Search

Et nyt værktøj om Open Science til forskerne.

The ever-growing competition among global biotech industries has led to high demands on production consistency. A statistical strategy of performance mapping for production optimization is therefore of great economic significance. Process analytical technology (PAT)-based sensors such as mid-infrared (MIR) spectroscopy enable process monitoring through substrate and by-product concentrations that directly represent the physiology of cells. Combined with multivariate statistics, MIR can be employed as a strategy for production performance mapping. This study describes the use of at-line spectroscopy, chemometric modeling, and post-process fitting to characterize Lactobacillus acidophilus fermentations. The emphasis is on alternative arrangements of the data and chemometric methods principle component analysis (PCA), multivariate curve resolution (MCR), and parallel factor analysis (PARAFAC). Two key parameters, rate constant and time of inflection, are extracted by post-process fitting on the outcomes of these different models. Their use as process performance descriptors to characterize the dynamics of substrate consumption, product formation and batch-to-batch variations is suggested. The unconstrained PCA primarily described biomass change, while the constrained models PARAFAC and MCR (both the augmented and individual-run configurations) could model the decrease in sugars and increase in lactic acid over time. It was concluded that MCR on individual batch data, followed by post-process fitting, is the preferred strategy for MIR spectroscopic monitoring.

Utilizing the full potential of spectroscopic calibrations in changing environments typically requires large amounts of maintenance and/or model updates as the presence of new sources of variation makes the calibration insufficient. In this paper, we propose the use of unlabelled data in order to automize such maintenance. We extend the Linear Joint Trained Framework by Ryan and Culp such that the shifts in mean value and covariance structure are modelled explicitly. The extension yields a more flexible framework, and thus we are able to regularize the final calibration in a more desirable manner. The proposed framework is tested on a simulated dataset where we simulate three different realistic scenarios that are either challenging for classic multivariate calibrations or challenging when adding unlabelled data. Furthermore, we test our framework on two real datasets across multiple data splits. We find that our framework not only achieves the same (and in some instances lower) error level as that of the baseline model (NARE), it also yields better calibration models than the Linear Joint Trained Framework.

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.

The salt content is one of the most important quality and safety parameters in the manufacturing process of marinated herring, which needs to be controlled during processing. Standard methods are often destructive and time consuming, and therefore a nondestructive and fast method is needed. Near-infrared (NIR) spectroscopy was measured on marinade samples from the herring marinating process in order to investigate the potential of NIR as a fast method to determine the salt content in marinade and in fish. The spectral region 1100–1300 nm had the highest positive correlation with the measured salt values. A principal component analysis performed on the NIR spectra showed that the first principal component described the evolution of the spectra according to the determined salt values. A partial least-squares regression model between the selected region of the NIR spectra and the salt content of the fish gave a correlation coefficient of 0.81 and a prediction error (RMSECV) of 0.41 g/100 g with the prerequisite that salt concentration in fish and marinade was in equilibrium. The results indicate that NIR spectroscopy can be used as a fast and non-destructive method for assessing the salt concentration in fish during the herring marinating process in order to ensure product safety.

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.

A presence of carbonyl compounds is very common in the food industry. The nature of such compounds is to be reactive and thus many products involve aldehydes/ketones in their synthetic routes. By contrast, the high reactivity of carbonyl compounds could also lead to formation of undesired compounds, such as genotoxic impurities. It can therefore be important to remove carbonyl compounds by implementing suitable removal techniques, with the aim of protecting final product quality. This work is focused on benzaldehyde as a model component, studying its removal from a water/ethanol mixture by applying different derivatization agents as the scavengers. Discovery chemistry is performed in the beginning as a screening procedure, followed by the process design of a small-scale continuous process for benzaldehyde removal with in-line real-time monitoring. Applications of tris(hydroxymethyl)aminomethane (TRIS) are found to provide above average removal of benzaldehyde.

The possibility of using mid-infrared spectroscopy for prediction of hydrolysis of whey protein was explored. Two different protein concentrations (5% and 8%) were trypsinated at different enzyme-to-substrate ratios (1:100 and 1:200) and followed over time (0–8 h). Degree of hydrolysis was determined by use of a fluorescamine assay, whereas size exclusion chromatography (SEC) was used to quantify the loss of intact protein. The results show spectral changes as hydrolysis proceeded over time. Increased absorbance intensities in the region from 1588 to 1540 cm−1 as well as increased absorbance intensities around 1400 cm−1 were observed with increasing hydrolysis. These changes were assigned to formation of primary amines as well as an increase in carboxyl groups as hydrolysis proceeded. As the level of free amino terminals correlates with spectral changes, infrared spectroscopy is a promising tool for prediction of degree of hydrolysis of whey proteins.

Fluorescence spectroscopy is a sensitive and selective technique, which can be of great value in bioprocesses to provide online, real-time measures of chemical compounds. Although fluorescence spectroscopy is a widely studied method, not much attention has been given to issues concerning intensity variations in the fluorescence landscapes due to pH fluctuations. This study elucidates how pH fluctuations cause intensity changes in fluorescence measurements and thereby decreases the quality of the subsequent quantification. A photo-degradation process of riboflavin was investigated using fluorescence spectroscopy and used as a model system. A two-step modeling approach, combining weighted PARAllel FACtor analysis (PARAFAC) with weighted nonlinear regression of the known reaction kinetics, is suggested as a way of handling the fluorescence intensity shifts caused by the pH changes. The suggested strategy makes it possible to compensate for uncertainties in the shifted data and thereby obtain more reliable concentration profiles for the chemical compounds and kinetic parameters of the reaction.

