[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.

[Hyperspectral optimum wavelengths and Fisher discrimination analysis to distinguish different concentrations of aflatoxin on corn kernel surface].

Aflatoxin is a toxic metabolite widely existing in corn. In the present paper, the feasibility of detecting aflatoxin on corn kernel surface by hyperspectral imaging technology was verified. The corn called pioneer with the same shape is provided by Toxicology and Mycotoxin Research Unit. With methanol configuration, four different concentrations of aflatoxin solutions were prepared and dripped on every 30 corn kernels. Also other clean 30 kernels without aflatoxin dripped were prepared to be the control samples. Among the 150 kernel samples, 103 training samples and 47 validation samples were prepared randomly. Firstly, hyperspectral image in the range of 400 to 1 000 nm was collected. For eliminating the deviations in original spectrum, standard normal variate transformation (SNV) was adopted as pretreatment method. And then several optimum wavelengths were selected by the principle of minimum misdiagnosis rate. After that the selected optimum wavelengths were taken as the input of the Fisher discrimination analysis to discriminate the different concentrations of aflatoxin on the corn. Finally, the discrimination model based on four optimum wavelengths (812.42, 873.00, 900.36 and 965.00 nm) was built and the accuracy of the model was tested. Results indicate that the classification accuracy of calibration and validation set was 87.4% and 80.9% respectively. This method provides basis for designing the corresponding portable instrument and distinguishing aflatoxin produced by naturally metabolism in corn.