Chemometric classification of Chinese lager beers according to manufacturer based on data fusion of fluorescence, UV and visible spectroscopies

Interactions between food matrices and odorants: A review

Currently, although odorants of various foods have been thoroughly studied, the regulation of food aromas is still difficult due to the interaction between odorants and food matrices. These complex matrices in food may interact with odorants to change the volatility of odorants, which in turn affect food aroma. Clarifying the interaction between them are promising for predicting food aroma formation, which will provide valuable support for a high-efficiency food industry. Herein, the research progresses on interactions between food matrices and odorants are reviewed. First, the analysis methods and their advantages and disadvantages are introduced and discussed emphatically, including sensory-analysis methods, characterization methods of the volatility changes of odorants, and the research methods of interaction mechanism. Further, the research advances of interactions among proteins, carbohydrates, lipids, and polyphenols with odorants are summarized briefly. Finally, the existing problems are discussed and the research prospects are proposed.

A fast and low-cost approach for the discrimination of commercial aged cachaças using synchronous fluorescence spectroscopy and multivariate classification

BACKGROUND

Cachaça is the distilled beverage typical of Brazil and can be subjected to the aging process in wooden barrels. In addition to oak barrels, cachaça is also aged in barrels of different Brazilian native woods, resulting in a wide variety of its sensory characteristics. In this work, 172 cachaças aged in bálsamo, jequitibá, oak, and umburana barrels were analyzed by synchronous fluorescence spectroscopy and by the classification methods of principal component analysis and partial least squares discriminant analysis. Spectra were preprocessed by the first derivative by Savitzky-Golay smoothing, using a filter width and polynomial order determined through face-centered central composite designs. Multivariate analysis was realized using the spectra recorded at different wavelength differences, and models were compared by the classification errors in the test sets.

RESULTS

The principal component analysis applied to the synchronous fluorescence spectra presented a tendency of separation by the wood used in the aging process, and the partial least squares discriminant analysis model constructed using the fluorescence spectra recorded at a wavelength difference of 30 nm provided better performance parameters (efficiency 91-97%, sensitivity 81-100%, and specificity 91-100%).

CONCLUSION

Synchronous fluorescence spectroscopy offers a promising approach for the classification of cachaças aged in bálsamo, oak, jequitibá, and umburana barrels, and the discriminant model can be used for routine analysis as a screening method. © 2022 Society of Chemical Industry.

An Overview of the Application of Multivariate Analysis to the Evaluation of Beer Sensory Quality and Shelf-Life Stability

Achieving beer quality and stability remains the main challenge for the brewing industry. Despite all the technologies available, to obtain a high-quality product, it is important to know and control every step of the beer production process. Since the process has an impact on the quality and stability of the final product, it is important to create mechanisms that help manage and monitor the beer production and aging processes. Multivariate statistical techniques (chemometrics) can be a very useful tool for this purpose, as they facilitate the extraction and interpretation of information from brewing datasets by managing the connections between different types of data with multiple variables. In addition, chemometrics could help to better understand the process and the quality of the product during its shelf life. This review discusses the basis of beer quality and stability and focuses on how chemometrics can be used to monitor and manage the beer quality parameters during the beer production and aging processes.

A single screen-printed electrode in tandem with chemometric tools for the forensic differentiation of Brazilian beers

In the present study a single screen-printed carbon electrode (SPCE) and chemometric techniques were utilized for forensic differentiation of Brazilian American lager beers. To differentiate Brazilian beers at the manufacturer and brand level, the classification techniques: soft independent modeling of class analogy (SIMCA), partial least squares regression discriminant analysis (PLS-DA), and support vector machines discriminant analysis (SVM-DA) were tested. PLS-DA model presented an inconclusive assignment ratio of 20%. On the other hand, SIMCA models had a 0 inconclusive rate but an sensitivity close to 85%. While the non-linear technique (SVM-DA) showed an accuracy of 98%, with 95% sensitivity and 98% specificity. The SPCE-SVM-DA technique was then used to distinguish at brand level two highly frauded beers. The SPCE coupled with SVM-DA performed with an accuracy of 97% for the classification of both brands. Therefore, the proposed electrochemicalsensor configuration has been deemed an appropriate tool for discrimination of American lager beers according to their producer and brands.

