Independent components analysis coupled with 3D-front-face fluorescence spectroscopy to study the interaction between plastic food packaging and olive oil

CFSA-AGD: An accurate crosstalk fluorescence spectroscopic decomposition method for identifying and quantifying FDOMs in aquatic environments

Fluorescent substances exist in various aquatic environments and other environmental media. It is a critical task to identify the components accurately and quantify their contents precisely. Based on the Crosstalk Fluorescence Spectroscopy Analysis (CFSA) model, a fluorescence spectroscopic decomposition using the Alternating Gradient Descent (AGD) algorithm is developed. By reducing the residual error of the model through alternating iterations, the CFSA-AGD method achieves unsupervised model training and automatic spectroscopic decomposition without extra experimental operations such as dilution or absorbance measurement, exempting from tedious modeling process. The objectives of this work are to validate that the CFSA-AGD method can comprehensively address the decomposition of fluorescence spectral crosstalk. Furthermore, the novel method is applied to the spectroscopic decomposition of natural FDOMs in aquatic environments as a standard tool. The spectral data analyzing the performance of this method is verified and compared with the conventional methods through the experiment on standard samples. The results indicate that CFSA-AGD has higher spectroscopic decomposition accuracy and gives more abundant information on the characteristic spectra with less residual error than parallel factor analysis. This means that the fluorescence spectra of natural FDOMs can be decomposed into the characteristic fluorescence emission spectra of single components with higher accuracy and the characteristic fluorescence absorption spectra that cannot be obtained by the conventional methods. Meanwhile, it improves the analytical precision of the contents (from R² ≥ 0.9778 to R² ≥ 0.9920) and reduces the ultimate residual error by two orders of magnitude (from 1.42 × 10^-1 to 4.68 × 10^-3) when the method is used to estimate the measured fluorescence spectra.

Towards Automated Classification of Zooplankton Using Combination of Laser Spectral Techniques and Advanced Chemometrics

Zooplankton identification has been the subject of many studies. They are mainly based on the analysis of photographs (computer vision). However, spectroscopic techniques can be a good alternative due to the valuable additional information that they provide. We tested the performance of several chemometric techniques (principal component analysis (PCA), non-negative matrix factorisation (NMF), and common dimensions and specific weights analysis (CCSWA of ComDim)) for the unsupervised classification of zooplankton species based on their spectra. The spectra were obtained using laser-induced breakdown spectroscopy (LIBS) and Raman spectroscopy. It was convenient to assess the discriminative power in terms of silhouette metrics (Sil). The LIBS data were substantially more useful for the task than the Raman spectra, although the best results were achieved for the combined LIBS + Raman dataset (best Sil = 0.67). Although NMF (Sil = 0.63) and ComDim (Sil = 0.39) gave interesting information in the loadings, PCA was generally enough for the discrimination based on the score graphs. The distinguishing between Calanoida and Euphausiacea crustaceans and Limacina helicina sea snails has proved possible, probably because of their different mineral compositions. Conversely, arrow worms (Parasagitta elegans) usually fell into the same class with Calanoida despite the differences in their Raman spectra.

A novel approach for discovering correlations between elemental and molecular composition using laser-based spectroscopic techniques

LIBS and Raman spectra of marine zooplankton processed together to study trends in anomalous lithium enrichment.

Instrumental Techniques to Classify Olive Oils according to Their Quality

This review includes an update of the publications on quality classification of olive oils into extra, virgin or lampante olive oil categories. Nowadays, the official method to carry out this classification is time-consuming and, sometimes, it is not systematic and/or objective. It is based on conventional physicochemical analysis and on a sensorial tasting of olive oils carried out by a panel of experts. The aim of this review was to explore and give value to the alternative techniques reported in the bibliography to complement the current official methods established for that classification of olive oils. Specifically considered were non-separation and separation analytical techniques which could contribute to correctly classify olive oils according to their physicochemical and/or sensorial characteristics. An in-depth description has been written on the methods used to differentiate these three types of olive oils and the main advantages and disadvantages of the proposed procedures. The techniques here reviewed could be a real and fast option to complement or even substitute some of the analysis included in the official method. Finally, general trends and detected difficulties found to address this issue have been discussed throughout the article.

