Quantification of Corn Adulteration in Wet and Dry-Processed Peaberry Ground Roasted Coffees by UV–Vis Spectroscopy and Chemometrics

In this present research, a spectroscopic method based on UV–Vis spectroscopy is utilized to quantify the level of corn adulteration in peaberry ground roasted coffee by chemometrics. Peaberry coffee with two types of bean processing of wet and dry-processed methods was used and intentionally adulterated by corn with a 10–50% level of adulteration. UV–Vis spectral data are obtained for aqueous samples in the range between 250 and 400 nm with a 1 nm interval. Three multivariate regression methods, including partial least squares regression (PLSR), multiple linear regression (MLR), and principal component regression (PCR), are used to predict the level of corn adulteration. The result shows that all individual regression models using individual wet and dry samples are better than that of global regression models using combined wet and dry samples. The best calibration model for individual wet and dry and combined samples is obtained for the PLSR model with a coefficient of determination in the range of 0.83–0.93 and RMSE below 6% (w/w) for calibration and validation. However, the error prediction in terms of RMSEP and bias were highly increased when the individual regression model was used to predict the level of corn adulteration with differences in the bean processing method. The obtained results demonstrate that the use of the global PLSR model is better in predicting the level of corn adulteration. The error prediction for this global model is acceptable with low RMSEP and bias for both individual and combined prediction samples. The obtained RPD_p and RER_p in prediction for the global PLSR model are more than two and five for individual and combined samples, respectively. The proposed method using UV–Vis spectroscopy with a global PLSR model can be applied to quantify the level of corn adulteration in peaberry ground roasted coffee with different bean processing methods.     

Hydrogen and Methane Production, Energy Recovery, and Organic Matter Removal from Effluents in a Two-Stage Fermentative Process

This study evaluates the potential for using different effluents for simultaneous H₂ and CH₄ production in a two-stage batch fermentation process with mixed microflora. An appreciable amount of H₂ was produced from parboiled rice wastewater (23.9?mL g^?1 chemical oxygen demand [COD]) and vinasse (20.8?mL g^?1 COD), while other effluents supported CH₄ generation. The amount of CH₄ produced was minimum for sewage (46.3?mL g^?1 COD), followed by parboiled rice wastewater (115.5?mL g^?1 COD) and glycerol (180.1?mL g^?1 COD). The maximum amount of CH₄ was observed for vinasse (255.4?mL g^?1 COD). The total energy recovery from vinasse (10.4?kJ g^?1 COD) corresponded to the maximum COD reduction (74.7?%), followed by glycerol (70.38?%, 7.20?kJ g^?1 COD), parboiled rice wastewater (63.91?%, 4.92?kJ g^?1 COD), and sewage (51.11?%, 1.85?kJ g^?1 COD). The relatively high performance of vinasse in such comparisons could be attributed to the elevated concentrations of macronutrients contained in raw vinasse. The observations are based on kinetic parameters of H₂ and CH₄ production and global energy recovery of the process. These observations collectively suggest that organic-rich effluents can be deployed for energy recovery with sequential generation of H₂ and CH₄.     

A Novel Methylotrophic Bacterial Consortium for Treatment of Industrial Effluents

Considering the importance of methylotrophs in industrial wastewater treatment, focus of the present study was on utilization of a methylotrophic bacterial consortium as a microbial seed for biotreatment of a variety of industrial effluents. For this purpose, a mixed bacterial methylotrophic AC (Ankleshwar CETP) consortium comprising of Bordetella petrii AC1, Bacillus licheniformis AC4, Salmonella subterranea AC5, and Pseudomonas stutzeri AC8 was used. The AC consortium showed efficient biotreatment of four industrial effluents procured from fertilizer, chemical and pesticide industries, and common effluent treatment plant by lowering their chemical oxygen demand (COD) of 950–2000 mg/l to below detection limit in 60–96 h in 6-l batch reactor and 9–15 days in 6-l continuous reactor. The operating variables of wastewater treatment, viz. COD, BOD, pH, MLSS, MLVSS, SVI, and F/M ratio of these effluents, were also maintained in the permissible range in both batch and continuous reactors. Therefore, formation of the AC consortium has led to the development of an efficient microbial seed capable of treating a variety of industrial effluents containing pollutants generated from their respective industries.     

