P-Stereogenic Ir-MaxPHOX: A Step toward Privileged Catalysts for Asymmetric Hydrogenation of Nonchelating Olefins.

The Ir-MaxPHOX-type catalysts demonstrated high catalytic performance in the hydrogenation of a wide range of nonchelating olefins with different geometries, substitution patterns, and degrees of functionalization. These air-stable and readily available catalysts have been successfully applied in the asymmetric hydrogenation of di-, tri-, and tetrasubstituted olefins (ee's up to 99%). The combination of theoretical calculations and deuterium labeling experiments led to the uncovering of the factors responsible for the enantioselectivity observed in the reaction, allowing the rationalization of the most suitable substrates for these Ir-catalysts.

Acid number, viscosity and end-point detection in a multiphase high temperature polymerisation process using an online miniaturised MEMS Fabry-Pérot interferometer.

Recent advances in the latest generation of MEMS (micro-electro-mechanical system) Fabry-Pérot interferometers (FPI) for near infrared (NIR) wavelengths has led to the development of ultra-fast and low cost NIR sensors with potential to be used by the process industry. One of these miniaturised sensors operating from 1350 to 1650 nm, was integrated into a software platform to monitor a multiphase solid-gas-liquid process, for the production of saturated polyester resins. Twelve batches were run in a 2 L reactor mimicking industrial conditions (24 h process, with temperatures ranging from 220 to 240 °C), using an immersion NIR transmission probe.

Process Monitoring of Moisture Content and Mass Transfer Rate in a Fluidised Bed with a Low Cost Inline MEMS NIR Sensor.

Purpose: The current trend for continuous drug product manufacturing requires new, affordable process analytical techniques (PAT) to ensure control of processing. This work evaluates whether property models based on spectral data from recent Fabry-Pérot Interferometer based NIR sensors can generate a high-resolution moisture signal suitable for process control.

Methods: Spectral data and offline moisture content were recorded for 14 fluid bed dryer batches of pharmaceutical granules.

Determination of chemical properties in 'calçot' (Allium cepa L.) by near infrared spectroscopy and multivariate calibration.

'Calçots', the immature floral stems of second-year onion resprouts, are an economically important traditional crop in Catalonia (Spain). Classical approaches to evaluating the chemical properties of 'calçots' are time consuming and expensive; near-infrared spectroscopy (NIRS) may be faster and cheaper. We used NIRS to develop partial least square (PLS) models to predict dry matter, soluble solid content, titratable acidity, and ash content in cooked 'calçots'.

Prediction of olive oil sensory descriptors using instrumental data fusion and partial least squares (PLS) regression.

Headspace-Mass Spectrometry (HS-MS), Fourier Transform Mid-Infrared spectroscopy (FT-MIR) and UV-Visible spectrophotometry (UV-vis) instrumental responses have been combined to predict virgin olive oil sensory descriptors. 343 olive oil samples analyzed during four consecutive harvests (2010-2014) were used to build multivariate calibration models using partial least squares (PLS) regression. The reference values of the sensory attributes were provided by expert assessors from an official taste panel.

Olive oil sensory defects classification with data fusion of instrumental techniques and multivariate analysis (PLS-DA).

Three instrumental techniques, headspace-mass spectrometry (HS-MS), mid-infrared spectroscopy (MIR) and UV-visible spectrophotometry (UV-vis), have been combined to classify virgin olive oil samples based on the presence or absence of sensory defects. The reference sensory values were provided by an official taste panel. Different data fusion strategies were studied to improve the discrimination capability compared to using each instrumental technique individually.

Data fusion methodologies for food and beverage authentication and quality assessment - a review.

The ever increasing interest of consumers for safety, authenticity and quality of food commodities has driven the attention towards the analytical techniques used for analyzing these commodities. In recent years, rapid and reliable sensor, spectroscopic and chromatographic techniques have emerged that, together with multivariate and multiway chemometrics, have improved the whole control process by reducing the time of analysis and providing more informative results. In this progression of more and better information, the combination (fusion) of outputs of different instrumental techniques has emerged as a means for increasing the reliability of classification or prediction of foodstuff specifications as compared to using a single analytical technique.

Identification of olive oil sensory defects by multivariate analysis of mid infrared spectra.

Mid-infrared (MIR) spectra (4000-600 cm(-1)) of olive oils were analyzed using chemometric methods to identify the four main sensorial defects, musty, winey, fusty and rancid, previously evaluated by an expert sensory panel. Classification models were developed using partial least squares discriminant analysis (PLS-DA) to distinguish between extra-virgin olive oils (defect absent) and lower quality olive oils (defect present). The most important spectral ranges responsible for the discrimination were identified.

Biomonitoring exposure assessment to contemporary pesticides in a school children population of Spain.

The exposure to pesticides amongst school-aged children (6-11 years old) was assessed in this study. One hundred twenty-five volunteer children were selected from two public schools located in an agricultural and in an urban area of Valencia Region, Spain. Twenty pesticide metabolites were analyzed in children's urine as biomarkers of exposure to organophosphate (OP) insecticides, synthetic pyrethroid insecticides, and herbicides.

Classification of edible oils and modeling of their physico-chemical properties by chemometric methods using mid-IR spectroscopy.

This research work describes two studies for the classification and characterization of edible oils and its quality parameters through Fourier transform mid infrared spectroscopy (FT-mid-IR) together with chemometric methods. The discrimination of canola, sunflower, corn and soybean oils was investigated using SVM-DA, SIMCA and PLS-DA. Using FT-mid-IR, DPLS was able to classify 100% of the samples from the validation set, but SIMCA and SVM-DA were not.

