Effect of ultrafiltration operating conditions for separation of ferulic acid from arabinoxylans in corn fibre alkaline extract

Corn fibre, a co-product of the starch industry, is rich in compounds with high added value, such as ferulic acid and arabinoxylans, which are released during alkaline extraction. This work aims to optimise an efficient separation method for the recovery of these two compounds from a corn fibre alkaline extract, allowing an efficient valorisation of this co-product. Ultrafiltration was selected as separation method, due to its potential to fractionate these compounds. In order to minimise the loss of membrane permeance, due to mass transfer limitations caused by the high arabinoxylan viscosity, the impact of relevant ultrafiltration operating parameters (membrane molecular weight cut-off, fluid dynamics conditions, transmembrane pressure, and operating temperature) were evaluated. A Nadir UP 150 membrane was found to be an adequate choice, allowing for an efficient separation of ferulic acid from arabinoxylans, with null rejection of ferulic acid, a high estimated rejection of arabinoxylans 98.0% ± 1.7%, and the highest permeance of all tested membranes. A response surface methodology (RSM) was used to infer the effect of ultrafiltration conditions (crossflow velocity, transmembrane pressure and operating temperature) on the rejection of ferulic acid, retention of arabinoxylans (assessed through apparent viscosity of the retentate stream), and permeance. Through mathematical modelling it was possible to determine that the best conditions are the highest operating temperature and initial crossflow velocity tested (66◦C and 1.06 m.s−1, respectively), and the lowest transmembrane pressure tested (0.7 bar).publishersversionpublishe

Fluorescence coupled with chemometrics for simultaneous monitoring of cell concentration, cell viability and medium nitrate during production of carotenoid-rich Dunaliella salina

This work was supported by the Associate Laboratory for Green Chemistry- LAQV which is financed by national funds from FCT / MCTES ( UID/QUI/50006/2019 ); by the European FP7 KBBE project “D-Factory” (contract no. 613870); by KAUST OSR award no. OSR-2016-CPF-2907-05; grant of FCT / MCTES : SFRH/BD/108894/2015. The authors would like to thank The Marine Biological Association (Devon, UK) and NBT Ltd (Israel).Two-dimensional (2D) fluorescence spectroscopy was investigated as a monitoring tool for cultivation, harvesting, and effluent treatment of Dunaliella salina with high carotenoid concentration; aiming to improve the production process and minimise costs. Chemometric analysis, namely Principal Component Analysis (PCA) and Projection to Latent Structures (PLS), were used to build models for estimation of cellular concentration, cellular viability, and nitrate concentration in media. The estimations were based on fluorescence excitation-emission matrices (EEMs) acquired directly from algal suspensions. Cell concentration during cultivation and harvesting can be predicted by a single model capturing 92.0% of the variance, and with R2 of 0.92 and 0.97, for training and validation, respectively. Cell viability during harvesting by ultrafiltration was modelled with 79% of variance and R2 of 0.79 for training and 0.73 for validation. Nitrate concentration was successfully predicted during cultivation and permeate treatment using a single model with 81.8% of variance and R2 of 0.82 for training and 0.80 for validation. Therefore, this work demonstrates the strong potential of combining 2D fluorescence and chemometrics for monitoring different processes during microalgae production.authorsversionpublishe

Development of a monitoring tool based on fluorescence and climatic data for pigments profile estimation in Dunaliella salina

When growing microalgae for biorefinery processes, a high product yield is desired. For that reason, monitoring the concentration of the desired products during growth and products induction procedure is of great interest. 2D Fluorescence spectroscopy is a fingerprinting technique, used in situ and at real time, with a high potential for online monitoring of biological systems. In this work, Dunaliella salina pigment content was monitored using fluorescence data coupled with chemometric tools. Climatic parameters were also used as input variables due to their impact on the pigments profile in outdoor cultivations. Predictive models were developed for chlorophyll content (a, b, and total) with variance captured between 50 and 90%, and R2 varying between 0.6 and 0.9 for both training and validation data sets. Total carotenoids models captured 70 to 80% of variance, and R2 between 0.7 and 0.9, for training and validation. Models for specific carotenoids (zeaxanthin, α-carotene, all-trans-β-carotene, and 9-cis-β-carotene) captured variance between 60 and 90%, with validation and training R2 between 0.6 and 0.9. With this methodology, it was possible to calibrate a monitoring tool for pigments quantification, as a bulk and as individual compounds, proving that 2D fluorescence spectroscopy and climatic data combined with chemometric tools can be used to assess simultaneously and at real time different pigments in D. salina biomass production.info:eu-repo/semantics/publishedVersio

A PHABULOSA/cytokinin feedback loop controls root growth in arabidopsis

The hormone cytokinin (CK) controls root length in Arabidopsis thaliana by defining where dividing cells, derived from stem cells of the root meristem, start to differentiate [ [1], [2], [3], [4], [5] and [6]]. However, the regulatory inputs directing CK to promote differentiation remain poorly understood. Here, we show that the HD-ZIPIII transcription factor PHABULOSA (PHB) directly activates the CK biosynthesis gene ISOPENTENYL TRANSFERASE 7 (IPT7), thus promoting cell differentiation and regulating root length. We further demonstrate that CK feeds back to repress both PHB and microRNA165, a negative regulator of PHB. These interactions comprise an incoherent regulatory loop in which CK represses both its activator and a repressor of its activator. We propose that this regulatory circuit determines the balance of cell division and differentiation during root development and may provide robustness against CK fluctuations

