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Visible near infrared spectroscopy combined with genetic algorithm and successive projection algorithm was investigated to detect soil organic matter (OM). A total of 394 soil samples were collected from Wencheng, Zhejiang province. In order to simplify calibration model, a total of 18 characteristic wavelengths were selected with using genetic algorithm and successive projections algorithm. These characteristic wavelengths were subjected to partial least squares regression (PLSR) with leave-one-out cross validation to establish calibration model of soil organic matter (OM) with coefficient of determination (R2) of 0.81, 0.83, RMSEP of 0.22, 0.20 and residual prediction deviation (RPD) of 2.31, 2.45 for the calibration set and prediction set respectively. The results showed that using genetic algorithm and successive projections algorithm can simplify the model greatly while the assessing indexes of model such as R2, RMSEP and RPD were not reduced greatly compared with indexes of model using full spectra data to develop calibration model. Therefore, genetic algorithm combined with successive projections algorithm can be used to simply the model to predict soil organic matter.

Hyperspectral imaging technology is a rapid, non-destructive, and non-contact technique which integrates spectroscopy and digital imaging to simultaneously obtain spectral and spatial information. Hyperspectral images are made up of hundreds of contiguous wavebands for each spatial position of a sample studied and each pixel in an image contains the spectrum for that specific position. With hyperspectral imaging, a spectrum for each pixel can be obtained and a gray scale image for each narrow band can be acquired, enabling this system to reflect componential and constructional characteristics of an object and their spatial distributions. In this study, hyperspectral image technology is used to discuss the application of hyperspectral imaging detection technology of Jiangxi navel orange surface of different concentrations of pesticide residue changes with time relationship. The pesticide was diluted to 1 : 20, 1 : 100 and 1 : 1 000 solution with distilled water. A 1×2 matrix of dilutions was applied to each of 30 cleaned samples with different density pesticide residue. After 0, 4 and 20 d respectively, hyperspectral images in the wavelength range from 900 to 1 700 nm are taken. The characteristic wavelengths are achieved by using principal component analysis (PCA) and the PC-2 image based on PCA using characteristic wavelengths (930, 980, 1 100, 1 210, 1 300, 1 400, 1 620 and 1 680 nm) as the classification and recognition of image. Based on these 8 characteristic wavelengths for a second principal component analysis, the application of PC-2 image and appropriate image processing methods for different concentrations and different days of placing pesticide residues in non-destructive testing were applied. Using hyperspectral imaging technology to detect three periods a higher dilution of the fruit surface pesticide residues are more obvious. This research shows that the technology of hyperspectral imaging can be used to effectively detect pesticide residue on Gannan navel surface.

The varieties of cabbage seeds directly affect the yield and quality of cabbage, in order to rapidly and nondestructively identify the varieties of cabbage seeds, near infrared spectra technique were applied in this study and reflectance spectrum of the cabbage seeds was obtained. Firstly, to excavate the effective information in the spectral data and improve signal to noise ratio, the raw spectra was pre-processed with the method of standard normal variate (SNV) and multiplicative scatter correction (MSC). Secondly, principal component analysis (PCA) was used to analyze the clustering of cabbage samples, then the characteristic differentia of three cabbage varieties was obtained through qualitative analysis. Six Effective wavelengths were selected by successive projections algorithm (SPA). Finally, the full spectra variable, the first three principal components (PCs) using PCA and selected effective wavelengths using SPA were respectively set as inputs of the partial least squares discriminant analysis (PLS-DA) and least-squares support vector machine (LS-SVM) models for the classification of cabbage seeds. As can be seen from the two dimensional plot drawn with the scores of PC1 and PC2 (the first two principle components), PC1 and PC2 had a good clustering effect for different kinds of cabbage seeds. LS-SVM models performed better than PLS-DA models, the correct rates of discrimination were 100% achieved with LS-SVM models. PLS-DA and LS-SVM models built based on the selected wavelengths performed better than the models built based on the first three principal components, moreover, the SPA-LS-SVM model obtained the best results among all models, with 100% discrimination accuracy for both the calibration set and the prediction set. The overall results show that SPA can extract wavelengths, and the LS-SVM model combined with SPA can obtain optimal classification results. So the present paper could offer an alternate approach for the rapid discrimination of cabbage seeds variety.

