In the photoacoustic microscopy coupled with the optical fiber, the photoacoustic intensity of the irradiated tissue is one of most important factors of Furthermore, the coupling coefficient of the fiber also impacts the final irradiated laser energy. Furthermore, the coupling coefficient of the fiber also impacts the final irradiated laser energy. At the characteristic wavelength of 532nm, the effects of the optical positions of the With the operations of parameters scanning, the optimal values of the optical positions of the focusing lens and optical fiber on the coupling efficiency of the fiber were investigated. values of the optical positions of the focusing lens and optical fiber were obtained under the maximum coupling efficiency of the fiber. After that, the effect of the fiber's mode field diameter on the coupling efficiency of fiber under the optimal positions of the focusing lens and fiber was also investigated. The coupling efficiencies of fiber corresponding to seven different mode field diameters of fiber from 1 to 9μm were computed, the The study results show that with the increase of the mode field Under the optimal positions of the focusing lens and the fiber, as well as the mode field diameter, the optical efficiency of fiber first increase then decrease. Under the optimal positions of the focusing lens and the fiber, as well as the mode field diameter, the optical efficiency of fiber can be improved from 23.174% to 91.638%. Therefore, the reasonable positions of the optical path and the mode field diameter of the fiber are all important to ensure the satisfactory optical Therefore, the reasonable positions of the optical path and the mode field diameter of the fiber are all important to ensure the satisfactory optical coupling efficiency in the photoacoustic microscopy system coupled with the optical fiber.
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 work, the photoacoustic detection of blood glucose with the interference of multiple factors was studied. A set of photoacoustic detection system of blood glucose was established, in which the interference of multiple factors including the laser energy, concentration, temperature, flow velocity and detection distance were combined into. Under different conditions of multiple factors, the time-resolved photoacoustic signals and peak-to-peak values of blood samples were all obtained. To accurately classify the concentration of blood glucose samples, back propagation (BP) neural network was employed to train the photoacoustic peak-to-peak values and the multiple factors. In BP neural network, five different Arabic numerals from 1 to 5 were labeled to denote five kinds of blood glucose levels ranged from2mmol/Lto14mmol/L. The photoacoustic peak-to-peak values, laser energy, temperature, flow velocity and detection distance were used as the input data, the labels denoted different concentrations were used as the output data. Meanwhile, the effects of neurons number in hidden layer and learning factor on the classification accuracy of blood glucose level were investigated. Under the optimal parameters of BP neural network, the accuracy of classifying concentration of blood glucose level reached 85.6% for the test blood glucose samples. Compared with the classification accuracy (71.2%) of blood glucose level based on support vector machine (SVM) algorithm, it is demonstrated that the photoacoustic spectroscopy combined with BP neural network has a good performance in qualitative classification of blood glucose under the interference of multiple factors.
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 the system of photoacoustic microscopy coupled with fiber, the coupling efficiency of fiber is one of most important factors to ensure the photoacoustic intensity under the adequate absorbed energy of pulsed laser for the irradiated tissue. In this work, the effects of aspheric lens parameters on the coupling efficiency of fiber with Gaussian pulsed laser were studied under their reasonable ranges of the parameters. The parameters of the aspheric lens includes the curvature radius, cone constant, thickness, tilt angle, radial deviation of vertical shaft. At the same distances between the aspheric lens, fiber, and the parameters of fiber and pulsed laser, the coupling efficiencies of optical fiber under different values of all parameters of aspheric lens were obtained and compared. The influence laws of all parameters of aspheric lens on the coupling efficiency of optical fiber were all obtained. Studies results show that under the optimal distances of aspheric lens and fiber, with the increase of curvature radius, cone constant, thickness, tilt angle, radial deviation of vertical shaft, the coupling efficiency of optical fiber all first increases then decreases with the Gaussian-liked function. Moreover, the parameters scan optimization method was used to obtain the optimal values of parameters of aspheric lens. Studies show that the coupling efficiency of optical fiber can reach 85.45% when curvature radius is 1.8903mm, cone constant is -2.0627 , thickness is 250μm, tilt angle is 0°, radial deviation is 0mm.
In this work, we used photoacoustic spectroscopy to distinguish the different types of blood including four kinds of true blood and two kinds of fake blood. The peak-to-peak spectra of blood were obtained in the wavelength from 700nm to 1064nm based on the established photoacoustic detection system of blood. To accurately discriminate the different types of blood, back propagation (BP) neural network was used to train the photoacoustic peak-to-peak spectra of training blood with 120 groups, the correct rate of distinguishing blood is 76.7% for 30 groups of test samples. Particle swarm optimization (PSO) algorithm was used to optimize the parameters of BP network. The effects of neurons number in the hidden layer, learning rate factor, inertia weight, two acceleration factors, iteration times and training times on the corret rate and mean square error were all investigated. Under the optimal parameters, the correct rate of BP-PSO algorithm was increased to 93.3%. To further improve the correct rate, the dynamic inertia weight strategy was used. Moreover, a kind of improved dynamic inertia weight strategy function was proposed. The correct rate of the improved dynamic inertia weight strategy function was compared with that of the static inertia weight and two other dynamic inertia weight strategy functions. Under the optimal value of the improved dynamic inertia weight, the correct rate reached 96.7%. Therefore, the BP-PSO algorithm combined with the improved dynamic inertia weight strategy function has a potential value in the photoacoustic discrimination of blood.
In this work, to study the effects of multiple factors on the blood glucose photoacoustic detection, five different factors including the laser energy, concentration, temperature, flow velocity and detection distance were considered, and a set of blood glucose photoacoustic detection system combined multiple influence factors was established. The time-resolved photoacoustic signals and peak-to-peak spectra of 625 groups of blood samples were obtained. To predict the blood glucose concentration with high accuracy under the influence of multiple factors, back propagation (BP) neural network was used to train five different factors and photoacoustic peak-to-peak values of 500 groups of blood samples, and 125 groups of blood samples were used as the test samples. Meanwhile, the effects of neurons number in the hidden layer, learning rate and training times on the root-mean-square error(RMSE) of predicting blood glucose concentration were investigated. Under the optimal parameters, the RMSE of blood glucose concentration for 125 groups of test blood samples is about 0.807679mmol/L. Compared with the results of partial least square (PLS) algorithm with RMSE of 1.78mmol/L, it is demonstrated that the BP algorithm has good performance in the prediction blood glucose concentration under multiple influence factors based on photoacoustic detection technology.
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