ABSTRACT
In order to realize the rapid and non-destructive detection of SO_2 content in sulphur-fumigated Achyranthis Bidentatae Radix, this paper first prepared the sulphur-fumigated Achyranthis Bidentatae Radix samples with the usage amount of sulphur being 0, 2.5%, and 5% of the mass of Achyranthis Bidentatae Radix pieces. The SO_2 content in different batches of sulphur-fumigated Achyranthis Bidentatae Radix was determined using the method in Chinese Pharmacopoeia, followed by the acquisition of their hyperspectral data within both visible-near infrared(435-1 042 nm) and short-wave infrared(898-1 751 nm) regions by hyperspectral imaging. Meanwhile, the first derivative, AUTO, multiplicative scatter correction, Savitzky-Golay(SG) smoothing, and standard normal variable transformation algorithms were used to pre-process the original hyperspectral data, which were then subjected to characteristic band extraction based on competitive adaptive reweighted sampling(CARS) and the partial least square regression analysis for building a quantitative model of SO_2 content in sulphur-fumigated Achyranthis Bidentatae Radix. It was found that the accuracy of the quantitative model built depending on the visible-near infrared spectra was high, with the determination coefficient of prediction set(R■) reaching 0.900 1. The established quantitative model has enabled the rapid and non-destructive detection of SO_2 content in sulphur-fumigated Achyranthis Bidentatae Radix, which can serve as an effective supplement to the method described in Chinese Pharmacopeia.
Subject(s)
Hyperspectral Imaging , Least-Squares Analysis , Plant Roots , SulfurABSTRACT
Near-infrared spectroscopy(NIRS) combined with band screening method and modeling algorithm can be used to achieve the rapid and non-destructive detection of the traditional Chinese medicine(TCM) production process. This paper focused on the ginkgo leaf macroporous resin purification process, which is the key technology of Yinshen Tongluo Capsules, in order to achieve the rapid determination of quercetin, kaempferol and isorhamnetin in effluent. The abnormal spectrum was eliminated by Mahalanobis distance algorithm, and the data set was divided by the sample set partitioning method based on joint X-Y distances(SPXY). The key information bands were selected by synergy interval partial least squares(siPLS); based on that, competitive adaptive reweighted sampling(CARS), successive projections algorithm(SPA) and Monte Carlo uninformative variable(MC-UVE) were used to select wavelengths to obtain less but more critical variable data. With selected key variables as input, the quantitative analysis model was established by genetic algorithm joint extreme learning machine(GA-ELM) algorithm. The performance of the model was compared with that of partial least squares regression(PLSR). The results showed that the combination with siPLS-CARS-GA-ELM could achieve the optimal model performance with the minimum number of variables. The calibration set correlation coefficient R_c and the validation set correlation coefficient R_p of quercetin, kaempferol and isorhamnetin were all above 0.98. The root mean square error of calibration(RMSEC), the root mean square error of prediction(RMSEP) and the relative standard errors of prediction(RSEP) were 0.030 0, 0.029 2 and 8.88%, 0.041 4, 0.034 8 and 8.46%, 0.029 3, 0.027 1 and 10.10%, respectively. Compared with the PLSR me-thod, the performance of the GA-ELM model was greatly improved, which proved that NIRS combined with GA-ELM method has a great potential for rapid determination of effective components of TCM.
Subject(s)
Algorithms , Ginkgo biloba , Least-Squares Analysis , Plant Leaves , Spectroscopy, Near-InfraredABSTRACT
Near-infrared spectroscopy (NIRS) combined with chemometrics can achieve rapid detection in process analysis. After variable selection, the redundant information is effectively removed and the characteristic variables related to the response values are selected. Compared with global model, the complexity is significantly reduced and the prediction accuracy is also improved. In this study, near-infrared spectroscopy analysis combined with different variable selection methods was applied to achieve the rapid detection of baicalin in the extraction process of Scutellaria baicalensis. Data sets were divided based on sample set portioning based on joint x-y distance (SPXY) method. Competitive adaptive weighted resampling method (CARS), random frog (RF) and successive projections algorithm (SPA) were applied to variable selection. Partial least squares (PLS) models were constructed based on above three methods, and the prediction results were compared. After CARS, RF and SPA method, 92, 10 and 17 variables were screened out respectively. According to the performance of the models, CARS method is found to be more effective and suitable than RF and SPA. Furthermore, the characteristic variables selected by CARS method have a better correspondence with the chemical structure of baicalin. The root mean square error (RMSEC) of the calibration set and the root mean square error (RMSEP) of the prediction set are 0.528 2 and 0.720 2 respectively. Compared with the global PLS model, the correlation coefficient of the calibration set (Rc) is increased to 0.979 9 from 0.917 0, and the relative standard errors of prediction (RSEP) is reduced to 5.59% from 10.58%.
