Long wavelength near-infrared transmission spectroscopy of barley seeds using a supercontinuum laser: Prediction of mixed-linkage beta-glucan content.

A supercontinuum laser was used to perform the first transmission measurements on intact seeds with long wavelength near-infrared spectroscopy. A total of 105 barley seeds from five different barley genotypes (Bomi, lys5.f, lys5.g, lys16 and lys95) were measured from 2275 to 2375 nm. The mixed-linkage (1→3,1→4)-ß-D-glucan (BG) and protein content was measured with wet chemical analysis for each single seed. A partial least squares model correlated the BG % (w/w) with the spectral measurements with a R2CV and R2PRED of 0.83 and 0.90, respectively. The predictive model for BG could be improved by averaging spectra from the same seed and by replacing the individual seed BG content with the average BG of each barley genotype.

Near-Infrared Spectroscopy Using a Supercontinuum Laser: Application to Long Wavelength Transmission Spectra of Barley Endosperm and Oil.

The supercontinuum laser is a new type of light source, which combines the collimation and intensity of a laser with the broad spectral region of a lamp. Using such a source therefore makes it possible to focus the light onto small sample areas without losing intensity and thus facilitate either rapid or high-intensity measurements. Single seed transmission analysis in the long wavelength (LW) near-infrared (NIR) region is one area that might benefit from a brighter light source such as the supercontinuum laser. This study is aimed at building an experimental spectrometer consisting of a supercontinuum laser source and a dispersive monochromator in order to investigate its capability to measure the barley endosperm using transmission experiments in the LW NIR region. So far, barley and wheat seeds have only been studied using NIR transmission in the short wavelength region up to 1100 nm. However, the region in the range of 2260-2380 nm has previously shown to be particularly useful in differentiating barley phenotypes using NIR spectroscopy in reflectance mode. In the present study, 350 seeds (consisting of 70 seeds from each of five barley genotypes) in 1 mm slices were measured by NIR transmission in the range of 2235-2381 nm and oils from the same five barley genotypes were measured in a cuvette with a 1 mm path length in the range of 2003-2497 nm. The spectra of the barley seeds could be classified according to genotypes by principal component analysis; and spectral covariances with reference analysis of moisture, ß-glucan, starch, protein and lipid were established. The spectral variations of the barley oils were compared to the fatty acid compositions as measured using gas chromotography-mass spectrometry (GC-MS).

In vitro and in silico derived relative effect potencies of ah-receptor-mediated effects by PCDD/Fs and PCBs in rat, mouse, and guinea pig CALUX cell lines.

For a better understanding of species-specific relative effect potencies (REPs), responses of dioxin-like compounds (DLCs) were assessed. REPs were calculated using chemical-activated luciferase gene expression assays (CALUX) derived from guinea pig, rat, and mouse cell lines. Almost all 20 congeners tested in the rodent cell lines were partial agonists and less efficacious than 2,3,7,8-tetrachlorodibenzo-p-dioxin (TCDD). For this reason, REPs were calculated for each congener using concentrations at which 20% of the maximal TCDD response was reached (REP20TCDD). REP20TCDD values obtained for PCDD/Fs were comparable with their toxic equivalency factors assigned by the World Health Organization (WHO-TEF), while those for PCBs were in general lower than the WHO-TEF values. Moreover, the guinea pig cell line was the most sensitive as indicated by the 20% effect concentrations of TCDD of 1.5, 5.6, and 11.0 pM for guinea pig, rat, and mouse cells, respectively. A similar response pattern was observed using multivariate statistical analysis between the three CALUX assays and the WHO-TEFs. The mouse assay showed minor deviation due to higher relative induction potential for 2,3,7,8-tetrachlorodibenzofuran and 2,3,4,6,7,8-hexachlorodibenzofuran and lower for 1,2,3,4,6,7,8-heptachlorodibenzofuran and 3,3',4,4',5-pentachlorobiphenyl (PCB126). 2,3,7,8-Tetrachlorodibenzofuran was more than two times more potent in the mouse assay as compared with that of rat and guinea pig cells, while measured REP20TCDD for PCB126 was lower in mouse cells (0.05) as compared with that of the guinea pig (0.2) and rat (0.07). In order to provide REP20TCDD values for all WHO-TEF assigned compounds, quantitative structure-activity relationship (QSAR) models were developed. The QSAR models showed that specific electronic properties and molecular surface characteristics play important roles in the AhR-mediated response. In silico derived REP20TCDD values were generally consistent with the WHO-TEFs with a few exceptions. The QSAR models indicated that, e.g., 1,2,3,7,8-pentachlorodibenzofuran and 1,2,3,7,8,9-hexachlorodibenzofuran were more potent than given by their assigned WHO-TEF values, and the non-ortho PCB 81 was predicted, based on the guinea-pig model, to be 1 order of magnitude above its WHO-TEF value. By combining in vitro and in silico approaches, REPs were established for all WHO-TEF assigned compounds (except OCDD), which will provide future guidance in testing AhR-mediated responses of DLCs and to increase our understanding of species variation in AhR-mediated effects.

Quantitative structure-activity relationship models for ready biodegradability of chemicals.

The European REACH regulation requires information on ready biodegradation, which is a screening test to assess the biodegradability of chemicals. At the same time REACH encourages the use of alternatives to animal testing which includes predictions from quantitative structure-activity relationship (QSAR) models. The aim of this study was to build QSAR models to predict ready biodegradation of chemicals by using different modeling methods and types of molecular descriptors. Particular attention was given to data screening and validation procedures in order to build predictive models. Experimental values of 1055 chemicals were collected from the webpage of the National Institute of Technology and Evaluation of Japan (NITE): 837 and 218 molecules were used for calibration and testing purposes, respectively. In addition, models were further evaluated using an external validation set consisting of 670 molecules. Classification models were produced in order to discriminate biodegradable and nonbiodegradable chemicals by means of different mathematical methods: k nearest neighbors, partial least squares discriminant analysis, and support vector machines, as well as their consensus models. The proposed models and the derived consensus analysis demonstrated good classification performances with respect to already published QSAR models on biodegradation. Relationships between the molecular descriptors selected in each QSAR model and biodegradability were evaluated.

Identification of cytochrome P450 2D6 and 2C9 substrates and inhibitors by QSAR analysis.

This paper presents four new QSAR models for CYP2C9 and CYP2D6 substrate recognition and inhibitor identification based on human clinical data. The models were used to screen a large data set of environmental chemicals for CYP activity, and to analyze the frequency of CYP activity among these compounds. A large fraction of these chemicals were found to be CYP active, and thus potentially capable of affecting human physiology. 20% of the compounds within applicability domain of the models were predicted to be CYP2C9 substrates, and 17% to be inhibitors. The corresponding numbers for CYP2D6 were 9% and 21%. Where the majority of CYP2C9 active compounds were predicted to be both a substrate and an inhibitor at the same time, the CYP2D6 active compounds were primarily predicted to be only inhibitors. It was demonstrated that the models could identify compound classes with a high occurrence of specific CYP activity. An overrepresentation was seen for poly-aromatic hydrocarbons (group of procarcinogens) among CYP2C9 active and mutagenic compounds compared to CYP2C9 inactive and mutagenic compounds. The mutagenicity was predicted with a QSAR model based on Ames in vitro test data.