japan and other countries by mineral composition

Onions (Allium cepa L.) are produced in many countries and are one of the most popular vegetables in the world, thus leading to an enormous amount of international trade. It is currently important that a scientific technique be developed for determining geographic origin as a means to detect fraudulent labeling. We have therefore developed a technique based on mineral analysis and linear discriminant analysis (LDA). The onion samples used in this study were from Hokkaido, Hyogo, and Saga, which are the primary onion-growing areas in Japan, and those from countries that export onions to Japan (China, the United States, New Zealand, Thailand, Australia, and Chile). Of 309 samples, 108 were from Hokkaido, 52 were from Saga, 77 were from Hyogo, and 72 were from abroad. Fourteen elements (Na, Mg, P, Mn, Co, Ni, Cu, Zn, Rb, Sr, Mo, Cd, Cs, and Ba) in the samples were determined by frame atomic adsorption spectrometry, inductively coupled plasma optical emission spectrometry, and inductively coupled plasma mass spectrometry. The models established by LDA were used to discriminate the geographic origin between Hokkaido and abroad, Hyogo and abroad, and Saga and abroad. Ten-fold cross-validations were conducted using these models. The discrimination accuracies obtained by cross-validation between Hokkaido and abroad were 100 and 86%, respectively. Those between Hyogo and abroad were 100 and 90%, respectively. Those between Saga and abroad were 98 and 90%, respectively. In addition, it was demonstrated that the fingerprint of an element pattern from a specific production area, which a crop receives, did not easily change by the variations of fertilization, crop year, variety, soil type, and production year if appropriate elements were chosen.     

除草剂胁迫下大麦叶片丙二醛含量的光谱快速检测方法

丙二醛(MDA)是植物衰老和抗性生理研究中的一个重要指标,传统检测方法程序复杂,检测费时。该研究应用近红外光谱技术实现了除草剂胁迫下大麦叶片丙二醛(MDA)含量的简便、无损、快速检测。采集75个大麦叶片样本的近红外光谱数据,比较了Savitzky-Golay平滑(SG)、变量标准化(SNV)、多元散射校正(MSC)等7种预处理方法,建立了大麦叶片丙二醛含量预测的最优偏最小二乘法(PLS)模型,将PLS提取的特征向量(LV)作为最小二乘-支持向量机(LS-SVM)模型的输入变量,建立了LV-LS-SVM模型。选用回归系数(RC)方法提取原始光谱的特征波长,将其分别作为PLS、MLR和LS-SVM的输入变量建立相应模型。将相关系数(r)和预测集均方根误差(RMSEP)作为模型的主要评价指标。结果表明,LV-LS-SVM模型效果优于PLS模型,LV-LS-SVM模型在SNV及MSC预处理后预测效果相同,其预测的r和RMSEP分别为0.9383和10.4598,获得了满意的预测效果。说明应用光谱技术检测大麦叶片中MDA含量是可行的,且预测精度较高,为大麦生长状况的大田监测及除草剂胁迫对大麦抗性等生理信息的快速检测提供了新的途径。     

Determination of olive oil oxidative status by selected ion flow tube mass spectrometry

The emergence of primary and secondary oxidation products in New Zealand extra virgin olive oil during accelerated thermal oxidation was measured and correlated with the concentrations of 13 headspace volatile compounds measured by selected ion flow tube mass spectrometry (SIFT-MS). SIFT-MS is a mass spectrometric technique that permits qualitative and absolute quantitative measurements to be made from whole air, headspace, or breath samples in real-time down to several parts per billion (ppb). It is well-suited to high-throughput analysis of headspace samples. Propanal, hexanal, and acetone were found at high concentrations in a rancid standard oil, while propanal, acetone, and acetic acid showed marked increases with oxidation time for the oils used in this study. A partial least-squares (PLS) regression model was constructed, which allowed the prediction of peroxide values (PV) for three separate oxidized oils. Sensory rancidity was also measured, although the correlations of headspace volatile compounds with sensory rancidity score were less satisfactory, and too few results were available for the construction of a PLS regression model. A fast (approximately 1 min), reliable method for prediction of olive oil PVs by SIFT-MS was developed.     

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.     

