Comparison of classification methods performance for defining the best reuse of waste wood material using NIR spectroscopy.

Recycling of post-consumer waste wood material is becoming an increasingly appealing alternative to disposal. However, its huge heterogeneity is calling for an assessment of the material characteristics in order to define the best recycling option and intended reuse. In fact, waste wood comes into a variety of uses/types of wood, along with several levels of contamination, and it can be divided into different categories based on its composition and quality grade. This study provides the measurement of more than a hundred waste wood samples and their characterisation using a hand-held NIR spectrophotometer. Three classification methods, i.e. K-nearest Neighbours (KNN), Principal Component Analysis - Linear Discriminant Analysis (PCA-LDA) and PCA-KNN, have been compared to develop models for the sorting of waste wood in quality categories according to the best-suited reuse. In addition, the classification performance has been investigated as a function of the number of the spectral measurements of the sample and as the average of the spectral measurements. The results showed that PCA-KNN performs better than the other classification methods, especially when the material is ground to 5 cm of particle size and the spectral measurements are averaged across replicates (classification accuracy: 90.9 %). NIR spectroscopy, coupled with chemometrics, turned out to be a promising tool for the real-time sorting of waste wood material, ensuring a more accurate and sustainable waste wood management. Obtaining real-time information about the quality and characteristics of waste wood material translates into a decision of the best recycling option, increasing its recycling potential.

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.

Multiway Decomposition Followed by Reconvolution of Fluorescence Time Decay Data.

The use of time-resolved emission spectroscopy (TRES) is increasing as these instruments become more available, and the prices are decreasing, especially with the development of cheaper LED light sources. In this article, we propose a new methodology for analyzing TRES data. It combines two existing methods: PARAFAC and reconvolution. PARAFAC is a soft modeling curve resolution technique which has been extensively applied to steady-state fluorescence data, and reconvolution is the most common method for fitting TRES data. The proposed method is compared to two well-established methods of analyzing these data, namely, global reconvolution and tail fitting. In addition, we compare our approach with the SLICING method proposed in 2021 by Devos et al. which is also based on a soft model, but does not include the reconvolution step. All of these methods follow the assumption that the measured fluorescence signal is a linear combination of the underlying fluorophores. The comparison is based on a measured TRES data set with a mixture of three fluorophores and two sets of simulated data sets with up to four fluorophores. The results show that global fitting works well as long as the signal-to-noise ratio (SNR) is high (more than 15 dB), independent of the spacing between the emission peak maxima. SLICING does not give as good estimates of the time decay, mainly due to the challenge of defining the tail. Our proposed method gives robust and accurate results, outperforming the other techniques in cases with broad instrument response functions and high noise levels with SNRs down to 5 dB.

Classification of waste wood categories according to the best reuse using FT-NIR spectroscopy and chemometrics.

In Europe, the volume of waste wood is increasing. Waste wood can be reused, promoting circular economy and avoiding landfills. It can be used as a bioenergy feedstock reducing the use of fossil fuels, or be reused for producing new composite wood material. Only wood with hazardous substances needs to be disposed. To this aim waste wood samples were collected from a panel board company and several recycling centres in Italy and Denmark. The samples were assigned to waste wood categories and analysed by Near Infrared Spectroscopy. Principal Component Analysis was used to investigate sample variability and Soft Independent Modelling of Class Analogies (SIMCA) for classifying the samples according to the appropriate reuse: energy production, panel board production or landfill. The results are good, with a classification rate of 90% for virgin wood material and 86.7% for treated wood material. The classification of waste wood is key for turning it into a secondary resource.

Chemometrics in Protein Formulation: Stability Governed by Repulsion and Protein Unfolding.

Therapeutic proteins can be challenging to develop due to their complexity and the requirement of an acceptable formulation to ensure patient safety and efficacy. To date, there is no universal formulation development strategy that can identify optimal formulation conditions for all types of proteins in a fast and reliable manner. In this work, high-throughput characterization, employing a toolbox of five techniques, was performed on 14 structurally different proteins formulated in 6 different buffer conditions and in the presence of 4 different excipients. Multivariate data analysis and chemometrics were used to analyze the data in an unbiased way. First, observed changes in stability were primarily determined by the individual protein. Second, pH and ionic strength are the two most important factors determining the physical stability of proteins, where there exists a significant statistical interaction between protein and pH/ionic strength. Additionally, we developed prediction methods by partial least-squares regression. Colloidal stability indicators are important for prediction of real-time stability, while conformational stability indicators are important for prediction of stability under accelerated stress conditions at 40 °C. In order to predict real-time storage stability, protein-protein repulsion and the initial monomer fraction are the most important properties to monitor.