Insights into chemical and sensorial aspects to understand and manage beer aging using chemometrics

Beer chemical instability remains, at present, the main challenge in maintaining beer quality. Although not fully understood, after decades of research, significant progress has been made in identifying "aging compounds," their origin, and formation pathways. However, as the nature of aging relies on beer manufacturing aspects such as raw materials, process variables, and storage conditions, the chemical profile differs among beers. Current research points to the impact of nonoxidative reactions on beer quality. The effect of Maillard and Maillard intermediates on the final beer quality has become the focus of beer aging research, as prevention of oxidation can only sustain beer quality to some extent. On the other hand, few studies have focused on tracing a profile of whose compound is sensory relevant to specific types of beer. In this matter, the incorporation of "chemometrics," a class of multivariate statistic procedures, has helped brewing scientists achieve specific correlations between the sensory profile and chemical data. The use of chemometrics as exploratory data analysis, discrimination techniques, and multivariate calibration techniques has made the qualitatively and quantitatively translation of sensory perception of aging into manageable chemical and analytical parameters. However, despite their vast potential, these techniques are rarely employed in beer aging studies. This review discusses the chemical and sensorial bases of beer aging. It focuses on how chemometrics can be used to their full potential, with future perspectives and research to be incorporated in the field, enabling a deeper and more specific understanding of the beer aging picture.

Excitation-emission matrix fluorescence spectroscopy coupled with multi-way chemometric techniques for characterization and classification of Chinese lager beers

This paper proposed excitation-emission matrix fluorescence spectroscopy coupled with multi-way chemometric techniques for characterization and classification of Chinese pale lager beers produced by different manufacturers. The undiluted and diluted beer samples presented different fluorescence fingerprints. Three-way and four-way parallel factor analysis (PARAFAC) were used to decompose the skillfully constructed three-way and four-way data arrays, respectively, to further achieve beer characterization and feature extraction. Based on the features extracted in different ways, four strategies for beer classification were proposed. In each strategy, three supervised classification methods including linear discriminant analysis (LDA), partial least squares discriminant analysis (PLS-DA) and k-nearest neighbor (kNN) were used to build discriminant models. By comparison, PARAFAC-data fusion-kNN method in strategy 3 and four-way PARAFAC-kNN method in strategy 4 obtained the best classification results. The classification strategy based on four-way sample-excitation-emission-dilution level data array was proposed to solve the problem of beer classification for the first time.

Detection of milk powder in liquid whole milk using hydrolyzed peptide and intact protein mass spectral fingerprints coupled with data fusion technologies

Detection of the presence of milk powder in liquid whole milk is challenging due to their similar chemical components. In this study, a sensitive and robust approach has been developed and tested for potential utilization in discriminating adulterated milk from liquid whole milk by analyzing the intact protein and hydrolyzed peptide using ultra‐performance liquid chromatography with quadrupole time‐of‐flight mass spectrometer (UPLC‐QTOF‐MS) fingerprints combined with data fusion. Two different datasets from intact protein and peptide fingerprints were fused to improve the discriminating ability of principle component analysis (PCA). Furthermore, the midlevel data fusion coupled with PCA could completely distinguish liquid whole milk from the milk. The limit of detection of milk powder in liquid whole milk was 0.5% (based on the total protein equivalence). These results suggested that fused data from intact protein and peptide fingerprints created greater synergic effect in detecting milk quality, and the combination of data fusion and PCA analysis could be used for the detection of adulterated milk.

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.

Tea types classification with data fusion of UV-Vis, synchronous fluorescence and NIR spectroscopies and chemometric analysis

