Usefulness of chemometrics and mass spectrometry-based electronic nose to classify Australian white wines by their varietal origin

A combination of mass spectrometry-based electronic nose (MS e_nose) and chemometrics was explored to classify two Australian white wines according to their varietal origin namely Riesling and unwooded Chardonnay. The MS e_nose data were analysed using principal components analysis (PCA), discriminant partial least squares (DPLS) and linear discriminant analysis (LDA) applied to principal components scores and validated using full cross validation (leave one out). DPLS gave the highest levels of correct classification for both varieties (>90%). LDA classified correctly 73% of unwooded Chardonnay and 82% of Riesling wines. Even though the conventional analysis provides fundamental information about the volatile compounds present in the wine, the MS e_nose method has a series of advantages over conventional analytical techniques due to simplicity of the sample-preparation and reduced time of analysis and might be considered as a more convenient choice for routine process control in an industrial environment. The work reported here is a feasibility study and requires further development with considerably more commercial samples of different varieties. Further studies are needed in order to improve the calibration specificity, accuracy and robustness, and to extend the discrimination to other wine varieties or blends.     

Trace metal studies of selected white wines: an alternative approach

A number of white wines from vineyards outside the metropolitan region of Melbourne were analyzed for the trace metals copper, iron, potassium, sodium, magnesium and calcium by atomic absorption spectrometry (AAS). The 24 wines analyzed included Chardonnay, Sauvignon Blanc, Riesling, Gewürztraminer and Pinot Gris. In addition, preliminary transmission near infrared (NIR) spectrometric analyses for these metals in the above wines indicated, using both multiple linear regression (MLR) and partial least squares (PLS), that promising results may be obtained for some metals. The squared correlation coefficients (R²) and the ratios of the standard error of cross validation (SECV) and the standard deviation (S.D.) for the calibration models for potassium, sodium, magnesium and calcium were acceptable, however those for iron and copper were not. Although, only a small number of wines were used in this study, the preliminary data indicated that NIR spectrometry may provide a suitable, rapid method for analysis for some metals in white wines, and warrants a further investigation using a larger number of white wines over a wider range of locations.     

Near infrared spectroscopy as a rapid tool to measure volatile aroma compounds in Riesling wine: possibilities and limits

Volatile chemical compounds responsible for the aroma of wine are derived from a number of different biochemical and chemical pathways. These chemical compounds are formed during grape berry metabolism, crushing of the berries, fermentation processes (i.e. yeast and malolactic bacteria) and also from the ageing and storage of wine. Not surprisingly, there are a large number of chemical classes of compounds found in wine which are present at varying concentrations (ng L⁻¹ to mg L⁻¹), exhibit differing potencies, and have a broad range of volatilities and boiling points. The aim of this work was to investigate the potential use of near infrared (NIR) spectroscopy combined with chemometrics as a rapid and low-cost technique to measure volatile compounds in Riesling wines. Samples of commercial Riesling wine were analyzed using an NIR instrument and volatile compounds by gas chromatography (GC) coupled with selected ion monitoring mass spectrometry. Correlation between the NIR and GC data were developed using partial least-squares (PLS) regression with full cross validation (leave one out). Coefficients of determination in cross validation (R ²) and the standard error in cross validation (SECV) were 0.74 (SECV: 313.6 μg L⁻¹) for esters, 0.90 (SECV: 20.9 μg L⁻¹) for monoterpenes and 0.80 (SECV: 1658 μg L⁻¹) for short-chain fatty acids. This study has shown that volatile chemical compounds present in wine can be measured by NIR spectroscopy. Further development with larger data sets will be required to test the predictive ability of the NIR calibration models developed.     

Coupling chemometrics and electrochemical-based sensor for detection of bacterial population

This work describes a home-made microelectrode array, based on reticulated vitreous carbon, which has been used to record the normal pulse voltammograms with the aim of obtaining the growth curves of Escherichia coli ATCC 13706 and Pseudomonas aeruginosa ATCC 27853 chosen as test microorganisms. The electrochemical signal data have been analysed with partial least squares (PLS) regression in order to highlight the useful analytical information and correlate with the data obtained from the aerobic plate counting. The obtained PLS models generally had a low root mean square error of cross-validation (RMSECV), a cross validated explained variance percentage near 90%.     

