Furong Huang

Detection of olive oil adulteration with waste cooking oil via Raman spectroscopy combined with iPLS and SiPLS

Olive oil adulteration with waste cooking oil was detected and quantified by combining optical Raman scattering spectroscopy and chemometrics. Spectra of 96 olive oil samples with waste cooking oil (2.5%, 5%, 10%, 20%, 30% and 50%) were collected by the portable Raman spectroscopy system. iPLS and SiPLS quantitative analysis models were established. The results revealed that spectral data after SNV processing are the best for synergy interval partial least square (SiPLS) modeling and forecast. The root mean squared error of calibration (RMSEC) is 0.0503 and the root mean squared error of validation (RMSEV) is 0.0485. The lower limit of application (LLA) of the proposed method is c[WCO]=0.5%. According to linear regression calculation, the theoretical limit of detection (LOD) of the proposed method is about c[WCO]=0.475%. The established model could make effective quantitative analysis on adulteration of waste cooking oil. It provides a quick accurate method for adulteration detection of waste cooking oil in olive oil.

Identification of Repeatedly Frozen Meat Based on Near-Infrared Spectroscopy Combined with Self-Organizing Competitive Neural Networks

A rapid, effective method of identifying repeatedly frozen meat by near-infrared spectroscopy (NIRS) combined with a self-organizing competitive neural network (SCNN) model was established. A total of 180 samples were adopted, including hot, cold, frozen, and repeatedly frozen meats. We compared the treatment effects of four pretreatment methods on the spectrogram samples, namely, multiplicative scatter correction (MSC), standard normal variables (SNV), first-order differential and second-order differential. The second differential pretreatment exerted the optimum effect. A total of 120 pork samples were randomly selected and used to establish a calibration model, and the remaining 60 samples were used for prediction. SCNN analysis revealed that classification performance was the highest when the learning number was 250. The recognition ratio of the 60 prediction collection was 93.3%, in which the recognition ratio of the repeatedly frozen meat was 100%. Thus, combined NIRS and SCNN can rapidly and accurately detect repeatedly frozen meat without destruction.

Quantitative analysis of glycated albumin in serum based on ATR-FTIR spectrum combined with SiPLS and SVM

A rapid quantitative analysis model for determining the glycated albumin (GA) content based on Attenuated total reflectance (ATR)-Fourier transform infrared spectroscopy (FTIR) combining with linear SiPLS and nonlinear SVM has been developed. Firstly, the real GA content in human serum was determined by GA enzymatic method, meanwhile, the ATR-FTIR spectra of serum samples from the population of health examination were obtained. The spectral data of the whole spectra mid-infrared region (4000-600 cm-1) and GAs characteristic region (1800-800 cm-1) were used as the research object of quantitative analysis. Secondly, several preprocessing steps including first derivative, second derivative, variable standardization and spectral normalization, were performed. Lastly, quantitative analysis regression models were established by using SiPLS and SVM respectively. The SiPLS modeling results are as follows: root mean square error of cross validation (RMSECVT) = 0.523 g/L, calibration coefficient (RC) = 0.937, Root Mean Square Error of Prediction (RMSEPT) = 0.787 g/L, and prediction coefficient (RP) = 0.938. The SVM modeling results are as follows: RMSECVT = 0.0048 g/L, RC = 0.998, RMSEPT = 0.442 g/L, and Rp = 0.916. The results indicated that the model performance was improved significantly after preprocessing and optimization of characteristic regions. While modeling performance of nonlinear SVM was considerably better than that of linear SiPLS. Hence, the quantitative analysis model for GA in human serum based on ATR-FTIR combined with SiPLS and SVM is effective. And it does not need sample preprocessing while being characterized by simple operations and high time efficiency, providing a rapid and accurate method for GA content determination.

Theoretical analysis of resonant mode splitting in a single microfiber knot-ring resonator

In this paper, we established a theoretical model and made an analysis of single knot-ring resonator by polarization transmission matrix. The theoretical analysis shows that two orthogonal polarization modes of knot-ring, which are originally resonant at the same wavelength, will be split into two resonant modes at two different wavelengths. The mode splitting owes to the twisted coupler of the knot-ring, which makes these two orthogonal polarization modes couple each other. This results can provide a novel method to implement coupled-resonator-induced transparency in a single knot-ring.

Cistanches identification based on fluorescent spectral imaging technology combined with principal component analysis and artificial neural network

In this study, fluorescent spectral imaging technology combined with principal component analysis (PCA) and artificial neural networks (ANNs) was used to identify Cistanche deserticola, Cistanche tubulosa and Cistanche sinensis, which are traditional Chinese medicinal herbs. The fluorescence spectroscopy imaging system acquired the spectral images of 40 cistanche samples, and through image denoising, binarization processing to make sure the effective pixels. Furthermore, drew the spectral curves whose data in the wavelength range of 450-680 nm for the study. Then preprocessed the data by first-order derivative, analyzed the data through principal component analysis and artificial neural network. The results shows: Principal component analysis can generally distinguish cistanches, through further identification by neural networks makes the results more accurate, the correct rate of the testing and training sets is as high as 100%. Based on the fluorescence spectral imaging technique and combined with principal component analysis and artificial neural network to identify cistanches is feasible.

