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Summary Most soil tests for available phosphorus (P) perform rather poorly in predicting crop response. This study was set up to compare different established soil tests in their capacity to predict crop response across contrasting types of soil. Soil samples from long-term field experiments, the oldest >100 years old, were collected in five European countries. The total number of soil samples (n = 218), which differed in cropping and P treatment, and originated from 11 different soil types, were analysed with five tests: ammonium oxalate (Ox), ammonium lactate (AL), Olsen P, 0.01 m CaCl2 and the diffusive gradient in thin film (DGT). The first three tests denote available P quantity (Q), whereas the last two indicate P intensity (I) of the soil solution. All five tests were positively related to the crop yield data (n = 317). The Q-tests generally outperformed I-tests when evaluated with goodness of fit in Mitscherlich models, but critical P values of the I-tests varied the least among different types of soil. No test was clearly superior to the others, except for the oxalate extraction, which was generally poor. The combination of Q- and I-tests performed slightly better for predicting crop yield than any single soil P test. This Q + I analysis explains why recent successes with I-tests (e.g. DGT) were found for soils with larger P sorption than for those in the present study. This systematic evaluation of soil tests using a unique compilation of established field trials provides critical soil P values that are valid across Europe. Highlights We compared soil P tests for predicting crop response across contrasting soil types. No test was clearly superior to the others except for the oxalate extraction, which was generally poor. This study suggests that intensity tests do not perform markedly better than quantity tests. The evaluation of soil P tests on this unique dataset provided critical soil P values across Europe.

Jack-ups are designed to withstand specific environmental conditions at a defined water depth in combination with certain types of soil conditions. A balanced design results in an almost full utilization of the design elements under such conditions. Typical design elements used are the holding system (either the leg fixation system if present, or the jacking system), leg chord elements or the preload capacity. This means that if an owner wants to use the jack-up beyond the original design conditions, for example to operate in deeper water or in harsher conditions or with bearing pressure limitations, some of these elements would have to be upgraded. However, these elements are fully integrated into the overall design of the jack-up and thus an upgrade is always a very costly and often also a time-consuming affair. A simpler way of upgrading the overall capacity of a jack-up is through the use of spudcan extensions. Spudcan extensions can be designed to replace the existing spudcan in the form of a new spudcan below the existing one at the bottom of the leg, a so-called add-on spudcan, or as a temporary or permanent extension of the original spudcan. In these cases, the impact of the spudcan extensions on the total jack-up are very limited, where in the best case the jack-up remains as-is and in the worst case only the lower ends of the legs are affected. The purpose of spudcan extensions is generally twofold: Limit the leg penetration into the seabed, effectively reducing the free leg length below the hull; Move part of the overall leg bending moment from the hull towards the spudcan, effectively reducing the loads acting at hull level; The first purpose is relatively simple and usually results in just an enlargement of the total horizontal projected area of the spudcan. In this case, the spudcan extension can be a welded permanent structure, or if the owner wants to be able to go back to the original design it will be designed to connect temporarily to the chords of the lower end of the legs or the top plate of the spudcan, fitting around the existing spudcan. The second purpose is a bit more complicated, as the extension must now be able to attract overall leg bending moments. This usually means that the extension must either be significantly bigger than the original spudcan or equipped with skirts, or a combination of both. The size and type of spudcan required depends heavily on the soil conditions of the intended area of operation. The end result is mostly in the shape of an add-on spudcan, but can also be executed as a permanent extension. GustoMSC shall present the design considerations of typical spudcan extensions, apply spudcan extension to three well known types of GustoMSC jack-ups and show the influence of spudcan extensions to the capacity of the jack-ups.

© 2017 Elsevier B.V. A crucial stage in the development of medical drugs is to study which additives, usually called excipients, impact the active ingredient stability. This type of study is generally named an excipient compatibility study and requires a mixture experiment. Subsequently, the effect of the storage conditions, more specifically the relative humidity and temperature, on the stability is investigated. This so-called accelerated life test involves a factorial type of experiment. It has become, however, customary to include the storage conditions in the compatibility study. This provides valuable information concerning potential interactions between excipient combinations and storage conditions. Experiments that combine a mixture experiment with a factorial experiment are generally named mixture-process variable experiments. A limited number of designs for mixture-process variable experiments are available in the literature. One problem is that the proposed designs offer little flexibility. Another is that the required number of runs becomes prohibitively large for large numbers of mixture components. In this paper, we examine flexible, optimal designs for realistic mixture-process variable experiments. Our motivation is to provide guidance to pharmaceutical formulation scientists concerning state-of-the art models and designs for excipient compatibility studies. Using several proof-of-concept examples, we demonstrate that I-optimal designs offer both flexibility and small variances of prediction. We also discuss a real-life example, which could be used as a blueprint for future studies. Because many excipient compatibility studies are not completely randomized, we pay special attention to their logistics and to the resulting randomization restrictions, which lead to split-plot and strip-plot experiments. ispartof: Chemometrics and Intelligent Laboratory Systems vol:171 pages:125-139 status: published

In the present study, proton transfer reaction-mass spectrometry (PTR-MS) in combination with partial least square-discriminant analysis (PLS-DA) was evaluated as a method for the prediction of the origin of European butters. Eighty-three commercial butters from three European regions were subjected to headspace analysis using PTR-MS. Data were collected for the mass range m/z 20-150 using a dwell time of 0.2 s mass−1, resulting in a cycle time just under 30 s. The log transformed headspace concentrations of the masses were subjected to PLS-DA in order to estimate classification models for the butter samples. One model predicted the region of origin; a second set of models predicted dichotomously whether or not a butter originated from a particular EU country. The performance of each model was evaluated by means of a 10-fold double cross validation procedure. For 76% of the butters the region of origin was predicted correctly in the cross validation. The success rate of the countries, averaged over all dichotomous models, was 88% but large differences between countries were observed. Additional work is required to study the underlying factors that determine the geographical differences in butter volatile compositions.