Key wavelengths screening using competitive adaptive reweighted sampling method for multivariate calibration

By employing the simple but effective principle ‘survival of the fittest’ on which Darwin's Evolution Theory is based, a novel strategy for selecting an optimal combination of key wavelengths of multi-component spectral data, named competitive adaptive reweighted sampling (CARS), is developed. Key wavelengths are defined as the wavelengths with large absolute coefficients in a multivariate linear regression model, such as partial least squares (PLS). In the present work, the absolute values of regression coefficients of PLS model are used as an index for evaluating the importance of each wavelength. Then, based on the importance level of each wavelength, CARS sequentially selects N subsets of wavelengths from N Monte Carlo (MC) sampling runs in an iterative and competitive manner. In each sampling run, a fixed ratio (e.g. 80%) of samples is first randomly selected to establish a calibration model. Next, based on the regression coefficients, a two-step procedure including exponentially decreasing function (EDF) based enforced wavelength selection and adaptive reweighted sampling (ARS) based competitive wavelength selection is adopted to select the key wavelengths. Finally, cross validation (CV) is applied to choose the subset with the lowest root mean square error of CV (RMSECV). The performance of the proposed procedure is evaluated using one simulated dataset together with one near infrared dataset of two properties. The results reveal an outstanding characteristic of CARS that it can usually locate an optimal combination of some key wavelengths which are interpretable to the chemical property of interest. Additionally, our study shows that better prediction is obtained by CARS when compared to full spectrum PLS modeling, Monte Carlo uninformative variable elimination (MC-UVE) and moving window partial least squares regression (MWPLSR).     

Multivariate methods on the excitation emission matrix fluorescence spectroscopic data of diesel-kerosene mixtures: a comparative study

Quantitative determination of kerosene fraction present in diesel has been carried out based on excitation emission matrix fluorescence (EEMF) along with parallel factor analysis (PARAFAC) and N-way partial least squares regression (N-PLS). EEMF is a simple, sensitive and nondestructive method suitable for the analysis of multifluorophoric mixtures. Calibration models consisting of varying compositions of diesel and kerosene were constructed and their validation was carried out using leave-one-out cross validation method. The accuracy of the model was evaluated through the root mean square error of prediction (RMSEP) for the PARAFAC, N-PLS and unfold PLS methods. N-PLS was found to be a better method compared to PARAFAC and unfold PLS method because of its low RMSEP values.     

Rapid analysis of grape aroma glycoconjugates using Fourier-transform infrared spectrometry and chemometric techniques

The varietal aroma of most wines from non-aromatic grapes is partly dependent on the qualitative and quantitative composition of glycosidic precursors in grapes. The only rapid method to assess these glycoconjugates (Red-Free Glycosyl-Glucose (G-G) method) allows only their total quantitation. We developed a new method using Fourier-transform infrared spectrometry (FT-IR) and chemometric techniques allowing these glycosidic precursors to be determined more finely. Vitis vinifera cv. Melon B. grapes grown in different areas of Muscadet vineyard (Northwest France) and harvested at different maturity stages were used to demonstrate the potentiality of this analysis method. Predictive partial least squares (PLS) regressions were established using 39 samples, representative of the glycoside variability. These models allowed the total levels of C13-norisoprenoidic and monoterpenic glycoconjugates, the most relevant aroma glycoconjugates for Muscadet wines, to be determined with a predictive error of 14 and 15%, respectively, and the total levels of the other classes of glycoconjugates with predictive errors ranging from 22% for volatile phenols to 36% for aliphatic and aromatic alcohols.     

Selection of individual variables versus intervals of variables in PLSR

The selection abilities of the two well‐known techniques of variable selection, synergy interval‐partial least‐squares (SiPLS) and genetic algorithm‐partial least‐squares (GA‐PLS), have been examined and compared. By using different simulated and real (corn and metabolite) datasets, keeping in view the spectral overlapping of the components, the influence of the selection of either intervals of variables or individual variables on the prediction performances was examined. In the simulated datasets, with decrease in the overlapping of the spectra of components and cases with components of narrow bands, GA‐PLS results were better. In contrast, the performance of SiPLS was higher for data of intermediate overlapping. For mixtures of high overlapping analytes, GA‐PLS showed slightly better performance. However, significant differences between the results of the two selection methods were not observed in most of the cases. Although SiPLS resulted in slightly better performance of prediction in the case of corn dataset except for the prediction of the moisture content, the improvement obtained by SiPLS compared with that by GA‐PLS was not significant. For real data of less overlapped components (metabolite dataset), GA‐PLS that tends to select far fewer variables did not give significantly better root mean square error of cross‐validation (RMSECV), cross‐validated R² (Q²), and root mean square error of prediction (RMSEP) compared with SiPLS. Irrespective of the type of dataset, GA‐PLS resulted in models with fewer latent variables (LVs). When comparing the computational time of the methods, GA‐PLS is considered superior to SiPLS. Copyright © 2010 John Wiley & Sons, Ltd.     

