Characterisation of humidity dependence of a metal oxide semiconductor (MOS) sensor array using Partial Least Squares (PLS)

Metal oxide semiconductor (MOS) sensors are a class of chemical sensor that have potential for being a practical core sensor module for an electronic nose system in various environmental monitoring applications. However, the responses of these sensors may be affected by changes in humidity and this must be taken into consideration when developing calibration models. This paper characterises the humidity dependence of a sensor array which consists of 12 MOS sensors. The results were used to develop calibration models using partial least squares. Effects of humidity on the response of the sensor array and predictive ability of partial least squares are discussed. It is shown that partial least squares can provide proper calibration models to compensate for effects caused by changes in humidity.

Characterisation of humidity dependence of a metal oxide semiconductor sensor array using partial least squares

Metal oxide semiconductor (MOS) sensors are a class of chemical sensors that have potential for being a practical core sensor module for an electronic nose system in various environmental monitoring applications. However, the responses of these sensors may be affected by changes in humidity and this must be taken into consideration when developing calibration models. This paper characterises the humidity dependence of a sensor array which consists of 12 MOS sensors. The results were used to develop calibration models using partial least squares (PLS). Effects of humidity on the response of the sensor array and predictive ability of partial least squares are discussed. It is shown that partial least squares can provide proper calibration models to compensate for effects caused by changes in humidity. Special Issue: Selected Paper from the 12th International Symposium on Olfaction and Electronic Noses - ISOEN 2007, International Symposium on Olfaction and Electronic Noses.

Quantitative assessment of total phenol contents of European oak (Quercus petraea and Quercus robur) by diffuse reflectance NIR spectroscopy on solid wood surfaces

Near infrared spectroscopy (NIRS) combined with multivariate analysis techniques was applied to assess phenol content of European oak. NIRS data were firstly collected directly from solid heartwood surfaces: in doing so, the spectra were recorded separately from the longitudinal radial and the transverse section surfaces by diffuse reflectance. The spectral data were then pretreated by several pre-processing procedures, such as multiplicative scatter correction, first derivative, second derivative and standard normal variate. The tannin contents of sawmill collected from the longitudinal radial and transverse section surfaces were determined by quantitative extraction with water/methanol (1:4, by vol). Then, total phenol contents in tannin extracts were measured by the Folin-Ciocalteu method. The NIR data were correlated against the Folin-Ciocalteu results. Calibration models built with partial least squares regression displayed strong correlation - as expressed by high determination correlation coefficient (r2) and high ratio of performance to deviation (RPD) - between measured and predicted total phenols content, and weak calibration and prediction errors (RMSEC, RMSEP). The best calibration was provided with second derivative spectra (r2 value of 0.93 for the longitudinal radial plane and of 0.91 for the transverse section plane). This study illustrates that the NIRS technique when used in conjunction with multivariate analysis could provide reliable, quick and non-destructive assessment of European oak heartwood extractives.

Absolute quantitation of Marek's disease virus and Herpesvirus of turkeys in chicken lymphocyte, feather tip and dust samples using real-time PCR

The further development of Taqman quantitative real-time PCR (qPCR) assays for the absolute quantitation of Marek's disease virus serotype 1 (MDV1) and Herpesvirus of turkeys (HVT) viruses is described and the sensitivity and reproducibility of each assay reported. Using plasmid DNA copies, the lower limit of detection was determined to be 5 copies for the MDV1 assay and 75 copies for the HVT assay. Both assays were found to be highly reproducible for Ct values and calculated copy numbers with mean intra- and inter-assay coefficients of variation being less than 5% for Ct and 20% for calculated copy number. The genome copy number of MDV1 and HVT viruses was quantified in PBL and feather tips from experimentally infected chickens, and field poultry dust samples. Parallelism was demonstrated between the plasmid-based standard curves, and standard curves derived from infected spleen material containing both viral and host DNA, allowing the latter to be used for absolute quantification. These methods should prove useful for the reliable differentiation and absolute quantitation of MDV1 and HVT viruses in a wide range of samples.

Estimating Hydrogen Cyanide in Forage Sorghum (Sorghum bicolor) by Near-Infrared Spectroscopy

Hydrogen cyanide (HCN) is a toxic chemical that can potentially cause mild to severe reactions in animals when grazing forage sorghum. Developing technologies to monitor the level of HCN in the growing crop would benefit graziers, so that they can move cattle into paddocks with acceptable levels of HCN. In this study, we developed near-infrared spectroscopy (MRS) calibrations to estimate HCN in forage sorghum and hay. The full spectral NIRS range (400-2498 nm) was used as well as specific spectral ranges within the full spectral range, i.e., visible (400-750 nm), shortwave (800-1100 nm) and near-infrared (NIR) (1100-2498 nm). Using the full spectrum approach and partial least-squares (PLS), the calibration produced a coefficient of determination (R-2) = 0.838 and standard error of cross-validation (SECV) = 0.040%, while the validation set had a R-2 = 0.824 with a low standard error of prediction (SEP = 0.047%). When using a multiple linear regression (MLR) approach, the best model (NIR spectra) produced a R-2 = 0.847 and standard error of calibration (SEC) = 0.050% and a R-2 = 0.829 and SEP = 0.057% for the validation set. The MLR models built from these spectral regions all used nine wavelengths. Two specific wavelengths 2034 and 2458 nm were of interest, with the former associated with C=O carbonyl stretch and the latter associated with C-N-C stretching. The most accurate PLS and MLR models produced a ratio of standard error of prediction to standard deviation of 3.4 and 3.0, respectively, suggesting that the calibrations could be used for screening breeding material. The results indicated that it should be feasible to develop calibrations using PLS or MLR models for a number of users, including breeding programs to screen for genotypes with low HCN, as well as graziers to monitor crop status to help with grazing efficiency.

