Fluorescence spectroscopy for discrimination of botrytized wines

Impact of different classification schemes on discrimination of proteins with noise-contaminated spectra using laboratory-measured fluorescence data

Biological agents are important to detect and identify with respect to environmental contamination and public health. Noise contamination in fluorescent spectra is one of the contributors to the uncertainties of identification. In order to investigate the noise-tolerant capability provided by laboratory-measured excitation-emission matrix (EEM) fluorescence spectra that are used as a database, fluorescence properties of four proteinaceous biotoxin samples and ten harmless protein samples were characterized by EEM fluorescence spectra, and the predicting performance of models trained by laboratory-measured fluorescence data was tested and verified from validation data with noise-contaminated spectra. By means of peak signal of noise (PSNR) as an indicator of noise levels, the potential impact of noise contaminations on the characterization and discrimination of these samples was evaluated quantitatively. Different classification schemes utilizing multivariate analysis techniques of Principal Component Analysis (PCA), Random Forest (RF), and Multi-layer Perceptron (MPL) coupled with feature descriptors of differential transform (DT), Fourier transform (FT) and wavelet transform (WT) were conducted under different PSNR values. We systematically analyzed the performance of classification schemes by the case study at 20 PSNR and by statistical analysis from 1-100 PSNR. The results show that the spectral features with EEM-WT decreased the demanding number of input variables while retaining high performances in sample classification. The spectral features with EEM-FT presented the worst performance although having the largest number of features. The distributions of feature importance and contribution were found sensitive to noise contaminations. The classification scheme of PCA prior to MPL with EEM-WT as input presented an improvement in lower PSNR. These results indicate that robust features extracted by corresponding techniques are critical to enhancing the spectral differentiation capabilities among these samples and play an important role in eliminating the noise effect. The study of classification schemes for discriminating protein samples with noise-contaminated spectra presents tremendous potential for future developments in the rapid detection and identification of proteinaceous biotoxins based on three-dimensional fluorescence spectrometry.

Comparative Analysis of Amino Acid and Biogenic Amine Compositions of Fermented Grape Beverages

Amino acids and biogenic amines are important components of food and beverages. In grape-derived products such as wine and wine vinegar, they can have different origins and can influence the odor and taste of the products. Their concentration is influenced by the grape variety, vintage, and winemaking process. In our study, we carried out an LC-MS-based comparative analysis of 22 grape-derived beverages, including three different wine types and four wine vinegar samples from the Tokaj region in Hungary. The concentrations of 23 amino acids and 10 biogenic amines were examined, and the differences among the sample types were analyzed. The differences in the concentrations of some metabolites between Aszú-Furmint pairs originating from the same wineries and year provide information on the effect of botrytized grape on wine composition. Our data can provide further evidence on how the production process shapes the metabolite content of beverages and highlight the nutritional value of wine vinegar.

Texture Feature Extraction from 1H NMR Spectra for the Geographical Origin Traceability of Chinese Yam

Adulteration is widespread in the herbal and food industry and seriously restricts traditional Chinese medicine development. Accurate identification of geo-authentic herbs ensures drug safety and effectiveness. In this study, ¹H NMR combined intelligent "rotation-invariant uniform local binary pattern" identification was implemented for the geographical origin confirmation of geo-authentic Chinese yam (grown in Jiaozuo, Henan province) from Chinese yams grown in other locations. Our results showed that the texture feature of ¹H NMR image extracted with rotation-invariant uniform local binary pattern for identification is far superior compared to the original NMR data. Furthermore, data preprocessing is necessary. Moreover, the model combining a feature extraction algorithm and support vector machine (SVM) classifier demonstrated good robustness. This approach is advantageous, as it is accurate, rapid, simple, and inexpensive. It is also suitable for the geographical origin traceability of other geographical indication agricultural products.

Performance of feature extraction method for classification and identification of proteins based on three-dimensional fluorescence spectrometry

Three-dimensional excitation emission matrix (EEM) fluorescence spectroscopy was employed to discriminate protein samples comprising bovine serum albumin, neurotensin, ovalbumin, ricin, trypsin from bovine pancreas and trypsin from porcine pancreas. Two methods of feature extraction with and without parameterization were applied to the spectral data in order to evaluate their performance of discrimination between protein samples. The discrimination of protein samples was conducted by k-means clustering algorithm and eigenvalue extracting procedure based on principal component analysis (PCA). It was found that the method of feature extraction without parameterization performed best, correctly attributing 100% of the spectral data in the condition of two principal components (PCs) captured. Features extracted with spectral parameterization failed to separate ricin and trypsin from bovine pancreas in same condition. Without spectral parameterization, less dimensionality and unique principal components captured by PCA indicates the spectrally-resolved features of corresponding protein samples. By clustering using each spectrum at fixed excitation wavelength, excitation wavelengths matched with common intrinsic fluorophores were found to be more sensitive to the classification accuracy. Contributions of spectral features extracted from EEM to the principal components were discussed and demonstrated their feature differentiation capabilities among six protein samples. These results reveal that appropriate extraction approach of features in combination with PCA analysis could be used in discrimination of protein samples at species level as a spectroscopic diagnostic tool. Our study provides fundamental references about computational strategies when EEM are used to explore proteins in ambient environment.

