[A Point Raman Scanning Method for Rapidly Detection of Spinach Quality and Safety Parameters].

According to actual market demand for nondestructive detection of vegetables quality and safety, combined with the heterogeneity of quality and safety parameters such as pesticide residues on leaf vegetables surface and to realize the automatic point scanning for the whole leaf vegetables samples, a suction device based on laboratory (self-designed) Raman spectroscopy hardware and a GUI application software based on the LabVIEW development platform were developed. This system can test the Raman spectroscopy of the whole spinach including the automatic collection, display and storage of the Raman signal of all the scanned points by set up different scan step. A new method to remove the Raman spectrum background was proposed based on data replacement with linear equation at the range of threshold spectrum on both sides of the effective peaks according to the characteristics of spinach original spectra. Its principle is to determine the starting position of linear fitting by judging whether there is trough on both sides of the crest, and then to generate and replace the original spectra data in peak position through the linear fitting equation. Spinach samples were used for the experiment showed that the chlorophyll content and distribution of chlorpyrifos pesticide residue on each scanning point can be obtained after scanning. Therefore, the point scanning Raman system could improve detection accuracy of the quality and safety parameters for the non-uniform samples effectively.

[Detection of Chlorpyrifos on Spinach Based on Surface Enhanced Raman Spectroscopy with Silver Colloids].

In this research, the surface enhanced Raman spectroscopy (SERS) technique is used to develop a nondestructive and fast detecting method for the detection of residual chlorpyrifos on spinach. Silver colloids used for SERS spectroscopy is prepared by the reduction of silver nitrate with hydroxylamine hydrochloride at alkaline pH. The prepared silver colloids are dropped onto spinach samples, then the SERS spectra are collected non-destructively with a self-developed Raman system. This method can be made without physical contact to samples, and rapidly completed without time-consuming sample pre-treatments, and suited to the development of real-time on-line detection methods for trace pesticide residues. SERS signals are collected from 20 points on each spinach sample with 450 mW laser power and 2.5 s exposure time. Chlorpyrifos concentrations in 24 samples are determined with gas chromatography after SERS spectra taken. Savitzky-Golay (SG) smoothing filter and effective peak linear fitting method are used to remove the random noise and the fluorescence background for improving the accuracy of SERS results. The SERS signals are collected from different parts of 50 spinach samples with the same concentration of chlorpyrifos but at different fresh degrees. The relative standard deviation (RSD) of chlorpyrifos' characteristic peak intensities is 13.4%. Although the differences of samples lead to differences in the curves of Raman spectrum, they have little influence on the characteristic peak intensities, which indicates the stability of the proposed detecting method. After the fluorescent background removed, the 20 curves of each sample are averaged. Correlation analysis is done between chlorpyrifos concentration and signal intensity at every Raman shift. Results show that correlation coefficients are higher than 0.85 in the range of 615.5~626.4 cm-1. Signals in this range are used to establish multiple linear regression (MLR) model for the prediction of residual chlorpyrifos. MLR model was developed for chlorpyrifos concentration versus Raman signal intensity at 615.5~626.4 cm-1 for predicting residual chlorpyrifos content in samples, the correlation coefficients of calibration (RC) and validation (RP) are 0.961 and 0.954, which indicate a good linear relationships between them. The minimum detectable threshold for this method is 0.05 mg·kg-1 which is close to the value limited by the national standard of China (0.1 mg·kg-1 for chlorpyrifos in spinach). The proposed practical method is sample, fast, without sample preparation, thus it shows great potential in safety detection of fruits and vegetables.

[Simultaneous Detection of Multiple Quality Parameters of Pork Based on Fused Dual Band Spectral].

For dual band visible/near infrared spectroscopy system (350~1 100 and 1 000~2 500 nm), there exsits a band overlap and for the same sample the reflectivity data were unlike due to the performance difference between instruments. A band connection and data fusion method was proposed in this paper to make better use of the dual-band data. A dual-band visible/near-infrared spectroscopy system was built in the study to collect 60 pork samples' reflectance spectra. The reflectance spectra of samples were performed with pretreatment methods of Savitzky-Golay (S-G) and standard normal variable transform to eliminate the spectral noise. Then partial least squares regression (PLSR) prediction models of pork quality attributes (color, pH and cooking loss) based on single-band spectrum and dual-band spectrum were established, respectively. For the cross of two band overlap, the data were connected and integrated using the method put forward in this paper and then PLSR models were established based on the integrated data. The PLSR model yielded prediction result with correlation coefficient of validation (R(p)) of 0.948 8, 0.920 0, 0.950 5, 0.930 1 and 0.903 5 for L(*), a(*), b(*), pH value and cooking loss, respectively. To simplify the model, uninformative variables elimination (UVE) was employed to select characteristic variables. The experimental results show that the proposed method was able to achieve a better fusion of the two band spectral data, and it was good for the establishment of a more simplified and better prediction model.

[Research on Identification and Determination of Pesticides in Apples Using Raman Spectroscopy].

Raman spectroscopy combined with chemometric methods has been thought to an efficient method for identification and determination of pesticide residues in fruits and vegetables. In the present research, a rapid and nondestructive method was proposed and testified based on self-developed Raman system for the identification and determination of deltamethrin and acetamiprid remaining in apple. The peaks of Raman spectra at 574 and 843 cm(-1) can be used to identify deltamethrin and acetamiprid, respectively, the characteristic peaks of deltamethrin and acetamiprid were still visible when the concentrations of the two pesticides were 0.78 and 0.15 mg · kg(-1) in apples samples, respectively. Calibration models of pesticide content were developed by partial least square (PLS) algorithm with different spectra pretreatment methods (Savitzky-Golay smoothing, first derivative transformation, second derivative transformation, baseline calibration, standard normal variable transformation). The baseline calibration methods by 8th order polynomial fitting gave the best results. For deltamethrin, the obtained prediction coefficient (Rp) value from PLS model for the results of prediction and gas chromatography measurement was 0.94; and the root mean square error of prediction (RMSEP) was 0.55 mg · kg(-1). The values of Rp and RMSEP were respective 0.85 and 0.12 mg · kg(-1) for acetamiprid. According to the detect performance, applying Raman technology in the nondestructive determination of pesticide residuals in apples is feasible. In consideration of that it needs no pretreatment before spectra collection and causes no damage to sample, this technology can be used in detection department, fruit and vegetable processing enterprises, supermarket, and vegetable market. The result of this research is promising for development of industrially feasible technology for rapid, nondestructive and real time detection of different types of pesticide with its concentration in apples. This supplies a rapid nondestructive and environmentally friendly way for the determination of fruit and vegetable quality and safety.