Fitting Precision Research Articles

Influence of pulsed vacuum drying on drying kinetics and nutritional value of corn kernels

AbstractEffects of different drying temperature (45, 50, 55, 60, and 65°C), vacuum duration time (VDT; 7, 11, 15, and 19 min) and pulsating cyclic time (PCT; 7:2, 11:3, 15:4 and 19:5 min min−1) on the drying characteristics and quality of corn kernels were investigated. Reduce the PCT could enhance the whole drying process. Weibull distribution function has high fitting precision for describing the pulsed vacuum drying (PVD) kinetics of corn kernels (R2>.99). The Ea value under PVD was calculated as 36.9 kJmol−1. Meanwhile, the main nutritional components of corn showed declining trend with the increase of temperature and PCT, while it oscillated with the prolonged VDT. The total score decreased with the heightening of drying temperature and fluctuated with the extension of VDT and PCT parameters. Considering the total drying time and quality comprehensive score of corn, the optimal drying parameters was confirmed at 50°C with the pulsing ratio of 7:2 min min−1.Practical ApplicationsCorn is treated as an important crop because of its various nutritional ingredients and has been commonly cultivated all over the world. Due to its water‐rich characteristics, fresh maize kernels would subject to various deterioration reactions during storage and hence reduce the content of nutrients. However, the samples have high compactness and tough epidermal structure, which limited the water diffusion efficiency during processing. Pulsed vacuum drying technique has been widely applied to agricultural products drying in China benefiting from its high heating efficiency. Currently this technology is successfully used in lemon, wolfberry, Chinese ginger, and seedless grape. This study will help in revealing the impact of PVD parameters on drying characteristics and quality of maize kernels, which is of great significance to optimize the processing efficiency through the alternative pressure circulation and consumer acceptance of dried products.

On the precision of full-spectrum fitting of simple stellar populations – I. Well-sampled populations

ABSTRACT We investigate the precision of the ages and metallicities of 21 000 mock simple stellar populations (SSPs) determined through full-spectrum fitting. The mock SSPs cover an age range of 6.8 < log (age/yr) < 10.2, for three wavelength ranges in the optical regime, using both Padova and MIST isochrone models. Random noise is added to the model spectra to achieve S/N ratios between 10 and 100 per wavelength pixel. We find that for S/N ≥ 50, this technique can yield ages of SSPs to an overall precision of ∆log (age/yr)∼01 for ages in the ranges 7.0 ≤ log (age/yr) ≤ 8.3 and 8.9 ≤ log (age/yr) ≤ 9.4. For the age ranges of 8.3 ≤ log (age/yr) ≤ 8.9 and log (age/yr) ≥ 9.5, which have significant flux contributions from asymptotic giant branch and red giant branch stars, respectively, the age uncertainty rises to about ±0.3 dex. The precision of age and metallicity estimation using this method depends significantly on the S/N and the wavelength range used in the fitting. We quantify the systematic differences in age predicted by the MIST and Padova isochrone models, due to their different assumptions about stellar physics in various important (i.e. luminous) phases of stellar evolution, which needs to be taken in consideration when comparing ages of star clusters obtained using these popular models. Knowing the strengths and limitations of this technique is crucial in interpreting the results obtained for real star clusters and for deciding the optimal instrument set-up before performing the observations.

Study on Stand Diameter Size Diversity in Pinus densata Natural Forest

Taking 48 plots of Pinus densata natural forest as research objects, the Shannon-Wiener Index and Simpson's diversity Index were used to quantify the overall and diameter size diversity of Pinus densata stands. Correlation analysis, general regression analysis and CCA ranking analysis were used to analyze the variation of diameter size diversity index of Pinus densata forest with stand factors, topographical factors, and understory vegetation factors. The results showed that: (1) when the dominant tree species had a larger composition, the diameter size diversity was consistent in the overall stand population and dominant tree species. (2) within the sample plot in the study area, the overall diameter size diversity was generally higher than that of Pinus densata. Other tree species had a certain influence on the diameter size diversity of Pinus densata. (3) the influence of environmental factors on the stand diameter size diversity was as follows: stand factor > topographical factor > understory vegetation factor. The average diameter at breast height of stand had the highest influence on the diameter size diversity of Pinus densata.. The maximum correlation coefficient could reach 0.756, and the fitting precision was the best, R2 could reach 0.6989. When CCA sort axis analysis was usd, the performance in the first axis was the best.