Near infrared (NIR) instruments in combination with multivariate data calibration models are useful tools in process monitoring. However, the need for expert assistance with calibration and maintenance of the models is a bottleneck limiting their more widespread use. In the previous article [ NIR news26(8), 13–16 (2015)], the MATLAB graphical application called Fulgur was introduced. Fulgur was developed as part of the BIOPRO “Hands Free” project with the goal of developing automated methods for handling data sets and calibrations of partial least squares models for NIR instruments. In this article, the Model-View-Controller programming design principles needed for developing the application are described in more depth as an introduction for potential developers who may want to use and expand the Fulgur framework. The programing design principles may also be of interest to chemometricians who want to program and develop their own GUI applications in MATLAB. The source code for the application together with more in-depth user and developer guides and tutorials for programming plugins is available for download from models.life.ku.dk.

NIR instruments in combination with multivariate data calibration models are useful tools in process monitoring. The calibration models often need to overcome matrix effects and must be re-calibrated over time to compensate for, e.g., changes in process parameters or raw materials, a task often involving manual expert assistance, which is a bottleneck for more widespread application of the technique. To ease the re-calibration, the BIOPRO consortium launched the “Hands free” project to research and develop automated methods for handling data sets and calibration of partial least squares (PLS) models. The intention was to find methods and algorithms which could be operated either by non-experts or fully automatically. Part of this effort was to develop a graphical user interface (GUI) application in MATLAB to enable rapid dissemination of methods to partners in the consortium and early testing by non-programmers. The resulting application, called Fulgur, can assist in handling near infrared (NIR) instrument calibration datasets, identifying outliers and developing and assessing PLS model performance with standard plots. Plots are focused on performance over time as it is usual to collect NIR data over longer time spans when monitoring industry processes. In this article, we present an overview of the application for end-users and explain the outlier detection and model performance plotting in more depth. Source code for the application together with a more in-depth user guide is available for download from models.life.ku.dk. In the next article, we will introduce the source code for potential developers and the principles behind the programming architecture used to develop the application.

AIM Necrotising enterocolitis (NEC) is often staged according to Bell's 1978 system, but today's NEC cases are more immature than the ones that were used to develop Bell's stages. Our aim was to explore the clinical and radiographic findings of contemporary cases of NEC and spontaneous intestinal perforation. METHODS We coded the clinical records of all cases of NEC stages I-III and spontaneous intestinal perforation born in 2006-2015 at the tertiary department of neonatology at Rigshospitalet, Denmark, for 16 clinical and radiographic symptoms and signs at disease onset and at climax. These variables were explored using principal component analysis, which can detect patterns in large datasets. RESULTS We reviewed 640 clinical records and included 158 cases of NEC or spontaneous intestinal perforation. When we entered the clinical and radiographic signs at disease climax, the cases were roughly grouped according to Bell's stages, except for a small group of NEC III cases, who were grouped with the cases of spontaneous intestinal perforation. CONCLUSION An analysis of the pattern of clinical and radiographic findings in a 2006-2015 population of NEC cases supported Bell's 1978 staging system. However, the separation between NEC and spontaneous intestinal perforation still poses a difficult task.

cope We aimed to investigate whether changes in the metabolome could explain observed changes in body composition in overweight adults after consumption of butter with high level of medium-chain fatty acids (MCFAs) in combination with casein or whey. Methods and results With GC-ToF and LC-Q/MS, we analyzed metabolites in plasma and urine from a 12-weeks randomized double-blinded human intervention including 52-abdominally overweight adults. The participants consumed 63 g/day of milk fat (high or low in MCFAs) and 60 g/day of protein (whey or casein). Urinary loss of the tricarboxylic acid cycle metabolites and a concomitantly increase of glycerol in blood were observed in the whey + high-MCFAs group, indicating potential lower anabolic processes, such as lipogenesis, by draining substrates. High intake of MCFAs resulted in elevated level of urinary adipic (independently of protein type) and plasma sebacic acid (with whey), indicating a potential increase in oxidation of MCFAs, which might lead to energy loss. Conclusion The type of protein showed highest effect on the overall metabolic profiles, but ω-oxidation of MCFAs in the liver seemed to be the main reason for the observed reduction in body fat mass after consumption of high MCFAs, independent of type of protein. This article is protected by copyright. All rights reserved