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.

Using fluorescence excitation-emission matrices to predict bitterness and pungency of virgin olive oil: A feasibility study.

Unlike other food products, virgin olive oil must undergo an organoleptic assessment that is currently based on a trained human panel, which presents drawbacks that might affect the efficiency and robustness. Therefore, disposing of instrumental methods that could serve as screening tools to support sensory panels is of paramount importance. The present work aimed to explore excitation-emission fluorescence spectroscopy (EEFS) to predict bitterness and pungency, since both attributes are related with fluorophore compounds, such as polar phenols. Bitterness and pungency intensities of 250 samples were provided by an official sensory panel and used to build and compare partial least squares regressions (PLSR) with the excitation-emission matrix. Both PARAFAC scores and two-way unfolded data led to successful PLSR. The most relevant PARAFAC scores agreed with virgin olive oil phenolic spectra, evidencing that EEFS would be the fit-for-purpose screening tool to support the sensory panel.

The glacial-terrestrial-fluvial pathway: A multiparametrical analysis of spatiotemporal dissolved organic matter variation in three catchments of Lake Nam Co, Tibetan Plateau.

The Tibetan Plateau (TP) is a sensitive alpine environment of global importance, being Asia's water tower, featuring vast ice masses and comprising the world's largest alpine grasslands. Intensified land-use and pronounced global climate change have put pressure on the environment of the TP. We studied the tempo-spatial variability of dissolved organic matter (DOM) to better understand the fluxes of nutrients and energy from terrestrial to aquatic ecosystems in the TP. We used a multiparametrical approach, based on inorganic water chemistry, dissolved organic carbon (DOC) concentration, dissolved organic matter (DOM) characteristics (chromophoric DOM, fluorescence DOM and δ13C of DOM) in stream samples of three catchments of the Nam Co watershed and the lake itself. Satellite based plant cover estimates were used to link biogeochemical data to the structure and degradation of vegetation zones in the catchments. Catchment streams showed site-specific DOM signatures inherited from glaciers, wetlands, groundwater, and Kobresia pygmaea pastures. By comparing stream and lake samples, we found DOM processing and unification by loss of chromophoric DOM signatures and a change towards an autochthonous source of lake DOM. DOM diversity was largest in the headwaters of the catchments and heavily modified in terminal aquatic systems. Seasonality was characterized by a minor influence of freshet and by a very strong impact of the Indian summer monsoon on DOM composition, with more microbial DOM sources. The DOM of Lake Nam Co differed chemically from stream water samples, indicating the lake to be a quasi-marine environment in regards to the degree of chemical modification and sources of DOM. DOM proved to be a powerful marker to elucidate consequences of land use and climatic change on biogeochemical processes in High Asian alpine ecosystems.

Processing Effects on Protein Structure and Physicochemical Properties.

Raw materials, whether it is from the animal or plant kingdom, undergo some kind of (domestic or industrial) processing prior to consumption [...].

In-line Fluorescence Spectroscopy for Quantification of Low Amount of Active Pharmaceutical Ingredient.

The pharmaceutical industry is currently implementing new manufacturing principles and modernizing the related processing solutions. A key element in this development is implementation of process analytical technologies (PAT) for measuring product quality in a real-time mode, ideally for a continuously operating processing line. Near-infrared (NIR) spectroscopy is widely used for this purpose, but has limited use for low concentration formulations, due to its inherent detection limit. Light-induced fluorescence (LIF) spectroscopy is a PAT tool that can be used to quantify low concentrations of active pharmaceutical ingredient, and recent development of instrumentation has made it available for in-line applications. In this study, the content of tryptophan in a dynamic powder flow could be measured as low as 0.10 w/w % with LIF spectroscopy with good accuracy of RMSEP = 0.008 w/w %. Both partial least squares regression and support vector machines (SVM) were investigated, but we found SVM to be the better option due to non-linearities between the calibration test and the in-line measurements. With the use of SVM, LIF spectroscopy is a promising candidate for low concentration applications where NIR is not suitable.