The correlation coefficient method for alignment of polarization maintaining fiber

The paper introduces the aligned method of polarization maintaining fiber (PMF) combined the characteristic value methods with the correlation coefficient method. This method is based on side-view of fiber. The PMF is irradiated by parallel ray from lateral side and the observation plane where set a camera is on the other side. The side-view of PMF from the camera is intercepted to get the light-intensity distribution of fiber cross section. Because the curve of the light-intensity distribution is changed when the PMF is rotated around the fiber core, and the results of simulation and experiment shows that the correlation coefficient of the curve can be used to align PMF and the speed of alignment is improved with the correlation coefficient method.

Measurement of triglycerides concentration in human serum using near-infrared transmission spectroscopy and interval PLS

In order to measurement of Triglycerides in human serum with reagent-less using near-infrared (NIR) spectroscopy. Interval partial least square (iPLS) was proposed as an effective variable selection approach for multivariate calibration. For this purpose, an independent sample set was employed to evaluate the prediction ability of the resulting model. The spectrum was split into different interval. Then, the informative region of Triglycerides (1654-1746nm), in which the PLS model has a low RMSEP with 0.157mmol/L and a high R with 0.967, is selected with 18 intervals. The results show that the informative region of Triglycerides can be obtained by iPLS and applied to design the simpler reagent-less NIR instruments for inexpensive Triglycerides measurement in future.

Strongly evanescent field coupling between nanofibers for sensing transverse optical force

Using full-vector finite element method, transverse optical forces induced by strongly evanescent coupling between two identical nanofibers is theoretically investigated. It shows that anti-symmetry and symmetry modes can induce attractive and repulsive force, respectively. When light power of the symmetry (anti-symmetry) mode at 980nm wavelength is 50mW, the gap between the nanofibers with 400nm diameter nears 392nm, the repulsive force reaches maximum (11.5 pN/μm), which results in 30nm displacement at the center of 100μm-long free-standing nanofiber. Based on pump-probe scheme, a novel potential method for optical force measurement is proposed. Using Euler-Bernoulli beam equation and coupled mode theory for waveguides, the deformation impact on the splitting ratio of coupling nanofibers is also investigated. It is found that, through the deformation, the repulsive force from 0.9 to 17 pN/μm can change the splitting ratio of coupling nanofibers from 0 to 600 when coupling length of nanofibers is fixed at 100μm, the gap is 400nm and probe light is at 808nm. It shows that measuring the splitting ratio of the strongly coupling nanofibers can potentially provide a high sensitive method for measuring the optical force.

Feasibility study for rapid determination of alanine aminotransferase concentration in whole blood by using visible and near infrared spectroscopy

Visible and Near infrared spectroscopy was applied for the fast determination of alanine aminotransferase with whole blood. First, spectra of different thickness (0.5mm, 1mm, 2mm, 4mm ) were investigated to explore Optimal Optical Path for determination. The results show that the whole blood sample with 0.5mm thickness is more suitable for spectral analysis. And then Near infrared spectroscopy of 176 samples were collected. Multiplicative scatter correction and second-order differential method have been used to spectral pretreatment. Stepwise multiple linear regression method and partial least squares regression method have been employed to establish quantitative detection model to predict content of alanine aminotransferase in whole blood. The alanine aminotransferase measured presents best result in calibration and prediction by Near-Infrared Spectroscopy with partial least squares regression calibration model, and the calibration correlation coefficient, the standard error of calibration and the standard error of prediction are 0.98, 2.42 and 7.22 respectively.

Optimal waveband and mathematical model for analysis of human whole blood glucose by near infrared transmission spectroscopy

The optimal waveband for quantitative analysis of the human whole blood glucose by near infrared transmission spectroscopy is discussed in this paper. First, whole blood samples of different thickness (0.5mm, 1mm, 2mm, 4mm ) of the near-infrared transmittance spectra were analyzed respectively. It shows that the sample thickness of 0.5mm is more suitable for spectral analysis. And then near infrared spectroscopy of 111 samples of 0.5mm thickness were collected. Finally, different pretreatment methods of scattering correction methods, derivatives and different modeling spectral regions were compared to find out their impact on mathematical prediction model. The result shows that the best prediction accuracy was obtained in the waveband of 1500~1900nm by using Standard Normal Variate(SNV),the second derivative spectra and Partial Least Square (PLS) regression. And the correlation coefficient(RP), the Root Mean Square Error of Prediction(RMSEP) and the Relative Root Mean square Error of Prediction(RRMSEP) for the corresponding model are 0.836, 0.271, 5.519%, respectively.