Shanhong Xia

Identification of wheat quality using THz spectrum

The terahertz (THz) spectra in the range of 0.2-1.6 THz (6.6-52.8 cm-1) of wheat grains with various degrees of deterioration (normal, worm-eaten, moldy, and sprouting wheat grains) were investigated by terahertz time domain spectroscopy. Principal component analysis (PCA) was employed to extract feature data according to the cumulative contribution rates; the top four principal components were selected, and then a support vector machine (SVM) method was applied. Several selection kernels (linear, polynomial, and radial basis functions) were applied to identify the four types of wheat grain. The results showed that the materials were identified with an accuracy of nearly 95%. Furthermore, this approach was compared with others (principal component regression, partial least squares regression, and back-propagation neural networks). The comparisons showed that PCA-SVM outperformed the others and also indicated that the proposed method of THz technology combined with PCA-SVM is efficient and feasible for identifying wheat of different qualities.

Characterization of Wheat Varieties Using Terahertz Time-Domain Spectroscopy

Terahertz (THz) spectroscopy and multivariate data analysis were explored to discriminate eight wheat varieties. The absorption spectra were measured using THz time-domain spectroscopy from 0.2 to 2.0 THz. Using partial least squares (PLS), a regression model for discriminating wheat varieties was developed. The coefficient of correlation in cross validation (R) and root-mean-square error of cross validation (RMSECV) were 0.985 and 1.162, respectively. In addition, interval PLS was applied to optimize the models by selecting the most appropriate regions in the spectra, improving the prediction accuracy (R = 0.992 and RMSECV = 0.967). Results demonstrate that THz spectroscopy combined with multivariate analysis can provide rapid, nondestructive discrimination of wheat varieties.

Quantitative determination of aflatoxin B1 concentration in acetonitrile by chemometric methods using terahertz spectroscopy.

Aflatoxins contaminate and colonize agricultural products, such as grain, and thereby potentially cause human liver carcinoma. Detection via conventional methods has proven to be time-consuming and complex. In this paper, the terahertz (THz) spectra of aflatoxin B1 in acetonitrile solutions with concentration ranges of 1-50μg/ml and 1-50μg/l are obtained and analyzed for the frequency range of 0.4-1.6THz. Linear and nonlinear regression models are constructed to relate the absorption spectra and the concentrations of 160 samples using the partial least squares (PLS), principal component regression (PCR), support vector machine (SVM), and PCA-SVM methods. Our results indicate that PLS and PCR models are more accurate for the concentration range of 1-50μg/ml, whereas SVM and PCA-SVM are more accurate for the concentration range of 1-50μg/l. Furthermore, ten unknown concentration samples extracted from mildewed maize are analyzed quantitatively using these methods.

Performance investigation of an integrated Young interferometer sensor using a novel prism-chamber assembly

A novel prism-chamber assembly was prepared for application in optical waveguide based chemical and biological sensors, making the sensor easily and reproducibly operate. By using the prism-chamber assembly, the performance of a composite waveguide based integrated Young interferometer sensor was investigated. The temporal interference pattern detected with a single-slit photodetector heavily relies on the slit width, and regular high-contrast patterns can be obtained under the condition that the slit width is smaller than the spatial periodicity of the sensor. Increasing the temperature of water in the chamber leads to a quasi-linear variation in the phase difference with Deltaphi/DeltaT approximately -9.1 degrees/degrees C. Significant dependence of the sensors sensitivity on the polarization state of the guided mode was also observed. The sensor is stable and reliable, capable of real-time detection of very slow bioreactions at the interface.

Integrated Young interferometer sensor with a channel-planar composite waveguide sensing arm.

A simple integrated Young interferometer sensor was prepared on a glass substrate with a pair of single-mode straight channel waveguides 75 microm apart. A tapered thin film of TiO2 was locally deposited on one channel to form a composite waveguide sensing arm with a greatly enhanced evanescent field. With the prism-coupling method a single beam of laser light was simultaneously launched into the two channels to yield a high-contrast interference pattern. Refractive-index sensitivity of the interferometer and its response to protein adsorption were investigated by using a slit-photodetector assembly to interrogate the sensor signal. The factors affecting the fringe contrast were analyzed.