For the first time, several second-order calibration models based on artificial neural network-residual bilinearization (ANN-RBL), unfolded-partial least squares-RBL (U-PLS/RBL), multidimensional-partial least squares-RBL (N-PLS/RBL), multivariate curve resolution-alternating least squares (MCR-ALS), and parallel factor analysis 2 (PARAFAC2) were used to exploiting second-order advantage to identify which technique offers the best predictions for the simultaneous quantification of norepinephrine (NE), paracetamol (AC), and uric acid (UA) in the presence of pteroylglutamic acid (FA) as an uncalibrated interference at an electrochemically oxidized glassy carbon electrode (OGCE). Three-way differential pulse voltammetric (DPV) arrays were obtained by recording the DPV signals at different pulse heights. The recorded three-way arrays were both non-bilinear and non-trilinear therefore, the observed shifts in the recorded DPV data were corrected using correlation optimised warping (COW) algorithm. All the algorithms achieved the second-order advantage and were in principle able to overcome the problem of the presence of unexpected interference. Comparison of the performance of the applied second-order chemometric algorithms confirmed the more superiority of U-PLS/RBL to resolve complex systems. The results of applying U-PLS/RBL for the simultaneous quantification of the studied analytes in human serum samples were also encouraging. Fil: Jalalvand, Ali R.. Razi University. Faculty of Chemistry. Department of Analytical Chemistry; Irán. Universidad Nacional del Litoral. Facultad de Ingeniería Química. Laboratorio de Química Analitica; Argentina Fil: Gholivand, Mohammad Bagher. Razi University. Faculty of Chemistry. Department of Analytical Chemistry; Irán Fil: Goicoechea, Hector Casimiro. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe; Argentina. Universidad Nacional del Litoral. Facultad de Ingeniería Química. Laboratorio de Química Analitica; Argentina Fil: Skov, Thomas. Universidad de Copenhagen; Dinamarca

OBJECTIVE: A systematic review and meta-analysis of the evidence on balloon Eustachian tuboplasty (BET) as a treatment modality for Eustachian tube dysfunction (ETD). We followed the PRISMA guideline and registered with PROSPERO No. CRD42014009461.DATA SOURCES: We searched 12 databases including PubMed and Embase from January 1, 2010, to April 7, 2014, for studies of BET. Endpoints: change in symptoms, middle ear pathology, eardrum status, Eustachian tube function tests, hearing, adverse events, complications, and health-related quality of life.REVIEW METHODS: Study quality was assessed using the modified Delphi technique quality appraisal tool for case series studies. Risk of bias was assessed using the Cochrane Collaboration's tool for assessing risk of bias.RESULTS: Nine case series studies with 443 patients (642 tubes) were included. Population size n = 4 (7 tubes) to n = 210 (320 tubes). All studies were of poor quality and featured a high risk of bias. We found reduction of patient symptoms in ETD questionnaire (P < .001), postoperative normalization of the tympanic membrane, conversion of type B or type C into type A tympanograms, reduced mucosal inflammation, increased number of positive Valsalva test and Swallowing tests, improvement in Eustachian tube score, reduction in Sino-Nasal Outcome Test (SNOT)-22 score (P = .001), and increased quality of life (P = .001). No serious adverse events were found.CONCLUSION: The evidence of BET is poor and biased. No firm conclusions can be made to identify patients who will benefit from the procedure or to accurately predict surgical results. Randomized controlled trials or case-control trials are needed.

For the first time, an analytical methodology based on differential pulse voltammetry (DPV) at a glassy carbon electrode (GCE) and integration of three efficient strategies including variable selection based on ant colony optimization (ACO), mathematical pre-processing selection by genetic algorithm (GA), and sample selection (SS) through a distance-based procedure to improve partial least squares-1 (PLS-1, ACO-GA-SS-PLS-1) multivariate calibration (MVC) for the simultaneous determination of five opium alkaloids including morphine (MOP), noscapine (NOP), thebaine (TEB), codeine (COD), and papaverine (PAP) was used and validated. The baselines of the DPV signals were modeled as a smooth curve, using P-splines, a combination of B-splines and a discrete roughness penalty. After subtraction of the baseline we got a signal with a two-component probability density. One component was for the peaks and it was approximated by a uniform distribution on the potential axis. The other component was for the observed noise around the baseline. Some sources of bi-linearity deviation for electrochemical data were discussed and analyzed. The lack of bi-linearity was tackled by potential shift correction using correlation optimized warping (COW) algorithm. The MVC model was developed as a quinternary calibration model in a blank human serum sample (drug-free) provided by a healthy volunteer to regard the presence of a strong matrix effect which may be caused by the possible interferents present in the serum, and it was validated and tested with two independent sets of analytes mixtures in the blank and actual human serum samples, respectively. Fortunately, the proposed methodology was successful in simultaneous determination of MOP, NOP, TEB, COD, and PAP in both blank and actual human serum samples and its results were satisfactory comparable to those obtained by applying the reference method based on high performance liquid chromatography-ultraviolet detection (HPLC-UV) Fil: Gholivand, Mohammad Bagher. Razi University. Faculty of Chemistry; Irán Fil: Jalalvand, Ali R.. Razi University. Faculty of Chemistry; Irán. Universidad Nacional del Litoral. Facultad de Ingeniería Química. Laboratorio de Química Analitica; Argentina Fil: Goicoechea, Hector Casimiro. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe; Argentina. Universidad Nacional del Litoral. Facultad de Ingeniería Química. Laboratorio de Química Analitica; Argentina Fil: Gargallo, Raimundo. Universidad de Barcelona; España Fil: Skov, Thomas. Universidad de Copenhagen; Dinamarca Fil: Paimard, Giti. Razi University. Faculty of Chemistry; Irán