The potential of selected spectroscopic methods - UV-Vis, synchronous fluorescence and NIR as well a data fusion of the measurements by these methods - for the classification of tea samples with respect to the production process was examined. Four classification methods - Linear Discriminant Analysis (LDA), Quadratic Discriminant Analysis (QDA), Regularized Discriminant Analysis (RDA) and Support Vector Machine (SVM) - were used to analyze spectroscopic data. PCA analysis was applied prior to classification methods to reduce multidimensionality of the data. Classification error rates were used to evaluate the performance of these methods in the classification of tea samples. The results indicate that black, green, white, yellow, dark, and oolong teas, which are produced by different methods, are characterized by different UV-Vis, fluorescence, and NIR spectra. The lowest error rates in the calibration and validation data sets for individual spectroscopies and data fusion models were obtained with the use of the QDA and SVM methods, and did not exceed 3.3% and 0.0%, respectively. The lowest classification error rates in the validation data sets for individual spectroscopies were obtained with the use of RDA (12,8%), SVM (6,7%), and QDA (2,7%), for the UV-Vis, SF, and NIR spectroscopies, respectively. NIR spectroscopy combined with QDA outperformed other individual spectroscopic methods. Very low classification errors in the validation data sets - below 3% - were obtained for all the data fusion data sets (SF + UV-Vis, SF + NIR, NIR + UV-Vis combined with the SVM method). The results show that UV-Vis, fluorescence and near infrared spectroscopies may complement each other, giving lower errors for the classification of tea types.

Traceability of wild Paris polyphylla Smith var. yunnanensis based on data fusion strategy of FT-MIR and UV-Vis combined with SVM and random forest

Paris polyphylla Smith var. yunnanensis (Franch.) Hand.-Mazz (PPY) was a frequently used herbal medicine in pharmaceutical field and different provenances might affect the clinical efficacy. Tracing the geographical origin was an important portion for PPY authentication and quality assessment. Present study was compared low-, mid- and high-level data fusion methodology for geographical traceability of PPY samples (161 batches) combined with multivariate classification methods such as support vector machine gird search (SVM-GS) and random forest (RF) on the basis of Fourier transform mid-infrared (FT-MIR) and ultraviolet-visible (UV-Vis) spectra. Compared with the low- and mid-level data fusion strategy results basing on SVM-GS algorithm, result of high-level data fusion method (calculated by RF) was more satisfying. Result of RF basing on high-level data fusion strategy showed that merely two samples were misclassified and one sample was multiple assigned after voting with fuzzy set theory. Values of specificity, sensitivity, and accuracy rates were exceeded 0.91, 0.99 and 90.91%, for each class respectively, satisfying results of these were shown in training and test sets for high-level data fusion method. This feasible result indicated that the RF algorithm could establish a reliable and good performance model in geographical traceability on the basis of high-level data fusion strategy. Combination of high-level data fusion and RF algorithm could consider as a good choice for establishing a discrimination multivariate model for origins identification of PPY samples.

A chemometric approach for the prediction of the aging levels of automatic transmission fluids by mid-infrared spectroscopy

Automatic transmission fluids (ATF) are highly complex multi-component systems with a variety of different additive packages which suffer from manifold aging processes due to interfering factors. This work describes the development of a straightforward approach to model the aging effects by means of Fourier Transform Infrared (FTIR) spectroscopy combined with multivariate data analysis. Therefore, ATF samples were artificially aged under defined conditions by considering effects of product type, temperature, storage time and exposure to metallic materials, yielding 144 samples. For multivariate data analysis, three different approaches have been applied and compared: supervised Fisher's Linear Discriminant Analysis of principal components (PCFDA), regularized FDA (RFDA) of variables, and unsupervised PCA after orthogonalization using Error Removal by Orthogonal Subtraction (EROS + PCA). All methods worked well in reducing unwanted effects and transforming the relevant information to the first components. Combined with k-Nearest-Neighbor (kNN) prediction, RFDA leads to the best model, improving the accuracy ratios by 13%, 41%, and 12% in comparison with direct kNN classification for the target classes storage temperature, additional material and aging level, respectively. These results suggest that RFDA is highly suitable for the reduction of unwanted effects in a dataset with manifold perturbation influences. The model also predicted a correct aging level ranking when applied to unknown field samples.

Analysis of Australian Beers Using Fluorescence Spectroscopy

Classification of a series of Australian beers was performed using synchronous scanning fluorescence spectroscopy and emission-excitation matrices based on the IR fingerprint regions. The results indicate that synchronous scanning fluorescence spectroscopy is a robust and valuable method to discriminate between Australian lager beers based on their brand name. In addition, a subsequent spoiling study revealed that when beers are opened and stored at 4 °C for 4 weeks, the results demonstrated that the beers were not statistically different. The methods and techniques outlined may be of interest to brewing companies and microbrewers to determine the unique beer spectrum.