Back-propagation Artificial Neural Network Research Articles

Genetic optimized Al–Mg alloy constitutive modeling and activation energy analysis

As an intelligent global optimization method, the genetic algorithm has tremendous potential for improving flow behavior modeling and analysis. Based on flow stress-true strain curves of Al–Mg AA5005 alloy under temperature 563∼773 K and strain rate 0.0003∼0.03s−1, a phenomenological model named Arrhenius-type (A-T) was established to describe the flow behavior. On this basis, the genetic optimized A-T (GA-T) model with higher precision was obtained by optimizing A-T parameters α, n, Q and lnA. To reduce the large computing power consumed by unnecessary complex topological network structure when conducting simulations by the back propagation artificial neural network (BP-ANN) model, a genetic optimized BP-ANN (GBP-ANN) model was designed through determining the initial values of weights, biases and hyper parameters. The presented GBP-ANN model inherits the advantage of the BP-ANN model’s high accuracy as well as maintaining the simplest structure. The statistical analysis demonstrates that the GBP-ANN model possesses the best flow behavior description ability among three established models. Moreover, the GBP-ANN also shows a better generalization performance than the GA-T model. Lastly, with the help of the GA-T model, the activation energy map was plotted to determine the desirable deformation condition analyze the deformation mechanism. Our work presents a combination of GBP-ANN model and genetic optimized Q analysis, thus shedding new light on high accuracy flow behavior modeling and deformation mechanism analysis.

A Backpropagation Artificial Neural Network Approach for Loan Status Prediction

Providing credit has become a main source of profit for financial and non-financial institutions. However, this transaction might lead into credit risk. This risk occurred if debtors unable to complete their obligations that will led loss for creditors. It is necessity for company to create assessment in distinguishing eligible or non-eligible prospective customer. Artificial Neural Network (ANN) is introduced in solving this typical classification case. Furthermore, one of learning algorithm in ANN namely Backpropagation is able to minimizing error of output in order to receive accurate result. This research aims to form models that capable in classifying the loan status of applicants by utilizing historical data. The method developed in this research is Backpropagation with activation function is a sigmoid function. In addition, this research formed two data model for analyzed; with first data model is every variable given in dataset and for the second data model is the variables that influenced the loan acceptance. Backpropagation shows high performance with more or less data variables. The results of this research show that the both data model has highest accuracy of prediction is 94.37% while the lowest accuracy prediction is 80.28%.

An Aeromagnetic Compensation Algorithm Based on Radial Basis Function Artificial Neural Network

Aeromagnetic exploration is a magnetic exploration method that detects changes of the earth’s magnetic field by loading a magnetometer on an aircraft. With the miniaturization of magnetometers and the development of unmanned aerial vehicles (UAV) technology, UAV aeromagnetic surveying plays an increasingly important role in mineral exploration and other fields due to its advantages of low cost and safety. However, in the process of aeromagnetic measurement data, due to the ferromagnetic material of the aircraft itself and the change of flight direction and attitude, magnetic field interference will occur and affect the measurement of the geomagnetic field by the magnetometer. The work of aeromagnetic compensation is to compensate for this part of the magnetic interference and improve the magnetic measurement accuracy of the magnetometer. This paper focused on the problems of UAV aeromagnetic survey data processing and improved the accuracy of UAV based aeromagnetic data measurement. Based on the Tolles–Lawson model, a numerical simulation experiment of magnetic interference of UAV-based aeromagnetic data was carried out, and a radial basis function (RBF) artificial neural network (ANN) algorithm was proposed for the first time to compensate the aeromagnetic data. Compared with classical backpropagation (BP) ANN, the test results of the synthetic data and real measured magnetic data showed that the RBF-ANN has higher compensation accuracy and stronger generalization ability.