Rapid computation of set boundaries of multi-scale grids and its application in coverage analysis of remote sensing images

With the rapid development of remote sensing technology, the amount of remote sensing data is increasing, service objects are increasingly extensive, and requests from users to query the coverage of remote sensing images under specific conditions have also increased. These conditions have increased efficiency and precision requirements for concurrent access and response. Remote sensing images provide multiple coverages of the same area, and the nested and overlapping relationships among data are complex. Therefore, calculating the range of multiple overlaps of images becomes increasingly difficult as the number of images increases. To query the range of a coverage area of multiple images is to calculate its boundary, which is an important part of spatial overlay analysis. Traditional vector methods are inefficient in processing many spatial objects or complex shapes. The overlay of traditional spatial grids is mostly addressed via single-scale methods, which have low computational efficiency and low boundary fitting precision. Combined with the current gridding management method for multi-source remote sensing images, we proposed a new algorithm for rapid computation of the set boundaries of multi-scale grids and applied it to coverage analysis of remote sensing images. The algorithm can effectively trace the boundaries of multi-scale grids formed in the regions covered by remote sensing images, and it can solve all types of complex boundaries, such as convex and concave boundaries, holes and islands. The experiments in this study show that the new algorithm greatly improves computational efficiency and boundary fitting precision compared with the single-scale grid methods. Compared with the vector algorithms of ArcGIS and other commercial software, this study's algorithm can greatly improve calculation efficiency while ensuring a precision above 99%. The new algorithm is suitable for rapid calculations of large areas and widespread coverage of remote sensing images.

Prediction of Individual Tree Diameter and Height to Crown Base Using Nonlinear Simultaneous Regression and Airborne LiDAR Data

The forest growth and yield models, which are used as important decision-support tools in forest management, are commonly based on the individual tree characteristics, such as diameter at breast height (DBH), crown ratio, and height to crown base (HCB). Taking direct measurements for DBH and HCB through the ground-based methods is cumbersome and costly. The indirect method of getting such information is possible from remote sensing databases, which can be used to build DBH and HCB prediction models. The DBH and HCB of the same trees are significantly correlated, and so their inherent correlations need to be appropriately accounted for in the DBH and HCB models. However, all the existing DBH and HCB models, including models based on light detection and ranging (LiDAR) have ignored such correlations and thus failed to account for the compatibility of DBH and HCB estimates, in addition to disregarding measurement errors. To address these problems, we developed a compatible simultaneous equation system of DBH and HCB error-in-variable (EIV) models using LiDAR-derived data and ground-measurements for 510 Picea crassifolia Kom trees in northwest China. Four versatile algorithms, such as nonlinear seemingly unrelated regression (NSUR), two-stage least square (2SLS) regression, three-stage least square (3SLS) regression, and full information maximum likelihood (FIML) were evaluated for their estimating efficiencies and precisions for a simultaneous equation system of DBH and HCB EIV models. In addition, two other model structures, namely, nonlinear least squares with HCB estimation not based on the DBH (NLS and NBD) and nonlinear least squares with HCB estimation based on the DBH (NLS and BD) were also developed, and their fitting precisions with a simultaneous equation system compared. The leave-one-out cross-validation method was applied to evaluate all estimating algorithms and their resulting models. We found that only the simultaneous equation system could illustrate the effect of errors associated with the regressors on the response variables (DBH and HCB) and guaranteed the compatibility between the DBH and HCB models at an individual level. In addition, such an established system also effectively accounted for the inherent correlations between DBH with HCB. However, both the NLS and BD model and the NLS and NBD model did not show these properties. The precision of a simultaneous equation system developed using NSUR appeared the best among all the evaluated algorithms. Our equation system does not require the stand-level information as input, but it does require the information of tree height, crown width, and crown projection area, all of which can be readily derived from LiDAR imagery using the delineation algorithms and ground-based DBH measurements. Our results indicate that NSUR is a more reliable and quicker algorithm for developing DBH and HCB models using large scale LiDAR-based datasets. The novelty of this study is that the compatibility problem of the DBH model and the HCB EIV model was properly addressed, and the potential algorithms were compared to choose the most suitable one (NSUR). The presented method and algorithm will be useful for establishing similar compatible equation systems of tree DBH and HCB EIV models for other tree species.