Predictive Models of Phytosterol Degradation in Rapeseeds Stored in Bulk Based on Artificial Neural Networks and Response Surface Regression

The need to maintain the highest possible levels of bioactive components contained in raw materials requires the elaboration of tools supporting their processing operations, starting from the first stages of the food production chain. In this study, artificial neural networks (ANNs) and response surface regression (RSR) were used to develop models of phytosterol degradation in bulks of rapeseed stored under various temperatures and water activity conditions (T = 12–30 °C and a_w = 0.75–0.90). Among ANNs, networks based on a multilayer perceptron (MLP) and a radial basis function (RBF) were tested. The model input constituted a_w, temperature and storage time, whilst the model output was the phytosterol level in seeds. The ANN-based modeling turned out to be more effective in estimating phytosterol levels than the RSR, while MLP-ANNs proved to be more satisfactory than RBF-ANNs. The approximation quality of the ANNs models depended on the number of neurons and the type of activation functions in the hidden layer. The best model was provided by the MLP-ANN containing nine neurons in the hidden layer equipped with the logistic activation function. The model performance evaluation showed its high prediction accuracy and generalization capability (R² = 0.978; RMSE = 0.140). Its accuracy was also confirmed by the elliptical joint confidence region (EJCR) test. The results show the high usefulness of ANNs in predictive modeling of phytosterol degradation in rapeseeds. The elaborated MLP-ANN model may be used as a support tool in modern postharvest management systems.     

Optimization of solar photocatalytic biodegradability of seawater using statistical modelling

The performance of zinc oxide (ZnO) as a photocatalyst was evaluated for the treatment of pollutants present in seawater. Batch experimental studies were carried out by varying the dosage of photocatalyst (1–4 ​g/L). The effect of reaction time, pH and the dosage of photocatalyst was evaluated with the percentage removal efficiencies of chemical oxygen demand (COD), biological oxygen demand (BOD), total organic carbon (TOC) and the biodegradability (BOD/COD) of the seawater. Response surface methodology-central composite design (RSM-CCD) and artificial neural network-Levenberg Marquardt (ANN-LM) statistical models were employed to optimize the photocatalytic biodegradability (BOD/COD). A quadratic polynomial statistical model was obtained to predict the percentage removal efficiencies of COD, TOC, BOD and biodegradability. For the experimental runs, the maximum percentage removal efficiencies for COD, TOC, BOD was found to be 62.3, 40.1, and 18.8%, respectively. Whereas, the maximum biodegradability was 0.036. As per RSM-CCD and ANN-LM statistical model method the maximum percentage removal efficiencies were found to be COD ​= ​58.14, 60.39%, TOC ​= ​33.74, 40.09%, BOD ​= ​18.47, 18.7% and Biodegradability ​= ​0.0315, 0.0360, respectively. The predicted values from statistical models were well correlated with experimental values. ANN modelling predicted better values for the responses with an average of R² ​= ​0.99697 than RSM modelling with average R² ​= ​0.8948.     

Application of genetic algorithm‐support vector regression (GA‐SVR) for quantitative analysis of herbal medicines

In this paper, a genetic algorithm‐support vector regression (GA‐SVR) coupled approach was proposed for investigating the relationship between fingerprints and properties of herbal medicines. GA was used to select variables so as to improve the predictive ability of the models. Two other widely used approaches, Random Forests (RF) and partial least squares regression (PLSR) combined with GA (namely GA‐RF and GA‐PLSR, respectively), were also employed and compared with the GA‐SVR method. The models were evaluated in terms of the correlation coefficient between the measured and predicted values (Rp), root mean square error of prediction, and root mean square error of leave‐one‐out cross‐validation. The performance has been tested on a simulated system, a chromatographic data set, and a near‐infrared spectroscopic data set. The obtained results indicate that the GA‐SVR model provides a more accurate answer, with higher Rp and lower root mean square error. The proposed method is suitable for the quantitative analysis and quality control of herbal medicines. Copyright © 2012 John Wiley & Sons, Ltd.     

