THE USE OF NEAR INFRARED REFLECTANCE SPECTROMETRY FOR CHARACTERIZATION OF BROWN ALGAL TISSUE1

Measuring qualitative traits of plant tissue is important to understand how plants respond to environmental change and biotic interactions. Near infrared reflectance spectrometry (NIRS) is a cost‐, time‐, and sample‐effective method of measuring chemical components in organic samples commonly used in the agricultural and pharmaceutical industries. To assess the applicability of NIRS to measure the ecologically important tissue traits of carbon, nitrogen, and phlorotannins (secondary metabolites) in brown algae, we developed NIRS calibration models for these constituents in dried Sargassum flavicans (F. K. Mertens) C. Agardh tissue. We then tested if the developed NIRS models could detect changes in the tissue composition of S. flavicans induced by experimental manipulation of temperature and nutrient availability. To develop the NIRS models, we used partial least squares regression to determine the statistical relationship between trait values determined in laboratory assays and the NIRS spectral data of S. flavicans calibration samples. Models with high predictive power were developed for all three constituents that successfully detected changes in carbon, nitrogen, and phlorotannin content in the experimentally manipulated S. flavicans tissue. Phlorotannin content in S. flavicans was inversely related to nitrogen availability, and nitrogen, temperature, and tissue age interacted to significantly affect phlorotannin content, demonstrating the importance of studies that investigate these three variables simultaneously. Given the speed of analysis, accuracy, small tissue requirements, and ability to measure multiple traits simultaneously without consuming the sample tissue, NIRS is a valuable alternative to traditional methods for determining algal tissue traits, especially in studies where tissue is limited.     

Predicting seasonal and spatial variations in diet quality of Pyrenean chamois (Rupicapra pyrenaica pyrenaica) using near infrared reflectance spectroscopy

Monitoring nutritional status of wildlife is key to understanding the population response to changes in food availability. Several direct and indirect methods have been proposed for such purposes, but faecal nitrogen (FN) is by far the most commonly used indirect non-invasive indicator of free-ranging ruminant diet quality. In this work, two methods were used to estimate the FN content in 291 pellet samples of Pyrenean chamois (Rupicapra pyrenaica pyrenaica) collected during a 2-year period (May 2009 to May 2011) in two chamois populations from the Catalan Pyrenees, Spain: the Dumas LECO analyzer and near-infrared reflectance spectroscopy (NIRS). In order to optimize further FN estimations we performed three different NIRS calibrations: (1) general, across year, season and populations; (2) population-specific; and (3) season-specific, i.e., different periods of vegetation growth. FN ranged from 1.66 % to 2.85 % (dry matter basis) and was higher in the main vegetation growing season than in winter. Concerning NIRS calibration, our study confirmed that FN of Pyrenean chamois can be accurately determined by NIRS, since the general calibration was accurate enough to predict FN. It is concluded that NIRS represents a non-destructive, emission-free and low-cost analytical technique which may reduce the use of conventional laborious methods for estimating FN in long-term wildlife monitoring.     

Assessment of Mass Flows and Fuel Quality During Mechanical Dehydration of Silages Using Near Infrared Reflectance Spectroscopy

This study was implemented to evaluate the potential of near-infrared reflectance spectroscopy (NIRS) technology to estimate chemical composition of dried press cake samples characterised by a wide range of parent materials. A total of 210 samples, derived from two studies on production of solid fuels from agricultural crops by application of the IFBB technology (Integrated production of solid fuels and biogas from biomass), were analysed to determine their chemical composition. A Foss XDS-spectrometer was used to obtain near-infrared spectra (400–2,500 nm). Prediction equations, developed for chemical components, showed that NIRS technology could predict N, inorganic ash (ash), crude fiber (CF), ether extract (EE) and nitrogen free extracts (NFE) accurately (RSQ_cal and SECV of 0.93 and 0.04 % DM, 0.89 and 0.48 % DM, 0.93 and 1.67 % DM, 0.87 and 0.28 % DM and 0.93 and 1.72 % DM, respectively). Mineral components could also be predicted with a moderate degree of accuracy using NIRS technology (RSQ_cal and SECV of 0.85 and 0.10 % DM (K), 0.77 and 0.01 % DM (P) and 0.84 and 0.02 % DM (Cl), respectively), whereas calibration of gross energy (GE) did not succeed. Subsequent, external validation confirmed these results. Regression of mass flows with measured and NIRS-predicted values showed accurate results (RSQ 0.72–0.99) and promise an accelerated quality management in working biogas plants.     