Survey on Methods for Investigating Protein Functionality and Related Molecular Characteristics.

Proteins from various sources are widely used in the food industry due to their unique functional performances in food products. The functional properties of proteins are somehow dictated by their molecular characteristics, but the exact relationship is not fully understood. This review gives a tangible overview of the methods currently available for determining protein functionality and related molecular characteristics in order to support further research on protein ingredients. The measurements of protein functionality include solubility, water holding capacity, oil holding capacity, emulsion property, foam property, and gelation. This review also provides a description of different methods of molecular characteristics including electrophoresis, surface hydrophobicity and charge, molecular interaction, and thermal property measurement. Additionally, we have put significant emphasis on spectroscopic methods (ultraviolet-visible, Fourier transform infrared, Raman, circular dichroism, fluorescence and nuclear magnetic resonance). In conclusion, first and foremost, there is a need to agree on a standardization of the analytical methods for assessing functional properties. Moreover, it is mandatory to couple different analyses of molecular characteristics to measure and monitor the structural changes obtained by different processing methods in order to gain knowledge about the relationship with functionality. Ideally, a toolbox of protein analytical methods to measure molecular characteristics and functionality should be established to be used in a strategic design of protein ingredients.

Two Statistical Tools for Assessing Functionality and Protein Characteristics of Different Fava Bean (Vicia faba L.) Ingredients.

Fava bean (Vicia faba L.) is a promising source of proteins that can be potentially used as nutritional and/or functional agents for industrial food applications. Fava ingredients are industrially produced, modified, and utilized for food applications. Their processing conditions influence physico-chemical protein properties that further impact ingredient functionality. To design a functionally suitable ingredient, an understanding of the interrelationships between different properties is essential. Hence, this work aimed to assess two statistical analytical tools, Pearson's correlation and Principal Component Analysis (PCA), for investigating the role of the process conditions of fava ingredients on their functional and protein properties. Fava concentrates were processed by pH (2, 4, 6.4 and 11), temperature (55, 75 and 95 ∘C) and treatment duration (30 and 360 min) into different modified ingredients. These were utilized under two application conditions (pH 4 and 7), and their foam and emulsion properties as well as their ingredient characteristics (charge, solubility, and intrinsic fluorescence) were measured. The results show that foam and emulsion properties are not correlated to each other. They are associated with different protein and non-protein attributes as fava concentrate is a multi-component matrix. Importantly, it is found that the results from the two statistical tools are not fully comparable but do complement each other. This highlights that both statistical analytical tools are equally important for a comprehensive understanding of the impact of process conditions on different properties and the interrelationships between them. Therefore, it is recommended to use Pearson's correlation and principal component analysis in future investigations of new plant-based proteins.

Physiological Genetics Reformed: Bridging the Genome-to-Phenome Gap by Coherent Chemical Fingerprints - the Global Coordinator.

Forward-focused molecular genetics is successfully framing DNA diversity and mapping primary gene functions. However, abandoning the classic Linnaean fingerprint link between the phenome and genome by suppressing gene interaction (pleiotropy), has resulted in a genome-to-phenome gap and poor utilization of molecular data. We demonstrate how to bridge this gap by using an example of a barley mutant seed model, where pleiotropy is observed as covarying global molecular patterns that define each endosperm. Global coherence was discovered as a covariate coordinator within and between local genotype specific fingerprints. This implies that any of these fingerprints can select its recombinant global phenotype variant, including composition. Introducing the law of coherence, and the movement of gene complexes by chemical fingerprint traits as selectors, introduces a revolution in understanding physiological molecular genetics and plant-breeding.

New Advanced Glycation End Products Observed in Rat Urine by Untargeted Metabolomics after Feeding with Heat-Treated Skimmed Milk Powder.