Tetracycline antibiotics in hospital and municipal wastewaters: a pilot study in Portugal

This study investigated the occurrence of tetracyclines (TCs), namely minocycline (MIN), TC, and its epimer epitetracycline (ETC), and doxycycline (DC), in four hospital wastewater effluents and its fate in municipal wastewater treatment plants (WWTPs), in Coimbra, Portugal. Analytical determination was carried out by solid-phase extraction followed by liquid chromatography with fluorescence detection. A gradient system with a mobile phase containing oxalic acid 0.02 M and acetonitrile was used. After postcolumn derivatization with magnesium reagent, TCs were detected at λ _exc 386 nm and λ _em 500 nm. The proposed method allowed good sensitivity, accuracy, and precision. LOQs were 0.5 μg l⁻¹ for ETC and TC and 15 and 5 μg l⁻¹ for MIN and DC, respectively. The recovery values ranged between 66.4% and 117.1%, and intraday and interday repeatability was lower than 6.8%. The method was successfully used to determine the presence of the above-mentioned TCs in 24 wastewater composite samples obtained from hospital effluents and from influent and effluent of the WWTP located in Coimbra, Portugal. MIN and TC were found in 41.7% of the samples; ETC and DC were found in 25% and 8.3% of the samples, respectively. The levels found ranged from 6 to 531.7 μg l⁻¹ in hospital effluents, while its concentrations in WWTP ranged from 95.8 to 915.3 μg l⁻¹. A seasonal influence in the concentrations found has also been observed, the levels found in samples collected during spring being higher than those observed in samples collected during autumn; however, these are only preliminary results. The WWTP removal rate ranged between 89.5% and 100%.     

Gamma radiation induced effects on slaughterhouse wastewater treatment

A preliminary study using gamma radiation on slaughterhouse wastewater samples was carried out. Chemical oxygen demand (COD), biochemical oxygen demand (BOD) and total suspended solids (TSS) results were obtained at a dose rate of 0.9 kGy h⁻¹. A decrease of COD, BOD and colour was observed after irradiation at high absorbed doses. The microbiological results, following irradiation in the same conditions, correlated with the BOD results. The results obtained highlight the potential of this technology for wastewater treatment.     

A method for determination of COD in a domestic wastewater treatment plant by using near-infrared reflectance spectrometry of seston

This paper proposes a method for determination of chemical oxygen demand (COD) in domestic wastewater. The proposed method is based on near-infrared reflectance (NIRR) measurements of seston collected from wastewater samples by filtration. The analysis does not require any special reagent, catalyst or solvent. Inherent baseline and noise features present in NIRR spectra are removed by a Savitzky-Golay derivative procedure followed by wavelet denoising. The resulting wavelet approximation coefficients are used for partial-least-squares modelling and subsequent prediction of COD values in new samples. The model is calibrated by using COD values obtained according to the American Public Health Association (APHA) reference method. The proposed method is applied to effluent samples from the anaerobic ponds of the Mangabeira municipal wastewater treatment plant in the city of João Pessoa (Paraíba, Brazil). By comparing the NIRR prediction results with the APHA reference values, a root-mean-square error of prediction (RMSEP) of 19 mg O₂ L⁻¹ and a correlation of 0.97 were obtained. Such results are deemed adequate in view of the joint estimate of the standard error of the reference method, which was calculated as 21 mg O₂ L⁻¹.     

One- and two-stage upflow anaerobic sludge-bed reactor pretreatment of winery wastewater at 4–10°C

The operating performance of a single and two (in series) laboratory upflow anaerobic sludge-bed (UASB) reactors (2.7-L working volume, recycle ratio varied from 1:1 to 1:18) treating diluted wine vinasse was investigated under psychrophilic conditions (4-10 degreesC). For a single UASB reactor seeded with granular sludge, the average organic loading rates (OLRs) applied were 4.7, 3.7, and 1.7 g of chemical oxygen demand (COD)/(L.d) (hydraulic retention times [HRTs] were about 1 d) at 9-11, 6 to 7, and 4 to 5 degreesC, respectively. The average total COD removal for preacidified vinasse wastewater was about 60% for all the temperature regimes tested. For two UASB reactors in series, the average total COD removal for treatment of non-preacidified wastewater exceeded 70% (the average OLRs for a whole system were 2.2, 1.8, and 1.3 g of COD/[L.d] under HRTs of 2 d at 10, 7, and 4 degreesC, respectively). In situ determinations of kinetic sludge characteristics (apparent Vm and Km) revealed the existence of substantial mass transfer limitations for the soluble substrates inside the reactor sludge bed. Therefore, application of higher recycle ratios is essential for enhancement of UASB pretreatment under psychrophilic conditions. The produced anaerobic effluents were shown to be efficiently posttreated aerobically: final effluent COD concentrations were about 0.1 g/L. Successful operation of the UASB reactors at quite low temperatures (4-10 degreesC) opens some perspectives for application of high-rate anaerobic pretreatment at ambient temperatures.     