Use of near-infrared reflectance spectroscopy to predict species composition in tree fine-root mixtures

Assessment of belowground interactions in mixed forests has been largely constrained by the ability to distinguish fine roots of different species. Here, we explored near infrared reflectance spectroscopy (NIRS) to predict the proportion of woody fine roots in mixed samples and analyzed whether the prediction quality of NIRS models is related to the complexity of the fine-root mixture. For model calibration and validation purposes, 11 series of artificial mixed species samples containing known amounts of fine roots of up to four temperate tree species and non-woody plants were prepared. Three types of models with different calibration/validation approaches were developed and tested against external independent data for additional validation. With these models the proportion of each species in root mixtures was predicted accurately with low standard error of prediction (RMSECV/RMSEP <6.5%) and high coefficient of determination (r²?>?0.93) for all fine-root mixtures. In addition, NIRS models also provided satisfactory estimates for samples with low (<15%) or no content of particular components. The predictive power of the NIRS models did not decrease substantially with increasing complexity of the root samples. The approach presented here is a promising alternative to hand sorting of fine roots, which may be influenced substantially by operator variation, and it will facilitate investigating belowground interactions between woody species.     

NIRS prediction of the feed value of temperate forages: efficacy of four calibration strategies

Near infrared reflectance spectroscopy (NIRS) of 924 fresh temperate forages were used to develop calibration models for chemical composition - crude ash (CA) and crude protein (CP) - organic matter digestibility (OMD) and voluntary intake (VI). We used 110 samples to assess the models. Four calibration strategies for determining forage quality were compared: (i) species-specific calibration, (ii) family-specific calibration, (iii) a global procedure and (iv) a local approach. Forage calibration data sets displayed CA values ranging from 52 to 205 g/kg of dry matter (DM), CP values from 50 to 280 g/kg DM, OMD values from 0.48 to 0.85 g/g and VI values from 22.5 to 115.2 g DM/kg metabolic body weight (BW0.75). The calibration models performed well for all the variables except for VI. For CA, local procedure showed lower standard error of prediction (SEP) than species-specific, family-specific or global models. For CP, the calibration models all showed similar SEP values (11.13, 11.08, 11.38 and 11.34 g/kg DM for species-specific, family-specific, global and local approaches). For OMD, the local procedure gave a similar SEP (0.024 g/g) to specific species and global procedures (0.027 g/g) and a lower SEP than the family-specific approach (0.028 g/g). For VI, the local approach and species-specific calibration showed lower SEP (7.08 and 7.16 g/kg BW0.75) than the broad-based calibrations (8.09 and 8.34 g/kg BW0.75 for family-specific model and global procedure, respectively). Local calibration may thus offer a practical way to develop robust universal equations for animal response determinations.     