SCOPE: Milk powder is commonly consumed throughout the world. However, advanced glycation end products (AGEs) will form in milk powder during thermal processing and long-term storage. This study aimed to identify such compounds with potential as new urinary biomarkers of intake of heat-treated skimmed milk powder (HSMP). METHODS AND RESULTS: A parallel study is performed with different dosages of HSMP as well as hydrolyzed HSMP and untreated skimmed milk powder (SMP) in 36 rats. The 24-h urine samples on day 7 or 8 are collected and profiled by untargeted UPLC-Qtof-MS metabolomics. Statistical analysis revealed 25 metabolites differentiating SMP and HSMP; nineteen of these structures are proposed as lysine- and arginine-derived AGEs, and heterocyclic compounds. CONCLUSION: These metabolites may potentially serve as biomarkers of food intake pending further validation to assess intakes of heat-processed dairy foods and thus help to elucidate the effects of HSMP consumption or dietary AGEs on human health.

The anserine to carnosine ratio: an excellent discriminator between white and red meats consumed by free-living overweight participants of the PREVIEW study.

BACKGROUND: Biomarkers of meat intake hold promise in clarifying the health effects of meat consumption, yet the differentiation between red and white meat remains a challenge. We measure meat intake objectively in a free-living population by applying a newly developed, three-step strategy for biomarker-based assessment of dietary intakes aimed to indicate if (1) any meat was consumed, (2) what type it was and (3) the quantity consumed. METHODS: Twenty-four hour urine samples collected in a four-way crossover RCT and in a cross-sectional analysis of a longitudinal lifestyle intervention (the PREVIEW Study) were analyzed by untargeted LC-MS metabolomics. In the RCT, healthy volunteers consumed three test meals (beef, pork and chicken) and a control; in PREVIEW, overweight participants followed a diet with high or moderate protein levels. PLS-DA modeling of all possible combinations between six previously reported, partially validated, meat biomarkers was used to classify meat intake using samples from the RCT to predict consumption in PREVIEW. RESULTS: Anserine best separated omnivores from vegetarians (AUROC 0.94-0.97), while the anserine to carnosine ratio best distinguished the consumption of red from white meat (AUROC 0.94). Carnosine showed a trend for dose-response between non-consumers, low consumers and high consumers for all meat categories, while in combination with other biomarkers the difference was significant. CONCLUSION: It is possible to evaluate red meat intake by using combinations of existing biomarkers of white and general meat intake. Our results are novel and can be applied to assess qualitatively recent meat intake in nutritional studies. Further work to improve quantitation by biomarkers is needed.

Study of Variability of Waste Wood Samples Collected in a Panel Board Industry.

Waste wood is becoming an appealing alternative material to virgin wood, and the main drivers are the increased demand for waste wood by the panel industry, the introduction of renewable energy policies, and the waste framework directive. In fact, the use of waste wood as a secondary resource is favored over both landfills and combustion. The best reuse and cascading use of the material are linked to its characteristics. That is why it is important to know the chemical composition and the variation in the properties of such a heterogeneous material. In this article, a sampling study was carried out in a panel board company located in the northern part of Italy. In order to investigate the heterogeneity of waste wood, all samples have been analyzed by near-infrared spectroscopy. Nested analysis of variance and principal component analysis have been used to evaluate the heterogeneity and the variation in sample properties. The approach gives information about how to ensure representative measurements and efficiently describe the variability of the material. The results suggest that it is important to have replicates or at least two subsamples for each lot and then measure each of these with at least 100 scans, in order to get representative measurements and describe the variability of the material. The determination of waste wood composition and variability is the focal point for improving the sorting process and increasing the reuse of waste wood, avoiding expensive landfills and risks for human health and the environment.

Isothermal Chemical Denaturation: Data Analysis, Error Detection, and Correction by PARAFAC2.

Characterization of a protein's conformational stability is a key step in the development of biotherapeutics, where protein unfolding leads to adverse properties, such as aggregation and loss of efficacy. Isothermal chemical denaturation (ICD) can be applied to determine chemical stability, aiming to identify the optimal solvent conditions, in terms of pH, salt concentration, and added excipients. For seven monoclonal antibodies, this study investigates the observed intrinsic protein fluorescence emission spectra as a function of denaturant concentration. Protein formulations are screened in two experimental series. We show how the peak shapes of folded and unfolded proteins are preserved under added salt (0-140 mM NaCl) and added excipients concentrations, as typically found in biotherapeutic formulations and that only minor effects in tryptophan fluorescence peak tailing are observed over a large pH range (5.5-9.0). The data of seven mAbs, where GuHCl was a suitable denaturant, are modeled using PARAFAC2. PARAFAC2, a linear decomposition method, is well suited for the data and yields robust, valid, and automated models that allow for the detection of erroneous measurements. Analysis of the errors show correlation with the well-based experimental setup, and differences in observed errors between the two experimental series. We additionally show a correction method for these outliers based on PARAFAC2 model scores, such that full transition curves can be retrieved, increasing the accuracy of any subsequent analysis.