Permutationally invariant polynomial potential energy surfaces for tropolone and H and D atom tunneling dynamics

We report permutationally invariant polynomial (PIP) fits to energies and gradients for 15-atom tropolone. These include standard, augmented, and fragmented PIP bases. Approximately, 6600 energies and their associated gradients are obtained from direct-dynamics calculations using DFT/B3LYP/6-31+G(d) supplemented by grid calculations spanning an energy range up to roughly 35 000 cm-1. Three fragmentation schemes are investigated with respect to efficiency and fit precision. In addition, several fits are done with reduced weight for gradient data relative to energies. These do result in more precision for the H-transfer barrier height. The properties of the fits such as stationary points, harmonic frequencies, and the barrier to H-atom transfer are reported and compared to direct calculations. A previous 1D model is used to obtain the tunneling splitting for the ground vibrational state and qualitative predictions for excited vibrational states. This model is applied to numerous fits with different barrier heights and then used to extrapolate the H and D atom tunneling splittings to values at the CCSD(T)-F12 barrier. The extrapolated values are 2.3 and 0.14 cm-1, respectively for H and D. These are about a factor of two larger than experiment, but within the expected level of agreement with experiment for the 1D method used and the level of the electronic structure theory.

Effect of zirconia ceramic sintering condition on the precision of fit in dental restorations

Effect of zirconia ceramic sintering condition on the precision of fit in dental restorations

Research on nonlinear prediction model of weld forming quality during hot-wire laser welding

In order to realize accurate prediction of the weld forming quality during hot-wire laser welding, in this study, a basic multivariate linear regression model regarding multiple process parameters and the weld forming quality is first established. And a residual analysis is then used to analyze the linear regression model. The results of the residual analysis illustrate that the influences of the hot-wire current, welding speed, and wire feeding rate on the weld forming quality are nonlinear, whereas the influences of the laser power and gap width on the weld forming quality are linear. In addition, the significance of the interactions among multiple process parameters on the weld forming quality is also obtained. Based on the results of the residual analysis, the linear regression model is modified in a targeted manner by adding the quadratic and interaction terms of the corresponding process parameters to establish a multivariate nonlinear regression model of the process parameters and the weld forming quality. Through comparing the goodness-of-fit, standard error, and standardized residual of the improved nonlinear regression model with the linear regression model and verifying the improved nonlinear regression model by verification experiment, it can be concluded that the improved nonlinear regression model has a better fitting precision and prediction precision. The results of the verification experiment indicate that the improved nonlinear regression model can correctly describe the relationship between the process parameters and the weld forming quality and can realize an accurate prediction of the weld forming quality.

Research on parameterization and optimization procedure of low-Reynolds-number airfoils based on genetic algorithm and Bezier curve

A systematic procedure of high-precision parameterization and multi-objective optimization for airfoils was proposed in this paper in order to improve the aerodynamic performance under full working conditions. The Bezier curve was applied in the airfoil parameterization with the coordinates of the 8 Bezier control points chosen to be the design variables. The control points were initialized by self-programming software and then optimized by direct search method to guarantee the fitting precision. The multi-objective optimization of low-Reynolds-number airfoil E387 was executed through NSGA II based on the result from XFOIL calculation, with the increment and variance of lift-drag ratio chosen to be the objective functions. Two representative solutions were selected from the Pareto front, and the performances of the new airfoils were promoted in different ways. The lift-drag ratio of the optimized airfoil with smaller variance is increased uniformly under different attack angles and maintains the similar characteristics with the original one. For the optimized airfoil with larger variance, the distribution of lift-drag ratio relative to the attack angle is altered obviously, and the performance under small attack angles and stall conditions are significantly ameliorated. The introduction of variance control in the objective function enables the differentiation of the optimized solutions for different requirement in engineering application.

Parameterization of metabolite and macromolecule contributions in interrelated MR spectra of human brain using multidimensional modeling.

