The Parallel Factor Analysis of Beer Fluorescence

A microbial quantity monitoring model based on 3D fluorescence data of the cucumber storeroom gas and its use in providing auxiliary early spoilage warning

A microbial quality prediction model for early warning of cucumber spoilage is proposed based on the fluorescence information of the cucumber storeroom gas.

Impact of temperature and sunlight exposition on locally brewed beers composition revealed by fluorescence spectroscopy coupled with chemometric methods

Fluorescence excitation-emission matrix (EEM) and synchronous scanning fluorescence (SF), coupled with parallel factor (PARAFAC) analysis, principal component analysis (PCA) and Linear discriminant analysis (LDA) methods were used to differentiate 49 lager beer samples and monitor the effects of temperature and sunlight exposition on their composition. EEMs were decomposed into independent fluorescent components. The beer samples were characterized by the presence of excitation/emission (exc/em) peaks at 290/350, 315/345, 340/410, 375/455, 360/420, 400/460, and 437/525 nm, which were ascribed, according to the known beer fluorescent components, respectively to aromatic amino acids, vitamin B6 (pyridoxal), vitamin B6 (pyridoxic acids), vitamin B3, iso-α-acids, vitamin B1, and vitamin B2. The variation of the relative concentration of iso-α-acids in the different beer brands presented the same trend with that of their relative IBU, thus revealing the potency of our method in the assessment of beer bitterness. The impact of temperature and sunlight was assessed by separately monitoring the modifications of the EEMs after 5 h exposition to 40°C temperature and sunlight respectively. Noticeably a variation of the peaks intensity of the iso-α-acids, carbonyl and polyphenols compounds were observed, accompanied by a decrease of the alcohol content, thus indicating beer aging. This method can be useful for the identification and monitoring of beer state during the technological production cycle and storage. PRACTICAL APPLICATION: The present work demonstrates the potency of the fluorescence technique used together with chemometric methods to give valuable information on beer bitterness. Development of rapid quantitative methods for beer bitterness assessment is of great importance for brewing industries.

Do Spectra Live in the Matrix? A Brief Tutorial on Applications of Factor Analysis to Resolving Spectral Datasets of Mixtures

In spite of a rapid growth of data processing software, that has allowed for a huge advancement in many fields of chemistry, some research issues still remain problematic. A standard example of a troublesome challenge is the analysis of multi-component mixtures. The classical approach to such a problem consists of separating each component from a sample and performing individual measurements. The advent of computers, however, gave rise to a relatively new domain of data processing - chemometry - focused on decomposing signal recorded for the sample rather than the sample itself. Regrettably, still a very few chemometric methods are practically used in everyday laboratory routines. The Authors believe that a brief 'user-friendly' guide-like article on several 'flagship' algorithms of chemometrics may, at least partly, stimulate an increased interest in the use of these techniques among researchers specializing in many fields of chemistry. In the paper, five different techniques of factor analysis are used for the analysis of a three-component system of fluorophores. These algorithms, applied on the excitation-emission spectra, recorded for the 'unknown' mixture, allowed to unambiguously determine its composition without the need for physical separation of the components. An example of using chemometric methods for physical chemistry research is also provided. For each presented technique of the data analysis, a short description of its theoretical background followed by an example of its practical performance is given. In addition, the Reader is supplemented with a basic information on matrix algebra, detailed experimental 'recipes', reference specialist literature and ready-to-use MATLAB codes.

Characterization of engine lubricants by fluorescence spectroscopy and chemometrics

In this study, principal component analysis (PCA) and parallel factor analysis (PARAFAC) combined with excitation-emission matrix fluorescence (EEMF) were used to determine the most efficient excitation wavelengths of engine lubricants; identify their fluorophores; classify them and look for correlations between their fluorescence and their physical parameters. EEMF spectra were obtained for the different samples in the range of 260 to 600 nm, and 300 to 700 nm for excitation and emission wavelengths respectively. PCA and PARAFAC showed that the efficient excitation wavelengths for engine lubricants are 300, 350, 400, 450 and 470 nm. These five wavelengths represented the maxima of the PARAFAC recovered excitation profiles, of which two were attributed to fluorene and pyrene. The relative proportions of the PARAFAC retrieved components were used to classify engine lubricants with a satisfactory percentage of classification of 70%. Finally, a good correlation was obtained between some physical parameters (particularly the viscosity) of engine lubricants and their fluorescence.

A Metabolomic Approach to Beer Characterization

The consumers’ interest towards beer consumption has been on the rise during the past decade: new approaches and ingredients get tested, expanding the traditional recipe for brewing beer. As a consequence, the field of “beeromics” has also been constantly growing, as well as the demand for quick and exhaustive analytical methods. In this study, we propose a combination of nuclear magnetic resonance (NMR) spectroscopy and chemometrics to characterize beer. ¹H-NMR spectra were collected and then analyzed using chemometric tools. An interval-based approach was applied to extract chemical features from the spectra to build a dataset of resolved relative concentrations. One aim of this work was to compare the results obtained using the full spectrum and the resolved approach: with a reasonable amount of time needed to obtain the resolved dataset, we show that the resolved information is comparable with the full spectrum information, but interpretability is greatly improved.

Rapid and label free detection of aflatoxin B1 in alcoholic beverages with a microfluid fiber device

A rapid and label free aflatoxin B₁ (AFB₁) microfluid sensor was proposed and tested. The device was fabricated with hollow-core photonics crystal fiber infiltrated with the AFB₁ solution. The autofluorescence emitting from the AFB₁ molecules was detected. The sensor length was optimized. The AFB₁ concentration was tested with a 4 cm long sensor. The best limit of detection was achieved as low as 1.34 ng/ml, which meets the test requirement of the national standards for AFB₁ in food. The effectiveness of this sensor being applied in beer solution was also verified to be a little more sensitive than in aqueous solution. Compared with traditional AFB₁ detection methods, the proposed single-ended device perfectly satisfies the demand of process control in alcoholic beverages manufacture.

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.