Optimization of enantiomer separation in flow-modulated comprehensive two-dimensional gas chromatography by response surface methodology coupled to artificial neural networks: Wine analysis case study

In spite of extensive applications of flow modulated comprehensive two-dimensional gas chromatography (FM-GG × GC) in different research areas, its application in the field of chiral separation is very limited. From a practical point of view, the establishment of experimental parameters for enantiomer separations is possibly more demanding in this case. Since the carrier gas flows in both dimensions, it affects not only the separation parameters, but also the fill/flush volumes of the modulator and its working efficiency. In this context, a multivariate design of experiment was applied to find the optimum experimental parameters of a reversed fill/flush (RFF) modulator for enantiomer separation of organic compounds present in botrytized wine samples. The results were described both with response surface methodology and artificial neural networks (ANN). The enantiomeric composition of chiral compounds present in the botrytized wines was used to identify their geographical origin, by principal component analysis (PCA). In addition, the developed one-class partial least squares (OC-PLS) model enabled recognition of the wine samples from the Tokaj wine region with 93% effectiveness in the presence of other samples.

Multispectral fluorescence sensitivity to acidic and polyphenolic changes in Chardonnay wines - The case study of malolactic fermentation

In this study, stationary and time-resolvedfluorescence signatures, were statistically and chemometrically analyzed among three typologies of Chardonnay wines (A, B and C) with the objectives to evaluate their sensitivity to acidic and polyphenolic changes. For that purpose, a dataset was built using Excitation Emission Matrices of fluorescence (N = 103) decomposed by a Parallel Factor Analysis (PARAFAC), andfluorescence decays (N = 22), mathematically fitted, using the conventional exponential modeling and the phasor plot representation. Wine PARAFAC component C4 coupledwith its phasor plot g and s values enable the description of malolactic fermentation (MLF) occurrence in Chardonnay wines. Such proxies reflect wine concentration modifications in total acidity, malic/lactic and phenol acids.Lower g values among fresh MLF + wines compared to MLF- wines are explained by a quenching effect on wine fluorophores by both organic and phenolic acids.The combination of multispectral fluorescence parametersopens a novel routinely implementable methodology to diagnose fermentative processes.

Varietal classification of white wines by fluorescence spectroscopy

The Slovak Tokaj region is one of the producers of high-quality white wine having protected designations of origin. The main grape varieties of this region are Furmint, Lipovina and Muscat blanc, which have specific sensory characteristics. This research work presents a strategy for the classification of three mentioned varieties of white wines using fluorescence spectroscopy with chemometrics. Emission and synchronous fluorescence spectra were obtained for bulk as well as diluted samples, principal component analysis (PCA) was applied for exploratory analysis and the scores of the selected PCs were used in linear discriminant analysis (LDA). For undiluted samples, the best PCA-LDA models based on either emission spectra excited at 370 nm or synchronous fluorescence spectra obtained at wavelength difference of 40 and 100 nm provided total correct classifications of 100, 100 and 93% for the calibration, validation and prediction steps, respectively. For diluted samples, the best PCA-LDA models (excitation at 280 nm; wavelength difference of 40 nm) again provided total correct classifications as mentioned above.

Coumarins content in wine: application of HPLC, fluorescence spectrometry, and chemometric approach

In this work, high performance liquid chromatography (HPLC) and fluorescence spectrometry methods for determination of natural coumarins in Tokaj wine were developed and compared. Molecularly imprinted solid phase extraction procedure was applied for sample preparation. The separation of esculin, coumarin, herniarin, 4-methylumbelliferone, scoparone, scopoletin was performed on core-shell C18 type of stationary phase (100 × 4.6 mm, 5 µm) with a gradient elution of mobile phase containing 1% aqueous solution of acetic acid and methanol, UV-VIS (280 nm for coumarin) and fluorescence detection (Ex 320 nm, Em 450 nm for other coumarins). The HPLC method was validated in term of linearity, limit of detection, limit of quantification, precision and accuracy. Fluorescence detection offers high sensitivity with limit of detection in the ng mL^-1 range. Scopoletine and 4-methylumbelliferone were identified and quantified in tested wines. Emission spectral data, synchronous fluorescence spectra of coumarins from fluorescence spectrometry and total concentrations of coumarins quantified by the HPLC method were used in the partial least squares regression. The linear regression between the concentrations predicted by the partial least squares model versus true values obtained by HPLC method confirmed good agreement between the two methods.