The volatile compositions of 192 olive oil samples from five different European countries were investigated by PTR-MS sample headspace analysis. The mass spectra of all samples showed many masses with high abundances, indicating the complex VOC composition of olive oil. Three different PLS-DA models were fitted to the data to classify samples into ‘country’, ‘region’ and ‘district’ of origin, respectively. Correct classification rates were assessed by cross-validation. The first fitted model produced an 86% success rate in classifying the samples into their country of origin. The second model, which was fitted to the Italian oils only, also demonstrated satisfactory results, with 74% of samples successfully classified into region of origin. The third model, classifying the Italian samples into district of origin, yielded a success rate of only 52%. This lower success rate might be due to either the small class set, or to genuine similarities between olive oil VOC compositions on this tight scale.

The potential of proton transfer reaction mass spectrometry (PTR-MS) as a tool for classification of milk fats was evaluated in relation to quality and authentication issues. Butters and butter oils were subjected to heat and off-flavouring treatments in order to create sensorially defective samples. The effect of the treatments was evaluated by means of PTR-MS analysis, sensory analysis and classical chemical analysis. Subsequently, partial least square-discriminant analysis models (PLS-DA) were fitted to predict the matrix (butter/butter oil) and the sensory grades of the samples from their PTR-MS data. Using a 10-fold cross-validation scheme, 84% of the samples were successfully classified into butter and butter oil classes. Regarding sensory quality, 89% of the samples were correctly classified. As the milk fats were fairly successfully classified by the combination of PTR-MS and PLS-DA, this combination seems a promising approach with potential applications in quality control and control of regulations.

SELDI-TOF-MS is rapidly gaining popularity as a screening tool for clinical applications of proteomics. Application of adequate statistical techniques in all the stages from measurement to information is obligatory. One of the statistical methods often used in proteomics is classification: the assignment of subjects to discrete categories, for example healthy or diseased. Lately, many new classification methods have been developed, often specifically for the analysis of Xomics data. For proteomics studies a good strategy for evaluating classification results is of prime importance, because usually the number of objects will be small and it would be wasteful to set aside part of these as a 'mere' test set. The present paper offers such a strategy in the form of a protocol which can be used for choosing among different statistical classification methods and obtaining figures of merit of their performance. This paper also illustrates the usefulness of proteomics in a clinical setting, serum samples from Gaucher disease patients, when used in combination with an appropriate classification method. © 2007 Wiley-VCH Verlag GmbH & Co. KGaA.

To predict macrofaunal community composition from environmental data a two-step approach is often followed: (1) the water samples are clustered into groups on the basis of the macrofauna data and (2) the groups are related to the environmental data, e.g. by discriminant analysis. For the cluster analysis in step 1 many hard, seemingly arbitrary choices have to be made that nevertheless influence the solution (similarity measure, clustering strategy, number of clusters). The stability of the solution is often of concern, e.g. in clustering by the twinspan program. In the discriminant analysis of step 2 it can occur that a water sample is misclassified on the basis of the environmental data but on further inspection happens to be a borderline case in the cluster analysis. One would then rather reclassify such a sample and iterate the two steps. Bayesian latent class analysis is a flexible, extendable model-based cluster analysis approach that recently has gained popularity in the statistical literature and that has the potential to address these problems. It allows the macrofauna and environmental data to be modelled and analyzed in a single integrated analysis. An exciting extension is to incorporate in the analysis prior information on the habitat preferences of the macrofauna taxa such as is available in lists of indicator values. The output of the analysis is not a hard assignment of water samples to clusters but a probabilistic (fuzzy) assignment. The number of clusters is determined on the basis of the Bayes factor. A standard feature of the Bayesian method is to make predictions and to assess their uncertainty. We applied this approach to a data set consisting of 70 water samples, 484 macrofauna taxa and four environmental variables for which previously a five cluster solution had been proposed. The standard for Bayesian estimation, the Gibbs sampler, worked fine on a subset with only 12 selected taxa but did not converge on the full set with 484 taxa. This is due to many configurations in which the assignment probabilities are all very close to either 0 or 1. This convergence problem is comparable with the local optima problem in classical cluster optimization algorithms, including the EM algorithm used in Latent Gold, a Windows program for latent class analysis. The convergence problem needs to be solved before the benefits of Bayesian latent class analysis can come to fruition in this application. We discuss possible solutions.

The identity of a variety of animal fats and vegetable oils was predicted by three different analytical techniques with help of chemometrics. The sample material of animal origin consisted of milk fat, cow fat, pig fat and poultry fat. The vegetable oils comprised coconut, palm and palm kernel oils. Each product group was composed of at least eight samples of independent batches. For the identity prediction of the fats/oils several (combinations of) datasets were used: absolute and relative measurements of fatty acid compositions, of triacylglycerol compositions, and of combined fatty acid and triacylglycerol compositions. Volatile organic compound compositions were used as well. Fatty acid and triacylglycerol compositions were determined by gas chromatography. Fingerprints of volatile compositions were acquired using Proton transfer reaction-mass spectrometry. The rates of successful prediction were high and varied between 89 and 100%. The 100% rate was obtained for the absolute combined fatty acid/triacylglycerol dataset. Proton transfer reaction-mass spectrometry resulted in 89% correct classifications, has the advantage that it allows very rapid measurements compared to the other techniques, but requires further studies.