Long-term effects of elevated CO2, nighttime warming and drought on plant secondary metabolites in a temperate heath ecosystem.

BACKGROUND AND AIMS: Plant secondary metabolites play critical roles in plant stress tolerance and adaptation, and are known to be influenced by the environment and climate changes, yet the impacts and interactions of multiple climate change components are poorly understood, particularly under natural conditions. METHODS: Accumulation of phenolics and emissions of volatile organic compounds (VOCs) were assessed on heather, Calluna vulgaris, an abundant evergreen dwarf shrub in European heathlands, after 6 years of exposure to elevated CO2, summer drought and nighttime warming. KEY RESULTS: Drought alone had the strongest effects on phenolic concentrations and compositions, with moderate effects of elevated CO2 and temperature. Elevated CO2 exerted the greatest impact on VOC emissions, mainly by increasing monoterpene emissions. The response magnitudes varied among plant tissue types and chemical constituents, and across time. With respect to interactive effects of the studied climate change components, the interaction between drought and elevated CO2 was most apparent. Drought mainly reduced phenolic accumulation and VOC emissions, while elevated CO2 mitigated such effects. CONCLUSIONS: In natural ecosystems, co-occurring climate factors can exert complex impacts on plant secondary metabolite profiles, which may in turn alter ecosystem processes.

Advancing Therapeutic Protein Discovery and Development through Comprehensive Computational and Biophysical Characterization.

Therapeutic protein candidates should exhibit favorable properties that render them suitable to become drugs. Nevertheless, there are no well-established guidelines for the efficient selection of proteinaceous molecules with desired features during early stage development. Such guidelines can emerge only from a large body of published research that employs orthogonal techniques to characterize therapeutic proteins in different formulations. In this work, we share a study on a diverse group of proteins, including their primary sequences, purity data, and computational and biophysical characterization at different pH and ionic strength. We report weak linear correlations between many of the biophysical parameters. We suggest that a stability comparison of diverse therapeutic protein candidates should be based on a computational and biophysical characterization in multiple formulation conditions, as the latter can largely determine whether a protein is above or below a certain stability threshold. We use the presented data set to calculate several stability risk scores obtained with an increasing level of analytical effort and show how they correlate with protein aggregation during storage. Our work highlights the importance of developing combined risk scores that can be used for early stage developability assessment. We suggest that such scores can have high prediction accuracy only when they are based on protein stability characterization in different solution conditions.

Novel non-linear curve fitting to resolve protein unfolding transitions in intrinsic fluorescence differential scanning fluorimetry.

In biotherapeutic protein research, an estimation of the studied protein's thermal stability is one of the important steps that determine developability as a function of solvent conditions. Differential Scanning Fluorimetry (DSF) can be applied to measure thermal stability. Label-free DSF measures amino acid fluorescence as a function of temperature, where conformational changes induce observable peak deformation, yielding apparent melting temperatures. The estimation of the stability parameters can be hindered in the case of multidomain, multimeric or aggregating proteins when multiple transitions partially coincide. These overlapping protein unfolding transitions are hard to evaluate by the conventional methodology, as peak maxima are shifted by convolution. We show how non-linear curve fitting of intrinsic fluorescence DSF can deconvolute highly overlapping transitions in formulation screening in a semi-automated process. The proposed methodology relies on synchronous, constrained fits of the fluorescence intensity, ratio and their derivatives, by combining linear baselines with generalized logistic transition functions. The proposed algorithm is applied to data from three proteins; a single transition, a double separated transition and a double overlapping transition. Extracted thermal stability parameters; apparent melting temperatures Tm,1, Tm,2 and melting onset temperature Tonset are obtained and compared with reference software analysis. The fits show R2 = 0.94 for single and R2 = 0.88 for separated transitions. Obtaining values and trends for Tonset in a well-described and automated way, will aid protein scientist to better evaluate the thermal stability of proteins.