The Parallel Factor Analysis of Beer Fluorescence

Fluorescence excitation-emission matrices were measured for 111 samples of different types of beer and studied by the parallel factor analysis (PARAFAC). The 5-component PARAFAC model was found to suitably describes the beer fluorescence, accounting for 99.4% of the fluorescence variance in the measured set of samples, and providing the completely resolved excitation and emission spectra of each component. The model was chosen based on a model's core consistency and split-half analysis. It is shown that beer fluorescence is the sum of fluorescence of aromatic amino acids (tryptophan, tyrosine, and phenylalanine), different forms of vitamin B, and phenolic compounds. Obtained PARAFAC model of beer fluorescence demonstrated the potential for the quantification and quality analysis of beer fluorophores and classification of different beer types.

Comparison of different fluorescence techniques in brandy classification by region of production

Fluorescence spectrometry coupled with chemometrics was used to discriminate between 44 brandies originating from different countries. The kind of spectrum (emission, total luminescence and synchronous fluorescence), the geometry of sample illumination (front-face and right angle), and the sample type (bulk and diluted) were considered to compare the brandy classification. Firstly, the emission and synchronous fluorescence spectra (SFS) were processed by the principal component analysis (PCA) and the excitation-emission matrix (EEM) fluorescence spectra were modeled by unfolded PCA and parallel factor analysis (PARAFAC). Secondly, the scores of PCA/PARAFAC components were used in the linear discriminant analysis (LDA). Finally, the quality of the PCA-LDA and PARAFAC-LDA models was compared. Total correct classification using emission spectra was poor, regardless of the experimental conditions. The highest total correct classification (95.5%) was achieved by processing the SFS recorded at wavelength difference of 20 and 60nm on the diluted samples. However, 90.9% observed for bulk samples and their SFS at wavelength difference of 20nm in the right angle geometry as well as EEM fluorescence spectra in both geometries is still an acceptable result.

Computational and spectroscopic analysis of interaction between food colorant citrus red 2 and human serum albumin

The main aim of this work was to gain insight into the binding properties between a food colorant, citrus red 2 (CR), and human serum albumin (HSA), which is the predominant protein in blood plasma. Here, computer simulations and multiple spectroscopies were applied to predict and characterize the interaction between CR and HSA. Docking and molecular dynamics presented a stable binding configuration with low fluctuations. Fluorescence spectroscopy and lifetime results suggested that the CR–HSA combination undergoes static quenching mechanism with binding constant of 10⁵ L/mol. Displacement analysis showed the binding of CR at site I of HSA, which agrees with the docking results. The binding process occured spontaneously and was mainly driven by electrostatic interactions. Synchronous fluorescence and circular dichroism measurements demonstrate the changes in the microenvironment residues and α-helix contents of HSA induced by CR. The computational and experimental techniques are complementary to clearly understand the food colorant transportation and bioaccumulative toxicity in the human body.

Determination of interactions between human serum albumin and niraparib through multi-spectroscopic and computational methods

The interactions between 2-{4-[(3S)-piperidin-3-yl] phenyl}-2H-indazole-7-carboxamide (niraparib) and human serum albumin (HSA) were investigated through fluorescence and computational studies. Fluorescence experiments showed that the static quenching mechanism and the binding constant of the HSA-niraparib system at a single binding site was approximately 4 × 10⁴ L mol^-1. Thermodynamic constants indicated that the binding of niraparib to HSA was mainly driven by electrostatic interactions. Competition experiments and molecular docking simulations revealed that niraparib bound to site III of HSA. Synchronous fluorescence and Fourier transform infrared spectroscopy (FT-IR) results suggested that interactions between niraparib and HSA could affect the conformation and microenvironment of HSA. Circular dichroism (CD) measurements revealed that the α-helix contents of HSA negligibly increased after binding with niraparib. Molecular dynamics simulations demonstrated the stability of the binary HSA-niraparib system and confirmed that electrostatic forces accounted for the dominant contribution to system energy between HSA and niraparib.

A theoretical method for the high-sensitivity fluorescence detection of PAEs through double-substitution modification

A highly sensitive method for the detection of phthalate acid ester (PAE) derivatives by fluorescence spectroscopy based on a double-substitution modification is reported. The fluorescence intensities of 18 PAE derivatives and the template PAEs after docking with the protein bovine serum albumin (BSA) are compared; the fluorescence intensities of the PAE derivatives increase significantly (28.20 to 110.00 times) with high sensitivities, but the functionalities (stability and insulation) are not affected. The persistence, mobility, and toxicity of the PAE derivatives are reduced by varying degrees, and their amounts of bioaccumulation are not significantly changed, indicating that the derivatives are more environmentally friendly. Within the wavelength range 240-420 nm in the fluorescence spectrum, there is no interference between the PAE derivatives and polycyclic aromatic hydrocarbons (PAHs), and the derivatives can be detected with good precision. Based on the analysis of fluorescence characteristics using a 2D quantitative structure-activity relationship (QSAR) model, the occupied orbital energies and Mulliken charge numbers are the main factors that influence the spectral characteristics of the PAE derivatives.