Resolving fluorescence spectra of Maillard reaction products formed on bovine serum albumin using parallel factor analysis.

Formation of Maillard reaction products (MRPs) is increasingly studied by the use of fluorescence spectroscopy, and most often, by measuring single excitation/emission pairs or use of unresolved spectra. However, due to the matrix complexity and potential co-formation of fluorescent oxidation products on tryptophan and tyrosine residues, this practice will often introduce errors in both identification and quantification. The present study investigates the combination of fluorescence excitation emission matrix (EEM) spectroscopy and parallel factor analysis (PARAFAC) to resolve the EEMs into its underlying fluorescent signals, allowing for better identification and quantification of MRPs. EEMs were recorded on a sample system of bovine serum albumin incubated at 40 °C for up to one week with either glucose, methylglyoxal or glyoxal added. Ten unique PARAFAC components were resolved, and assignment was attempted based on similarity with fluorescence of pure standards of MRPs and oxidation products and reported data from literature. Of the ten fluorescent PARAFAC components, tyrosine and buried and exposed tryptophan were resolved and identified, as well as the formation of specific MRPs (argpyrimidine and Nα-acetyl-Nδ-(5-methyl-4-imidazolon-2-yl)ornithine) and tryptophan oxidation products (kynurenine and dioxindolylalanine). The formation of the PARAFAC resolved protein modifications were qualitatively validated by liquid chromatography-mass spectrometry.

Colorimetric pH indicators based on well-defined amylose and amylopectin matrices enriched with anthocyanins from red cabbage.

This study aimed to prepare a novel colorimetric indicator film from virtually pure (99 %) amylose (AM) and anthocyanins extracted from red cabbage (RCA). The AM used was a unique engineered bulk material extracted from transgenic barley grains. Films produced by solution casting were compared to normal barely starch (NB) and pure barley amylopectin (AP), with amylose contents of 30 % and 0 %, respectively. The pH-indicator films were produced by incorporation of RCA into the different starch support matrices with different amylose contents. Barrier, thermal, and mechanical properties, photo degradation stability, and release behavior data revealed that RCA interact differently through the glucan matrices. Microstructural observations showed that RCA were evenly dispersed in the glucan matrix, and AM+RCA indicator films showed high UV-barrier and mechanical performance over normal starch. FTIR revealed that RCA was properly affected by the AM matrix. Moreover, the AM+RCA films showed sensitive color changes in the pH range (2-11) and a predominant Fickian diffusion release mechanism for RCA. This study provides for the first time data regarding AM films with RCA and their promising potential for application as support matrices in responsive food and other industrial biodegradable packaging materials.

Detection of protein oxidation products by fluorescence spectroscopy and trilinear data decomposition: Proof of concept.

Current analytical methods studying protein oxidation modifications require laborious sample preparation and chromatographic methods. Fluorescence spectroscopy is an alternative, as many protein oxidation products are fluorescent. However, the complexity of the signal causes misinterpretation and quantification errors if single emission spectra are used. Here, we analyzed the entire fluorescence excitation-emission matrix using the trilinear decomposition method parallel factor analysis (PARAFAC). Two sample sets were used: a calibration set based on known mixtures of tryptophan, tyrosine, and four oxidation products, and a second sample set of oxidized protein solutions containing UV-illuminated ß-lactoglobulin. The PARAFAC model succeeded in resolving the signals of the model systems into the pure fluorophore components and estimating their concentrations. The estimated concentrations for the illuminated ß-lactoglobulin samples were validated by liquid chromatography-mass spectrometry. Our approach is a promising tool for reliable identification and quantification of fluorescent protein oxidation products, even in a complex protein system.

Can We Trust Score Plots?

In this paper, we discuss the validity of using score plots of component models such as partial least squares regression, especially when these models are used for building classification models, and models derived from partial least squares regression for discriminant analysis (PLS-DA). Using examples and simulations, it is shown that the currently accepted practice of showing score plots from calibration models may give misleading interpretations. It is suggested and shown that the problem can be solved by replacing the currently used calibrated score plots with cross-validated score plots.

Monitoring yeast fermentations by nonlinear infrared technology and chemometrics-understanding process correlations and indirect predictions.