A data-driven approach for understanding the structure dependence of redox activity in humic substances

Humic substances (HS) can facilitate electron transfer during biogeochemical processes due to their redox properties, but the structure-redox activity relationships are still difficult to describe and poorly understood. Herein, the linear (Partial Least Squares regressions; PLS) and nonlinear (artificial neural network; ANN) models were applied to monitor the structure dependence of HS redox activities in terms of electron accepting (EAC), electron donating (EDC) and overall electron transfer capacities (ETC) using its physicochemical features as input variables. The PLS model exhibited a moderate ability with R2 values of 0.60, 0.53 and 0.65 to evaluate EAC, EDC and ETC, respectively. The variable influence in the projection (VIP) scores of the PLS identified that the phenols, quinones and aromatic systems were particularly important for describing the redox activities of HS. Compared with the PLS model, the back-propagation ANN model achieved higher performance with R2 values of 0.81, 0.65 and 0.78 for monitoring the EAC, EDC and ETC, respectively. Sensitivity analysis of the ANN separately identified that the EAC highly depended on quinones, aromatics and protein-like fluorophores, while the EDC depended on phenols, aromatics and humic-like fluorophores (or stable free radicals). Additionally, carboxylic groups were the best indicator for evaluating both the EAC and EDC. Good model performances were obtained from the selected features via the PLS and sensitivity analysis, further confirming the accuracy of describing the structure-redox activity relationships with these analyses. This study provides a potential approach for identifying the structure-activity relationships of HS and an efficient machine-learning model for predicting HS redox activities.

Generic compressive strength prediction model applicable to multiple lithologies based on a broad global database

In this study, we present a global database of ten parameters, which include measurements of rock index properties, strength stiffness and dynamic properties. Hoek–Brown constant mi, is included, and was estimated, using Hoek and Brown proposed guidelines for determining mi values for different rock types that can be used for preliminary design when triaxial tests are not available. This broad database is compiled from 96 studies and is labelled as “ROCK/10/4025”, to describe the type of geomaterial, the number of the parameters, and the number of the data samples included. It consists of 35.4 % igneous, 54.8 % sedimentary, and 9.2% metamorphic rocks. The purpose of this paper is to propose a generic soft computing model applicable to multiple lithologies, that can become more reliable and perhaps more suitable for a specific site study when used in order to densify often limited similar site-specific data. To this end four broad samples of data were selected, and served as training data sets for developed machine learning models, to develop a generic compression strength prediction model applicable to multiple lithologies. The suggested algorithms in this study are Back-Propagation Artificial Neural Networks, Artificial Neuro-Fuzzy Inference Systems, Support Vector Machines, Nearest Neighbour classifiers and Ensemble Bagged Trees. According to the findings of this study, Artificial Neuro-Fuzzy Inference Systems model performance was found to be marginally superior, while Back Propagation Artificial Neural Networks, Support Vector Machines and Ensemble Bagged Trees models were found to have good performance. Constant mi seems to be an important training parameter when training predictive models centred on data from multiple lithologies. As a result, we can suggest that these models are powerful tools that allow for a reliable estimation of compressive strength, based on the performance indicators. The performance was found to be 70%–82% when the problem of compressive strength prediction was approached as a classification problem (that is successful prediction of class from very weak to very strong), and 80%–96% when solved as a function approximation problem.

Adaptive fuze-warhead coordination method based on BP artificial neural network

The appropriate fuze-warhead coordination method is important to improve the damage efficiency of air defense missiles against aircraft targets. In this paper, an adaptive fuze-warhead coordination method based on the Back Propagation Artificial Neural Network (BP-ANN) is proposed, which uses the parameters of missile-target intersection to adaptively calculate the initiation delay. The damage probabilities at different radial locations along the same shot line of a given intersection situation are calculated, so as to determine the optimal detonation position. On this basis, the BP-ANN model is used to describe the complex and highly nonlinear relationship between different intersection parameters and the corresponding optimal detonating point position. In the actual terminal engagement process, the fuze initiation delay is quickly determined by the constructed BP-ANN model combined with the missile-target intersection parameters. The method is validated in the case of the single-shot damage probability evaluation. Comparing with other fuze-warhead coordination methods, the proposed method can produce higher single-shot damage probability under various intersection conditions, while the fuze-warhead coordination effect is less influenced by the location of the aim point.