Abordagem geriátrica ampla na promoção de um envelhecimento ativo e saudável: componentes do modelo de intervenção AGA@4life

info:eu-repo/semantics/publishedVersio

Hormonal input in plant meristems: a balancing act

Plant hormones are a group of chemically diverse molecules that control virtually all aspects of plant development. Classical plant hormones were identified many decades ago in physiology studies that addressed plant growth regulation. In recent years, biochemical and genetic approaches led to the identification of many molecular components that mediate hormone activity, such as hormone receptors and hormone-regulated genes. This has greatly contributed to the understanding of the mechanisms underlying hormone activity and highlighted the intricate crosstalk and integration of hormone signalling and developmental pathways. Here we review and discuss recent findings on how hormones regulate the activity of shoot and root apical meristems

Multivariate statistically-based modelling of a membrane bioreactor for wastewater treatment using 2D fluorescence monitoring data

This work presents the development of multivariate statistically-based models for monitoring several key performance parameters of membrane bioreactors (MBR) for wastewater treatment. This non-mechanistic approach enabled the deconvolution of 2D fluorescence spectroscopy data, a powerful technique that has previously been shown to capture important information regarding MBR performance. Projection to latent structure (PLS) modelling was used to integrate 2D fluorescence data, after compression through parallel factor analysis (PARAFAC), with operation and analytical data to describe an MBR fouling indicator (transmembrane pressure, TMP), five descriptors of the effluent quality (total COD, soluble COD, concentration of nitrite and nitrate, total nitrogen and total phosphorus in the permeate) and the biomass concentration in the bioreactor (MLSS). A multilinear correlation was successfully established for TMP, CODtp and CODsp, whereas the optimised models for the remaining outputs included quadratic and interaction terms of the compressed 2D fluorescence matrices. Additionally, the coefficients of the optimised models revealed important contributions of some of the input parameters to the modelled outputs. This work demonstrates the applicability of 2D fluorescence and statistically-based models to simultaneously monitor multiple key MBR performance parameters with minimal analytical effort. This is a promising approach to facilitate the implementation of MBR technology for wastewater treatment

Development of a hybrid model strategy for monitoring membrane bioreactors

In the present study, the performance of a membrane bioreactor (MBR) was modelled using a hybrid approach based on the activated sludge model number 3 (ASM3) combined with projection to latent structures (PLS) to predict the residuals of the ASM. The application of ASM to MBRs requires frequent re-calibration to adjust the model to variations in influent characteristics, determined through time-consuming analysis and batch tests. Considering this problem, the objective of this study was to improve ASM prediction ability with minimal additional monitoring effort. Hybrid models were developed to predict three MBR performance parameters: mixed liquor suspended solids (MLSS), COD in the permeate (CODp) and nitrite and nitrate concentration in the permeate (NOxp). For PLS modelling of ASM residuals three input strategies were used: (1) analytic and operating data; (2) operating data plus 2D fluorescence spectroscopy; (3) all the data. The first input strategy improved ASM prediction of the three selected outputs, and highlighted the lack of detailed and real-time information from wastewater and operating parameters in the ASM used in this study. In the second input strategy, the incorporation of updated data from 2D fluorescence spectroscopy resulted on better model fitting than in the first input strategy, for all the output parameters studied. Through the hybrid modelling approach it was possible to significantly improve the ASM predictions in real-time using 2D fluorescence measurements and other relevant parameters acquired on-line, without requiring further laboratory analysis. Furthermore, the third input strategy, incorporating all the collected data, did not significantly improve the prediction of the outputs beyond the second strategy. This shows that 2D fluorescence spectroscopy is a comprehensive monitoring tool, able to capture on-line the required information to complement, through hybrid modelling, the mechanistic information described by an ASM

Two-dimensional fluorescence as a fingerprinting tool for monitoring wastewater treatment systems

Background: The use of two-dimensional (2D) fluorescence for monitoring complex biological systems requires careful assessment of the effect of chemical species present, which may be fluorescent and/or may interfere with the fluorescence response of target fluorophores. Given the complexity of fluorescence data (excitation emission matrices-EEMs), the challenge is how to recover the information embedded into those EEMs that can be related quantitatively with the observed performance of the biological processes under study. Results: This work shows clearly that interference effects (such as quenching and inner filter effects) occur due to the presence of multiple species in complex biological media, such as natural water matrices, wastewaters and activated sludge. A statistical multivariate analysis is proposed to recover quantitative information from 2D fluorescence data, correlating EEMs with the observed performance. A selected case study is discussed, where 2D fluorescence spectra obtained from the effluent of a membrane bioreactor were compressed using PARAFAC and successfully correlated with the effluent chemical oxygen demand, using projection to latent structures modelling. Conclusion: This study demonstrates the potential of using 2D fluorescence spectroscopy as a status fingerprint. Additionally, it is shown how statistical multivariate data analysis can be used to correlate EEMs with selected performance parameters for monitoring of biological systems