Spectropolarimetric interferometer based on single-mode glass waveguides.

A novel simple spectropolarimetric interferometer was developed based on single-mode potassium ion-exchanged (PIE) glass waveguides that generally have a large birefringence due to the compressive stress induced in the ion-exchanged layers. By using the spectropolarimetric interferometry, wavelength dependence of the modal birefringence of single-mode PIE waveguides was accurately obtained in a broad bandwidth, without need to measure individual modal indices. The modal birefringence decreases with increasing wavelength. The spectropolarimetric interferometer was demonstrated to be responsive to changes occurring within the penetration depth of the evanescent field. A refractive-index change of Deltan = 0.002 was easily detected in the case of a 2-cm-long interaction path length.

Discrimination of moldy wheat using terahertz imaging combined with multivariate classification

Terahertz (THz) imaging was employed to develop a novel method for discriminating wheat of varying states of moldiness. Spectral data, in the range of 0.2–1.6 THz, were extracted from regions of interest (ROIs) in the THz images. Principal component analysis (PCA) was used to evaluate the spectral data and determine the cluster trend. Six optimal frequencies were selected by implementing PCA directly for each images ROI. Classification models for moldy wheat identification were established using the support vector machine (SVM) method, a partial least-squares regression analysis, and the back propagation neural network method. The models developed from these methods were based on the full and optimal frequencies, using the top three principal components as input variables. The PCA-SVM method achieved a prediction accuracy of over 95%, and was implemented at every pixel in the images to visually demonstrate the moldy wheat classification method. Our results indicate that THz imaging combined with chemometric algorithms is efficient and practical for the discrimination of moldy wheat.

Early detection of germinated wheat grains using terahertz image and chemometrics.

In this paper, we propose a feasible tool that uses a terahertz (THz) imaging system for identifying wheat grains at different stages of germination. The THz spectra of the main changed components of wheat grains, maltose and starch, which were obtained by THz time spectroscopy, were distinctly different. Used for original data compression and feature extraction, principal component analysis (PCA) revealed the changes that occurred in the inner chemical structure during germination. Two thresholds, one indicating the start of the release of α-amylase and the second when it reaches the steady state, were obtained through the first five score images. Thus, the first five PCs were input for the partial least-squares regression (PLSR), least-squares support vector machine (LS-SVM), and back-propagation neural network (BPNN) models, which were used to classify seven different germination times between 0 and 48 h, with a prediction accuracy of 92.85%, 93.57%, and 90.71%, respectively. The experimental results indicated that the combination of THz imaging technology and chemometrics could be a new effective way to discriminate wheat grains at the early germination stage of approximately 6 h.

Miniaturized Optical System for Detection of Ammonia Nitrogen in Water Based on Gas-Phase Colorimetry

A small-size gas-tight optical measuring system for detection of ammonia nitrogen in water was prepared based on gas-phase ammonia induced color change of the sensing element that was made by loading bromothymol blue (BTB) in a transparent porous glass fiber membrane. The gas-tight optical measuring system consists of a gas-testing and a liquid-sample chamber connected with each other by means of tubes and a mini-pump that cycles the gas between the two chambers. A 625-nm light emitting diode (LED), a photodetector and a sensing element were mounted in the gas-testing chamber for optical response to ammonia gas released from the water in the liquid-sample chamber. Release of ammonia gas was realized by alkalinizing the water sample with NaOH. Owing to the amount accumulation of ammonia gas in the sealing system, the ammonia nitrogen detection limit of the device can be very low. A small concentration of ammonia nitrogen, as low as 0.05 mg/L, was detected. The two linear-response ranges from 0.05 mg/L to 0.26 mg/L and from 0.26 mg/L to 2.62 mg/L were obtained. A relative standard deviation of ≤1% was determined by multiple measurements of the same sample.