Prediction of tree species composition in fine root mixed samples using near-infrared reflectance spectroscopy

Fine roots ( ≤ 2 mm diameter) are of great value when investigating belowground interactions among different plant species and soil nutrient cycling in forest ecosystems. However, fine root separation and species identification are labor-intensive and time-consuming processes. This study aimed to evaluate the aptitude of near-infrared reflectance spectroscopy (NIRS) in predicting tree species composition in fine root mixed samples. The coniferous species Cunninghamia lanceolata and Pinus massoniana, the deciduous species Alniphyllum fortunei and Liquidambar formosana, and the evergreen broadleaved species Cyclobalanopsis glauca represent the five subtropical tree species selected for this investigation. To obtain near-infrared reflectance spectral data, 20 samples taken in the field and 70 artificially mixed samples of the five species were produced after root samples were oven-dried and ground. Calibration was performed with partial least squares regression and leave-one-out cross-validation. Root mass proportions of the mixed samples showed good predictive capacity for C. lanceolata, P. massoniana, and C. glauca with low root mean square error of prediction ( < 6.82%) and high determination coefficients (R²>0.944). Predictions for A. fortunei and L. formosana were acceptable with R²>0.819. NIRS shows potential in predicting tree species composition with suitable accuracy.     

Performance,gut morphology and carcass characteristics of fattening rabbits as affected by particle size of pelleted diets

A review of past literature revealed inconsistencies in recommended feed particle size for optimal growth and productive performance of rabbits. Changing diet formulation and subsequent processing conditions may improve pellet texture and potentially affect rabbit performance. In the current study, two isoenergetic and isonitrogenous pelleted diets were formulated, which varied in the particle size of the concentrates (2 and 8 mm, respectively). The objective was to evaluate the effect of different particle sizes of compound diets on performance, nutrient utilisation, gut morphology, and carcass characteristics of fattening Italian White breed rabbits. The finely ground diet led to a significant improvement in feed efficiency and apparent digestibility of crude protein, ether extract, crude fibre and NDF, without any negative effect on gut morphology. Furthermore, a smaller particle size of concentrates in pelleted diets improved carcass traits. Meat colour parameters showed significant differences in longissimus lumborum and biceps femoris due to dietary treatments, but in both muscles pH values 1 h and 24 h after slaughter remained unchanged. It is concluded that a finely ground pelleted diet can be used to improve growth performance of rabbits without affecting carcass parameters.     

At-line near-infrared spectroscopy for monitoring concentrations in temperature-triggered glutamate fermentation

Rapid development in the glutamate fermentation industry has dictated the need for effective fermentation monitoring by rapid and precise methods that provide real-time information for quality control of the end-product. In recent years, near-infrared (NIR) spectroscopy and multivariate calibration have been developed as fast, inexpensive, non-destructive and environmentally safe techniques for industrial applications. The purpose of this study was to develop models for monitoring glutamate, glucose, lactate and alanine concentrations in the temperature-triggered process of glutamate fermentation. NIR measurements of eight batches of samples were analyzed by partial least-squares regression with several spectral pre-processing methods. The coefficient of determination (R ²), model root-mean square error of calibration (RMSEC), root-mean square error of prediction (RMSEP) and residual predictive deviation (RPD) of the test calibration for the glutamate concentration were 0.997, 3.11 g/L, 2.56 g/L and 19.81, respectively. For the glucose concentration, R ², RMSEC, RMSEP and RPD were 0.989, 1.37 g/L, 1.29 g/L and 9.72, respectively. For the lactate concentration, R ², RMSEC, RMSEP and RPD were 0.975, 0.078 g/L, 0.062 g/L and 6.29, respectively. For the alanine concentration, R ², RMSEC, RMSEP and RPD were 0.964, 0.213 g/L, 0.243 g/L and 5.29, respectively. New batch fermentation as an external validation was used to check the models, and the results suggested that the predictive capacity of the models for the glutamate fermentation process was good.     