Macromolecular signals are crucial constituents of short echo-time 1 H MR spectra with potential clinical implications in themselves as well as essential ramifications for the quantification of the usually targeted metabolites. Their parameterization, needed for general fitting models, is difficult because of their unknown composition. Here, a macromolecular signal parameterization together with metabolite signal quantification including relaxation properties is investigated by multidimensional modeling of interrelated 2DJ inversion-recovery (2DJ-IR) datasets. Simultaneous and iterative procedures for defining the macromolecular background (MMBG) as mono-exponentially or generally decaying signals over TE are evaluated. Varying prior knowledge and restrictions in the metabolite evaluation are tested to examine their impact on results and fitting stability for two sets of three-dimensional spectra acquired with metabolite-cycled PRESS from cerebral gray and white matter locations. One dataset was used for model optimization, and also examining the influence of prior knowledge on estimated parameters. The most promising model was applied to a second dataset. It turned out that the mono-exponential decay model appears to be inadequate to represent TE-dependent signal features of the MMBG. TE-adapted MMBG spectra were therefore determined. For a reliable overall quantification of implicated metabolite concentrations and relaxation times, a general fitting model had to be constrained in terms of the number of fitting variables and the allowed parameter space. With such a model in place, fitting precision for metabolite contents and relaxation times was excellent, while fitting accuracy is difficult to judge and bias was likely influenced by the type of fitting constraints enforced. In summary, the parameterization of metabolite and macromolecule contributions in interrelated MR spectra has been examined by using multidimensional modeling on complex 2DJ-IR datasets. A tightly restricted model allows fitting of individual subject data with high fitting precision documented in small Cramér-Rao lower bounds, good repeatability values and a relatively small spread of estimated concentration and relaxation values for a healthy subject cohort.

A new method for multivariable nonlinear coupling relations analysis in complex electromechanical system

Coupling relations analysis of monitoring variables in complex electromechanical system is a powerful and useful means for abnormal detection, false monitoring information identification and fault diagnosis. However, due to the characteristics of multivariable, nonlinear and non-stationarity in the actual production process, it is difficult to achieve multivariable coupling modeling of complex electromechanical systems. In this paper, a new coupling modeling method is proposed by combining causality analysis with RBF neural network. First, considering the multivariate and nonlinearity of complex system, the conditional Granger and nonlinear Granger is combined to analyze the causal relations of process variables and obtain the cause variable set of any variable in complex system. Second, the RBF neural network is applied to achieve the nonlinear fitting and the parameter is optimized to ensure the fitting precision. Finally, the effectiveness of the proposed method is verified by an analysis of one case study of real compressor groups data set in chemical production system. This new approach can handle general coupling modeling problems and obtain a quantitative nonlinear coupling model by determine the dependent and independent variables in system and the functional relationship between them. Which dedicated to studying quantitative functional relationship of variable coupling, not just considering the direction or strength of coupling as in the existing, and does not need any prior knowledge about the physical structure. Thus, the proposed method can be effectively used in coupling modeling of complex electromechanical systems and formulate the foundation of anomaly detection, information quality assessment, and failure propagation mechanism, as well as other engineering applications.

A simplified method to predict the local stress field and N-SIF of V-shaped notch

The V-notch stress formula based on linear elastic theory shows that the notch stress at the corner tends to infinity due to stress singularity effect. In fact, infinite stress will not be obtained by the results of finite element analysis, and the stress results depend on the mesh size. Based on the concept of singular intensity value 'as', this paper presents a simple formula to evaluate the notch stress distribution and notch stress intensity factor (N-SIF). The simple notch stress evaluation formula has higher fitting precision and practicability. Meanwhile, in order to analyze the influence of mesh size on notch stress, a series of stress results of notched specimens with different opening angles and crack depths are systematically analyzed and compared. On this basis, the relationship equation between singular strength value 'as' and mesh size is derived, which provides a basis for accurately obtaining the N-SIF of V-notch under coarse mesh. After a series of verification, it is found that the result of fatigue strength evaluated by the simplified formula has a smaller scatter band.

Robust Locally Weighted Regression for Profile Measurement of Magnesium Alloy Tube in Hot Bending Process

Section flattening often occurs in the hot bending process of magnesium alloy tube with large curvature. In order to control the forming quality of the tube, it is necessary to measure the section profile of the magnesium alloy pipe online. In this paper, the laser vision system is used to measure the profile of magnesium alloy tube. Due to the influence of the environment and the surface quality of the pipe, there are obviously isolated outliers in the profile data, which seriously affects the accuracy and precision of the tube measurement. An outlier identification algorithm based on robust locally weighted regression and PaйTa criterion is proposed. This algorithm is used to identify the typically isolated outliers in the measurement process and discuss its identification ability. Meanwhile, it is compared with the moving mean identifier and the Hampel identifier. Subsequently, the ellipse fitting of profile data was carried out, and the fitting ellipse parameters and fitting precision of the curved section were obtained. At the same time, the fitting results were compared before and after the outliers are eliminated. The experiment proves that the outlier identification method based on robust locally weighted regression and PaйTa criterion can effectively identify outliers in profile data, especially for spot outliers. This algorithm is a robust, accurate, and efficient outlier identification method, which can effectively improve the laser profile measurement accuracy of the pipe section and has great significance for the quality control of magnesium alloy tube.

