Can homeopathic verum and placebo globules be distinguished by UV spectroscopy?

Purpose: Homeopathic preparations are used in homeopathy and anthroposophically extended medicine. Previous studies described differences in UV transmission between homeopathic preparations of CuSO4 and controls. The aim of the present study was to investigate whether statistically significant differences can be found between homeopathic verum and placebo globules by UV spectroscopy. Methods: Verum (aconitum 30c, calcium carbonate/quercus e cortice) and placebo globules used in two previous clinical trials were dissolved in distilled water at 10mg/ml 20-23h prior to the measurements. Absorbance was measured at 190 – 340nm with a Shimadzu UV-1800 double beam spectrophotometer. Duplicates of each sample were measured in a randomized order 4 times on each of the 5 measurement days. To correct for differences between measurement days, average absorbance of all samples on one day was deduced from absorbance of the individual samples. The Kruskal-Wallis test was used to determine group differences between the samples, and finally the coding of the samples was revealed. Results: First analysis showed significant differences (p≤0.05) in average UV absorbance at 200 – 290nm between the samples and a tendency of a correlation (p≤0.1) between absorbance and globule weight. More results will be presented at the conference. Conclusion: Since the absorbance of the samples at the wavelengths between 200 and 290nm was small, a number of aspects had to be considered and should be corrected for if they are present when performing UV spectroscopy on homeopathic globules: 1. Exact weighing of the globules. 2. Measurement error of the spectrophotometer at small absorbances. 3. Drift of the spectrophotometer during a measurement day. 4. Differences between measurement days. The question remains what caused the differences in absorbance found in these experiments: the use of the original material for the production of the verum globules, differences in the production of verum and placebo globules, or other context factors.

Independent measurement of biogenic silica in sediments by FTIR spectroscopy and PLS regression

We present an independent calibration model for the determination of biogenic silica (BSi) in sediments, developed from analysis of synthetic sediment mixtures and application of Fourier transform infrared spectroscopy (FTIRS) and partial least squares regression (PLSR) modeling. In contrast to current FTIRS applications for quantifying BSi, this new calibration is independent from conventional wet-chemical techniques and their associated measurement uncertainties. This approach also removes the need for developing internal calibrations between the two methods for individual sediments records. For the independent calibration, we produced six series of different synthetic sediment mixtures using two purified diatom extracts, with one extract mixed with quartz sand, calcite, 60/40 quartz/calcite and two different natural sediments, and a second extract mixed with one of the natural sediments. A total of 306 samples—51 samples per series—yielded BSi contents ranging from 0 to 100 %. The resulting PLSR calibration model between the FTIR spectral information and the defined BSi concentration of the synthetic sediment mixtures exhibits a strong cross-validated correlation ( R2cv = 0.97) and a low root-mean square error of cross-validation (RMSECV = 4.7 %). Application of the independent calibration to natural lacustrine and marine sediments yields robust BSi reconstructions. At present, the synthetic mixtures do not include the variation in organic matter that occurs in natural samples, which may explain the somewhat lower prediction accuracy of the calibration model for organic-rich samples.

Visible near infra-red (VisNIR) spectroscopy for predicting soil organic carbon in Ethiopia

Over the past few decades, the advantages of the visible-near infra-red (VisNIR) diffuse reflectance spectrometer (DRS) method have enabled prediction of soil organic carbon (SOC). In this study, SOC was predicted using regression models for samples taken from three sites (Gununo, Maybar and Anjeni) in Ethiopia. SOC was characterized in laboratory using conventional wet chemistry and VisNIR-DRS methods. Principal component analysis (PCA), principal component regression (PCR) and partial least square regression (PLS) models were developed using Unscrambler X 10.2. PCA results show that the first two components accounted for a minimum of 96% variation which increased for individual sites and with data treatments. Correlation (r), coefficient of determination (R2) and residual prediction deviation (RPD) were used to rate four models built. PLS model (r, R2, RPD) values for Anjeni were 0.9, 0.9 and 3.6; for Gununo values 0.6, 0.3 and 1.2; for Maybar values 0.6, 0.3 and 0.9, and for the three sites values 0.7, 0.6 and 1.5, respectively. PCR model values (r, R2, RPD) for Anjeni were 0.9, 0.8 and 2.7; for Gununo values 0.5, 0.3 and 1; for Maybar values 0.5, 0.1 and 0.7, and for the three sites values 0.7, 0.5 and 1.2, respectively. Comparison and testing of models shows superior performance of PLS to PCR. Models were rated as very poor (Maybar), poor (Gununo and three sites) and excellent (Anjeni). A robust model, Anjeni, is recommended for prediction of SOC in Ethiopia.