An industrial scale biomass production using batch or fed-batch fermentations usually optimized by selection of bacterial strains, tuning fermentation media, feeding strategy, and temperature. However, in-depth investigation of the biomass metabolome during the production may reveal new knowledge for better optimization. In this study, for the first time, the authors investigated seven fermentation batches performed on five Streptoccoccus thermophilus strains during the biomass production at Chr. Hansen (Denmark) in a real life large scale fermentation process. The study is designed to investigate effects of batch fermentation, fermentation time, production line, and yeast extract brands on the biomass metabolome using untargeted GC-MS metabolomics. Processing of the raw GC-MS data using PARAFAC2 revealed a total of 90 metabolites out of which 64 are identified. Partitioning of the data variance according to the experimental design was performed using ASCA and revealed that batch and fermentation time effects and their interaction term were the most significant effects. The yeast extract brand had a smaller impact on the biomass metabolome, while the production line showed no effect. This study shows that in-depth metabolic analysis of fermentation broth provides a new tool for advanced optimization of high-volume-low-cost biomass production by lowering the cost, increase the yield, and augment the product quality.

Foodomics is a newly developed discipline that has become more and more important in the last years where focus on food and the understanding of food systems has increased significantly. In this review, the flow of a typical foodomics study will be followed with a focus on the core components, where chemometric expertise is more deeply involved. These are: how to acquire sound data, how to exploit an experimental design, how to use classification in a proper way, how to look at more analytical platforms at the same time and, not the least, how to understand the limitations when interpreting the developed models. For each of these phases, the most common data issues will be highlighted and some of the most recent chemometric methods that are able to help solving them, will be presented.

Foodomics studies are normally concerned with multifactorial problems and it makes good sense to explore and to measure the same samples on complementary, synergistic analytical platforms that comprise multifactorial sensors and separation methods. However, the challenge of exploring, extracting and describing the data increases exponentially. Moreover, the risk of becoming flooded with non-informative data increases concomitantly. Acquisition of data from different analytical platforms provides opportunities for checking the validity of the data, comparing analytical platforms and ensuring proper data (pre)processing – all in the context of correlation studies. We provide practical and pragmatic tools to validate and to deal advantageously with data from more than one analytical platform. We emphasize the need for complementary correlation studies within and between blocks of data to ensure proper data handling, interpretation and dissemination. Correlation studies are a preliminary step prior to multivariate data analysis or as an introduction to more advanced multi-block methods.

A computationally engineered impedimetric naltrexone (NLT) biosensor based on immobilization of bovine serum albumin (BSA) onto fullerene-C60/glassy carbon electrode (FLR/GCE) has been developed using initial characterization by computational methods and complementing them by experimental ones. Computational results showed that BSA hydrophobically binds to FLR which is energetically favorable and leads to the spontaneous formation of the stable nanobiocomposite and also showed that interaction of NLT with BSA is mainly driven by hydrogen bonding and hydrophobic interactions. Besides complementing the computational studies, experimental results showed that addition of FLR to the surface of the electrode facilitated electron transfer reactions, and also showed that the presence of BSA inhibits the interfacial electron transfer in some extent due to the nonconductive properties of BSA. The presence of NLT may form a negatively charged electroactive complex with BSA which repels the negatively charged redox probe and decelerates interfacial electron transfer leading to obvious faradaic impedance change. The faradaic impedance responses were linearly related to naltrexone concentration between 0.1 nM and 80 nM and limit of detection (LOD) was calculated to be 0.01 nM (3Sb/b). Finally, the proposed biosensor was successfully applied to determination of NLT in urine samples of both healthy and addict volunteers. Fil: Gholivand, Mohammad Bagher. Razi University. Faculty of Chemistry; Irán Fil: Jalalvand, Ali R.. Razi University. Faculty of Chemistry; Irán. Universidad Nacional del Litoral; Argentina Fil: Paimard, Giti. Razi University. Faculty of Chemistry; Irán Fil: Goicoechea, Hector Casimiro. Universidad Nacional del Litoral; Argentina Fil: Skov, Thomas. Universidad de Copenhagen; Dinamarca Fil: Farhadi, Reza. Razi University. Faculty of Chemistry; Irán Fil: Ghobadi, Sirous. Razi University. Faculty of Science. Department of Biology; Irán Fil: Moradi, Nozar. Razi University. Faculty of Chemistry; Irán Fil: Nasirian, Vahid. Razi University. Faculty of Chemistry; Irán