Artificial neural network based delamination prediction in composite plates using vibration signals

Dynamic loading on composite components may induce damages such as cracks, delaminations, etc. and development of an early damage detection technique for delaminations is one of the most important aspects in ensuring the integrity and safety of composite components. The presence of damages such as delaminations on the composites reduces its stiffness and further changes the dynamic behaviour of the structures. As the loss in stiffness leads to changes in the natural frequencies, mode shapes, and other aspects of the structure, vibration analysis may be the ideal technique to employ in this case. In this research work, the supervised feed-forward multilayer back-propagation Artificial Neural Network (ANN) is used to determine the position and area of delaminations in GFRP plates using changes in natural frequencies as inputs. The natural frequencies were obtained by finite element analysis and results are validated by experimentation. The findings show that the suggested technique can satisfactorily estimate the location and extent of delaminations in composite plates.

Coupled Gold Nanoparticles with Aptamers Colorimetry for Detection of Amoxicillin in Human Breast Milk Based on Image Preprocessing and BP-ANN.

Antibiotic residues in breast milk can have an impact on the intestinal flora and health of babies. Amoxicillin, as one of the most used antibiotics, affects the abundance of some intestinal bacteria. In this study, we developed a convenient and rapid process that used a combination of colorimetric methods and artificial intelligence image preprocessing, and back propagation-artificial neural network (BP-ANN) analysis to detect amoxicillin in breast milk. The colorimetric method derived from the reaction of gold nanoparticles (AuNPs) was coupled to aptamers (ssDNA) with different concentrations of amoxicillin to produce different color results. The color image was captured by a portable image acquisition device, and image preprocessing was implemented in three steps: segmentation, filtering, and cropping. We decided on a range of detection from 0 µM to 3.9 µM based on the physiological concentration of amoxicillin in breast milk and the detection effect. The segmentation and filtering steps were conducted by Hough circle detection and Gaussian filtering, respectively. The segmented results were analyzed by linear regression and BP-ANN, and good linear correlations between the colorimetric image value and concentration of target amoxicillin were obtained. The R2 and MSE of the training set were 0.9551 and 0.0696, respectively, and those of the test set were 0.9276 and 0.1142, respectively. In prepared breast milk sample detection, the recoveries were 111.00%, 98.00%, and 100.20%, and RSDs were 6.42%, 4.27%, and 1.11%. The result suggests that the colorimetric process combined with artificial intelligence image preprocessing and BP-ANN provides an accurate, rapid, and convenient way to achieve the detection of amoxicillin in breast milk.

Rapid qualitative detection of titanium dioxide adulteration in persimmon icing using portable Raman spectrometer and Machine learning

Persimmon icing is the white crystalline powder that adheres to the surface of persimmon cakes when the sugar in the persimmon spills over during processing, which is considered the essence of persimmon. Titanium dioxide is a food additive that is commonly added to the surface of persimmon cakes to impersonate high-quality persimmon cakes. However, excessive titanium dioxide can be harmful to humans, so a quick method is needed to identify persimmon cakes as adulterated. Raman spectroscopy with distinctive advantages of water-insensitivity, real-time, field-deployable, label-free, and fingerprinting-identification has been rapidly developed and used in food quality assurance and safety monitoring. In this study, we investigated Raman spectroscopy integrated with machine learning to assess titanium dioxide adulteration in dried persimmon icing. The adaptive iterative reweighting partial least squares (air-PLS) algorithm as an effective algorithm was used to remove fluorescent background signals in raw Raman spectroscopy. Principal components analysis (PCA) was employed to analyze the spectral data and determine the class memberships, and results showed that 99.9% of information could be explained by PC-1 and PC-2. Compared with extreme learning machine (ELM), support vector machine (SVM), back propagation artificial neural network (BP-ANN), and random forest (RF) models, one-dimensional stack auto encoder convolutional neural network (1D-SAE-CNN) could provide the highest detection accuracy of 0.9825, precision of 0.9824, recall of 0.9825, and f1-score of 0.9824. This study shows that Raman spectroscopy coupled with 1D-SAE-CNN is a promising method to detect titanium dioxide adulteration in persimmon icing.