Rapid characterization of transgenic and non-transgenic soybean oils by chemometric methods using NIR spectroscopy.

Near infrared (NIR) spectroscopy and multivariate classification were applied to discriminate soybean oil samples into non-transgenic and transgenic. Principal Component Analysis (PCA) was applied to extract relevant features from the spectral data and to remove the anomalous samples. The best results were obtained when with Support Vectors Machine-Discriminant Analysis (SVM-DA) and Partial Least Squares-Discriminant Analysis (PLS-DA) after mean centering plus multiplicative scatter correction.

Outlier detection for the Generalized Rank Annihilation Method in HPLC-DAD analysis.

The Generalized Rank Annihilation Method (GRAM) is a second-order calibration method that is used in chromatography to quantify analytes that coelute with interferences. For a correct quantification, the peak of the analyte in the standard and in the test sample must be aligned and have the same shape (i.e.

Establishment of multivariate specifications for food commodities with discriminant partial least squares.

A novel method for establishing multivariate specifications of food commodities is proposed. The specifications are established for discriminant partial least squares (DPLS) by setting limits on the predictions of the DPLS model together with Hotelling T(2) and square error of prediction (SPE). These limits can be tuned depending on whether type I error (i.

Multi-class classification with probabilistic discriminant partial least squares (p-DPLS).

This work describes multi-classification based on binary probabilistic discriminant partial least squares (p-DPLS) models, developed with the strategy one-against-one and the principle of winner-takes-all. The multi-classification problem is split into binary classification problems with p-DPLS models. The results of these models are combined to obtain the final classification result.

Classification from microarray data using probabilistic discriminant partial least squares with reject option.

Microarrays are used to simultaneously determine the expressions of thousands of genes. An important application of microarrays is in the classification of samples into classes of interest (e.g.

Bagged k-nearest neighbours classification with uncertainty in the variables.

An analytical result should be expressed as x+/-U, where x is the experimental result obtained for a given variable and U is its uncertainty. This uncertainty is rarely taken into account in supervised classification. In this paper, we propose to include the information about the uncertainty of the experimental results to compute the reliability of classification.

Excitation-emission fluorescence spectroscopy combined with three-way methods of analysis as a complementary technique for olive oil characterization.

This paper shows the potential of excitation-emission fluorescence spectroscopy (EEFS) and three-way methods of analysis [parallel factor analysis (PARAFAC) and multiway partial least-squares (N-PLS) regression] as a complementary technique for olive oil characterization. The fluorescence excitation-emission matrices of a set of Spanish extra virgin, virgin, pure, and olive pomace oils were measured, and the relationship between them and some of the quality parameters of olive oils (peroxide value, K232, and K270) was studied. N-PLS was found to be more suitable than PARAFAC combined with multiple linear regression for correlating fluorescence and quality parameters, yielding better fits and lower prediction errors.

Prediction of heterofullerene stabilities: a combined DFT and chemometric study of C56Pt2, C57Pt2 and C81Pt2.

A systematic search of the regioisomers of the heterofullerenes, C57Pt2 and C56Pt2, has been carried out by means of density functional calculations to find the most stable structures. Both heterofullerenes incorporate two metal atoms into the fullerene surface. In the case of C57Pt2, one platinum atom substitutes one carbon atom of C60 and the other platinum atom replaces a C--C bond, whereas in C56Pt2 each platinum atom replaces one C--C bond.

Cluster analysis applied to the exploratory analysis of commercial spanish olive oils by means of excitation-emission fluorescence spectroscopy.

Olive oil fluorescence is related to oil composition. Here it is shown that the natural clustering of different types of commercial Spanish olive oils depends on their fluorescence excitation-emission matrices (EEMs). Fifty-six commercial samples of olive oil (29 virgin olive oils, 20 pure olive oils, and 7 olive-pomace oils) were used.

Fuzzy logic for identifying pigments studied by Raman spectroscopy.

Fuzzy logic and linguistic variables are used for the automatic interpretation of Raman spectra obtained from pigments found in cultural heritage art objects. Featured bands are extracted from a Raman spectrum of a reference pigment and the methodology for constructing the library is illustrated. An unknown spectrum is then interpreted automatically and a process for identifying the corresponding pigment is described.

Quantification from highly drifted and overlapped chromatographic peaks using second-order calibration methods.

For determining low levels of pesticides and phenolic compounds in river and wastewater samples by high performance liquid chromatography (HPLC), solid phase extraction (SPE) is commonly used before the chromatographic separation. This preconcentration step is not necessarily selective for the analytes of interest and it may retain other compounds of similar characteristics as well. In this case, we present, humic and fulvic acids caused a large baseline drift and overlapped the analytes to be quantified.

Using second-order calibration to identify and quantify aromatic sulfonates in water by high-performance liquid chromatography in the presence of coeluting interferences.

We used the Generalized Rank Annihilation Method (GRAM), a second-order calibration method, to quantify aromatic sulfonates in water with high-performance liquid chromatography (HPLC) when interferences coeluted with the analytes of interest. With GRAM, we can quantify in only two chromatographic analyses, one for a calibration sample and one for the unknown sample. The calculated concentrations were not statistically different to those obtained when the chromatographic separation of the unknown sample was modified in order to completely separate the analyte from the interferences before univariate calibration.