Analysis and optimisation of a bearingless induction motor's suspension force and unbalanced magnetic pulling force mathematical model

Aiming at the problem that the two general mathematical models of suspension force and unbalanced magnetic pulling force (UMPF) are affected by the actual factors of a bearingless induction motor, a non-linear model fitting method is proposed. Firstly, by finite element method (FEM), the coupling influence of the torque winding and suspension winding is taken into account, and the maximum voltage of the latter is obtained. Secondly, considering the motor magnetic saturation and combing the least squares method (LSM), the suspension force is estimated by a binary function. Similarly, considering the magnetic saturation and cogging effect, the UMPF is estimated by a ternary function. Finally, comparing the fitting precision under different orders, the two functions with their smallest errors are obtained, and the two original linear models are optimised, respectively. The theoretical study and experimental results show that the motor based on the optimised non-linear model control system has a better suspension performance.

Modeling Height–Diameter Relationship for Poplar Plantations Using Combined-Optimization Multiple Hidden Layer Back Propagation Neural Network

Relationship of total height and diameter at breast height (hereafter diameter) of the trees is generally nonlinear, and therefore has complex characteristics, which can be accurately described by the height-diameter model developed using the back propagation (BP) neural network approach. The multiple hidden layered-BP neural network has several hidden layers and neurons, and is therefore considered more appropriate modeling approach compared to the single hidden layered-BP neural network approach. However, the former approach is not widely applied for tree height prediction due to absence of the effective optimization method, but it can be done using the BP neural network modeling approach. The poplar (Populus spp. L.) plantation data acquired from Guangdong province of China were used for evaluating the BP neural network modeling approach and compared its results with those obtained from the traditional regression modeling and mixed-effects modeling approaches. We determined the best BP neural network structure with two hidden layers and five neurons in each layer, and logistic sigmoid transfer functions. Relative to the Mitscherlich height-diameter model that had the highest fitting precision among the six traditional height-diameter models evaluated, coefficient of determination (R2) of the neural network height-diameter model increased by 10.3%, root mean squares error (RMSE) and mean absolute error (MAE) decreased by 12% and 13.5%, respectively. The BP neural network height-diameter model also appeared more accurate than the mixed-effects height-diameter model. Our study proposes the method of determining the optimal numbers of hidden layers, neurons of each layer, and transfer functions in the BP neural network structure. This method can be useful for other modeling studies of similar or different types, such as tree crown modeling, height, and diameter increments modeling, and so on.

Comparison of BDS Station Clock Short-term Prediction Models and their Applications in Precise Orbit Determination

BDS (BeiDou Navigation Satellite System) ground tracking stations are equipped with high accuracy atomic clocks, and they are synchronized with the BDS time scale (BDT) via the Precise Orbit Determination (POD) processing. During the periods of satellite maneuver and post-maneuver, station clocks are kept fixed as known values in the POD processing. To improve the real-time POD capability, station clocks need to be predicted. In this paper, the performance of three clock prediction models is evaluated, including quadratic polynomial model (QP), periodical term model (PM), and grey model (GM). The precision of clock fitting and prediction, as well as the performance of the prediction models in POD are compared. Data of six stations are used for test, and the results show that: the mean fitting accuracy of quadratic polynomial model, periodical term model, and grey model is 0.14ns, 0.05ns, 0.27ns, respectively; the 1h and 2h prediction precision of the three models is 1.17ns, 0.88ns, 1.28ns, and 2.72ns, 2.09ns, 2.53ns, respectively. Applying the 1h and 2h predicted station clocks in the POD, the 3D orbit accuracy reaches the best using the periodical term model, while the radial accuracy of satellite orbit is rather close for the three models with the difference within 3cm.