Identification of toxic compounds in wastewater treatment plants during a field experiment

Chemical analysis and toxicity bioassays were used in conjunction to determine the toxic compounds present in wastewater. This combined methodology was applied to wastewater samples collected at two wastewater treatment plants (WWTP) from the area of Barcelona (Spain), during a field experiment carried out from 1-4 April 2000. The efficiency of the WWTP was evaluated by collecting and analyzing samples at various stages of the water treatment process. The samples corresponded to the raw influent, from first settlement—before biological treatment—and from the effluent.Two bioluminescence inhibition assays: ToxAlert^®10 and ToxAlert^®100 from Merck both based on the bioluminescence inhibition of Vibrio fischeri (marine bacterial specie) were used. ToxAlert^®10 is a portable device with no temperature control and uses freeze-dried bacterial reagent and ToxAlert^®100 uses liquid-dried bacterial reagent and the incubation takes place at controlled temperature. Both tests showed similar results.Besides the toxicity studies, the wastewater samples were characterized by various analytical protocols involving the use of solid phase extraction (SPE) followed by liquid chromatography-mass spectrometry (LC-MS). Phenols, non-ionic surfactants, linear alkyl benzene sulphonates, benzene and naphthalene sulphonates and micro-pollutants with high endocrine-disrupting effects like estradiol and ethynyl estradiol were identified at the WWTP.The toxic responses obtained for the samples collected at WWTP were defined by the 50% effective concentration (EC₅₀), the Toxicity Units (TU) and the toxicity impact index (TII₅₀). The toxic effect at the different steps of the WWTP was attributed to the compounds identified and quantified by LC-MS like transformation products of nonylphenol polyethoxylate such as nonylphenol and nonylphenol carboxylate.     

Long-term operation of a domestic wastewater treatment plant with membrane filtration

Possibilities of membrane technology and the use of membrane processes in wastewater treatment were investigated. The main focus was the monitoring of the starting phase of a domestic wastewater treatment plant. Experimental part of the study was realized at the municipal wastewater treatment plant (WWTP) Devínska Nová Ves — Bratislava during the period from February 2005 to September 2006. The system was stable without any external chemical treatment of the membrane modules and the permeate quality was very high. Observed decrease of COD and BOD₅ values ranged between 91 % and 98 %. The process of nitrification was very successful considering its high efficiency (> 95 %). Presented at the 34th International Conference of the Slovak Society of Chemical Engineering, Tatranské Matliare, 21–25 May 2007.     

A Voltammetric Electronic Tongue for the Quantitative Analysis of Quality Parameters in Wastewater

The use of a voltammetric electronic tongue for the quantitative analysis of quality parameters in influent wastewater from a wastewater treatment plant (WWTP) that treats domestic and industrial wastewater is proposed. The electronic voltammetric tongue consists of a set of four noble electrodes (iridium, rhodium, platinum and gold) housed inside a stainless steel cylinder. These noble metals have high durability and are low maintenance‐demanding, as required for developing future automated equipment. A pulse voltammetry study was conducted in 35 wastewater samples to determine ammonia (NH₄⁺‐N), nitrates (NO₃⁻‐N), total phosphate (tot‐P), soluble chemical oxygen demand (CODs) and conductivity. These parameters were also determined in these samples by routine analytical methods in the WWTP laboratory. A partial least squares (PLS) analysis was run to obtain a model to predict each parameter. Twenty‐five samples were included in the calibration set and 10 in the validation set. Calibration and validation sets were selected randomly, except for the extreme values of each parameter, which were included in the calibration set. Variable selection was performed on the voltammetric data using Genetic Algorithms in the calibration data set for each parameter. The electronic tongue showed good predictive power to determine the concentrations of NH₄⁺‐N, NO₃⁻‐N and tot‐P and CODs.     