The Predicted Model of the Sensory Quality of Refrigerated Tilapia Skin Established Based on Characteristic Near-Infrared Spectrum

ABSTRACT The predicted model of the sensory quality of refrigerated tilapia skin at 8°C was constructed using near-infrared spectroscopy (NIRS) based on the principle of sample packaging integrity. The characteristic spectral intervals were chosen from preprocessed spectral data using synergy interval partial least squares (siPLS), principal component analysis, and Jordan–Elman back propagation-artificial neural network (JENN) was used to build a prediction model for tilapia skin quality parameter. The results showed that the multiple scatter correction (MSC) + 2nd derivative was the best-preprocessed method, and the four characteristic spectral intervals were 680–740, 742–800, 980–1040, and 1150–1210 nm. Further, the cumulative contribution rate of the first three principal components was 99.15%. Additionally, the root mean square error of cross-validation set (RMSECV) of transfer function (tanh) of the model was 0.386, the determinant coefficient for prediction () was 0.973, and the RMSECV and were 0.393 and 0.971 for unknown samples, respectively. The results showed that NIRS combined with JENN could allow rapid and accurate evaluation of tilapia skin quality in the range of 73.00–97.00 scores.

Constitutive modeling of Ta-rich particle reinforced Zr-based bulk metallic composites in the supercooled liquid region by using evolutionary artificial neural network

In this study, a series of in-situ Ta-rich particle reinforced Zr-based bulk metallic glass composites were successfully fabricated by arc-melting copper-mold spray casting. The effects of Ta content on the room temperature plasticity, compressive strength and thermoplastic formability were studied. (Zr55Cu30Al10Ni5)94Ta6 showed good comprehensive performance, and it was selected to systematically study the deformation behavior in the supercooled liquid region. Different from the strain softening after stress overshoot in bulk metallic glass, the composites showed work hardening in the late stage. Some classical constitutive models cannot accurately describe these phenomena. The back-propagation artificial neural network optimized by particle swarm optimization and genetic algorithm was used to establish the constitutive model. The particle-swarm-optimization back-propagation network with the optimal topology showed high accuracy and good generalization ability. The results predicted with this model were consistent with the experimental data, providing a powerful approach for describing the hot-deformation behavior of these Zr-based bulk metallic glass composites in the supercooled liquid region.

Prediction of Pork Supply Based on Improved Mayfly Optimization Algorithm and BP Neural Network

Focusing on the issues of slow convergence speed and the ease of falling into a local optimum when optimizing the weights and thresholds of a back-propagation artificial neural network (BPANN) by the gradient method, a prediction method for pork supply based on an improved mayfly optimization algorithm (MOA) and BPANN is proposed. Firstly, in order to improve the performance of MOA, an improved mayfly optimization algorithm with an adaptive visibility coefficient (AVC-IMOA) is introduced. Secondly, AVC-IMOA is used to optimize the weights and thresholds of a BPANN (AVC-IMOA_BP). Thirdly, the trained BPANN and the statistical data are adopted to predict the pork supply in Heilongjiang Province from 2000 to 2020. Finally, to demonstrate the effectiveness of the proposed method for predicting pork supply, the pork supply in Heilongjiang Province was predicted by using AVC-IMOA_BP, a BPANN based on the gradient descent method and a BPANN based on a mixed-strategy whale optimization algorithm (MSWOA_BP), a BPANN based on an artificial bee colony algorithm (ABC_BP) and a BPANN based on a firefly algorithm and sparrow search algorithm (FASSA_BP) in the literature. The results show that the prediction accuracy of the proposed method based on AVC-IMOA and a BPANN is obviously better than those of MSWOA_BP, ABC_BP and FASSA_BP, thus verifying the superior performance of AVC-IMOA_BP.

Artificial Neural Network based Model for Fruit Grade Empirical Thresholding and Feature Extraction based Back Propagation

