Aiguo Ouyang

Determination of dimethoate and phosmet pesticides with surface-enhanced Raman spectroscopy

ABSTRACT Surface-enhanced Raman spectroscopy spectra of dimethoate and phosmet pesticides were recorded using a Klarite substrate. Significant enhancements were achieved with dimethoate over a concentration range of 0.5–10 µg mL−1 and phosmet over a concentration range of 0.1–10 µg mL−1. The best prediction model for dimethoate pesticide was achieved with a correlation coefficient of 0.940 and a root mean square error of prediction of 0.864 µg mL−1, with the first derivative and standard normalized variate data preprocessing, and the best prediction model of phosmet pesticide was achieved with a correlation coefficient of 0.949 and a root mean square error of prediction of 0.741 µg mL−1 with the first derivative data preprocessing. Our study shows that pesticides, including dimethoate and phosmet, could be quantitatively measured at as low as 0.5 µg mL−1 level using surface-enhanced Raman technology coupled with a Klarite substrate and the results indicated that surface-enhanced Raman spectroscopy with a Klarite substrate has potential for the analysis of dimethoate and phosmet residues.

Determination of Copper and Zinc Pollutants in Ludwigia prostrata Roxb Using Near-Infrared Reflectance Spectroscopy (NIRS)

The feasibility of using near-infrared reflectance spectroscopy (NIRS) to determine the concentrations of copper (Cu) and zinc (Zn) in Ludwigia prostrata Roxb plants was investigated. Ludwigia prostrata Roxb plants were grown over a full growth cycle under controlled laboratory conditions in soils contaminated with heavy metals. The Cu and Zn concentrations in 72 L. prostrata Roxb samples were analyzed using fame atomic absorption spectrometry, and NIRS spectra were collected in the 1099–2500 nm range. Five mathematical treatments of the spectral data were compared prior to developing the calibration models (n = 48) using partial least squares regression methods. The two calibration models for Cu and Zn concentrations were evaluated according to the correlation coefficient of cross-validation (Rcv) and root mean squares error of cross-validation. The highest Rcv and the lowest RMSECV were obtained for Cu (0.9 and 7.24 mg kg−1) and Zn (0.94 and 19.17 mg kg−1), respectively. The results showed that near-infrared diffuse refectance spectroscopy can be used for the rapid determination of Cu and Zn in leaves of L. prostrata Roxb plants.

Potable NIR spectroscopy predicting soluble solids content of pears based on LEDs

A portable near-infrared (NIR) instrument was developed for predicting soluble solids content (SSC) of pears equipped with light emitting diodes (LEDs). NIR spectra were collected on the calibration and prediction sets (145:45). Relationships between spectra and SSC were developed by multivariate linear regression (MLR), partial least squares (PLS) and artificial neural networks (ANNs) in the calibration set. The 45 unknown pears were applied to evaluate the performance of them in terms of root mean square errors of prediction (RMSEP) and correlation coefficients (r). The best result was obtained by PLS with RMSEP of 0.62°Brix and r of 0.82. The results showed that the SSC of pears could be predicted by the portable NIR instrument.

On-line NIR predicting soluble solids content of intact pears combination with wavelet transform and support vector regression

The objective of this paper was to predicting soluble solids content of intact pears using on-line near-infrared spectroscopy (NIRS) combination with wavelet transform (WT) and least squares-support vector machine (LS-SVM). Spectra of 200 pears were collected in the wavelength range of 840∼950nm at the speed of 5 pears per second. All samples were divided into two sets: calibration set (n=150) and validation set (n=50). The spectra were pretreated with the preprocessing method of multiplicative scatter correction (MSC), first derivative (1st D), second derivative (2nd D) and wavelet transform (WT). Partial least squares (PLS) and LS-SVM models were developed with the treated spectra. By comparison the LS-SVM model was super to PLS ones with r of 0.87 and RMSEP of 0. 43oBrix using WT treated spectra. The results indicated that LS-SVM combined with WT could be utilized as a precision method in predicting SSC of intact pears.

Predicting brix of intact pears by a portable NIR spectrometry with LS-SVM

A portable near-infrared (NIR) spectrometry was developed to predict brix of intact pears nondestructively. The spectra of 190 pears collected in the wavelength range of 800–950nm, were dealt with the preprocessing methods of smooth, derivative and standard normal transformation (SNV). The calibration models for brix were developed by least squares support vector machines (LS-SVM), partial least squares (PLS) and multiple linear regression (MLR) with the calibration set of 135 pears. 45 remaining samples were used to evaluate the performance of them. Meanwhile, the capabilities of LS-SVM with different kernel function (RBF_kernel and lin_kernel) were comparatively analyzed. By contrast, the combination of LS-SVM with the RBF kernel, SNV preprocessing and PLS latent variables gave more excellent predictions of brix in the pears, with coefficients of correlation (r) and standard error of prediction (SEP) of (0.87, 0.48°Birx).The results showed that the portable NIR combination with LS-SVM was a feasible method to predict brix of intact pears nondestructively.

A portable Vis-NIR spectrometer to determine soluble solids content in Gannan navel orange by LS-SVM and EWs selection

The objective of this paper was to determine soluble solids content (SSC) of intact Gannan navel orange by a portable near-infrared (NIR) spectrometer with the optical fiber in the wavelength range of 551~950nm. The effective wavelength regions (EWs) were chosen from the spectra pro-processed by second derivative by interval partial least square (iPLS) and backward interval partial least square (Bipls). Then the partial least square (PLS) and least square support vector machine (LS-SVM) models were developed with EWs. 60 unknown samples were used to evaluate the performance of them. The LS-SVM model was better than others with EWs chosen by Bilps. The correlation coefficient (R) and root mean square error of prediction (RMSEP) for LS-SVM (Bipls) were 0.86 and 0.55°Brix. The results showed that the portable NIR combination with LS-SVM was a feasible method to determine SSC of intact Gannan navel orange nondestructively.