In this study, 100 groups of apples with different sweetness were measured in transmission mode using visible light spectroscopy (VIS). The absorption spectra of all samples were obtained in the wavelength range of 400-800 nm with a step of 5 nm. To classify and identify the sweetness of apples, a qualitative classification model of apple absorption spectra and sweetness was constructed using BP neural network. The sweetness of all apples was classified into three different classes and labeled with Arabic numbers from one to three. In the experiment, 80 groups of apples were randomly selected as training samples and 20 groups of apples as test samples. Through the test, the sweetness classification accuracy of the test samples based on BP neural network reached 75%. To further improve the classification accuracy of sweetness, a Particle Swarm Optimization (PSO) algorithm was used to optimize the parameters of the BP neural network. With the optimal values of BP-PSO model parameters, the sweetness classification accuracy reached 90% for 20 sets of test samples. Finally, traditional classification models of extreme learning machine (ELM), competitive neural network (CNN) and self-organizing mapping neural network (SOMNN) were established to compare the classification accuracy of different algorithms, and the accuracy of 50%, 35% and 65% was achieved using ELM, CNN and SOMNN models, respectively. The results show that the classification using BP-PSO model has higher classification accuracy. Therefore, the BP-PSO model can be applied to the quality identification and classification of apples based on VIS technique.
In this study, the visible light spectroscopy was used to achieve the sweetness quantitative measurement of apple. In the experiments, the absorption spectra of apple samples in total of 100 groups were obtained in the waveband from 400-800nm with interval of 5nm by using the visible light spectroscopy. At the same time, the real sweetness values of all apples were measured by using a commercial fruit sugar meter. To achieve the sweetness quantitative spectral measurement, the back propagation (BP) neural network was used to supervised train the absorption spectral for 80 groups of training samples, and 20 groups of apples were utilized as the test samples. The effects of neuron numbers in the hidden layer, learning rate factor and the training times on the root-mean-square error (RMSE) of sweetness were investigated. Under the optimal parameters of BP neural network, the RMSE of sweetness for the test apple samples can reach 0.12218%, which is superior to that of the commercial fruit sugar meter (0.2%). Compared with the correlation coefficients for the training samples and test samples based on the partial least square (PLS) algorithm, it can be demonstrated that the visible light spectroscopy combined with BP neural network has the potential superiority and application value in the sweetness quantitative spectral measurement of fruit.
In this work, a set of photoacoustic detection system of blood was established to identify the true blood and fake blood. The time-resolved photoacoustic signals and peak-to-peak spectra of blood samples were obtained in the wavelength from 700nm to 1064nm. In experiments, five kinds of blood in total of 150 groups were used, where three kinds of blood were the animal true blood, two others were the fake blood. The experimental results demonstrated that the true and fake blood can be easily and accurately identified from the time-resolved photoacoustic signals or peak-to-peak spectra due to the overlapping of signals or spectra. To accurately identify the true and fake blood, back propagation (BP) neural network was used to supervised train the peak-to-peak values of training blood sample. The correct rate of identifying true and fake blood based on BP is 76.7%. To improve the correct rate, the particle swarm optimization (PSO) was employed to optimize the parameters of BP including weights and thresholds. Moreover, the effects of neurons number, learning rate factor, inertia weight, two acceleration factors, iteration times and training times on the correct rate were all investigated and compared with BP. Under the optimal parameters, the correct rate of BP-PSO algorithm was improved to 96.7%. Therefore, the photoacoustic spectroscopy combined with BP-PSO algorithm has the potential value in the identification of blood.
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