分提橄榄油中主要脂肪酸成分傅里叶变换红外光谱法测量（英）

为了测量从橄榄油中分提的高、低熔点油脂的脂肪酸成分,在4 000～600 cm-1 的范围测量了31个含有不同脂肪酸成分植物油的傅里叶变换红外光谱,用于建立偏最小二乘（PLS）回归分析校正模型。在油脂的傅里叶变换红外光谱变量和脂肪酸组成变量之间建立了交叉验证的PLS校正模型。为了校正油酸和亚油酸含量,在4?000～600 cm-1 的频率范围,经平滑,二阶导数,规范化处理的红外光谱获得了最好的交叉验证校正模型和最佳的预测结果。PLS校正模型预测结果表明,与高熔点橄榄油（油酸,72.29%,亚油酸,9.98%）相比,低熔点橄榄油含有较高的油酸含量和亚油酸含量（油酸,77.46%,亚油酸,12.51%）,预测的结果与气相色谱测量的结果有很好的一致性。建立的PLS校正模型预测橄榄油的不饱和脂肪酸含量具有较好的相关性。该研究为分提油脂质量的判别评价提供了便捷的方法。     

Classification of pumpkin seed oils according to their species and genetic variety by attenuated total reflection Fourier-transform infrared spectroscopy

Attenuated total reflection Fourier-transform infrared spectroscopy (ATR-FTIR), followed by multivariate treatment of the spectral data, was used to classify seed oils of the genus Cucurbita (pumpkins) according to their species as C. maxima, C. pepo, and C. moschata. Also, C. moschata seed oils were classified according to their genetic variety as RG, Inivit C-88, and Inivit C-2000. Up to 23 wavelength regions were selected on the spectra, each region corresponding to a peak or shoulder. The normalized absorbance peak areas within these regions were used as predictors. Using linear discriminant analysis (LDA), an excellent resolution among all categories concerning both Cucurbita species and C. moschata varieties was achieved. The proposed method was straightforward and quick and can be easily implemented. Quality control of pumpkin seed oils is important because Cucurbita species and genetic variety are both related to the pharmaceutical properties of the oils.     

The use of carbohydrate profiles and chemometrics in the characterization of natural honeys of identical geographical origin

A study of the real possibilities of carbohydrate profiles and chemometrics to characterize the botanical origin of honey from a single geographical area, the Province of Soria (Spain), is presented. To this end, 14 carbohydrates were quantified using high-performance liquid chromatography (HPLC) with pulsed amperometric detection (PAD) in 77 natural honeys, the botanical origins of which were ling, spike lavender, French lavender, thyme, forest, and multifloral. Principal component analysis has been employed as a first approach to characterize the honey samples analyzed, showing similarities between spike lavender and multifloral honeys. The best discrimination among groups is obtained when four canonical discriminant analyses were carried out sequentially, origin by origin, achieving an overall percentage of success of 90% following cross-validation.     

Origin of French virgin olive oil registered designation of origins predicted by chemometric analysis of synchronous excitation-emission fluorescence spectra

The authentication of virgin olive oil samples requires usually the use of sophisticated and very expensive analytical techniques, so there is a need for fast and inexpensive analytical techniques for use in a quality control methodology. Virgin olive oils present an intense fluorescence spectra. Synchronous excitation-emission fluorescence spectroscopy (SEEFS) was assessed for origin determination of virgin olive oil samples from five French registered designation of origins (RDOs) (Nyons, Vallée des Baux, Aix-en-Provence, Haute-Provence, and Nice). The spectra present bands between 600 and 700 nm in emission due to chlorophylls a and b and pheophytins a and b. The bands between 275 and 400 nm in emission were attributed to alpha-, beta-, and gamma-tocopherols and to phenolic compounds, which characterize the virgin olive oils compared to other edible oils. The chemometric treatment (PLS1) of synchronous excitation-emission fluorescence spectra allows one to determine the origin of the oils from five French RDOs (Baux, Aix, Haute-Provence, Nice, and Nyons). Results were quite satisfactory, despite the similarity between two denominations of origin (Baux and Aix) that are composed by some common cultivars (Aglandau and Salonenque). The interpretation of the regression coefficients shows that RDOs are correlated to chlorophylls, pheophytins, tocopherols, and phenols compounds, which are different for each origin. SEEFS is part of a global analytic methodology that associates spectroscopic and chromatographic techniques. This approach can be used for traceability and vindicates the RDOs.     

Multielement analysis of argentinean lemon juices by instrumental neutronic activation analysis and their classification according to geographical origin

Multielement analysis of lemon juices from different Argentinean regions was carried out by instrumental neutronic activation analysis (INAA) with the aim at developing a reliable method in the traceability of the origin of lemon juices. This work presents a characterization of 44 lemon juice samples selected from three different geographical origins in the Northwest region of Argentina. Multivariate chemometric techniques such principal component analysis and lineal discriminant analysis (LDA) were used with the aim of classifying the juices and identifying the most significant parameters. Eleven elements were determined (Br, As, Na, Rb, La, Cr, Sc, Fe, Co, Zn, and Sb). The analytical method was validated by analyzing the standard reference material IAEA V-10 (hay powder); the results are within +/-10% of the reported values for the majority of the elements. Biplots of LDA scores for the INAA data illustrate clear separation between each sample.     

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.