BACKGROUND In-process monitoring of fermentation processes (at-line, on-line or in-line measurements) is essential to control productivity and ensure high product quality. A number of different monitoring techniques are available for this purpose and one possible categorization among this variety of techniques is based on the different structures generated by the measurements and the potential of these with respect to holding and extracting process information. In this study lactic fermentation processes is monitored by different techniques (brix, pH, NIR- and fluorescence-spectroscopy) providing different data structures (zero-, first- and second-order). Multivariate data analysis (PCA and PARAFAC) was applied on the first- and second-order data sets, and the different measurement signals or derivatives of these were combined by a multiblock strategy. The aim of this work is to present and clarify the advantages and variations of the different data structures. RESULTS The zero-order pH and brix measurements (a commonly used measure for total sugar content in wine fermentations) decreased in a smooth and logical pattern from 6.4 to 4.4 and from 10.5% to 6.2%, respectively – provided valuable critical quality attributes, communicating the fermentation process is progressing over time in accordance with biological and engineering intuition. The first-order NIR measurements modelled with PCA showed an increasing trend over time on PC1. This increasing trend corresponds to the lactic bacterial growth. This trend could be distinguished by statistical modelling from a second trend (PC2), reproducible for all production batches. Based on the second-order fluorescence measurements modelled by PARAFAC and its statistical uniqueness properties, three distinctive fluorescence compounds were found to vary over process time. Most probably these three compounds represent riboflavin, tryptophan and lumichrome or NADH. Using multiblock PCA the combined sensor signals identified two distinguished, reproducible time profiles for all batch runs. CONCLUSIONS The most interpretable chemical information was obtained by fluorescence spectroscopy due to the uniqueness properties of second-order measurements. The first-order technique NIR spectroscopy also provided valuable process information, though the process trends can only be interpreted indirectly and if interfering species had been encountered they could not have been modelled. The multiblock data set provided by zero-, first- and second-order measurements recorded over time highlighted important relationships among the different variables that provide chemical information when multivariate data analysis is applied. Although, first- and second-order measurements seem to obtain more information than the zero-order measurements, it is important to keep in mind that zero-order measurements can provide valuable information about the process, especially in combination with different sensors. © 2014 Society of Chemical Industry

We demonstrate how to process comprehensive two-dimensional gas chromatograms (GC × GC chromatograms) to remove nonsample information (artifacts), including background and retention time shifts. We also demonstrate how this, combined with further reduction of the influence of irrelevant information, allows for data analysis without integration or peak deconvolution (pixel-based analysis).

Malt fermentability is a difficult and time consuming trait to measure. The Rapid Visco Analyser (RVA) was assessed as an alternative rapid method to indicate potential fermentability. This study evaluated changes in rheological profiles under different operational conditions and compared these changes with reference to malt fermentability from a limited number of samples. Viscosity measurements of samples were also made using different RVA (models 3D+and 4) analysis conditions including a brewhouse time-temperature profile, heating/cooling rate, particle size and enzyme activity. Rheological measurements using the RVA-3D+gave similar results compared with the RVA-4, indicating adequate sensitivity of the RVA 3D+for discriminatory purposes. Use of a time-temperature profile of a commercial brewery mashing process was unsuitable. When malt enzymes were inactivated with silver nitrate, differences in viscosities were observed. However, this eliminated the ability to discriminate on fermentability. Increasing or decreasing the heating rate influenced the time available for enzyme action, which affected the degree of discrimination. This also provided some insight into physical and biochemical processes affected by differences in particle size. RVA has the potential to be used as a tool to discriminate between poor and good fermentability barley malts. RVA conditions when using the 'Kilned Malt' method with an appropriate mashing malt-water ratio provided a fast and reliable indication of malt performance prior to conducting lengthy fermentability tests.

Fecal samples were obtained from a cohort of 330 healthy Danish infants at 9, 18, and 36 months after birth, enabling characterization of interbacterial relationships by use of quantitative PCR targeting 31 selected bacterial 16S rRNA gene targets representing different phylogenetic levels. Nutritional parameters and measures of growth and body composition were determined and investigated in relation to the observed development in microbiota composition. We found that significant changes in the gut microbiota occurred, particularly from age 9 to 18 months, when cessation of breastfeeding and introduction of a complementary feeding induce replacement of a microbiota characterized by lactobacilli, bifidobacteria, and Enterobacteriaceae with a microbiota dominated by Clostridium spp. and Bacteroides spp. Classification of samples by a proxy enterotype based on the relative levels of Bacteroides spp. and Prevotella spp. showed that enterotype establishment occurs between 9 and 36 months. Thirty percent of the individuals shifted enterotype between 18 and 36 months. The composition of the microbiota was most pronouncedly influenced by the time of cessation of breastfeeding. From 9 to 18 months, a positive correlation was observed between the increase in body mass index and the increase of the short-chain-fatty-acid-producing clostridia, the Clostridum leptum group, and Eubacterium hallii . Considering previously established positive associations between rapid infant weight gain, early breastfeeding discontinuation, and later-life obesity, the corresponding microbial findings seen here warrant attention.

Apples are normally produced with focus on the fresh fruit marked, which imply high priority on storability and shelf life. The fruits used for juice are normally those discarded along the post-harvest chain. If the main goal is changed to production of high-quality juice with emphasis on single cultivar characteristic flavours, focus can be on optimal fruit ripening for flavour development. To investigate the juice quality potentials during post-harvest ripening, four apple cultivars: ‘Aroma’, ‘Holsteiner Cox’, ‘Ingrid Marie’ and ‘Rajka’ were sampled and juiced after 5 and 10 days of post-harvest storage at room temperature (20°C). The soluble solids content (SSC) of ‘Ingrid Marie’ fruits increased most, and after 10 days, it had the lowest starch content of the four cultivars studied. The mid-to-late season cultivars ‘Ingrid Marie’, ‘Holsteiner Cox’ and ‘Rajka’ had higher SSC/acidity ratio than the middle early season ‘Aroma’ ‘and all of them showed an increase of the ratio SSC/acidity during storage’. ...

