Fingerprinting of complex mixtures with the use of high performance liquid chromatography, inductively coupled plasma atomic emission spectroscopy and chemometrics

The molecular and metal profile fingerprints were obtained from a complex substance, Atractylis chinensis DC—a traditional Chinese medicine (TCM), with the use of the high performance liquid chromatography (HPLC) and inductively coupled plasma atomic emission spectroscopy (ICP-AES) techniques. This substance was used in this work as an example of a complex biological material, which has found application as a TCM. Such TCM samples are traditionally processed by the Bran, Cut, Fried and Swill methods, and were collected from five provinces in China. The data matrices obtained from the two types of analysis produced two principal component biplots, which showed that the HPLC fingerprint data were discriminated on the basis of the methods for processing the raw TCM, while the metal analysis grouped according to the geographical origin. When the two data matrices were combined into a one two-way matrix, the resulting biplot showed a clear separation on the basis of the HPLC fingerprints. Importantly, within each different grouping the objects separated according to their geographical origin, and they ranked approximately in the same order in each group. This result suggested that by using such an approach, it is possible to derive improved characterisation of the complex TCM materials on the basis of the two kinds of analytical data. In addition, two supervised pattern recognition methods, K-nearest neighbors (KNNs) method, and linear discriminant analysis (LDA), were successfully applied to the individual data matrices—thus, supporting the PCA approach.

Retention and degradation of organics in Rundle waste shale/retort water mixtures

Batch, column and field lysimeter studies have been conducted to evaluate the concept of codisposal of retort water with Rundle (Queensland, Australia) waste shales. The batch studies indicated that degradation of a significant proportion of the total organic load occurs if the mixture is seeded with soil or compost. These results are compared with those from laboratory column studies and from the field lysimeter at the Rundle site. G.c.-m.s. analysis of some of the eluants indicated that significant degradation of the base-neutral fraction occurs even if no soil seed is added, and that degradation of this fraction was higher under anaerobic conditions.

Adhesion and dispersion of salmeterol xinofoate from lactose interactive mixtures for inhalation - can surface roughness of carriers determine performance

The objective was to understand the influence of the surface roughness of lactose carriers on the adhesion and dispersion of salmeterol xinafoate (SX) from interactive mixtures. The surface roughness of lactose carriers was determined by confocal microscopy. Particle images and adhesion forces between SX and lactose particles were determined by Atomic Force Microscopy. The dispersion of SX (2.5%) from interactive mixtures with lactose was determined using a twin-stage impinger (TSI) with a Rotahaler® at an airflow rate of 60L/min. SX was analysed using a validated HPLC assay. The RMS Rq of lactose carriers ranged from 0.93-2.84μm, the Fine Particle Fraction (FPF) of SX ranged between 4 and 24 percent and average adhesion force between a SX and lactose particles ranged between 49 and 134 nN. No direct correlation was observed between the RMS Rq of lactose carriers and either the FPF of SX for the interactive mixtures or the adhesion force of a SX on the lactose particles; however, the presence of fine lactose associated with the carrier surface increased the FPF of SX. Dispersion through direct SX detachment from the carrier surface was not consistent with the poor correlations described and was more likely to occur through complex particulate interactions involving fine lactose.

Design of experiments for bivariate binary responses modelled by Copula functions

Optimal design for generalized linear models has primarily focused on univariate data. Often experiments are performed that have multiple dependent responses described by regression type models, and it is of interest and of value to design the experiment for all these responses. This requires a multivariate distribution underlying a pre-chosen model for the data. Here, we consider the design of experiments for bivariate binary data which are dependent. We explore Copula functions which provide a rich and flexible class of structures to derive joint distributions for bivariate binary data. We present methods for deriving optimal experimental designs for dependent bivariate binary data using Copulas, and demonstrate that, by including the dependence between responses in the design process, more efficient parameter estimates are obtained than by the usual practice of simply designing for a single variable only. Further, we investigate the robustness of designs with respect to initial parameter estimates and Copula function, and also show the performance of compound criteria within this bivariate binary setting.