The absorption spectra of Ⅰ and Ⅲ polymorphs of chlorpropamide were measured with terahertz time-domain spectroscopy (THz-TDS) technique, FT-IR and FT-Raman at room temperature. The results showed that there were distinct differences of Ⅰ and Ⅲ polymorphs of chlorpropamide in those spectra. The IR spectra of two polymorphs of chlorpropamide had showed differences both in the frequencies of the vibrational bands and in the relative band intensities. The obvious differences in Raman spectra of polymorphs of chlorpropamide were that the characteristic peaks of Ⅲ- form were more than Ⅰ-form in 100~1 800 cm-1 region. Absorption peaks observing in the terahertz spectra ofⅠ-form were at 0.90, 1.09 and 1.29 THz and Ⅲ-form were at 0.92, 1.11, 1.23 and 1.63 THz. The maximum difference between Ⅰ-form and Ⅲ-form was that the strong peak appeared at 1.63 THz of Ⅲ-form. The characteristic absorption bands of two polymorphs of chlorpropamide were assigned based on the simulation results of DFT calculation. The simulation result is in accordance with the experiment. From simulation results, it is found that the vibration modes of Ⅰ-form and Ⅲ-form were similar in IR and Raman spectra, but there were more differences in terahertz spectra. The study can provide experimental and theoretical references for the application of FT-IR , FT-Raman and THz-TDS spectra techniques to detect pharmaceutical polymorphs.

Near infrared spectroscopy analysis as a reliable, rapid, little sample preparation requirement, low-cost, convenient, nondestructive and green technique becomes more and more important in the area of soil nutrition measurement. Near infrared spectroscopy are highly sensitive to C-H, O-H and N-H bonds of soil components such as total nitrogen (TN) making their use in the agricultural and environmental sciences particularly appropriate. The analytical abilities of near infrared spectroscopy depend on the repetitive and broad absorption of light by C-H, O-H and N-H bonds. A total of 243 soil samples were collected from wencheng, Zhejiang province. Raw spectra and wavelength-reduced spectra with 3 different pretreatment methods (Savitzky-Golay smoothing (SG), Reduce (RD), and Wavelet Transform (WT)) were compared to determine the optimal wavelength range and pretreatment method for analysis. Spectral variable selection is an important strategy in spectrum modeling analysis, because it tends to parsimonious data representation and can lead to multivariate models with better performance. In order to simply calibration models, the preprocessed spectra were then used to select sensitive wavelengths by competitive adaptive reweighted sampling (CARS), Random frog and Successive Projections Algorithm (SPA) methods. Different numbers of sensitive wavelengths were selected by different variable selection methods with Wavelet Transform (WT) preprocessing method. Partial least squares (PLS) was used to build models with the full spectra, and Extreme Learning Machine (ELM) and LS-SVM were applied to build models with the selected wavelength variables. The overall results showed that PLS and LS-SVM models performed better than ELM models, and the LS-SVM models with the selected wavelengths based on SPA obtained the best results with the determination coefficient (R2), RMSEP and RPD were 0.63, 0.007 9 and 1.58 for prediction set. The results indicated that it was feasible to use portable short wave near-infrared spectral technology to predict soil total nitrogen and wavelengths selection could be very useful to reduce redundancy of spectra.

Ultraviolet/visible (UV/Vis) spectroscopy technology was used to measure water COD. A total of 135 water samples were collected from Zhejiang province. Raw spectra with 3 different pretreatment methods (Multiplicative Scatter Correction (MSC), Standard Normal Variate (SNV) and 1st Derivatives were compared to determine the optimal pretreatment method for analysis. Spectral variable selection is an important strategy in spectrum modeling analysis, because it tends to parsimonious data representation and can lead to multivariate models with better performance. In order to simply calibration models, the preprocessed spectra were then used to select sensitive wavelengths by competitive adaptive reweighted sampling (CARS), Random frog and Successive Genetic Algorithm (GA) methods. Different numbers of sensitive wavelengths were selected by different variable selection methods with SNV preprocessing method. Partial least squares (PLS) was used to build models with the full spectra, and Extreme Learning Machine (ELM) was applied to build models with the selected wavelength variables. The overall results showed that ELM model performed better than PLS model, and the ELM model with the selected wavelengths based on CARS obtained the best results with the determination coefficient (R2), RMSEP and RPD were 0.82, 14.48 and 2.34 for prediction set. The results indicated that it was feasible to use UV/Vis with characteristic wavelengths which were obtained by CARS variable selection method, combined with ELM calibration could apply for the rapid and accurate determination of COD in aquaculture water. Moreover, this study laid the foundation for further implementation of online analysis of aquaculture water and rapid determination of other water quality parameters.