Monocolonization of germ-free (GF) mice enables the study of specific bacterial species in vivo. Lactobacillus acidophilus NCFM(TM) (NCFM) is a probiotic strain; however, many of the mechanisms behind its health-promoting effect remain unknown. Here, we studied the effects of NCFM on the metabolome of jejunum, cecum, and colon of NCFM monocolonized (MC) and GF mice using liquid chromatography coupled to mass-spectrometry (LC-MS). The study adds to existing evidence that NCFM in vivo affects the bile acid signature of mice, in particular by deconjugation. Furthermore, we confirmed that carbohydrate metabolism is affected by NCFM in the mouse intestine as especially the digestion of oligosaccharides (penta- and tetrasaccharides) was increased in MC mice. Additionally, levels of α-tocopherol acetate (vitamin E acetate) were higher in the intestine of GF mice than in MC mice, suggesting that NCFM affects the vitamin E acetate metabolism. NCFM did not digest vitamin E acetate in vitro, suggesting that direct bacterial metabolism was not the cause of the altered metabolome in vivo. Taken together, our results suggest that NCFM affects intestinal carbohydrate metabolism, bile acid metabolism and vitamin E metabolism, although it remains to be investigated whether this effect is unique to NCFM.

For the first time, several multivariate calibration (MVC) models including partial least squares-1 (PLS-1), continuum power regression (CPR), multiple linear regression-successive projections algorithm (MLRSPA), robust continuum regression (RCR), partial robust M-regression (PRM), polynomial-PLS (PLY-PLS), spline-PLS (SPL-PLS), radial basis function-PLS (RBF-PLS), least squares-support vector machines (LS-SVM), wavelet transform-artificial neural network (WT-ANN), discrete wavelet transform-ANN (DWT-ANN), and back propagation-ANN (BP-ANN) have been constructed on the basis of non-bilinear first order square wave voltammetric (SWV) data for the simultaneous determination of ascorbic acid (AA), uric acid (UA), dopamine (DP) and nitrite (NT) at a glassy carbon electrode (GCE) to identify which technique offers the best predictions. The compositions of the calibration mixtures were selected according to a simplex lattice design (SLD) and validated with an external set of analytes' mixtures. An asymmetric least squares splines regression (AsLSSR) algorithm was applied for correcting the baselines. A correlation optimized warping (COW) algorithm was used to data alignment and lack of bilinearity was tackled by potential shift correction. The effects of several pre-processing techniques such as genetic algorithm (GA), orthogonal signal correction (OSC), mean centering (MC), robust median centering (RMC), wavelet denoising (WD), and Savitsky–Golay smoothing (SGS) on the predictive ability of the mentioned MVC models were examined. The best preprocessing technique was found for each model. According to the results obtained, the RBF-PLS was recommended to simultaneously assay the concentrations of AA, UA, DP and NT in human serum samples. Fil: Gholivand, Mohammad Bagher. Razi University. Faculty of Chemistry; Irán Fil: Jalalvand, Alí R.. Razi University. Faculty of Chemistry; Irán. Universidad Nacional del Litoral. Cátedra de Química Analítica I. Laboratorio de Desarrollo Analítico y Quimiometría; Argentina Fil: Goicoechea, Hector Casimiro. Universidad Nacional del Litoral. Cátedra de Química Analítica I. Laboratorio de Desarrollo Analítico y Quimiometría; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Santa Fe; Argentina Fil: Skov, Thomas. University of Copenhagen. Faculty of Life Sciences. Department of Food Science. Quality and Technology group; Dinamarca

Parallel factor analysis 2 (PARAFAC2) has been shown to be a powerful tool for resolution of complex overlapping peaks in chromatographic analyses. It is particularly useful because of its ability to handle shifts in the elution time mode and peak shape changes. Like all curve resolution techniques, PARAFAC2 will only find chemically meaningful parameters (elution time profiles and mass spectra) if the correct number of factors are determined. So far, the primary way to determine an appropriate number of factors, when using PARAFAC2, is to calculate models with different number of factors and then inspect the models manually. This approach is time consuming, and the result may be biased because of the manual assessment of the model quality, making PARAFAC2 inaccessible for analytical chemists in general. Here, we develop a method that can determine an appropriate number of factors in an automated way. The automation is based on a number of model diagnostics (quality criteria) collected from models with different numbers of factors. Combining these diagnostics, it is possible to assess what the appropriate number of components is. In this work, only gas chromatography–mass spectrometry data are considered. However, it will most likely be fairly straightforward to expand the work to also cover liquid chromatography data (with a multivariate detector). Automating the model quality evaluation of the PARAFAC2 model enables both the inexperienced and trained user to perform comprehensive and advanced analysis of chromatographic data with a minimum of manual work. © 2013 The Authors. Journal of Chemometrics Published by John Wiley & Sons Ltd.

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.

