Near-Infrared (NIR) Spectrometry as a Fast and Reliable Tool for Fat and Moisture Analyses in Olives.

The evaluation of fat and moisture contents for olive fruits is crucial for both olive growers and olive oil processors. Reference methods, such as Soxhlet extraction, used for fat content determination in olive fruits are time- and solvent- consuming and labor intensive. Near-infrared (NIR) spectroscopy is proposed as a solution toward rapid and nondestructive analyses of olive fruit fat and moisture contents. In the present work, comparative studies of the fat and moisture quantification methods were performed on four cultivars (Arbosana, Arbequina, Chiquitita, and Koroneiki) during six different harvesting time points to determine the potential of NIR as an alternative methodology. The impact of olive paste crushing degree on NIR performance was also investigated using three different grid sizes (4, 6, and 8 mm) on a hammer mill, in addition to a blade crusher. Results indicate a satisfactory correlation between the reference Soxhlet and NIR methods with R 2 = 0.995. A comparison study of moisture content was also done on NIR and the use of conventional oven with the R 2 value of 0.995. The crushing blade produced higher values in both moisture and fat contents in comparison to the hammer mill. The evaluation indicates that when building a chemometric model, all crush sizes and blade sizes should be represented in the model for highest accuracy.

Pesticide transport with runoff from turf: observations compared with TurfPQ model simulations.

Pesticides applied to turf grass have been detected in surface waters raising concerns of their effect on water quality and interest in their source, hydrological transport and use of models to predict transport. TurfPQ, a pesticide runoff model for turf grass, predicts pesticide transport but has not been rigorously validated for larger storms. The objective of this study was to determine TurfPQ's ability to accurately predict the transport of pesticides with runoff following more intense precipitation. The study was conducted with creeping bentgrass [Agrostis palustris Huds.] turf managed as a golf course fairway. A pesticide mixture containing dicamba, 2,4-D, MCPP, flutolanil, and chlorpyrifos was applied to six adjacent 24.4 by 6.1 m plots. Controlled rainfall simulations were conducted using a rainfall simulator designed to deliver water droplets similar to natural rain. Runoff flow rates and volume were measured and water samples were collected for analysis of pesticide concentrations. Six simulations yielded 13 events with which to test TurfPQ. Measured mean percentage of applied pesticide recovered in the runoff for dicamba, 2,4-D, MCPP, flutolanil, and chlorpyrifos was 24.6, 20.7, 14.9, 5.9, and 0.8%, respectively. The predicted mean values produced by TurfPQ were 13.7, 15.6, 15.5, 2.5, and 0.2%, respectively. The model produced correlations of r=0.56 and 0.64 for curve number hydrology and measured hydrology, respectively. Comparisons of the model estimates with our field observations indicate that TurfPQ under predicted pesticide runoff during 69.5+/-11.4 mm, 1.9+/-0.2 h, simulated storms.

Digital filtering and model updating methods for improving the robustness of near-infrared multivariate calibrations.

Fourier transform near-infrared (NIR) transmission spectra are used for quantitative analysis of glucose for 17 sets of prediction data sampled as much as six months outside the timeframe of the corresponding calibration data. Aqueous samples containing physiological levels of glucose in a matrix of bovine serum albumin and triacetin are used to simulate clinical samples such as blood plasma. Background spectra of a single analyte-free matrix sample acquired during the instrumental warm-up period on the prediction day are used for calibration updating and for determining the optimal frequency response of a preprocessing infinite impulse response time-domain digital filter. By tuning the filter and the calibration model to the specific instrumental response associated with the prediction day, the calibration model is given enhanced ability to operate over time. This methodology is demonstrated in conjunction with partial least squares calibration models built with a spectral range of 4700-4300 cm(-1). By using a subset of the background spectra to evaluate the prediction performance of the updated model, projections can be made regarding the success of subsequent glucose predictions. If a threshold standard error of prediction (SEP) of 1.5 mM is used to establish successful model performance with the glucose samples, the corresponding threshold for the SEP of the background spectra is found to be 1.3 mM. For calibration updating in conjunction with digital filtering, SEP values of all 17 prediction sets collected over 3-178 days displaced from the calibration data are below 1.5 mM. In addition, the diagnostic based on the background spectra correctly assesses the prediction performance in 16 of the 17 cases.

Blank augmentation protocol for improving the robustness of multivariate calibrations.

An updating procedure is described for improving the robustness of multivariate calibration models based on near-infrared spectroscopy. Employing a single blank sample containing no analyte, repeated spectra are acquired during the instrumental warm-up period. These spectra are used to capture the instrumental profile on the analysis day in a way that can be used to update a previously computed calibration model. By augmenting the original spectra of the calibration samples with a group of spectra collected from the blank sample, an updated model can be computed that incorporates any instrumental drift that has occurred. This protocol is evaluated in the context of an analysis of physiological levels of glucose in a simulated biological matrix designed to mimic blood plasma. Employing data of calibration and prediction samples acquired over approximately six months, procedures are studied for implementing the algorithm in conjunction with calibration models based on partial least squares (PLS) regression. Over the range of 1-20 mM glucose, the final algorithm achieves a standard error of prediction (SEP) of 0.79 mM when the augmented PLS model is applied to data collected 176 days after the collection of the calibration spectra. Without updating, the original PLS model produces a seriously degraded SEP of 13.4 mM.

Detection and classification of gaseous sulfur compounds by solid electrolyte cyclic voltammetry of cermet sensor array.

Electrochemical sensors composed of a ceramic-metallic (cermet) solid electrolyte are used for the detection of gaseous sulfur compounds SO(2), H(2)S, and CS(2) in a study involving 11 toxic industrial chemical (TIC) compounds. The study examines a sensor array containing four cermet sensors varying in electrode-electrolyte composition, designed to offer selectivity for multiple compounds. The sensors are driven by cyclic voltammetry to produce a current-voltage profile for each analyte. Raw voltammograms are processed by background subtraction of clean air, and the four sensor signals are concatenated to form one vector of points. The high-resolution signal is compressed by wavelet transformation and a probabilistic neural network is used for classification. In this study, training data from one sensor array was used to formulate models which were validated with data from a second sensor array. Of the 11 gases studied, 3 that contained sulfur produced the strongest responses and were successfully analyzed when the remaining compounds were treated as interferents. Analytes were measured from 10 to 200% of their threshold-limited value (TLV) according to the 8-h time weighted average (TWA) exposure limits defined by the National Institute of Occupational Safety and Health (NIOSH). True positive classification rates of 93.3, 96.7, and 76.7% for SO(2), H(2)S, and CS(2), respectively, were achieved for prediction of one sensor unit when a second sensor was used for modeling. True positive rates of 83.3, 90.0, and 90.0% for SO(2), H(2)S, and CS(2), respectively, were achieved for the second sensor unit when the first sensor unit was used for modeling. Most of the misclassifications were for low concentration levels (such 10-25% TLV) in which case the compound was classified as clean air. Between the two sensors, the false positive rates were 2.2% or lower for the three sulfur compounds, 0.9% or lower for the interferents (eight remaining analytes), and 5.8% or lower for clean air. The cermet sensor arrays used in this analysis are rugged, low cost, reusable, and show promise for multiple compound detection at parts-per-million (ppm) levels.