Intake,feeding behaviour,digestibility, performance,carcass characteristics and meat quality of lambs fed different levels of semi-purified glycerine in the diet

The aim of this study was to evaluate the effect of different inclusion levels of semi-purified glycerine in the diet of feedlot lambs on feeding behaviour, nutrient intake and digestibility, carcass characteristics, meat quality and in vitro degradability. Thirty-two Dorper × (Texel × Suffolk) crossbred intact male lambs (22.2 ± 5.51 kg) were fed glycerine (90% purity) at 0, 120, 240 or 360 g/kg dry matter (DM) in a total mixed ration with a roughage to concentrate ratio of 40:60 for 84 d. In vitro degradability was not affected by glycerine supplementation. Feeding behaviour and digestibility of DM, crude protein and fibre and production performance were similar among treatments. Ether extract digestibility was lower at the highest inclusion level. Glycerine level had no effect on ruminal pH, carcass characteristics and meat quality, except for subcutaneous fat thickness which was lower for lambs fed glycerine at 240 and 360 g/kg DM. Scores for unpleasant taste, unpleasant odour, succulence and softness of meat were not affected by dietary glycerine level. These data suggest that there are no adverse effects on carcass quality and performance when semi-purified glycerine is provided up to 360 g/kg DM in the diet of growing lambs fed a forage to concentrate ratio of 40:60.     

Petroleum Degradation,Biosurfactant and Laccase Production by Fusarium neocosmosporiellum RH-10: A Microcosm Study

The aim of this study was to evaluate the potential of crude oil removal by fungal strains isolated from Arak refinery. The results showed that the RH10 strain is a potent strain as a surfactant producer and degrader of petrochemical hydrocarbons. The strain was identified as a Fusarium neocosmosporiellum and could degrade 58% of hydrocarbons in the minimal medium and reduce the surface tension from 45 to 26.5 mN m^?1. Moreover, residual crude oil analysis with Fourier transform infrared spectrophotometry showed that this strain was able to degrade 50% of aliphatic compounds. To investigate the mechanism of degradation, oxidase enzymes were assayed and it was found that F. neocosmosporiellum can produce 1.94 U L^?1 of laccase in 10 g L^?1 crude oil. Carbon, nitrogen, phosphorus and soil pattern optimization in a microcosm study showed that this strain removed 44% and 27% of the crude oil from contaminated soil in 1% and 5% crude oil concentrations, respectively. Under optimum condition, 9.66 g kg^?1 crude oil was removed by F. neocosmosporiellum when the initial oil concentration was 50 g kg^?1, at the end of 150 days microcosm experiment. The results demonstrated the promising potential of fungi strain for cleaning of contaminated soil.     

Characterisation of particle dynamics and turnover in the gastrointestinal tract of Holstein cows fed forage diets differing in fibre and protein contents

An improved understanding of the role of forage quality on the processes of particle dynamics and turnover is important for the development of healthier and cost-effective feeding strategies that aim at lowering the proportions of concentrates in the diets of cattle. The aim of this study was to evaluate the effects of feeding hays of different qualities on particle dynamics, digestion kinetics and turnover in the gastrointestinal tract (GIT). Three non-lactating, rumen fistulated Holstein cows were fed diets consisting exclusively of hay with either low quality [Group LH; 605 ± 12.4 g/kg neutral detergent fibre (NDF) and 63 ± 4.7 g/kg crude protein (CP)] or good quality (Group GH; 551 ± 20.1 g/kg NDF and 116 ± 3.6 g/kg CP). Data showed that in situ dry matter (DM) disappearance of the soluble fraction was greater for Group GH (p < 0.05). Feeding good quality hay also lowered the proportion of particles >1.18 mm particularly during the eating process (p < 0.05). Changes in the particle size occurring afterwards were greater for Group GH as well (p < 0.05); approximately 30% in the comminution in the particle size occurred postruminally. Feeding hay of good quality lowered DM content of solid rumen digesta (p < 0.05), accelerated (p < 0.05) the turnover rate of DM and NDF in the GIT and increased DM intake (p < 0.05). In conclusion, feeding forages of better quality significantly promoted degradation processes and kinetics in the GIT with positive effects on turnover rate of digesta and feed intake in Holstein cows.     