Chemometric data analysis of pollutants in wastewater—a case study

In this study, chemometric techniques such as cluster analysis (CA), discriminant analysis (DA), principal component analysis (PCA) and partial least squares (PLS) were used to analyse the wastewater dataset to identify the factors which affect the composition of sewage of domestic origin, spatial and temporal variations, similarity/dissimilarity among the wastewater characteristics of cis- and trans-drains and discriminating variables. Samples collected from 24 wastewater drains in Lucknow city and from three sites on Gomti river in the month of January/February, May, August and November during the period of 5 years (1994-1999) were characterized for 32 parameters. The multivariate techniques successfully described the similarities/dissimilarities among the sewage drains on the basis of their wastewater characteristics and sources signifying the effect of routine domestic/commercial activities in respective drainage areas. Spatial and seasonal variations in wastewater composition were also determined successfully. CA generated six groups of drains on the basis of similar wastewater characteristic. PCA provided information on seasonal influence and compositional differences in sewage generated by domestic and industrial waste dominated drains and showed that drains influenced by mixed industrial effluents have high organic pollution load. DA rendered six variables (TDS, alkalinity, F, TKN, Cd and Cr) discriminating between cis- and trans-drains. PLS-DA showed dominance of Cd, Cr, NO₃, PO₄ and F in cis-drains wastewater. The results suggest that biological-process based STPs could treat wastewater both from the cis- as well as trans-drains, however, prior removal of toxic metals will be required from the cis-drains sewage. Further, seasonal variations in wastewater composition and pollution load could be the guiding factor for determining the STPs design parameters. The information generated would be useful in selection of process type and in designing of the proposed sewage treatment plants (STPs) for safe disposal of wastewater.     

Evaluation of perfluorinated compounds in seven wastewater treatment plants in Beijing urban areas

The presence of perfluorinated compounds(PFCs)in seven major wastewater treatment plants(WWTPs)in Beijing was investigated in the current study.We detected nine PFCs in all the wastewater and sludge samples.Perfluorooctane acid(PFOA)is the dominant PFCs in influents and effluents,while perfluorooctane sulfonate(PFOS)is the major contaminant in sludge.The highest PFC concentration was found in plants at Qinghe and Jiuxianqiao WWTP,while the lowest was found at Fangzhuang WWTP.The total values of PFC range fr...     

Comparative studies on ultrasound assisted treatment of tannery effluent using multiple oxy-catalysts using response surface methodology

Advanced oxidation of wastewater is a promising technique for tannery wastewater treatment, as it consumes less chemical addition and energy and it doesn’t liberate any secondary effluents. However, advanced oxidation can be improved by conjoining it with energy sources like ultraviolet radiation, ultrasound, etc. Catalysts capable of oxidation like titanium dioxide and iron oxide have been utilized for advanced oxidation of tannery effluent. The present work studies the synergic effect of ultrasound assisted advanced oxidation using two oxy-catalysts, namely zinc oxide and silicon dioxide. The effect of variables like time of treatment, catalyst loading, and power of ultrasound on the reduction of BOD, COD, and TDS were estimated and the results indicated a proficient reduction of contaminants. Upon treatment with silicon dioxide under ultrasound, the COD, BOD, and TDS reduction were found to be 88%, 89%, and 88% respectively, while zinc oxide catalyst indicated 89%, 85%, and 88% reduction. Response Surface Methodology has been utilized for derivation of a mathematical model for COD, BOD and TDS reduction. The spent catalysts were analyzed using Scanning Electron Microscopy and X-ray Diffraction to understand the changes in the characteristics of the spent catalyst. The deposition of contaminants on the catalysts and slight changes in the surface morphology were evident. Hence silicon dioxide and zinc oxide are promising catalysts for the treatment of tannery effluent combined with ultrasound.     

