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Measurement Devices and Methods|5 Article(s)
Research on modeling method of continuous spectrum water quality online detection based on random forest
Wen LI, Sijia HAO, Hao ZHOU, and Ying and LIU
It's common to use the method of continuous spectroscopy in water quality testing. But there're some problems with it. For example, the scanning results have a large number of nonlinear signals, and the covariance between variables is serious, which can lead to a decrease in the model prediction accuracy. In this paper, the standard solutions of nitrate nitrogen (NO3-N) and nitrite nitrogen (NO2-N) were used as the subject to be tested, and the data of the scanned waves and absorbance were obtained by use of spectral detector. The data were processed by noise reduction first and then the random forest (RF) algorithm was adopted to establish the regression relationship between concentration and absorbance. For comparison, partial least squares (PLS) and support vector machine (SVM) algorithm models were also established. For the same given data, the three reverse models can make the projection of the concentration respectively. The experimental results show that the RF algorithm predicts NO2-N concentrations significantly better than the SVM algorithm and PLS algorithm. This proves that the RF algorithm has good prediction ability in spectral water quality detection because of its high model accuracy and better adaptability, which could be a reference for similar research on continuous spectral water quality online detection. It's common to use the method of continuous spectroscopy in water quality testing. But there're some problems with it. For example, the scanning results have a large number of nonlinear signals, and the covariance between variables is serious, which can lead to a decrease in the model prediction accuracy. In this paper, the standard solutions of nitrate nitrogen (NO3-N) and nitrite nitrogen (NO2-N) were used as the subject to be tested, and the data of the scanned waves and absorbance were obtained by use of spectral detector. The data were processed by noise reduction first and then the random forest (RF) algorithm was adopted to establish the regression relationship between concentration and absorbance. For comparison, partial least squares (PLS) and support vector machine (SVM) algorithm models were also established. For the same given data, the three reverse models can make the projection of the concentration respectively. The experimental results show that the RF algorithm predicts NO2-N concentrations significantly better than the SVM algorithm and PLS algorithm. This proves that the RF algorithm has good prediction ability in spectral water quality detection because of its high model accuracy and better adaptability, which could be a reference for similar research on continuous spectral water quality online detection.
Optoelectronics Letters
- Publication Date: Jan. 01, 2023
- Vol. 19, Issue 2, 95 (2023)
Combining urine surface-enhanced Raman spectroscopy with PCA-SVM algorithm for improving the identification of colorectal cancer at different stages
Jinyong LIN, Shangyuan FENG, and Xianzeng ZHANG
Cancer staging detection is important for clinician to assess the patients’ status and make optimal therapy decision. In this study, the machine learning algorithm based on principal component analysis (PCA) and support vector machine (SVM) was combined with urine surface-enhanced Raman scattering (SERS) spectroscopy for improving the identification of colorectal cancer (CRC) at early and advanced stages. Two discriminant methods, linear discriminant analysis (LDA) and SVM were compared, and the results indicated that the diagnostic accuracy of SVM (93.65%) was superior to that of LDA (80.95%). This exploratory study demonstrated the great promise of urine SERS spectra along with PCA-SVM for facilitating more accurate detection of CRC at different stages. Cancer staging detection is important for clinician to assess the patients’ status and make optimal therapy decision. In this study, the machine learning algorithm based on principal component analysis (PCA) and support vector machine (SVM) was combined with urine surface-enhanced Raman scattering (SERS) spectroscopy for improving the identification of colorectal cancer (CRC) at early and advanced stages. Two discriminant methods, linear discriminant analysis (LDA) and SVM were compared, and the results indicated that the diagnostic accuracy of SVM (93.65%) was superior to that of LDA (80.95%). This exploratory study demonstrated the great promise of urine SERS spectra along with PCA-SVM for facilitating more accurate detection of CRC at different stages.
Optoelectronics Letters
- Publication Date: Jan. 01, 2023
- Vol. 19, Issue 2, 101 (2023)
Reflectivity measurement technology of special high reflective mirrors and uncertainty analysis of measurement results
Xiaolin LIANG, Songqing ZHOU, Xiaowu LI, Ling ZHOU, and Huihuang and CHEN
In order to meet the reflectivity measurement requirements of any incident angle at different points of the large size special high mirror, a rotating cavity ring-down spectroscopy high reflectivity measurement system was built, in which the rotation function of the resonant cavity was set, and the lifting and parallel travelling mechanism of the measured mirror was added. Furthermore, the uncertainty of the measurement results was analyzed and calculated. The results showed that the reflectivity of a high reflective mirror measured by the system was 99.979 5%, the measurement accuracy reached the order of 10-6, and the combined standard uncertainty of reflectivity measurement was 0.002 8%. Collectively, these results provide a detection guarantee for the maintenance of the large size special high mirror, and provide ideas and methods for the uncertainty analysis of measurement results of similar equipment parameters. In order to meet the reflectivity measurement requirements of any incident angle at different points of the large size special high mirror, a rotating cavity ring-down spectroscopy high reflectivity measurement system was built, in which the rotation function of the resonant cavity was set, and the lifting and parallel travelling mechanism of the measured mirror was added. Furthermore, the uncertainty of the measurement results was analyzed and calculated. The results showed that the reflectivity of a high reflective mirror measured by the system was 99.979 5%, the measurement accuracy reached the order of 10-6, and the combined standard uncertainty of reflectivity measurement was 0.002 8%. Collectively, these results provide a detection guarantee for the maintenance of the large size special high mirror, and provide ideas and methods for the uncertainty analysis of measurement results of similar equipment parameters.