Co-cultures of specific lactic and propionic acid bacteria have been shown to have an antagonistic effect against yeast and moulds in dairy systems. In studies of these co-cultures by bioassay-guided fractionation and analysis, numerous compounds have been reported to inhibit yeast and moulds. Although active, the compounds do not account for the full effect observed. Instead, the inhibitory action in the co-culture is believed to be a result of synergy between known exo-metabolites, depletion of nutrients, and/or compounds not yet identified. Untargeted metabolomics or metabolic footprinting could be a potent approach to elucidation of the mechanism. The purpose of this review is to discuss the two pre-requisites for such a study--the compound classes expected in the co-cultures, and on the basis of these, the most suitable analytical technique(s). Ultrahigh-performance liquid chromatography (UPLC) coupled to high-resolution mass spectrometry (MS) via electrospray ionisation (ESI) operated in both positive and negative modes is regarded as the optimum instrumental technique. The applicability of a range of liquid chromatographic techniques ranging from ion-pair (IPC) and hydrophilic interaction (HILIC) to reversed-phase chromatography (RPC) is discussed in terms of the expected metabolome. Use of both HILIC and RPC is suggested, on account of the complementarity of these modes. The most promising strategy uses a combination of the two electrospray polarities and two modes of LC. The strategy recommended in this study does not include all compound classes, and suggestions for supplementary methods are listed.

BACKGROUND Visible–near infrared spectroscopy remains a method of increasing interest as a fast alternative for the evaluation of fruit quality. The success of the method is assumed to be achieved by using large sets of samples to produce robust calibration models. In this study we used representative samples of an early and a late season apple cultivar to evaluate model robustness (in terms of prediction ability and error) on the soluble solids content (SSC) and acidity prediction, in the wavelength range 400–1100 nm. RESULTS A total of 196 middle–early season and 219 late season apples (Malus domestica Borkh.) cvs ‘Aroma’ and ‘Holsteiner Cox’ samples were used to construct spectral models for SSC and acidity. Partial least squares (PLS), ridge regression (RR) and elastic net (EN) models were used to build prediction models. Furthermore, we compared three sub-sample arrangements for forming training and test sets (‘smooth fractionator’, by date of measurement after harvest and random). Using the ‘smooth fractionator’ sampling method, fewer spectral bands (26) and elastic net resulted in improved performance for SSC models of ‘Aroma’ apples, with a coefficient of variation CVSSC = 13%. The model showed consistently low errors and bias (PLS/EN: R2cal = 0.60/0.60; SEC = 0.88/0.88°Brix; Biascal = 0.00/0.00; R2val = 0.33/0.44; SEP = 1.14/1.03; Biasval = 0.04/0.03). However, the prediction acidity and for SSC (CV = 5%) of the late cultivar ‘Holsteiner Cox’ produced inferior results as compared with ‘Aroma’. CONCLUSION It was possible to construct local SSC and acidity calibration models for early season apple cultivars with CVs of SSC and acidity around 10%. The overall model performance of these data sets also depend on the proper selection of training and test sets. The ‘smooth fractionator’ protocol provided an objective method for obtaining training and test sets that capture the existing variability of the fruit samples for construction of visible–NIR prediction models. The implication is that by using such ‘efficient’ sampling methods for obtaining an initial sample of fruit that represents the variability of the population and for sub-sampling to form training and test sets it should be possible to use relatively small sample sizes to develop spectral predictions of fruit quality. Using feature selection and elastic net appears to improve the SSC model performance in terms of R2, RMSECV and RMSEP for ‘Aroma’ apples. © 2013 Society of Chemical Industry

Quantification of 62 aroma compounds in three commercial semi-hard cheeses with different fat contents and maturity levels was pursued. Dynamic headspace sampling and gas chromatography–mass spectrometry (GC–MS) was applied after spiking each of the three cheeses with the aroma compounds, and PARAllel FACtor analysis was used to resolve co-eluting compounds. By the use of linear regression, half of the spiked aroma compounds exhibited similar slopes in the three cheeses whereas the volatilities of 15 other compounds were dependent on their hydrophilicity relative to the fat content of the cheese. For nine aroma compounds, satisfactory coefficients of determination were obtained in one or two of the cheeses only, and seven compounds could not be quantified at all.

Green color and texture are important attributes for the perception of freshness of wild rocket. Packaging of green leafy vegetables can postpone senescence and yellowing, but a drawback is the risk of anaerobic respiration leading to loss of tissue integrity and development of an olive-brown color. The hypothesis underlying this paper is that color and textural quality of packaged wild rocket leaves can be predicted by multispectral imaging for faster evaluation of visual quality of leafy green vegetables in scientific experiments. Multispectral imaging was correlated to sensory evaluation of packaged wild rocket quality. CIELAB values derived from the multispectral images and from a spectrophotometer changed during storage, but the data were insufficient to describe variation in sensory perceived color and texture. CIELAB values from the multispectral images allowed for a more detailed determination of color compared to measurement with a spectrophotometer with an 8 mm aperture. Furthermore, the multispectral images enabled subtraction of background information of the sample and gave more accurate results on visual color of wild rocket leaves. The combination of wavelengths in the NIR range describing textural changes, and wavelengths in the VIS range describing color changes was optimal for quantification of sensory perceived color and textural quality of packaged wild rocket.