The potential of Raman spectroscopy for characterisation of the fatty acid unsaturation of salmon

Raman spectroscopy has been evaluated for characterisation of the degree of fatty acid unsaturation (iodine value) of salmon (Salmo salar). The Norwegian Quality Cuts from 50 salmon samples were obtained, and the samples provided an iodine value range of 147.8-170.0 g I₂/100 g fat, reflecting a normal variation of farmed salmon. Raman measurements were performed both on different spots of the intact salmon muscle, on ground salmon samples as well as on oil extracts, and partial least squares regression (PLSR) was utilised for calibration. The oil spectra provided better iodine value predictions than the other data sets, and a correlation coefficient of 0.87 with a root mean square error of cross-validation of 2.5 g I₂/100 g fat was achieved using only one PLSR component. The ground samples provided comparable results, but at least two PLSR components were needed. Higher prediction errors were obtained from Raman spectra of intact salmon muscle, and this may partly be explained by sampling uncertainties in the relation between Raman measurements and reference analysis. All PLSR models obtained were based on chemically sound regression coefficients, and thus information regarding fatty acid unsaturation is readily available from Raman spectra even in systems with high contents of protein and water. The accuracy, the robustness and the low complexity of the PLSR models obtained suggest Raman spectroscopy as a promising method for rapid in-process control of the degree of unsaturation in salmon samples.     

Sludge of wastewater treatment plants as Co2+ ions sorbent

Sludges produced in huge amounts by wastewater treatment plants (WWTP) display high fertility properties; however, the presence of heavy metals restricts their use for agricultural purposes. Sorption capacity of sludge is generally much higher and it can also be considered as a cheap sorbent of heavy metals. The paper describes cobalt sorption by dried activated sludge (DAS) obtained from the aerobic phase of a WWTP. DAS was characterized by FT-IR spectroscopy, cation exchange capacity (CEC), and atomic absorption spectrometry (AAS) analysis. Sorption capacity of DAS (Q _eq) increased with the initial concentration (C ₀) of Co²⁺ (CoCl₂) within the range from 100 μmol g^?1 to 4000 μmol g^?1, reaching 15 μmol g^?1 and 200 μmol g^?1, respectively. The maximum uptake capacity (Q _max) at pH 6.0 calculated from the Langmuir isotherm model was (256 ± 9) μmol g^?1 for Co²⁺ ions. Obtained Q values were dependent on pH within the range from 3.0 to 7.0. Competitive effect of other bivalent cations such as Ni²⁺ in Co²⁺ sorption equilibrium was confirmed; which is in agreement with the hypothesis of the decisive role of ion-exchange mechanism in metal sorption. The obtained data are discussed from the point of view of potential utilization of sludges as sorbents, i.e. in non-agricultural application.     

Rapid estimation of biochemical oxygen demand

The feasibility of using columnar reactors containing immobilized microorganisms for the rapid estimation of BOD was demonstrated in this study. Dilutions of three types of industrial effluents were tested by the BOD₅ test and by this experimental system. A high degree of correlation (r = 0.98) was observed between results of the two tests. The mean standard error of estimation of the experimental system was 11%.     

Near-infrared diffuse reflectance spectroscopy and neural networks for measuring nutritional parameters in chocolate samples

A rapid and non-destructive method has been developed for the characterization of chocolate samples based on diffuse reflectance near-infrared Fourier transform spectroscopy (DRIFTS) and artificial neural networks (ANNs). This methodology provides a potentially useful alternative to time-consuming chemical methods of analysis. To assess its utility, 36 chocolate samples purchased from the Spanish market were analyzed for the determination of the main nutritional parameters like carbohydrates, fat, proteins, energetic value and cocoa content.Direct triplicate measurements of each sample were carried out by DRIFTS. Cluster hierarchical analysis was used for selecting calibration and validation data sets, resulting in a calibration set comprised of 19 samples and a validation data set of 17 samples. As it is common the presence of non-linear effect in reflectance spectroscopy, ANNs was chosen for data pretreatment. The root-mean-square error of prediction (RMSEP) values obtained for carbohydrates, fat, energetic value and cocoa were 1.0% (w/w), 1.0% (w/w), 50 kJ (100 g)⁻¹ and 1.4%, respectively. The mean difference (d_x-y) and standard deviation of mean differences (s_x-y) of the carbohydrates, fat, proteins content, energetic value and cocoa content were 0.9 and 2.4% (w/w), 0.2 and 1.0% (w/w), 9.1 and 50 kJ (100 g)⁻¹, and −0.5 and 1.4%, respectively. The maximum relative error for the prediction (QC) of any of these parameters for a new sample did not exceed 5.2%.