Progresses in Food Packaging, Food Quality, and Safety-Controlled-Release Antioxidant and/or Antimicrobial Packaging

Food packaging is designed to protect foods, to provide required information about the food, and to make food handling convenient for distribution to consumers. Packaging has a crucial role in the process of food quality, safety, and shelf-life extension. Possible interactions between food and packaging are important in what is concerning food quality and safety. This review tries to offer a picture of the most important types of active packaging emphasizing the controlled/target release antimicrobial and/or antioxidant packaging including system design, different methods of polymer matrix modification, and processing. The testing methods for the appreciation of the performance of active food packaging, as well as mechanisms and kinetics implied in active compounds release, are summarized. During the last years, many fast advancements in packaging technology appeared, including intelligent or smart packaging (IOSP), (i.e., time-temperature indicators (TTIs), gas indicators, radiofrequency identification (RFID), and others). Legislation is also discussed.

Autofluorescence excitation-emission matrices as a quantitative tool for the assessment of meat quality

Commercially produced meat is currently graded by a complex and partly subjective multiparameter methodology; a quantitative method of grading, using small samples would be desirable. Here, we investigate the correlation between commercial grades of beef and spectral signatures of native fluorophores in such small samples. Beef samples of different commercial grades were characterized by fluorescence spectroscopy complemented by biochemical and histological assessment. The excitation-emission matrices of the specimens reveal five prominent native autofluorescence signatures in the excitation range from 250 to 350 nm, derived mainly from tryptophan and intramuscular fat. We found that these signatures reflect meat grade and can be used for its determination.

A 3D-Fluorescence Fingerprinting Approach to Detect Physiological Modifications Induced by Pesticide Poisoning in Apis mellifera: A Preliminary Study

The combined use of 3D-fluorescence spectroscopy and independent component analysis using a differential fingerprinting approach has been applied with success to detect physiological effects of dimethoate in honeybees. Biochemical determinations combined with the identification of fluorescence zones that may correspond to proteins, NADH or neurotransmitters/neurohormones (octopamine, dopamine and serotonin) related to the physiological stress caused by the pesticide enabled phenomenological modeling of the physiological response in the honeybee using a simple and rapid method. The signals associated with the fluorophores involved in the response to stress were extracted from the fluorescence spectra using an unsupervised algorithm such as independent component analysis. The signals of different neurotransmitters were isolated on separated factorial components, thus facilitating their biochemical interpretation.

Independent components analysis (ICA) at the "cocktail-party" in analytical chemistry

Independent components analysis (ICA) is a probabilistic method, whose goal is to extract underlying component signals, that are maximally independent and non-Gaussian, from mixed observed signals. Since the data acquired in many applications in analytical chemistry are mixtures of component signals, such a method is of great interest. In this article recent ICA applications for quantitative and qualitative analysis in analytical chemistry are reviewed. The following experimental techniques are covered: fluorescence, UV-VIS, NMR, vibrational spectroscopies as well as chromatographic profiles. Furthermore, we reviewed ICA as a preprocessing tool as well as existing hybrid ICA-based multivariate approaches. Finally, further research directions are proposed. Our review shows that ICA is starting to play an important role in analytical chemistry, and this will definitely increase in the future.

Evaluation of total phenolic content in virgin olive oil using fluorescence excitation-emission spectroscopy coupled with chemometrics

BACKGROUND

Determination of the total phenolic content (TPC) in olive oils is of great interest, as phenolic compounds affect the health benefits, sensory attributes and oxidative stability of olive oils. The aim of this study was to explore the feasibility of direct front-face fluorescence measurements coupled with chemometrics for developing multivatiate models for discrimination between virgin olive oils with low and high TPC and determination of TPC concentration.