Near‐infrared spectroscopy for metabolite quantification and species identification

Near‐infrared (NIR) spectroscopy is a high‐throughput method to analyze the near‐infrared region of the electromagnetic spectrum. It detects the absorption of light by molecular bonds and can be used with live insects. In this study, we investigate the accuracy of NIR spectroscopy in determining triglyceride level and species of wild‐caught Drosophila. We employ the chemometric approach to produce a multivariate calibration model. The multivariate calibration model is the mathematical relationship between the changes in NIR spectra and the property of interest as determined by the reference analytical method. Once the calibration model was developed, we used an independent set to validate the accuracy of the calibration model. The optimized calibration model for triglyceride quantification yielded coefficients of determination of 0.73 for the calibration test set and 0.70 for the independent test set. Simultaneously, we used NIR spectroscopy to discriminate two species of Drosophila. Flies from independent sets were correctly classified into Drosophila melanogaster and Drosophila simulans with accuracy higher than 80%. These results suggest that NIRS has the potential to be used as a high‐throughput screening method to assess a live individual insect's triglyceride level and taxonomic status.     

Monitoring complex media fermentations with near-infrared spectroscopy: comparison of different variable selection methods

Near-infrared spectroscopy (NIRS) is known to be a suitable technique for rapid fermentation monitoring. Industrial fermentation media are complex, both chemically (ill-defined composition) and physically (multiphase sample matrix), which poses an additional challenge to the development of robust NIRS calibration models. We investigated the use of NIRS for at-line monitoring of the concentration of clavulanic acid during an industrial fermentation. An industrial strain of Streptomyces clavuligerus was cultivated at 200-L scale for the production of clavulanic acid. Partial least squares (PLS) regression was used to develop calibration models between spectral and analytical data. In this work, two different variable selection methods, genetic algorithms (GA) and PLS-bootstrap, were studied and compared with models built using all the spectral variables. Calibration models for clavulanic acid concentration performed well both on internal and external validation. The two variable selection methods improved the predictive ability of the models up to 20%, relative to the calibration model built using the whole spectra.     

Optimization of multi-environment trials for genomic selection based on crop models

Key message

We propose a statistical criterion to optimize multi-environment trials to predict genotype × environment interactions more efficiently, by combining crop growth models and genomic selection models.

Abstract

Genotype × environment interactions (GEI) are common in plant multi-environment trials (METs). In this context, models developed for genomic selection (GS) that refers to the use of genome-wide information for predicting breeding values of selection candidates need to be adapted. One promising way to increase prediction accuracy in various environments is to combine ecophysiological and genetic modelling thanks to crop growth models (CGM) incorporating genetic parameters. The efficiency of this approach relies on the quality of the parameter estimates, which depends on the environments composing this MET used for calibration. The objective of this study was to determine a method to optimize the set of environments composing the MET for estimating genetic parameters in this context. A criterion called OptiMET was defined to this aim, and was evaluated on simulated and real data, with the example of wheat phenology. The MET defined with OptiMET allowed estimating the genetic parameters with lower error, leading to higher QTL detection power and higher prediction accuracies. MET defined with OptiMET was on average more efficient than random MET composed of twice as many environments, in terms of quality of the parameter estimates. OptiMET is thus a valuable tool to determine optimal experimental conditions to best exploit MET and the phenotyping tools that are currently developed.

     

人血清中胆固醇近红外光谱快速检测初步研究

以全自动生化分析仪测定结果为参考值,采用傅利叶变换近红外透射光谱技术,结合偏最小二乘法,建立人血清中胆固醇和甘油三酯的定标模型。利用内部交叉验证和自动优化功能对预测模型进行了优化,确定了最优建模参数。模型对人血清中胆固醇和甘油三酯定标样品集的预测值与参考值的相关系数r分别为0．9011、0．9593,预测校正标准误RMSECV分别为15．0mg／dL,21．6mg／dL。表明利用近红外光谱分析技术实现血清中胆固醇和甘油三酯快速检测是可行的。     