Pharmaceutical cocrystal can improve physical and chemical properties of active pharmaceutical ingredient (API), meanwhile this feature has shown great potential in improving the pharmaceutical's properties and characteristics. In this study, cocrystal formation between piracetam and 3-hydroxybenzoic acid (3HBA) using grinding method has been characterized by Fourier transform infrared (FTIR), Raman and terahertz (THz) spectroscopical techniques. The vibrational modes of different motions are obtained by the assignment of the peaks in the spectra of the starting materials and the cocrystal components. FTIR, Raman and THz spectroscopical results show that the vibrational modes of the cocrystal are different from those of the starting materials. In addition, the dynamic process of the above cocrystal formation is investigated in-depth with Raman and THz spec- tra. Piracetam-3HBA cocrystal is formed pretty fast in first several minutes, and then the formation rate becomes slow. After 35 minutes, such formation process has been completed. The results offer the theoretical benchmark and unique means for real-time monitoring pharmaceutical cocrystal formation and also the corresponding quantitative analysis in the pharmaceutical field.

Visible near spectra tecnnology was adopted to detect soil total nitrogen content. 394 soil samples were collected from Wencheng, Zhejiang province to be used for calibration model (n=263) and independent prediction set (n=131). Raw spectra and wavelength-reduced spectra with five different pretreatment methods (SG smoothing, SNV, MSC, 1st-D and 2nd-D) were compared to determine the optimal wavelength range and pretreatment method for analysis. The results with 5 different pretreatment methods were not improved compared to that both of full spectra PLS model and wavelength reduction spectra model. Spectral variable selection is an important strategy in spectrum modeling analysis, because it tends to parsimonious data representation and can lead to multivariate models with better performance. In order to simply calibration models, the wavelength variables selected by two different variable selection methods (i. e. regression coefficient analysis (RCA) and successive projections algorithm (SPA) were proposed to be the inputs of calibration methods of PLS, MLR and LS-SVM models separately. These calibration models were also compared to select the best model to predict soil TN. In total, 9 different models were built ahd the best results indicated that PLS, MLR and LS-SVM obtained the highest precision with determination coefficient of prediction R2(pre) =0. 81, RMSEP=0. 0031 and RPD=2. 26 based on wavelength variables selected by RCA (0. 0002) and SPA as inputs of models. SPA-MLR model and other three models based on 7 sensitive variables selected by RC using 0. 0002 regression coefficient threshold value obtained the best result with R2(pre), RMSEP and RPD as 0. 81, 0. 0031 and 2. 26. This prediction accuracy is classied to be very good. For all the models, it could be concluded that RCA and SPA could be very useful ways to selected sensitive wavelengths, and the selected wavelengths were effective to estimate soil TN. It is recommended to adopt SPA variable selection or RCA variable selection method with both linear and nonlinear calibration models for measurement of the soil TN using Vis-NIR spectroscopy technology, and wavelengths selection could be very useful to reduce collinearity and redundancies of spectra.

Ultraviolet/visible (UV/Vis) spectroscopy was studied for the rapid determination of chemical oxygen demand (COD), which was an indicator to measure the concentration of organic matter in aquaculture water. In order to reduce the influence of the absolute noises of the spectra, the extracted 135 absorbance spectra were preprocessed by Savitzky-Golay smoothing (SG), EMD, and wavelet transform (WT) methods. The preprocessed spectra were then used to select latent variables (LVs) by partial least squares (PLS) methods. Partial least squares (PLS) was used to build models with the full spectra, and back- propagation neural network (BPNN) and least square support vector machine (LS-SVM) were applied to build models with the selected LVs. The overall results showed that BPNN and LS-SVM models performed better than PLS models, and the LS-SVM models with LVs based on WT preprocessed spectra obtained the best results with the determination coefficient (r2) and RMSE being 0. 83 and 14. 78 mg · L(-1) for calibration set, and 0.82 and 14.82 mg · L(-1) for the prediction set respectively. The method showed the best performance in LS-SVM model. The results indicated that it was feasible to use UV/Vis with LVs which were obtained by PLS method, combined with LS-SVM calibration could be applied to the rapid and accurate determination of COD in aquaculture water. Moreover, this study laid the foundation for further implementation of online analysis of aquaculture water and rapid determination of other water quality parameters.