RESULTS

Parallel factor analysis and principal component analysis of fluorescence excitation-emission matrices (EEMs) of virgin olive oils revealed different fluorescent properties for samples with low and high TPC. A perfect discrimination of oils with low and high TPC was achieved using partial least squares (PLS) discriminant analysis. The best regression model for the prediction of TPC was based on the PLS analysis of the unfolded entire EEMs (R²  = 0.951, RPD = 4.0).

CONCLUSIONS

The results show the potential of fluorescence spectroscopy for direct screening of virgin olive oils for TPC. This may contribute to the development of fast screening methods for TPC assessment, providing an alternative to conventional assays. The procedure is environmentally friendly and fulfils the requirements for green analytical chemistry. © 2018 Society of Chemical Industry.

Processing Excitation-Emission Matrix Fluorescence and Total Synchronous Fluorescence Spectroscopy Data Sets with Constraint Randomised Non-negative Factor Analysis: a Novel Fluorescence Based Analytical Procedure to Analyse the Multifluorophoric Mixtures

The present work successfully shows the application of novel chemometric approach constraint randomised non-negative factor analysis (CRNNFA) for the analyses of the composite multidimensional fluorescence data sets. The CRNNFA involves the initialisation of the spectral variables in a constraint fashion thus ensures that algorithm does not wander with chemically and spectro-chemically irrelevant variables. The CRNNFA approach does not require that there must be pure variables for each fluorophores of the multifluorophoric mixture. One of the biggest advantages of CRNNFA is that it does not involve any convergence criteria thus circumventing the premature convergence of the algorithm. The CRNNFA achieves the termination only when the iteration limit is reached. The CRNNFA analysis s carried out under the non-negativity constraints therefore the mathematically retrieved profiles can easily be compared with those obtained experimentally. In the present work, both trilinear as well as non-trilinear multidimensional data sets are subjected to CRNNFA to validate its applicability. Excitation emission matrix fluorescence (EEMF) spectral profiles of Catechol, Hydroquinone, Indole and Tryptophan mixtures is used as the source of trilinear data sets. Total synchronous fluorescence spectroscopy (TSFS) spectral profiles of Benzo[a] Pyrene, Chrysene and Pyrene mixtures are used as the source of non-trilinear data sets. The CRNNFA approach is found to work equally well with trilinear as well with non-trilinear data sets. Thus, CRNFFA clearly does not have any prerequisite in the data structure. The obtained results clearly shows that CRNNFA algorithm in combination with EEMF and TSFS data sets are potential analytical tool for the analysis of complex-multifluorophoric mixtures.

An untargeted evaluation of food contact materials by flow injection analysis-mass spectrometry (FIA-MS) combined with independent components analysis (ICA)

Food contact materials (FCMs), especially plastics, are known to be a potential source of contaminants in food. In fact, various groups of additives are used to protect the integrity of the material during processing and life time. However, these intentionally added substances (IAS) could also lead to degradation products called non-intentionally added substances (NIAS), due to reactions occurring in the polymeric material. Complex mixtures of components may therefore be generated within the material, creating a source of potential migrating substances towards food in contact. In this context, an innovative analytical approach is proposed in order to assess IAS and NIAS in plastic FCMs for a fast screening of their composition. For this purpose, solvent extracts of polyethylene (PE) pellets, containers and films were analyzed by flow injection analysis-mass spectrometry (FIA-MS). This direct approach offers the ability to perform a large number of analyses in a short time. Mass spectral fingerprints were then treated by a multivariate data analysis technique called independent components analysis (ICA) in order to overcome the complexity of such data and to highlight hidden information related to IAS and NIAS molecules. ICA applied on mass spectral fingerprints of PE extracts highlighted group discriminations related to different m/z values which were putatively assigned to IAS and also to NIAS. In order to confirm these putative annotations, a hybrid LTQ-Orbitrap was used for high resolution mass spectrometry analysis. Moreover, MS/MS experiments were performed on some discriminant ions to improve their putative identification. The proposed methodology combining FIA-MS fingerprints and ICA proved its efficiency in identifying IAS and NIAS in plastic FCMs and its capability to discriminate different PE samples, in a relatively fast approach compared to classical analytical techniques. This approach may help the FCMs classification for compounders in the selection of the starting substances in plastic formulation and for plastic converters in the control of manufacturing processes as well as for the monitoring of final products.