Optoelectronics Letters
- Publication Date: Jan. 01, 2023
- Vol. 19, Issue 1, 49 (2023)
Rapid detection of cAMP content in red jujube using near-infrared spectroscopy
Wen-li YAN, Shui-ying REN, Xia-xia YUE, Jun TANG, Chen CHEN, Xiao-yi Lü, and Jia-qing MO
In this paper, a new method for the rapid, economical and convenient detection of cyclic adenosine monophosphate (cAMP) in jujube is proposed and verified. Based on near-infrared (NIR) fiber spectroscopy combined with stoichio-metric analysis, the cAMP content in red jujube can be quickly detected. 68 red jujube samples were used for the NIR spectroscopy data acquisition and the corresponding chemical values were determined. The sample set was adjusted based on the joint XY distance (SPXY) to select the correction sample set. After different preprocessing on the spectra, the partial least squares (PLS) method was used to establish the model, and the smoothed and normalized PLS model result was obtained better. The model's correction correlation coefficient (Rc), correction set mean square error (RMSEC), prediction correlation coefficient (Rp), and prediction and mean square error (RMSEP) are 0.951 5, 25.793 7, 0.910 8 and 28.228 0, respectively. The results show that NIR combined with specific chemometric methods can achieve rapid de-tection of cAMP in red jujube. In this paper, a new method for the rapid, economical and convenient detection of cyclic adenosine monophosphate (cAMP) in jujube is proposed and verified. Based on near-infrared (NIR) fiber spectroscopy combined with stoichio-metric analysis, the cAMP content in red jujube can be quickly detected. 68 red jujube samples were used for the NIR spectroscopy data acquisition and the corresponding chemical values were determined. The sample set was adjusted based on the joint XY distance (SPXY) to select the correction sample set. After different preprocessing on the spectra, the partial least squares (PLS) method was used to establish the model, and the smoothed and normalized PLS model result was obtained better. The model's correction correlation coefficient (Rc), correction set mean square error (RMSEC), prediction correlation coefficient (Rp), and prediction and mean square error (RMSEP) are 0.951 5, 25.793 7, 0.910 8 and 28.228 0, respectively. The results show that NIR combined with specific chemometric methods can achieve rapid de-tection of cAMP in red jujube.
Optoelectronics Letters
- Publication Date: Jan. 01, 2018
- Vol. 14, Issue 5, 380 (2018)
Three-dimensional measurement of bubble volume based on dual perspective imaging
Ting XUE, Shao-jie ZHANG, and Bin WU
This paper presents a new three-dimensional (3D) volume measurement approach of bubble in gas-liquid two-phase flow. According to the dual perspective imaging principle, bubble feature images can be captured from two different view angles. The least square ellipse fitting algorithm is used to figure out the feature parameters from the captured images. Then the 3D volume of bubble can be quantitatively measured. Compaerd with the traditional volume estimation methods based on single perspective imaging, it can effectively reduce the loss of bubble feature information. In the experiment, the 3D volume reconstruction of bubbles from dual perspective images is conducted, and the variation of bubble volume in the bubble rising process is studied. The results show that the measurement accuracy based on the proposed 3D method is higher than those based on traditional methods. The volume of rising bubble is periodically changed, which indicates that bubble achieves periodic rotation and deformation in the rising process. This paper presents a new three-dimensional (3D) volume measurement approach of bubble in gas-liquid two-phase flow. According to the dual perspective imaging principle, bubble feature images can be captured from two different view angles. The least square ellipse fitting algorithm is used to figure out the feature parameters from the captured images. Then the 3D volume of bubble can be quantitatively measured. Compaerd with the traditional volume estimation methods based on single perspective imaging, it can effectively reduce the loss of bubble feature information. In the experiment, the 3D volume reconstruction of bubbles from dual perspective images is conducted, and the variation of bubble volume in the bubble rising process is studied. The results show that the measurement accuracy based on the proposed 3D method is higher than those based on traditional methods. The volume of rising bubble is periodically changed, which indicates that bubble achieves periodic rotation and deformation in the rising process.
Optoelectronics Letters
- Publication Date: Jan. 01, 2017
- Vol. 13, Issue 1, 70 (2017)
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