This study proposed a new method using visible and near infrared (Vis/NIR) hyperspectral imaging for the detection and visualization of the chilling storage time for turbot flesh rapid and nondestructively. A total of 160 fish samples with 8 different storage days were collected for hyperspectral image scanning, and mean spectra were extracted from the region of interest (ROD inside each image. Partial least squares regression (PLSR) was applied as calibration method to correlate the spectral data and storage time for the 120 samples in calibration set. Then the PLSR model was used to predict the storage time for the 40 prediction samples, which achieved accurate results with determination coefficient (R2) of 0.966 2 and root mean square error of prediction (RMSEP) of 0.679 9 d. Finally, the storage time of each pixel in the hyperspectral images for all prediction samples was predicted and displayed in different colors for visualization based on pseudo-color images with the aid of an IDL program. The results indicated that hyperspectral imaging technique combined with chemometrics and image processing allows the determination and visualization of the chilling storage time for fish, displaying fish freshness status and distribution vividly and laying a foundation for the automatic processing of aquatic products.

Hyperspectral imaging technology was developed to identify different brand famous green tea based on PCA information and image information fusion. First 512 spectral images of six brands of famous green tea in the 380 approximately 1 023 nm wavelength range were collected and principal component analysis (PCA) was performed with the goal of selecting two characteristic bands (545 and 611 nm) that could potentially be used for classification system. Then, 12 gray level co-occurrence matrix (GLCM) features (i. e., mean, covariance, homogeneity, energy, contrast, correlation, entropy, inverse gap, contrast, difference from the second-order and autocorrelation) based on the statistical moment were extracted from each characteristic band image. Finally, integration of the 12 texture features and three PCA spectral characteristics for each green tea sample were extracted as the input of LS-SVM. Experimental results showed that discriminating rate was 100% in the prediction set. The receiver operating characteristic curve (ROC) assessment methods were used to evaluate the LS-SVM classification algorithm. Overall results sufficiently demonstrate that hyperspectral imaging technology can be used to perform classification of green tea.

Visible and short wave infrared spectroscopy (Vis/SW-NIRS) was investigated in the present study for measurement of soil organic matter (OM) and available potassium (K). Four types of pretreatments including smoothing, SNV, MSC and SG smoothing+first derivative were adopted to eliminate the system noises and external disturbances. Then partial least squares regression (PLSR) and least squares-support vector machine (LS-SVM) models were implemented for calibration models. The LS-SVM model was built by using characteristic wavelength based on successive projections algorithm (SPA). Simultaneously, the performance of LSSVM models was compared with PLSR models. The results indicated that LS-SVM models using characteristic wavelength as inputs based on SPA outperformed PLSR models. The optimal SPA-LS-SVM models were achieved, and the correlation coefficient (r), and RMSEP were 0. 860 2 and 2. 98 for OM and 0. 730 5 and 15. 78 for K, respectively. The results indicated that visible and short wave near infrared spectroscopy (Vis/SW-NIRS) (325 approximately 1 075 nm) combined with LS-SVM based on SPA could be utilized as a precision method for the determination of soil properties.