This study investigated the overall consumer experience of a common complex food product by comparing its traditional and modern form. The aim of the study was to uncover the mechanisms, occurring in the consumers' perception, responsible for defining the key elements of the product and therefore its uniqueness. Descriptive analysis was carried out for 59 sensory descriptive attributes covering odour, appearance: colour and shape, basic taste, flavour, texture, mouth feeling and after taste. Moreover, conceptual measurements of the descriptors, familiar, common, complexity, harmonious, balance, luxury, elegant, inviting, interesting, feminine and masculine were also measured for all sandwiches. Sensory, conceptual and design relationships were analysed by ANOVA Partial Least Squares Regression (APLSR). The conjoint results indicate that the combination of appearance, and texture sensory attributes were able to affect the overall conceptual understanding of this culturally defined complex food product and were considered key modalities in terms of the product quality assessment. Thus, the results stressed the fact that the overall consumer experience is a multisensory process in which cross-modal associations play a predominant role. Further the findings confirmed that cognitive processes, and therefore conceptualisations are responsible for this cross-modal associations and therefore of the overall consumer perception. However, retro nasal perception similar in both traditional and modern versions could be considered as core to the products' implicit definition in terms of uniqueness.

In a setting where multiple spectroscopic instruments are used for the same measurements it may be convenient to develop the calibration model on a single instrument and then transfer this model to the other instruments. In the ideal scenario, all instruments provide the same predictions for the same samples using the transferred model. However, sometimes the success of a model transfer is evaluated by comparing the transferred model predictions with the reference values. This is not optimal, as uncertainties in the reference method will impact the evaluation. This paper proposes a new method for calibration model transfer evaluation. The new method is based on comparing predictions from different instruments, rather than comparing predictions and reference values. A total of 75 flour samples were available for the study. All samples were measured on ten near infrared (NIR) instruments from two instrumental platforms, five NIR instruments from each platform. Protein content was quantified for all 75 samples and used as the reference variable during modelling by partial least squares regression. By adding artificial noise to first the spectroscopic measurements and then the reference values, this paper highlights the problems of including reference values in the evaluation of a model transfer, as uncertainties in the reference method impact the evaluation. At the same time, this paper highlights the power of the proposed model transfer evaluation, which is based on comparing predictions obtained from the different instruments. In this way, the impact of uncertainties originating from the reference method is minimised.

Predicting protein fractions and coagulation properties in bovine milk using Fourier transform infrared (FT-IR) measurements is desirable. However, such predictions may rely on correlations with total protein content. The aim of this study was to show how correlations between total protein content, protein fractions, and coagulation properties are responsible for the successful prediction of protein fractions and rennet-induced coagulation properties in milk samples. This study comprised 832 bovine milk samples from 2 breeds (426 Holstein and 406 Jersey). Holstein samples were collected from 20 Danish dairy herds from October to December 2009; Jersey samples were collected from 22 Danish dairy herds from February to April 2010. All samples were from conventional herds and taken while cows were housed. The results showed that κ-CN, αS1-CN, αS1-CN with 8 phosphorylated groups attached (αS1-CN 8P), and curd firming rate could be predicted from FT-IR measurements of the milk samples (with coefficients of determination between 0.66 and 0.71). However, the success of these FT-IR-based predictions was based on indirect relationships with total protein content. Hence, the FT-IR predictions relied on covariance structures with total protein content rather than absorption bands directly associated with the protein fractions and coagulation properties. If covariance structures between the protein fractions, coagulation properties, and total protein content used to calibrate partial least squares models were not conserved in future samples, these samples would show incorrect predictions of the protein fractions and coagulation properties. We demonstrated this using samples from 1 breed to calibrate and samples from the other breed to validate partial least squares models for β-CN. The 2 breeds had different covariance structures between β-CN and total protein content, and the validation samples yielded incorrect predictions. This finding may limit the usefulness of FT-IR-based predictions of protein fractions in milk recording, because indirect covariance structures in the calibration set must be valid for future samples, or future samples will show incorrect predictions.

The task of identifying individual compounds within complex gas chromatography - mass spectrometry (GC-MS) chromatograms is made more difficult by interferences between peaks with similar mass spectra eluting at the same time, typically against a background of chemical and electronic noise. Although chemometric techniques like parallel factor analysis and multivariate curve resolution can help to purify spectra and improve correlations with reference compounds, file incompatibilities between GC-MS acquisition software and modeling software prevent the modeled spectra from being easily compared to spectra in reference libraries. In this paper we present an enhancement to OpenChrom, an open-source software for chromatography and mass spectrometry, which implements the automated cross-matching of modeled spectra to NIST08 and NIST11 mass spectral databases. The benefits of this approach are demonstrated using a complex environmental dataset consisting of non-methane volatile organic compound emissions sampled on an Australian poultry farm. © 2012 Elsevier B.V.

The marama bean is an underutilized traditional source of food for the indigenous population in Southern Africa such as the Herero, Tswana and Khoisan people and is mostly eaten as a snack after roasting in hot sand. The beans have a high content of protein and oil (mainly unsaturated lipid). This composition gives the marama bean a significant potential for production of various nutritious food products such as roasted beans. The aim of this study was to investigate the influence of different storage conditions on the oxidative stability of roasted marama beans. This was evaluated by packing the roasted marama beans in the presence or absence of oxygen and storing them with or without exposure to light for seven months. During the storage period, the changes in flavor related oxidation products (i.e. secondary oxidation products) were investigated. It was found that roasted marama beans should preferably be stored in darkness in bags containing a low amount of oxygen. Under these conditions the beans could be stored for at least five months without obtaining undesirable odors caused by lipid oxidation. Hexanal was found to be the limiting storage factor as it was the first volatile exceeding its odor detection threshold.