Sanguk Lee

Investigation of the Effect of Free Ammonia Concentration Upon Leachate Treatment by Shortcut Biological Nitrogen Removal Process

Abstract A shortcut biological nitrogen removal (SBNR) process was operated to treat an ammonium rich landfill leachate using a pilot-scale reactor. The SBNR process was intended to oxidize ammonia to nitrite and, then, to reduce it to nitrogen gas. When the hydraulic retention time was 4–3 days, a half of the ammonium oxidized was accumulated as nitrite in the oxidation tank. The nitrite was denitrified completely in the anoxic tank when recycled. The average free ammonia (FA) concentration in the ammonium oxidation tank was 3.7 mg/L. The specific substrate utilization rates of ammonium oxidizers and nitrite oxidizers were investigated at varying FA concentrations through batch experiments. The highest specific ammonium oxidation rate was observed when the FA concentration was 10 mg/L. The rate decreased slightly when the FA concentration was increased to 20 or 50 mg/L, or decreased significantly when it was 5 mg/L. In case of nitrite oxidation, the specific nitrite utilization rate decreased significantly with increasing FA concentration up to 10 mg/L. Consequently, the optimal FA concentration in leachate treatment was 10 mg/L for maximum nitrite accumulation and maximum ammonium removal, or 5 mg/L for lower ammonium concentration and reasonable nitrite accumulation.

Simple transmission Raman measurements using a single multivariate model for analysis of pharmaceutical samples contained in capsules of different colors.

Direct transmission Raman measurements for analysis of pharmaceuticals in capsules are advantageous since they can be used to determine active pharmaceutical ingredient (API) concentrations in a non-destructive manner and with much less fluorescence background interference from the capsules themselves compared to conventional back-scattering measurements. If a single calibration model such as developed from spectra simply collected in glass vials could be used to determine API concentrations of samples contained in capsules of different colors rather than constructing individual models for each capsule color, the utility of transmission measurements would be further enhanced. To evaluate the feasibility, transmission Raman spectra of binary mixtures of ambroxol and lactose were collected in a glass vial and a partial least squares (PLS) model for the determination of ambroxol concentration was developed. Then, the model was directly applied to determine ambroxol concentrations of samples contained in capsules of 4 different colors (blue, green, white and yellow). Although the prediction performance was slightly degraded when the samples were placed in blue or green capsules, due to the presence of weak fluorescence, accurate determination of ambroxol was generally achieved in all cases. The prediction accuracy was also investigated when the thickness of the capsule was varied.

Use of temperature dependent Raman spectra to improve accuracy for analysis of complex oil-based samples: lube base oils and adulterated olive oils.

A simple and effective strategy to improve accuracy for Raman spectroscopic analysis of complex mixture samples by probing a measurement temperature yielding enhanced spectral selectivity has been demonstrated. For the evaluation, the determination of Kinematic Viscosity at 40 °C (KV@40) of lube base oil (LBO) samples was initially attempted. Partial least squares (PLS) was used to determine the KV@40 using Raman spectra of the samples collected at 8 different temperatures from 20 to 90 °C with 10 °C increments. Interestingly, the distinct temperature-induced spectral variation among the samples occurred at 50 °C, thereby resulting in the improved accuracy for determination of KV@40. Two-dimensional (2D) correlation analysis was also performed to find an additional supportive rationale for the improved accuracy. The strategy was further evaluated for the identification of soybean oil-adulterated olive oils using linear discriminant analysis (LDA). Similarly, the discrimination accuracy was improved around 80-90 °C due to the enhanced spectral selectivity between olive and soybean oils. In overall, these two results successfully demonstrate analytical effectiveness of the strategy.