The effects of temperature and diet on age grading and population age structure determination in Drosophila

The age structure of natural population is of interest in physiological, life history and ecological studies but it is often difficult to determine. One methodological problem is that samples may need to be invasively sampled preventing subsequent taxonomic curation. A second problem is that it can be very expensive to accurately determine the age structure of given population because large sample sizes are often necessary. In this study, we test the effects of temperature (17 °C, 23 °C and 26 °C) and diet (standard cornmeal and low calorie diet) on the accuracy of the non-invasive, inexpensive and high throughput near-infrared spectroscopy (NIRS) technique to determine the age of Drosophila flies. Composite and simplified calibration models were developed for each sex. Independent sets for each temperature and diet treatments with flies not involved in calibration model were then used to validate the accuracy of the calibration models. The composite NIRS calibration model was generated by including flies reared under all temperatures and diets. This approach permits rapid age measurement and age structure determination in large population of flies as less than or equal to 9 days, or more than 9 days old with 85–97% and 64–99% accuracy, respectively. The simplified calibration models were generated by including flies reared at 23 °C on standard diet. Low accuracy rates were observed when simplified calibration models were used to identify (a) Drosophila reared at 17 °C and 26 °C and (b) 23 °C with low calorie diet. These results strongly suggest that appropriate calibration models need to be developed in the laboratory before this technique can be reliably used in field. These calibration models should include the major environmental variables that change across space and time in the particular natural population to be studied.     

Bright light exposure before bedtime impairs response inhibition the following morning: a non-randomized crossover study

Introduction: Bright light exposure in the late evening can affect cognitive function the following morning either by changing the biological clock and/or disturbing sleep, but the evidence for this effect is scarce, and the underlying mechanism remains unknown. In this study, we first aimed to evaluate the effect of bright light exposure before bedtime on frontal lobe activity the following morning using near-infrared spectroscopy (NIRS) during a Go/NoGo task. Second, we aimed to evaluate the effects of bright light exposure before bedtime on polysomnographic measures and on a frontal lobe function test the following morning.

Methods: Twenty healthy, young males (mean age, 25.5 years) were recruited between September 2013 and August 2014. They were first exposed to control light (150 lux) before bedtime (from 20:00 h to 24:00 h) for 2 days and then to bright light (1,000 lux) before bedtime for an additional 5 days. We performed polysomnography (PSG) on the final night of each light exposure period (on nights 2 and night 7) and performed NIRS, which measures the concentrations of oxygenated and deoxygenated hemoglobin (OxyHb and DeoxyHb, respectively), coupled with a Go/NoGo task the following morning (between 09:30 h and 11:30 h). The participants also completed frontal lobe function tests the following morning.

Results: NIRS showed decreased hemodynamic activity (lower OxyHb and a tendency toward higher DeoxyHb concentration) in the right frontal lobe during the NoGo block after 1000-lux light exposure compared with that during the NoGo block after 150-lux light exposure. The commission error rate (ER) during the Go/NoGo task was higher after 1000-lux light exposure than that during the Go/NoGo task after 150-lux light exposure (1.24 ± 1.09 vs. 0.6 ± 0.69, P = 0.002), suggesting a reduced inhibitory response.

Conclusion: This study shows that exposure to bright light before bedtime for 5 days impairs right frontal lobe activation and response inhibition the following morning.     