In the present study, hyperspectral imaging combined with chemometrics was successfully proposed to identify different varieties of black bean. The varieties of black bean were defined based on the three different colors of the bean core. The hy-perspectral images in the spectral range of 380-1,030 nm of black bean were acquired using the developed hyperspectral imaging system, and the reflectance spectra were extracted from the region of interest (ROD) in the images. The average spectrum of a ROI of the sample in the images was used to represent the spectrum of the sample and build classification models. In total, 180 spectra of 180 samples were extracted. The wavelengths from 440 to 943 nm were used for analysis after the removal of the spec- tral region with absolute noises, and 440-943 nm spectra were preprocessed by multiplicative scatter correction (MSC). Five classification methods, including partial least squares discriminant analysis (PLS-DA), soft independent modeling of class analogy (SIMCA), K-nearest neighbor algorithm (KNN), support vector machine (SVM) and extreme learning machine (ELM), were used to build discriminant models using the preprocessed full spectra, the feature information extracted by principal component analysis (PCA) and the feature information extracted by wavelet transform (WT) from the preprocessed spectra, respectively. Among all the classification models using the preprocessed full spectra, ELM models obtained the best performance; among all the classification models using the feature information extracted from the preprocessed spectra by PCA, ELM model also obtained the best classification accuracy; and among all the classification models using the feature information extracted from the preprocessed spectra by WT, ELM models obtained the best classification performance with 100% accuracy in both the calibration set and the prediction set. Among all classification models, WT-ELM model obtained the best classification accuracy. The overall results indicated that it was feasible to identify black bean varieties nondestructively by using hyperspectral imaging, and WT could effectively extract feature information from spectra and ELM algorithm was effective to build high performance classification models.

The potential of hyperspectral imaging technology was evaluated for discriminating three types of waxed apples. Three types of apples smeared with fruit wax, with industrial wax, and not waxed respectively were imaged by a hyperspectral imaging system with a spectral range of 308-1 024 nm. ENVI software processing platform was used for extracting hyperspectral image object of diffuse reflection spectral response characteristics. Eighty four of 126 apple samples were selected randomly as calibration set and the rest were prediction set. After different preprocess, the related mathematical models were established by using the partial least squares (PLS), the least squares support vector machine (LS-SVM) and BP neural network methods and so on. The results showed that the model of MSC-SPA-LSSVM was the best to discriminate three kinds of waxed apples with 100%, 100% and 92.86% correct prediction respectively.

A hundred and ninety one representative brown rice samples from the Chinese Rice Genebank and the DH population derived from the cross of japonica upland rice IRAT109 with paddy rice Yuefu were selected for this study. Their protein content range was 5.90%-14.50%. Near-infrared diffusive spectroscopy (NIDRS) and partial least square (PLS) were used to determine protein content with different wavelength ranges and data preprocessing methods for regression and information extraction. The object function [R/(1+RMSECV)] of quantitative model was defined, and the samples of calibration and validation tests were classified by projective distribution of PLS loadings. These methods were applied to the optimization of the calibration model. It is demonstrated that the calibration model developed by the spectral data pretreatment of the first derivative + standard vector normalization with the same spectral region (5 000-9 000 cm(-1) resulted in the best determination of protein content in brown rice when the maximum values of the object function were reached. The maximum values of the object functions of calibration and validation sets were 0.701 and 0.687, respectively. Projective distributions of PLS loadings were used to validate the models, and the result was the same as that of validating model by object function [R/(1+RMSECV)].

A new resonance light scattering (RLS) assay is presented in this paper. At the optimum pH = 4.0, the weak RLS of chlorophenol red can be greatly enhanced by the addition of proteins due to the interaction between protein and chlorophenol red. A new quantitative determination method for proteins has been developed. The linear range is 0-1.0 mg.L-1 for BSA, and 0-0.75 mg.L-1 for HSA with detection limit of 20.3 micrograms.L-1 for BSA, and 20.4 micrograms.L-1 for HSA, respectively. Besides high sensitivity, the method is characterized by good reproducibility, rapidity of reaction, good stability and few inferring substances. The determination results of the proteins in human serum samples by this method are very close to those obtained using biuret spectrophotometric method, with relative stand deviation of 1.17%-2.42%.

A new Rayleigh light scattering (RLS) assay is presented in this paper. At the optimum pH = 2.72, the weak RLS of fuchsine acid can be greatly enhanced by the addition of proteins due to the interaction between protein and fuchsine acid. A new quantitative determination method for proteins has been developed. The linear range for human serum albumin is 0-4.0 mg.L-1 with detection limit of 23 micrograms.L-1. Besides high sensitivity, the method is characterized by good reproducibility, rapidity of reaction, good stability and few interfering substances. The determination results of the proteins in human serum and urine samples are very close those obtained using Biuret method, with relative stand deviation of 0.94%-4.93%.