This research proposed to design a prediction model based on Radial Basis Function (RBF) neural network and Near Infrared Reflectance Spectroscopy (NIRS) in detecting concentration of Benzoyl Peroxide (BPO) in flour. Near Infrared Reflectance (NIR) spectra acquired from 100 different concentration samples were pre-processed by Standard Normal Variate (SNV) method, detection of leverage and student residual. NIRS models were designed to predict BPO in the 36 samples by means of Partial Least Squares (PLS), BP neural network and RBF, respectively. The Downloaded by [Deakin University Library] at 13:53 12 August 2015A c c e p t e d M a n u s c r i p t 2 results demonstrated that the RBF model, with prediction correlation coefficient (R), root mean squared error of prediction (RMSEP) and ratio of performance to standard deviate (RPD) reaching 0.9937, 15.5095 and 8.8216, respectively, had optimal prediction accuracy and feasibility providing quality evaluation and dynamic monitoring service for quality inspection department and consumers.
Taking a variety of edible oils as the research object, including soybean oil, peanut oil, rapeseed oil, a method based on Near-Infrared Spectroscopy (NIRS) to identify the frying times is proposed to evaluate the quality of frying oil. Ten rounds of frying experiments are carried out for each of the three oils. The spectra of the first eight rounds are used to build the model, and the last two are used for model testing. First, all the original spectra are preprocessed using the first derivative (1D). Then, the correlation coefficient between the sequence of frying times and absorbance is calculated, and the characteristic wavelengths with a high correlation coefficient are extracted. Finally, a differential prediction model is established based on the characteristic wavelengths. The results show that the differential prediction model accurately predicts the frying times of various edible oils and provides a new method for quality inspection of frying oil, and the predicted accuracy of the frying times of three frying oils is 100% within the allowable range of error.
Abstract. Near Infrared Reflectance (NIR) spectroscopy is a 'green' nondestructive testing technology and it has been widely used in grain crop analysis. The experimental data were collected using 161 wheat samples from the major wheat-producing area in China. The original spectral data was represented by four characteristic variables extracted by Partial Least Squares based Dimension Reduction (PLSDR). Besides, Mahalanobis distance method, second derivative and SNV were used to preprocess spectra. A two-tier classification model based on SVM algorithm was used to achieve the classification of wheat quality. The experimental results indicated that the two-tier SVM classification model was effective in identifying the quality of wheat grain with the recognition rates of common, strong-gluten, middle-gluten and weak-gluten wheat samples being 93.3%, 87.5%, 72.7% and 92.3%, respectively, and the rejection rates of them being 90.0%, 97.4%, 100.0% and 95.2%, respectively. The model realized rapid and accurate classification of wheat, besides it could be applied to the detection system of wheat quality.
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