Determination of polyphenols in oats by near-infrared spectroscopy (NIRS) and two-dimensional correlation spectroscopy

Two-dimensional correlation spectroscopy (2DCOS) and near-infrared spectroscopy (NIRS) were used to determine the polyphenol content in oat grain. A partial least squares (PLS) algorithm was used to perform the calibration. A total of 116 representative oat samples from four locations in China were prepared and the corresponding near-infrared spectra were measured. Two-dimensional correlation spectroscopy was employed to select wavelength bands for the PLS regression model for the polyphenol determination. The number of PLS components and intervals was optimized according to the coefficients of determination (R²) and root mean square error of cross validation (RMSECV) in the calibration set. The performance of the final model was evaluated using the correlation coefficient (R) and the root mean square error of validation (RMSEV) in the prediction set. The results showed the band corresponding to the optimal calibration model was between 1350 and 1848?nm and the optimal spectral preprocessing combination was second derivative with second smoothing. The optimal regression model was obtained with an R² of 0.8954 and an RMSECV of 0.06651 in the calibration set and R of 0.9614 and RMSEV of 0.04573 in the prediction set. These measurements reveal the calibration model had qualified predictive accuracy. The results demonstrated that the 2DCOS with PLS was a simple and rapid method for the quantitative determination of polyphenols in oats.     

Application of an electronic tongue based on FT-MIR to emulate the gustative mouthfeel “tannin amount” in red wines

In this work, the ability of an electronic tongue based on Fourier-Transform Mid Infrared (FT-MIR) spectroscopy as a gustative sensor is assessed by emulating the responses of a tasting panel for the gustative mouthfeel “tannin amount”. The FT-MIR spectra were modeled against the sensory responses evaluated in 37 red wines by means of partial least squares (PLS) regression models. In order to find the wavenumbers more correlated with the sensorial attribute and thus providing the best predictive models, six different variable selection techniques were tested. The iterative predictor weighting IPW-PLS technique showed the best results with the smallest RMSEC and RMSECV values (0.07 and 0.13, respectively) using 20 selected wavenumbers. The coincident wavenumbers selected by the six variable selection techniques were interpreted based on the absorption bands of tannin and then a calibration model using these wavenumbers was built to validate the interpretation made.     

Prediction of phenolic compounds in red wine fermentations by visible and near infrared spectroscopy

Near infrared (NIR) spectroscopy was used to simultaneously predict the concentrations of malvidin-3-glucoside (M3G), pigmented polymers (PP) and tannins (T) in red wine. A total of 495 samples from 32 commercial scale red wine fermentations over two vintages using two grape varieties (Cabernet Sauvignon and Shiraz), and also including as additional variables two types of fermenters, two different yeasts, and three fermentation temperatures were used. Samples were scanned in transmission mode (400-2500 nm) using a monochromator instrument (NIRSystems6500). Calibration equations were developed from high performance liquid chromatography (HPLC) and NIR data using partial least squares (PLS) regression with internal cross validation. Using PLS regression, very good calibration statistics (R_cal²>0.80) were obtained for the prediction of M3G, PP and T with standard deviation (S.D.)/standard error in cross validation (SECV) ratio (residual predictive deviation, RPD)) ranging from 1.8 to 5.8. It was concluded that near infrared spectroscopy could be used as rapid alternative method for the prediction of the concentration of phenolic compounds in red wine fermentations.     