In‐situ monitoring of Saccharomyces cerevisiae ITV01 bioethanol process using near‐infrared spectroscopy NIRS and chemometrics

The application feasibility of in‐situ or in‐line monitoring of S. cerevisiae ITV01 alcoholic fermentation process, employing Near‐Infrared Spectroscopy (NIRS) and Chemometrics, was investigated. During the process in a bioreactor, in the complex analytical matrix, biomass, glucose, ethanol and glycerol determinations were performed by a transflection fiber optic probe immersed in the culture broth and connected to a Near‐Infrared (NIR) process analyzer. The NIR spectra recorded between 800 and 2,200 nm were pretreated using Savitzky‐Golay smoothing and second derivative in order to perform a partial least squares regression (PLSR) and generate the calibration models. These calibration models were tested by external validation and then used to predict concentrations in batch alcoholic fermentations. The standard errors of calibration (SEC) for biomass, ethanol, glucose and glycerol were 0.212, 0.287, 0.532, and 0.296 g/L and standard errors of prediction (SEP) were 0.323, 0.369, 0.794, and 0.507 g/L, respectively. Calibration and validation criteria were defined and evaluated in order to generate robust and reliable models for an alcoholic fermentation process matrix. © 2016 American Institute of Chemical Engineers Biotechnol. Prog., 32:510–517, 2016     

Proposal of cutoff points for pediatric daytime sleepiness scale to identify excessive daytime sleepiness

The objective of the present study was to propose cutoff points for the Pediatric Daytime Sleepiness Scale (PDSS) through sensitivity and specificity analyses in order to identify excessive daytime sleepiness, considering parameters such as duration and quality of sleep, health perception, stress control and depressive moods (feelings of sadness) in adolescents. A total of 1,132 adolescents, aged 14–19 years old, of both sexes, from the public high school of São José – SC, answered the questionnaire with information on age, daytime sleepiness, sleep duration, health perception, stress management, depressive moods (feelings of sadness) and quality of sleep. The Receiver Operating Characteristic (ROC) curve was used to estimate cutoff points considering the sensitivity and specificity values ??that best identify adolescents with excessive daytime sleepiness, using independent variables as a reference. The majority of the sample was female (54.2%), aged 14–16 years. The girls presented worse quality of sleep (66.4%), and the boys had a more positive perception of health (74.8%), better stress control (64.8%) and lower depressive moods (feelings of sadness) (63.3%). The largest area in the ROC curve was the one that considered sleep quality as a parameter in both sexes (area of the curve = 0.709 and 0.659, respectively, for boys and girls, p < 0.001). Considering sleep quality as a reference, the cutoff point for excessive daytime sleepiness was 15 points. The other parameters used were also significant (p < 0.005). Poor sleep quality was the parameter most strongly related to daytime sleepiness, and a cutoff of 15 points for the PDSS for both sexes should be used in the definition of excessive daytime sleepiness. For the other parameters, stress management, depressive mood (feelings of sadness) and health perception, different cutoff points are suggested for boys and girls.     

Enzymatic degradation of the antibiotic sulfamethazine by using crude extracts of different halophytic plants

Abstract

The biodegradation of the antibiotic sulfamethazine (SMT) by using different crude extracts of halophytes was investigated. For this purpose, crude water extracts of the halophytes Chenopodium quinoa, Sesuvium portulacastrum, and Tripolium pannonicum were prepared. Different amounts of SMT were added to the different extracts (final concentration of 1, 2, and 5?mg L^?1) and incubated at 37?°C. Crude extracts of T. pannonicum were further used to evaluate the degradation rate over time. In order to evaluate the influence of endophytic or naturally plant-associated microorganisms on the biodegradation of SMT, extracts from plants grown in sterile and non-sterile conditions were compared. SMT was analyzed by liquid chromatography coupled to positive ion electrospray mass spectrometry (ESI LC-MS). Based on the findings, crude extracts of T. pannonicum have a high potential to biodegrade SMT with a decrease up to 85.4% (4.27?±?0.10?mg L^?1) from an initial concentration of 5?mg L^?1. The lowest activity was obtained using extracts of C. quinoa with degradation of 4.5%. Extracts of plants cultivated under sterile and non-sterile conditions do not have any significant difference in SMT degradation. Therefore, microorganisms and their enzymatic activities do not seem to play a significant role during this process.