Fermentation processes are still compromised by a lack of monitoring strategies providing integrated process data online, ensuring process understanding, control, and thus, optimal reactor efficiency. The crucial demand for online monitoring strategies, not only encouraged by the PAT initiative but also motivated by modern paradigms such as circular economy and sustainability, has driven research and industry to provide "next-generation process technology": in other words, technology tailored toward industrial needs. Mid-infrared (MIR) spectroscopy as such is superior to near-infrared (NIR) spectroscopy since it provides significantly enhanced selectivity. However, due to high costs and a lack of instrumental robustness, MIR spectroscopy is outcompeted by NIR when it comes to industrial application. The lack of chemometric expertise, model understanding, and practical guidance might add to the slow acceptance of industrial MIR application. This work demonstrates the use of novel MIR, so-called non-linear infrared (NLIR) technology and the importance of model understanding, exemplarily investigated on a lab-scale yeast fermentation process. The six analytes glucose, ethanol, glycerol, acetate, ammonium, and phosphate were modeled by partial least squares (PLS) based on spectral data, demonstrating the potential of the novel technology facilitating online data acquisition and the necessity of investigating indirect predictions. KEY POINTS: • NLIR spectra were acquired online during a yeast fermentation process • PLS models were constructed for six components based on uncorrelated samples • Glucose, ethanol, ammonium, and phosphates were modeled with errors of less than 15% • Acetate and glycerol were shown to rely on indirect predictions.

Fast measurement of phosphates and ammonium in fermentation-like media: A feasibility study.

Real-time monitoring of bioprocesses plays a key-role in modern industries, providing new information on full-scale production, thus enabling control of the process and allowing it to run at optimal conditions while minimizing waste. Monitoring of phosphates and ammonium in fermentation processes has a twofold interest: they are important nutrients for living organisms while at the same time constituting environmental nutrient pollutants, for which unnecessary use and disposal must be avoided. In this report, the possibility of simultaneous analysis of phosphates and ammonium in fermentations was verified using spectroscopy-based methods combined with chemometrics to construct calibration models. To achieve this, the models were based on synthetic samples mimicking real fermentation media, providing a dataset where the analytes were completely uncorrelated. Different at-line techniques (mid- and near- infrared spectroscopy, MIR and NIR) were evaluated for their ability to monitor quickly both analytes, in a wide range of concentrations (10-100 mM), in three media of different complexities. Partial Least Squares (PLS) models on MIR spectroscopy gave very good results, with prediction errors lower than 5 % for both analytes in all datasets. In contrast, the results for PLS models on NIR spectroscopy were inferior (prediction errors between 3 and 26 %) for both analytes, as, in the case of phosphate, it could be demonstrated that the model was based on based on indirect predictions.

Raw material quality assessment approaches comparison in pectin production.

Different opportunities are explored to evaluate quality variation in raw materials from biological origin. Assessment of raw materials attributes is an important step in a bio-based production as fluctuations in quality are a major source of process disturbance. This can be due to a variety of biological, seasonal, and supply scarcity reasons. The final properties of a product are invariably linked with the initial properties of the raw material. Thus, the operational conditions of a process can be tuned to drive the product to the required specification based on the quality assessment of the raw material being processed. Process analytical technology tools which enable this assessment in a far more informative and rapid manner than current industrial practices that rely on rule-of-thumb decisions are assessed. An example with citrus peels is used to demonstrate the conceptual and performance differences of distinct quality assessment approaches. The analysis demonstrates the advantage of characterization through multivariate data analysis coupled with a complementary spectroscopic technique, near-infrared spectroscopy. The quantitative comparative analysis of three different approaches, discriminant classification based on expert-knowledge, unsupervised classification, and spectroscopic correlation with reference physicochemical variables, is performed in the same dataset context. © 2018 Her Majesty the Queen in Right of Canada © 2018 American Institute of Chemical Engineers Biotechnol. Prog., 35: e2762, 2019.

Solubilization of industrial grade plant protein by enzymatic hydrolysis monitored by vibrational and nuclear magnetic resonance spectroscopy: A feasibility study.

Protein hydrolysates are of great interest in the food industry due to their nutritional and functional properties, but their use often implies solubilization in water and therefore hamper the use of plant proteins with inherent low water solubility. Protein solubility in water can be modified by enzymatic hydrolysis, but during this process several collateral properties of the protein hydrolysates changes. It is therefore important to determine the end-point of the process and to monitor its development. In this feasibility study, we demonstrated the potential of different spectroscopic techniques (1H NMR and IR) coupled with chemometrics analysis in monitoring the hydrolysis of five different industrial grade plant proteins by the enzyme Alcalase. Logarithmic modeling of the PCA (Principal Component Analysis) scores confirmed that they can represent a measurement of the solubilized protein material released and resulted in kinetic parameters describing the suitability of protein sources as substrates for the hydrolysis. This way, we showed that a qualitative evaluation of the degree of hydrolysis is possible using fast at-line technologies and PCA.

Application of Rapid Visco Analyser (RVA) viscograms and chemometrics for maize hardness characterisation.