The establishment and evaluation of near infrared universal model to determinate the effective ingredient content in pesticide rapidly

A near infrared universal quantitative analysis model was established to determinate the effective ingredient content in pesticide EC(hikemalisation) by the PLS(partial least squares) algorithm,the model predictive ability was evaluated by the external inspection method.The model was established among samples containing the same active ingredient from five different companies, and the model determination coefficient R~2 and RMSECV(root mean square error of cross validation) were 0.9997 and 0.0223, respectively,the relative error between predicted value and chemical value of the testing set samples was between—2.71%and 3.36%,which indicated that the method to determinate the effective ingredient content in pesticide EC by the established universal model can meet the need of pesticide market monitoring.     

Rapid monitoring of grapevine reserves using ATR–FT-IR and chemometrics

Predictions of grapevine yield and the management of sugar accumulation and secondary metabolite production during berry ripening may be improved by monitoring nitrogen and starch reserves in the perennial parts of the vine. The standard method for determining nitrogen concentration in plant tissue is by combustion analysis, while enzymatic hydrolysis followed by glucose quantification is commonly used for starch. Attenuated total reflectance Fourier transform infrared spectroscopy (ATR–FT-IR) combined with chemometric modelling offers a rapid means for the determination of a range of analytes in powdered or ground samples. ATR–FT-IR offers significant advantages over combustion or enzymatic analysis of samples due to the simplicity of instrument operation, reproducibility and speed of data collection. In the present investigation, 1880 root and wood samples were collected from Shiraz, Semillon and Riesling vineyards in Australia and Germany. Nitrogen and starch concentrations were determined using standard analytical methods, and ATR–FT-IR spectra collected for each sample using a Bruker Alpha instrument. Samples were randomly assigned to either calibration or test data sets representing two thirds and one third of the samples respectively. Signal preprocessing included extended multiplicative scatter correction for water and carbon dioxide vapour, standard normal variate scaling with second derivative and variable selection prior to regression. Excellent predictive models for percent dry weight (DW) of nitrogen (range: 0.10–2.65% DW, median: 0.45% DW) and starch (range: 0.25–42.82% DW, median: 7.77% DW) using partial least squares (PLS) or support vector machine (SVM) analysis for linear and nonlinear regression respectively, were constructed and cross validated with low root mean square errors of prediction (RMSEP). Calibrations employing SVM-regression provided the optimum predictive models for nitrogen (R² = 0.98 and RMSEP = 0.07% DW) compared to PLS regression (R² = 0.97 and RMSEP = 0.08% DW). The best predictive models for starch was obtained using PLS regression (R² = 0.95 and RSMEP = 1.43% DW) compared to SVR (R² = 0.95; RMSEP = 1.56% DW). The RMSEP for both nitrogen and starch is below the reported seasonal flux for these analytes in Vitis vinifera. Nitrogen and starch concentrations in grapevine tissues can thus be accurately determined using ATR–FT-IR, providing a rapid method for monitoring vine reserve status under commercial grape production.     

应用遗传算法和PLS的近红外光谱预测玉米中淀粉含量的研究

以普通玉米籽粒为试验材料,在应用遗传算法结合偏最小二乘回归法对近红外光谱数据进行特征波长选择的基础上,应用偏最小二乘回归法建立了特征波长测定玉米籽粒中淀粉含量的校正模型．试验结果表明,基于11个特征波长所建立的校正模型,其校正误差（RMSEC）、交叉检验误差（RMSECV）和预测误差（RMSEP）分别为0.30％、0.35％和0.27％,校正数据集和独立的检验数据集的预测值与实际测定值之间的相关系数分别达到0.9279和0.9390,与全光谱数据所建立的预测模型相比,在预测精度上均有所改善,表明应用遗传算法和PLS进行光谱特征选择,能获得更简单和更好的模型,为玉米籽粒中淀粉含量的近红外测定和红外光谱数据的处理提供了新的方法与途径．     

Detecting Aflatoxin B1 in Peanuts by Fourier Transform Near-Infrared Transmission and Diffuse Reflection Spectroscopy