Field inoculation with arbuscular-mycorrhizal fungi overcomes phosphorus and zinc deficiencies of linseed (Linum usitatissimum) in a vertisol subject to long-fallow disorder

Long-fallow disorder is expressed as exacerbated deficiencies of phosphorus (P) and/or zinc (Zn) in field crops growing after long periods of weed-free fallow. The hypothesis that arbuscular-mycorrhizal fungi (AMF) improve the P and Zn nutrition, and thereby biomass production and seed yield of linseed (Linum usitatissimum) was tested in a field experiment. A factorial combination of treatments consisting of +/- fumigation, +/- AMF inoculation with Glomus spp., +/- P and +/- Zn fertilisers was used on a long-fallowed vertisol. The use of such methods allowed an absolute comparison of plants growing with and without AMF in the field for the first time in a soil disposed to long-fallow disorder. Plant biomass, height, P and Zn concentrations and contents, boll number and final seed yield were (a) least in fumigated soil with negligible AMF colonisation of the roots, (b) low initially in long-fallow soil but increased with time as AMF colonisation of the roots developed, and (c) greatest in soil inoculated with AMF cultures. The results showed for the first time in the field that inflows of both P and Zn into linseed roots were highly dependent on %AMF-colonisation (R-2 = 0.95 for P and 0.85 for Zn, P < 0.001) in a soil disposed to long-fallow disorder. Relative field mycorrhizal dependencies without and with P+Zn fertiliser were 85 % and 86 % for biomass and 68 % and 52 % for seed yield respectively. This research showed in the field that AMF greatly improved the P and Zn nutrition, biomass production and seed yield of linseed growing in a soil disposed to long-fallow disorder. The level of mycorrhizal colonisation of plants suffering from long-fallow disorder can increase during the growing season resulting in improved plant growth and residual AMF inoculum in the soil, and thus it is important for growers to recognise the cause and not terminate a poor crop prematurely in order to sow another. Other positive management options to reduce long fallows and foster AMF include adoption of conservation tillage and opportunity cropping.

Fast FIR Algorithms for the Continuous Wavelet Transform From Constrained Least Squares

New algorithms for the continuous wavelet transform are developed that are easy to apply, each consisting of a single-pass finite impulse response (FIR) filter, and several times faster than the fastest existing algorithms. The single-pass filter, named WT-FIR-1, is made possible by applying constraint equations to least-squares estimation of filter coefficients, which removes the need for separate low-pass and high-pass filters. Non-dyadic two-scale relations are developed and it is shown that filters based on them can work more efficiently than dyadic ones. Example applications to the Mexican hat wavelet are presented.

High-Throughput Prediction of Acacia and Eucalypt Lignin Syringyl/Guaiacyl Content Using FT-Raman Spectroscopy and Partial Least Squares Modeling

High-throughput techniques are necessary to efficiently screen potential lignocellulosic feedstocks for the production of renewable fuels, chemicals, and bio-based materials, thereby reducing experimental time and expense while supplanting tedious, destructive methods. The ratio of lignin syringyl (S) to guaiacyl (G) monomers has been routinely quantified as a way to probe biomass recalcitrance. Mid-infrared and Raman spectroscopy have been demonstrated to produce robust partial least squares models for the prediction of lignin S/G ratios in a diverse group of Acacia and eucalypt trees. The most accurate Raman model has now been used to predict the S/G ratio from 269 unknown Acacia and eucalypt feedstocks. This study demonstrates the application of a partial least squares model composed of Raman spectral data and lignin S/G ratios measured using pyrolysis/molecular beam mass spectrometry (pyMBMS) for the prediction of S/G ratios in an unknown data set. The predicted S/G ratios calculated by the model were averaged according to plant species, and the means were not found to differ from the pyMBMS ratios when evaluating the mean values of each method within the 95 % confidence interval. Pairwise comparisons within each data set were employed to assess statistical differences between each biomass species. While some pairwise appraisals failed to differentiate between species, Acacias, in both data sets, clearly display significant differences in their S/G composition which distinguish them from eucalypts. This research shows the power of using Raman spectroscopy to supplant tedious, destructive methods for the evaluation of the lignin S/G ratio of diverse plant biomass materials.