Feasibility for non-destructive discrimination of natural and beryllium-diffused sapphires using Raman spectroscopy

Raman spectroscopy based non-destructive discrimination between natural and beryllium-diffused (Be-diffused) sapphires has been attempted. The initial examination of Raman image acquired on a sapphire revealed that microscopic structural and compositional heterogeneity was apparent in the sample, so acquisition of spectra able to represent a whole body of sapphire rather than a localized area was necessary for a reliable discrimination. For this purpose, a wide area illumination (WAI) scheme (illumination area: 28.3mm(2)) providing a large sampling volume was employed to collect representative Raman spectra of sapphires. Upon the diffusion of Be into a sapphire, the band shift originated from varied lattice structure by substitution of Be at cation sites was observed and utilized as a valuable spectral signature for the discrimination. In the domain of principal component (PC) scores, the groups of natural and Be-diffused sapphires were identifiable with minor overlapping and the cross-validated discrimination error was 7.3% when k-Nearest Neighbor (k-NN) was used as a classifier.

Feasibility Study for Detection of Turnip yellow mosaic virus (TYMV) Infection of Chinese Cabbage Plants Using Raman Spectroscopy

Raman spectroscopy provides many advantages compared to other common analytical techniques due to its ability of rapid and accurate identification of unknown specimens as well as simple sample preparation. Here, we described potential of Raman spectroscopic technique as an efficient and high throughput method to detect plants infected by economically important viruses. To enhance the detection sensitivity of Raman measurement, surface enhanced Raman scattering (SERS) was employed. Spectra of extracts from healthy and Turnip yellow mosaic virus (TYMV) infected Chinese cabbage leaves were collected by mixing with gold (Au) nanoparticles. Our result showed that TYMV infected plants could be discriminated from non-infected healthy plants, suggesting the current method described here would be an alternative potential tool to screen virus-infection of plants in fields although it needs more studies to generalize the technique.

New discrimination method combining hit quality index based spectral matching and voting

A new discrimination method, called hit quality index (HQI)-voting, that uses the HQI for discriminant analysis has been developed. HQI indicates the degree of spectral matching between two spectra as known. In this method, a library sample yielding the highest HQI value for an unknown sample was initially searched and a group containing this sample was chosen as the group for the unknown sample. When overall spectral features of two groups are quite close to each other, many library samples with similar HQI values could be available for an unknown sample. In this situation, the simultaneous consideration of multiple votes (several library samples with close HQI values) for final decision would be more robust. In order to evaluate the discrimination performance of HQI-voting, three different near-infrared (NIR) spectroscopic datasets composed of two sample groups were used: (1) domestic and imported sesame samples, (2) domestic and imported Angelica gigas samples, and (3) diesel and light gas oil (LGO) samples. For the purpose of comparison, principal component analysis-linear discriminant analysis (PCA-LDA), partial least squares-discriminant analysis (PLS-DA) as well as k-nearest neighbor (k-NN) were also performed using the same datasets and the resulting accuracies were compared. The discrimination performances improved with the use of HQI-voting in comparison with those resulted from PCA-LDA and PLS-DA. The overall results support that HQI-voting is a comparable discrimination method to that of existing factor-based multivariate methods.

Spectral Collection of Polyethylene Pellets at nearly Cryogenic Temperature to Improve Selectivity of Raman Measurement

Raman spectroscopy has been extensively used for analysis of diverse polymer samples. Normally, Raman spectral collection of samples is routinely performed at room temperature for convenience. However, the feasibility of improving spectral selectivity and the resulting quantitative accuracy, when samples are measured at nearly cryogenic temperature, has not been investigated. For this purpose, we attempted to measure the density of polyethylene (PE) pellets at cryogenic temperatures and the resulting accuracies were compared with that from room temperature measurement. Initially, each of 25 PE sample was allowed to cool down to cryogenic temperature and the corresponding Raman spectra were continuously collected while the temperature of sample increased. When the temperature of sample was at cryogenic temperature, the resulting band widths were narrower compared to those at room temperature, thereby improving the accuracy of density measurement. In overall, the proposed Raman scheme is simple and efficient; therefore, it could be further applied for analysis of other polymers.