Concept and Performance Evaluation of a Novel UAV-Borne Topo-Bathymetric LiDAR Sensor

We present the sensor concept and first performance and accuracy assessment results of a novel lightweight topo-bathymetric laser scanner designed for integration on Unmanned Aerial Vehicles (UAVs), light aircraft, and helicopters. The instrument is particularly well suited for capturing river bathymetry in high spatial resolution as a consequence of (i) the low nominal flying altitude of 50–150 m above ground level resulting in a laser footprint diameter on the ground of typically 10–30 cm and (ii) the high pulse repetition rate of up to 200 kHz yielding a point density on the ground of approximately 20–50 points/m2. The instrument features online waveform processing and additionally stores the full waveform within the entire range gate for waveform analysis in post-processing. The sensor was tested in a real-world environment by acquiring data from two freshwater ponds and a 500 m section of the pre-Alpine Pielach River (Lower Austria). The captured underwater points featured a maximum penetration of two times the Secchi depth. On dry land, the 3D point clouds exhibited (i) a measurement noise in the range of 1–3 mm; (ii) a fitting precision of redundantly captured flight strips of 1 cm; and (iii) an absolute accuracy of 2–3 cm compared to terrestrially surveyed checkerboard targets. A comparison of the refraction corrected LiDAR point cloud with independent underwater checkpoints exhibited a maximum deviation of 7.8 cm and revealed a systematic depth-dependent error when using a refraction coefficient of n = 1.36 for time-of-flight correction. The bias is attributed to multi-path effects in the turbid water column (Secchi depth: 1.1 m) caused by forward scattering of the laser signal at suspended particles. Due to the high spatial resolution, good depth performance, and accuracy, the sensor shows a high potential for applications in hydrology, fluvial morphology, and hydraulic engineering, including flood simulation, sediment transport modeling, and habitat mapping.

Liquisolid pellets: Mixture experimental design assessment of critical quality attributes influencing the manufacturing performance via extrusion-spheronization

A Quality by Design approach was taken to assess the influence of several formulation parameters on critical quality attributes of liquisolid pellets (LPs) obtained by extrusion-spheronization. Two mixture experimental designs were used, one for each of the non-volatile solvents (NVS) studied: Kolliphor® EL or PEG 400. The studied parameters were the concentrations of NVS (A-Kolliphor® EL, 0–40% w/w; or PEG 400, 0–31.1% w/w), coating material (B-crospovidone, 0–50.3% w/w) and carrier (C-microcrystalline cellulose, MCC, 27–100% w/w). The critical quality attributes assessed were amount of granulation liquid required for extrusion/spheronization, residual moisture, particle size, particle size distribution, bulk density, tapped density, true density, Carr's Index, Hausner Ratio, aspect ratio, angle of repose and friability. All hierarchical terms and interactions were evaluated to determine the statistical relevance of each term. Mathematical models were determined by linear regression, taking into account statistically relevant terms. Lack of Fit, R2, adjusted R2 and adequate precision were assessed to determine the fitness of each mathematical model. It was possible to identify and understand parameters affecting critical quality attributes of LPs, as well as the magnitude of these effects. The mathematical models developed were robust and can be used to predict optimal LPs excipient compositions within the experimental domains assessed. This study provides an overall understanding of the impact of excipients on quality attributes of blank LPs, a critical knowledge to the implementation of this novel application of liquisolid systems.

Improved method for damping coefficient measurement based on spectral analysis of a self-mixing signal.

In this paper, the self-mixing interference subject to weak optical feedback has been used to measure the damping vibration. By analyzing the spectrum of the signal, the damping coefficient can be extracted precisely from the nth-order Bessel functions, which are determined by the dominant harmonic order of the frequency spectrum. Theoretical derivation and signal processing are presented. Four kinds of vibrating targets with different damping coefficients are measured. Experimental results show that standard deviation and root mean square error of data are less than 0.2 and 0.1, respectively, which means fitted values are stable as well as having a very high fitting precision.

Surface Natural Spectrum BRDF Modeling of Commonly Used Materials on Space Targets

This paper measures and analyzes bidirectional reflectance distribution function (BRDF) on material surface of space target under natural light within spectrum range of 400nm-900nm. Through optimization of five-parameter model, the paper carries out modeling and optimal fitting in combination with genetic algorithm and the least square method. Results show the combination of genetic algorithm and the least square method could improve fitting precision of computing results and experimental measurement results, save time, and prove the effectiveness and practical performance of the method.