Permeation studies of PVC pipes with near infrared spectroscopy

Polyvinyl chloride (PVC) is commonly used for drinking water pipes. Although PVC is resistant to natural environmental conditions, organic solvents may attack the pipe wall causing swelling, softening, water lines failure and drinking water pollution. Permeation is linked to underground storage tanks and random accidental spills of organic solvents or fuel derivates. Although rare, several cases have been reported. This thesis was developed with the idea of increasing the understanding of permeation process, developing new laboratory methods for permeation testing, and helping pipe companies in assess about pipe selection in zones of high risk. Near infrared spectroscopy (NIRs) was selected as the methodology. NIR is already used in both organic solvents and polymer fields for discriminative and quantitative analysis of organic compounds. The objective of the first study was to track the permeation of PVC pipes by two major organic solvents (toluene and benzene) at different concentrations. The development of principal least squares (PLS) calibrations with NIR spectra and reference data provided by the Civil Engineering lab in Iowa State University gave accurate models with the following statistic values: R2 above 90, Relative performance determinant (RPD) higher than 3, and standard errors of prediction (SEP) values similar to the standard error of the laboratory (SEL). These models could predict the permeation state of studied pipes in mm of solvent moving front, weight gain or days under permeation. The second study lead to the correlation of the pipe permeation susceptibility in pure toluene in mm/h1/2 to the pipe spectra. Spectra differences from several pipe brands and sizes were modeled with locally weighted regression (LWR) and resulted in several models which can predict with accuracy (RPD above 5) the pipe susceptibility to permeation. In summary, NIR is a suitable tool for permeation studies and may contribute to the better understanding of permeation

Measurement of Single Soybean Seed Attributes by Near-Infrared Technologies. A Comparative Study

Four near-infrared spectrophotometers, and their associated spectral collection methods, were tested and compared for measuring three soybean single-seed attributes: weight (g), protein (%), and oil (%). Using partial least-squares (PLS) and four preprocessing methods, the attribute that was significantly most easily predicted was seed weight (RPD > 3 on average) and protein the least. The performance of all instruments differed from each other. Performances for oil and protein predictions were correlated with the instrument sampling system, with the best predictions using spectra taken from more than one seed angle. This was facilitated by the seed spinning or tumbling during spectral collection as opposed to static sampling methods. From the preprocessing methods utilized, no single one gave the best overall performances but weight measurements were often more successful with raw spectra, whereas protein and oil predictions were often enhanced by SNV and SNV + detrending.Posted with permission from Journal of Agricultural and Food Chemistry 60 (2012): 8314–8322, doi:10.1021/jf3012807. Copyright 2012 American Chemical Society.</p

Permeation studies of PVC pipes with near infrared spectroscopy

Polyvinyl chloride (PVC) is commonly used for drinking water pipes. Although PVC is resistant to natural environmental conditions, organic solvents may attack the pipe wall causing swelling, softening, water lines failure and drinking water pollution. Permeation is linked to underground storage tanks and random accidental spills of organic solvents or fuel derivates. Although rare, several cases have been reported. This thesis was developed with the idea of increasing the understanding of permeation process, developing new laboratory methods for permeation testing, and helping pipe companies in assess about pipe selection in zones of high risk. Near infrared spectroscopy (NIRs) was selected as the methodology. NIR is already used in both organic solvents and polymer fields for discriminative and quantitative analysis of organic compounds. The objective of the first study was to track the permeation of PVC pipes by two major organic solvents (toluene and benzene) at different concentrations. The development of principal least squares (PLS) calibrations with NIR spectra and reference data provided by the Civil Engineering lab in Iowa State University gave accurate models with the following statistic values: R2 above 90, Relative performance determinant (RPD) higher than 3, and standard errors of prediction (SEP) values similar to the standard error of the laboratory (SEL). These models could predict the permeation state of studied pipes in mm of solvent moving front, weight gain or days under permeation. The second study lead to the correlation of the pipe permeation susceptibility in pure toluene in mm/h1/2 to the pipe spectra. Spectra differences from several pipe brands and sizes were modeled with locally weighted regression (LWR) and resulted in several models which can predict with accuracy (RPD above 5) the pipe susceptibility to permeation. In summary, NIR is a suitable tool for permeation studies and may contribute to the better understanding of permeation.</p

Single seed discriminative applications using near infrared technologies

Near infrared spectroscopy (NIRS) have been utilized in a wide selection of single seed applications because it provides fast and non-destructive measurements. Despite the limitation of small seed sizes, NIRS has led to successful results. In this dissertation we explored the feasibility of NIRS for several discriminative applications for corn and soybean seeds. The first application focused on discrimination of conventional and genetically modified Roundup Readyy soybeans. Classification accuracies ranged from 75 to 99% percent. The highest accuracies were obtained with a light tube instrument and with locally weighted principal component regression (LW-PCR) models with few samles represented. Artificial Neural Network (ANN) and Support Vector Machines models gave simmilar accuracies. The technologies performing worse were the low ressolution chemical imaging unit and the Fourier Transform transmittance instrument due to their sensitivity to seed positioning. Discrimination within a single variety was possible above 95% accuracies for most of the varieties. Moisture was proven to impact the classification due to interactions between water and carbohydrates (fiber). For this reason, this application would be feasible for breeders working in controlled seed moistures. Other applications such as discrimination of damaged corn kernels (heat and frost damage) and viability of corn and soybeans with NIRS were analyzed. Only discrimination of heat-damaged corn kernels was successful (accuracies above 95% using partial least squares discriminant analysis, PLS-DA); frost-damaged kernels and non-viable seeds could not be discriminated with any of the tested algorithms. This indicates that NIRS only detects changes in seeds due to damage and there is no relationship with its viability. The final remaining question is what the extent of damage that a seed may suffer to be detected by NIRS would be.</p

Food Chemistry

Previous studies showed that Near Infrared Spectroscopy (NIRS) could distinguish between Roundup Ready® (RR) and conventional soybeans at the bulk and single seed sample level, but it was not clear which compounds drove the classification. In this research the varieties used did not show significant differences in major compounds between RR and conventional beans, but moisture content had a big impact on classification accuracies. Four of the five RR samples had slightly higher moistures and had a higher water uptake than their conventional counterparts. This could be linked with differences in their hulls, being either compositional or morphological. Because water absorption occurs in the same region as main compounds in hulls (mainly carbohydrates) and water causes physical changes from swelling, variations in moisture cause a complex interaction resulting in a large impact on discrimination accuracies.This article is from Food Chemistry 141 (2013): 1895–1901, doi:10.1016/j.foodchem.2013.04.087.</p

Differences between conventional and glyphosate tolerant soybeans and moisture effect in their discrimination by near infrared spectroscopy

Previous studies showed that Near Infrared Spectroscopy (NIRS) could distinguish between Roundup Ready® (RR) and conventional soybeans at the bulk and single seed sample level, but it was not clear which compounds drove the classification. In this research the varieties used did not show significant differences in major compounds between RR and conventional beans, but moisture content had a big impact on classification accuracies. Four of the five RR samples had slightly higher moistures and had a higher water uptake than their conventional counterparts. This could be linked with differences in their hulls, being either compositional or morphological. Because water absorption occurs in the same region as main compounds in hulls (mainly carbohydrates) and water causes physical changes from swelling, variations in moisture cause a complex interaction resulting in a large impact on discrimination accuracies.This article is from Food Chemistry 141 (2013): 1895–1901, doi:10.1016/j.foodchem.2013.04.087. </p