Using pH variations to improve the discrimination of wines by 3D front face fluorescence spectroscopy associated to Independent Components Analysis

Wine composition in polyphenols is related to the variety of grape that it contains. These polyphenols play an essential role in its quality as well as a possible protective effect on human health. Their conjugated aromatic structure renders them fluorescent, which means that 3D front-face fluorescence spectroscopy could be a useful tool to differentiate among the grape varieties that characterize each wine. However, fluorescence spectra acquired simply at the natural pH of wine are not always sufficient to discriminate the wines. The structural changes in the polyphenols resulting from modifications in the pH induce significant changes in their fluorescence spectra, making it possible to more clearly separate different wines. 9 wines belonging to three different grape varieties (Shiraz, Cabernet Sauvignon and Pinot Noir) and from 9 different producers, were analyzed over a range of pHs. Independent Components Analysis (ICA) was used to extract characteristic signals from the matrix of unfolded 3D front-face fluorescence spectra and showed that the introduction of pH as an additional parameter in the study of wine fluorescence improved the discrimination of wines.

3D-front-face fluorescence spectroscopy and independent components analysis: A new way to monitor bread dough development

Following bread dough development can be a hard task as no reliable method exists to give the optimal mixing time. Dough development is linked to the evolution of gluten proteins, carbohydrates and lipids which can result in modifications in the spectral properties of the various fluorophores naturally present in the system. In this paper, we propose to use 3-D-front-face-fluorescence (3D-FFF) spectroscopy in the 250-550nm domain to follow the dough development as influenced by formulation (addition or not of glucose, glucose oxidase and ferulic acid in the dough recipe) and mixing time (2, 4, 6 and 8min). In all the 32 dough samples as well as in flour, three regions of maximum fluorescence intensities have been observed at 320nm after excitation at 295nm (Region 1), at 420nm after excitation at 360nm (Region 2) and 450nm after excitation at 390nm (Region 3). The principal components analysis (PCA) of the evolution of these maxima shows that the formulations with and without ferulic acid are clearly separated since the presence of ferulic acid induces a decrease of fluorescence in Region 1 and an increase in Regions 2 and 3. In addition, a kinetic effect of the mixing time can be observed (decrease of fluorescence in the Regions 1 and 2) mainly in the absence of ferulic acid. The analysis of variance (ANOVA) on these maximum values statistically confirms these observations. Independent components analysis (ICA) is also applied to the complete 3-D-FFF spectra in order to extract interpretable signals from spectral data which reflect the complex contribution of several fluorophores as influenced by their environment. In all cases, 3 signals can be clearly separated matching the 3 regions of maximal fluorescence. The signals corresponding to regions 1 and 2 can be ascribed to proteins and ferulic acid respectively, whereas the fluorophores associated with the 3rd signal (corresponding to region 3) remain unidentified. Good correlations are obtained between the IC score values of the 3 signals and the fluorescence intensities in Region 1, Region 2 and Region 3. Ferulic acid addition increases fluorescence in Region 2 and decreases fluorescence in Region 1, probably via a reabsorption of the protein fluorescence by ferulic acid. These phenomena are less pronounced when glucose oxidase is present. The enzymatic oxidation of ferulic acid by the glucose oxidase-peroxidase association could explain some of these effects.