It has been established in this study that the Rapid Visco Analyser (RVA) can describe maize hardness, irrespective of the RVA profile, when used in association with appropriate multivariate data analysis techniques. Therefore, the RVA can complement or replace current and/or conventional methods as a hardness descriptor. Hardness modelling based on RVA viscograms was carried out using seven conventional hardness methods (hectoliter mass (HLM), hundred kernel mass (HKM), particle size index (PSI), percentage vitreous endosperm (%VE), protein content, percentage chop (%chop) and near infrared (NIR) spectroscopy) as references and three different RVA profiles (hard, soft and standard) as predictors. An approach using locally weighted partial least squares (LW-PLS) was followed to build the regression models. The resulted prediction errors (root mean square error of cross-validation (RMSECV) and root mean square error of prediction (RMSEP)) for the quantification of hardness values were always lower or in the same order of the laboratory error of the reference method.

1H NMR-based urinary metabolic profiling reveals changes in nicotinamide pathway intermediates due to postnatal stress model in rat.

The maternal separation protocol in rodents is a widely recognized model of early life stress allowing acute and chronic physiological consequences to be studied. An (1)H NMR-based metabolomic approach was applied to urines to evaluate the systemic metabolic consequences of maternal separation stress in female rats after the beginning of weaning and 4 weeks later when the rats were reaching adulthood. Furthermore, because maternal separation is considered as a model mimicking the inflammatory bowel syndrome, the lactulose/mannitol test was used to evaluate the influence of postnatal maternal separation on gut permeability and mucosal barrier function by (1)H NMR spectroscopy analysis of urine. The results showed no statistical differences in gut permeability due to maternal separation. The application of ANOVA simultaneous component analysis allowed the contributions of physiological adaptations to the animal's development to be separated from the metabolic consequences due to postnatal stress. Systemic metabolic differences in the maternally separated pups were mainly due to the tryptophan/NAD pathway intermediate levels and to the methyladenosine level. Urinary NMR-based metabolic profiling allowed us to disentangle the metabolic adaptive response of the rats to postnatal stress during the animal's growth, highlighting the metabolic changes induced by weaning, gut closure, and maturity.

Local classification: Locally weighted-partial least squares-discriminant analysis (LW-PLS-DA).

The possibility of devising a simple, flexible and accurate non-linear classification method, by extending the locally weighted partial least squares (LW-PLS) approach to the cases where the algorithm is used in a discriminant way (partial least squares discriminant analysis, PLS-DA), is presented. In particular, to assess which category an unknown sample belongs to, the proposed algorithm operates by identifying which training objects are most similar to the one to be predicted and building a PLS-DA model using these calibration samples only. Moreover, the influence of the selected training samples on the local model can be further modulated by adopting a not uniform distance-based weighting scheme which allows the farthest calibration objects to have less impact than the closest ones. The performances of the proposed locally weighted-partial least squares-discriminant analysis (LW-PLS-DA) algorithm have been tested on three simulated data sets characterized by a varying degree of non-linearity: in all cases, a classification accuracy higher than 99% on external validation samples was achieved. Moreover, when also applied to a real data set (classification of rice varieties), characterized by a high extent of non-linearity, the proposed method provided an average correct classification rate of about 93% on the test set. By the preliminary results, showed in this paper, the performances of the proposed LW-PLS-DA approach have proved to be comparable and in some cases better than those obtained by other non-linear methods (k nearest neighbors, kernel-PLS-DA and, in the case of rice, counterpropagation neural networks).

Chemometric classification techniques as a tool for solving problems in analytical chemistry.

Supervised pattern recognition (classification) techniques, i.e., the family of chemometric methods whose aim is the prediction of a qualitative response on a set of samples, represent a very important assortment of tools for solving problems in several areas of applied analytical chemistry. This paper describes the theory behind the chemometric classification techniques most frequently used in analytical chemistry together with some examples of their application to real-world problems.

Rheoreversible hydrogels in paper restoration processes: a versatile tool.

BACKGROUND: Paper based artworks are probably ones of the most difficult materials to restore, because of their complexity and fragile structure. Cleaning of paper artifacts, one of the process commonly carried out during restoration, usually involves the use of solvents (organic or not), that may cause several troubles, like swelling and dissolution of some components, and may also be harmful to the users. RESULTS: Innovative procedure for cleaning paper artworks is reported in this paper. It is based on the use of rheoreversible, biocompatible hydrogels containing poly(ethylene oxide) or poly(ethylene oxide)-poly(propylene oxide)-poly(ethylene oxide) and α-cyclodextrin. We have studied two types of polymer with different hydrophobic properties in order to obtain two different hydrogels with slightly different cleaning capabilities. Our overall strategy has been to develop innovative systems based on these hydrogels so as to better confront the problems that a restorer faces during the cleaning of paper samples. Rheoreversible hydrogels are intriguing materials because their application and removal is not invasive and does not require a liquid treatment that could induce damage to the paper. CONCLUSIONS: These hydrogels have been applied in the cleaning of both new and aged paper samples and their cleaning efficiency has been established. Moreover, by comparison with traditional methods, the greater efficacy of the proposed procedure has been demonstrated.To assess the cleaning efficacy of these hydrogels, a multidisciplinary approach, combining non-invasive spectroscopic infrared techniques together with scanning electron microscopy, chromatographic (HPLC) analysis and pH investigations has been used. Near infrared spectroscopy spectra were coupled with a chemometric analysis to achieve a better interpretation of data.This work constitutes a preliminary step towards focused study in the development of α-cyclodextrin/polymer hydrogel family which will allow cleaning of paper artifacts with peculiar characteristics.