Aflatioxin B₁ (AFB₁) has been recognized by the International Agency of Research on Cancer as a group 1 carcinogen in animals and humans. A fast, batch, and real-time control and no chemical pollution method was developed for the discrimination and quantification prediction of AFB₁-infected peanuts by applying Fourier transform near-infrared (FT-NIR) coupled with chemometrics. Initially, the near-infrared transmission (NIRT) and diffuse reflection (NIRR) modules were applied to collect spectra of the samples. The principal component analysis (PCA) method was employed to extract the characteristic wavelength, followed by different preprocessing methods (seven methods) to build an effective linear discriminant analysis (LDA) classification and partial least squares (PLS) quantification models. The results showed that, for both the NIRT or NIRR modules, the LDA classification models satisfactorily distinguished peanuts infected with AFB₁ or from those not infected, with external validation showing a 100% correct identification rate and a 0% misjudgment rate. In addition, combined with the concentration of AFB₁ in peanuts determined by enzyme-linked immunoassay assay, the best partial least squares (PLS) models were established, with a combination of the first derivative and the Norris derivative filter smoothing pretreatment (R_c² = 0.937 and 0.984, RMSECV = 3.92% and 2.22%, RPD = 3.98 and 7.91 for NIRR and NIRT, respectively). The correlation coefficient between the predicted value and the reference value in the external verification was 0.998 and 0.917, respectively. This study highlights that both spectral acquisition modules meet the requirements of online, rapid, and accurate identification of peanut AFB₁ infection in the early stages.     

Application of Fourier transform infrared spectroscopy and orthogonal projections to latent structures/partial least squares regression for estimation of procyanidins average degree of polymerisation

Fourier transform infrared (FTIR) spectroscopy has being emphasised as a widespread technique in the quick assess of food components. In this work, procyanidins were extracted with methanol and acetone/water from the seeds of white and red grape varieties. A fractionation by graded methanol/chloroform precipitations allowed to obtain 26 samples that were characterised using thiolysis as pre-treatment followed by HPLC-UV and MS detection. The average degree of polymerisation (DPn) of the procyanidins in the samples ranged from 2 to 11 flavan-3-ol residues. FTIR spectroscopy within the wavenumbers region of 1800-700 cm⁻¹ allowed to build a partial least squares (PLS1) regression model with 8 latent variables (LVs) for the estimation of the DPn, giving a RMSECV of 11.7%, with a R² of 0.91 and a RMSEP of 2.58. The application of orthogonal projection to latent structures (O-PLS1) clarifies the interpretation of the regression model vectors. Moreover, the O-PLS procedure has removed 88% of non-correlated variations with the DPn, allowing to relate the increase of the absorbance peaks at 1203 and 1099 cm⁻¹ with the increase of the DPn due to the higher proportion of substitutions in the aromatic ring of the polymerised procyanidin molecules.     

Effect of Hanseniaspora uvarum–Saccharomyces cerevisiae Mixed Fermentation on Aroma Characteristics of Rosa roxburghii Tratt,Blueberry, and Plum Wines

Hanseniaspora uvarum, a non-Saccharomyces cerevisiae species, has a crucial effect on the aroma characteristics of fruit wines, thus, attracting significant research interest in recent years. In this study, H. uvarum–Saccharomyces cerevisiae mixed fermentation was used to ferment Rosa roxburghii Tratt, blueberry fruit wine, and plum fruit wines using either a co-inoculated or a sequentially inoculated approach. The three fruit wines’ volatile aroma characteristics were analyzed by headspace–solid-phase microextraction–gas chromatography-mass spectrometry (HS–SPME–GC–MS). The results showed that the mixed inoculation of H. uvarum and S. cerevisiae reduced the alcoholic content of Kongxinli fruit wine. Moreover, H. uvarum–S. cerevisiae fermented Rosa roxburghii Tratt, blueberry, and plum fruit wines and further enriched their flavor compounds. The overall flavor characteristics of sequentially inoculated fruit wines differed significantly from those fermented with S. cerevisiae alone, although several similarities were also observed. Sequential inoculation of H. uvarum and S. cerevisiae positively affected the mellowness of the wine and achieved a better harmony of the overall wine flavors. Therefore, H. uvarum–Saccharomyces cerevisiae mixed fermentation can improve the complexity of the wines’ aromatic composition and empower them with a unique identity. In particular, H. uvarum–Saccharomyces cerevisiae blueberry wine produced by mixed fermentation had the widest variety and content of aroma compounds among the fermented wines. Therefore, H. uvarum–Saccharomyces cerevisiae mixed-fermentation inoculation in the three fermented fruit wines significantly increased the aroma compound variety and content, thus, enriching their aroma richness and complexity. This study is the first comparative evaluation of the aroma characteristics of different fruit wines fermented with a mixed inoculation of H. uvarum and S. cerevisiae and provides a preliminary guide for these fruit wines produced with non-Saccharomyces yeast.