ABSTRACT
OBJECTIVE: To analyze and study the characteristic variables of wavelength in near-infrared spectroscopy of artificial cow-bezoar. METHODS: A method of near-infrared spectroscopy coupled with competitive adaptive reweighted sampling(CARS) was performed in characteristic variables of wavelength screening for the qualitative and quantitative researches, respectively. RESULTS: Some characteristic variables of wavelength, 0.48%-4.44% of all variables of wavelength, were screened out by CARS for different models. Not only the number of variables for building models decreased significantly, but also the index parameters for evaluating model became better. CONCLUSION: This method is suitable for quality evaluation and quality control for artificial cow-bezoar.
ABSTRACT
Objective: To determine the content of chlorogenic acid in Lonicerae Japonicae Flos by the combined near-infrared and variable selection methods. Methods: Synergy interval partial least squares (SIPLS), competitive adaptive reweighted sampling method (CARS), variable importance in projection (VIP), and successive projections algorithm (SPA) were used to build a chlorogenic acid quantitative model in Lonicerae Japonicae Flos and compare. High performance liquid chromatography (HPLC) was used as a reference to select the optimum variable screening method. Results: Study results showed that SIPLS was the most desirable method for chlorogenic acid in regression performance with Rpre2 at 0.990 3 and RMSEP at 2.316%. Conclusion: The quantitative model of chlorogenic acid established by NIR combined with SIPLS has good performance and meets the requirement of real-time analysis of traditional Chinese medicine production process.
ABSTRACT
To improve the yield of industrial fermentation, a method based on near infrared spectroscopy was presented to predict the growth of yeast.The spectral data of fermentation sample were measured by Fourier-transform near-infrared (FT-NIR) spectrometer in the process of yeast culture.Each spectrum was acquired over the range of 10000-4000 cm1.Meanwhile, the optical density (OD) of fermentation sample was determined with photoelectric turbidity method.After that, a method based on competitive adaptive reweighted sampling (CARS) was used to select characteristic wavelength variables of NIR data, and then extreme learning machine (ELM) algorithm was employed to develop the categorization model about the four growth processes of yeast.Experimental result showed that, only 30 characteristic wavelength variables of NIR data were selected by CRAS algorithms, and the prediction accuracies of training set and test set of the CARS-ELM model were 98.68% and 97.37%, respectively.The research showed that the near infrared spectrum analysis technology was feasible to predict the growth process of yeast.
ABSTRACT
Near infrared spectroscopy (NIR) was used to detect trans fatty acids (TFA) in edible vegetable oils quantitatively. And prediction model of TFA was optimized through band selection, pretreatment method, variable selection and modeling method. NIR spectra of 98 edible vegetable oil samples were collected in spectral range of 4000-10000 cm-1 using an Antaris Ⅱ Fourier transform near infrared spectrometer, and the true content of TFA was measured by gas chromatography. First, optimization of waveband and pretreatment method was conducted on original spectra. On this basis, competitive adaptive reweighted sampling (CARS) was used to select important variables that related to TFA. Finally, the prediction models of TFA content in edible vegetable oils were established using principal component regression ( PCR), partial least square (PLS) and least square support vector machine (LS-SVM). The results indicated that NIR spectroscopy was feasible for detecting TFA content in edible vegetable oils, R2 of the best prediction model after optimized in calibration and prediction sets were 0. 992 and 0. 989, and root mean square error of calibration (RMSEC) and root mean square error of prediction ( RMSEP) were 0. 071% and 0. 075% , respectively. Only 26 variables were used in the best prediction model, accounting for 0. 854% of the whole waveband variables. In addition, compared with the full waveband PLS prediction model, the R2 in prediction set increased from 0. 904 to 0. 989, and RMSEP decreased from 0. 230% to 0. 075% . It shows that model optimization is very necessary, CARS method can select important variables related to TFA effectively and immensely reduce the number of modeling variables, so it can simplify the prediction model, and greatly improve the accuracy and stability of prediction model.
ABSTRACT
Rapid detection and classification of bacteria colonies ( Escherichia coli, Listeria monocytogens and Staphylococcus aureus) were investigated by using hyperspectral imaging. The hyperspectral reflectance images (390-1040 nm ) of bacterial colonies on agar plates were collected. Bacterial spectra were extracted automatically based on the masks produced by segmenting a band difference image using the OTSU method. Full wavelength and simplified PLS-DA models were established for classification of bacterial colonies. For the full wavelength model, the overall correct classification rate ( OCCR) and confident OCCR for the prediction set were 100% and 95. 9%, respectively. Besides, competitive adaptive reweighted sampling ( CARS), genetic algorithm ( GA ) and least angle regression-least absolute shrinkage and selection operator ( LARS-Lasso) were used to select feature wavelengths for the development of simplified models. Among them, the CARS-model outperformed the other two in terms of precision, stability and classification accuracy with OCCR and confident OCCR of 100% and 98. 0% for the prediction set, respectively. It was demonstrated that hyperspectral imaging was an effective technology for nondestructive detection of bacterial colonies with high accuracy and high speed. The allocated feature wavelengths by CARS could lay theoretical basis for developing low cost multispectral imaging systems for bacterial colony detection.