Assessment of the applicability of a "toolbox" designed for microbially assisted phytoremediation: the case study at Ingurtosu mining site (Italy).

The paper describes the fieldwork at the Italian test site of the abandoned mine of sphalerite and galena in Ingurtosu (Sardinia), with the aim to assess the applicability of a "toolbox" to establish the optimized techniques for remediation of soils contaminated by mining activities. A preliminary characterization-including (hydro)geochemistry, heavy metal concentration and their mobility in soil, bioprospecting for microbiology and botany-provided a data set for the development of a toolbox to deliver a microbially assisted phytoremediation process. Euphorbia pithyusa was selected as an endemic pioneer plant to be associated with a bacterial consortium, established with ten selected native strains, including metal-tolerant bacteria and producers of plant growth factors. The toolbox was firstly assessed in a greenhouse pot experiment. A positive effect of bacterial inoculum on E. pithyusa germination and total plant survival was observed. E. pithyusa showed to be a well-performing metallophyte species, and only inoculated soil retained a microbial activity with a high functional diversity, expanding metabolic affinity also towards root exudates. These results supported the decision to proceed with a field trial, investigating different treatments used singly or in combination: bioaugmentation with bacterial consortia, mycorrhizal fungi and a commercial mineral amendment. Microbial activity in soil, plant physiological parameters and heavy metal content in plants and in soil were monitored. Five months after the beginning, an early assessment of the toolbox under field conditions was carried out. Despite the cold season (October-March), results suggested the following: (1) the field setup as well as the experimental design proved to be effective; (2) plant survival was satisfactory; (3) soil quality was increased and bioaugmentation improved microbial activity, expanding the metabolic competences towards plant interaction (root exudates); and (4) multivariate analysis supported the data provided that the proposed toolbox can be established and the field trial can be carried forward.

Application of near infrared (NIR) spectroscopy coupled to chemometrics for dried egg-pasta characterization and egg content quantification.

Dried egg pasta is an important and traditional food in the Italian cuisine, and the eggs in pasta improve its nutritional value and organoleptic properties. For this reason the percentage of eggs present in the products sold as "egg pasta" has to always be clearly reported in the label. In this respect, the present research addresses the possibility of developing a method which would allow fast, simple and economic determination of egg content in dried egg-pasta, using near-infrared spectroscopy and chemometric analysis. However, as it is very likely that the spectroscopic fingerprint can also be affected by the manufacturing process of this product, in particular by drying temperature and time, the effect of the manufacturing process on the spectral profile of egg-pasta samples was thoroughly investigated, using experimental design coupled to a multivariate exploratory data analytical technique called ANOVA-Simultaneous Component Analysis (ASCA). Moreover, once confirmed the significance of the drying effect on spectral shape, with the aim of building a calibration model to quantify the egg content in pasta samples irrespective of the manufacturing protocol adopted, a non-linear approach based on local regression, namely LWR-PLS, was investigated. This method allowed the determination of the egg content in external validation samples with low error (RMSEP=1.25), resulting in predictions more accurate and precise than those obtained by a global PLS model.

Tracing the origin of extra virgin olive oils by infrared spectroscopy and chemometrics: a case study.

In this paper, the potential of coupling mid- and near-infrared spectroscopic fingerprinting techniques and chemometric classification methods for the traceability of extra virgin olive oil samples from the PDO Sabina was investigated. To this purpose, two different pattern recognition algorithm representative of the discriminant (PLS-DA) and modeling (SIMCA) approach to classification were employed. Results obtained after processing the spectroscopic data by PLS-DA evidenced a rather high classification accuracy, NIR providing better predictions than MIR (as evaluated both in cross-validation and on an external test set). SIMCA confirmed these results and showed how the category models for the class Sabina can be rather sensitive and highly specific. Lastly, as samples from two harvesting years (2009 and 2010) were investigated, it was possible to evidence that the different production year can have a relevant effect on the spectroscopic fingerprint. Notwithstanding this, it was still possible to build models that are transferable from one year to another with good accuracy.