This study details a novel attribute retrieval method for use in pre-processing images, and then applies it to the development of an "artificial neural network" system based on back propagation for identifying fruits in photographs. The “Scale Conjugate Gradient” (SCG) technique is used For back propagation. In this paper, there are three stages to the process. First, MATLAB was used to process a variety of external image-based apple properties. Since merely colour is insufficient to judge the quality, size and weight characteristics were also taken into consideration. Second, features extraction was carried out during picture pre-processing to simplify the method by concentrating only on important features. The Support Vector Machine (SVM) algorithm is a favourite for creating classification models that are relatively small in weight. The classification in this work is done using the MATLAB-ANN (Artificial Neural Network) toolkit. A single hidden layer BP-ANN (Back propagation- artificial neural network) was employed with sigmoid activation functions,. The outcome was determined by the appropriate output variables, which is the apple's quality class, which was determined to be Class A, Class B, Class C, and Class D, respectively. The modeling result indicates the tremendous match between the data used in training and assumed output values. It also has shorter calculation time due to the SCG algorithm. It is also possible for apple producers and distributors to classify their fruit using this model and reduce the cost by avoiding manual classification.

Image Segmentation Using Haar Wavelet Transform and Artificial Network for Identification of Ant Nest Pattern

Ant nest is a powerful medicinal plant to treat various diseases such as heart disease, tumors, cancer, hemorrhoids, tuberculosis, rheumatism, ulcers and prostate disorders. The many benefits generated cause the ant nest to have a selling value in the community, so it is not uncommon to trigger ant nest forgery. This study aims to identify the image of an ant nest by testing the accuracy of the backpropagation network and haar wavelet. Before the identification process with the backpropagation method is carried out, first the process of taking the characteristics of the ant nest is carried out which will later be used as test data input in the identification process. The output of the network will be compared with the target to get the error output. Then these errors are propagated again to increase the network weight to minimize errors. This research resulted in an ant nest identification application. The backpropagation ANN architecture for the training data used is 0.95 momentum, 0.01 target error with MSE criteria, the maximum number of training iterations (epochs) is set at 5000, the learning rate is 0.1 and the number of hidden layers is 20 by using wavelet haar feature extraction level 3 and the backpropagation momentum method can be used. used to identify ant nest images with 100% accuracy.

Intelligent prediction of wear life of automobile brake pad based on braking conditions

PurposeTo avoid braking accidents caused by excessive wear of brake pad, this study aims to achieve online prediction of brake pad wear life (BPWL).Design/methodology/approachA simulated braking test bench for automobile disc brake was used. The correlation and mechanism between the three braking condition parameters of initial braking speed, braking pressure and initial braking temperature and the tribological performance were analyzed. The different artificial neural network (ANN) models of wear loss were discussed. Genetic algorithm was used to optimize the ANN model. The structure scheme of the online prediction system of BPWL was discussed and completed.FindingsThe results showed that the braking conditions were positively correlated with the wear loss, but negatively correlated with the friction coefficient. The prediction accuracy of back propagation (BP) ANN model was higher. The model was optimized by genetic algorithm, and the average deviation of prediction results was 4.67%. By constructing the online monitoring system of automobile braking conditions, the online prediction of BPWL based on the ANN model could be realized.Originality/valueThe research results not only have important theoretical significance for the study of BPWL but also have practical value for guiding the maintenance and replacement of automobile brake pads and avoiding the occurrence of braking accidents.

Analysis of long term water quality variations driven by multiple factors in a typical basin of Beijing-Tianjin-Hebei region combined with neural networks

Water environment can be affected by multiple natural and human factors.This study aimed to explore the impacts on water quality from various driving factors including land use, landscape, social economy and climate in a typical basin of Beijing-Tianjin-Hebei Region combined with neural networks like self-organizing map and back propagation artificial neural network based on the water quality monitoring data.And redundancy analysis as well as gray correlation analysis were also adopted to explore the relationships between water quality and landscape indices as well as socioeconomic factors, respectively. The results showed that different land uses had different impacts on water quality. Agricultural additions in cropland had a great impact on surrounding water environment. The forest and grassland purified water to some degree, while the vegetation purification became weaker when there was serious pollution. The fragmentated landscape patches with high patch density caused by human activities also worsened water quality because its retention of pollutants was weakened. Besides, the per capita disposable income of rural residents and the per capita disposable urban income had the highest correlation degrees with most water quality indexes in all socioeconomic factors. In addition, temperature rise would promote dissolved oxygen consumption and eutrophication. Increasing precipitation brought pollutants into water via rainfall contains, nutrient transportation and soil erosion. The main conclusions were that both agricultural activities and urban development had impacts on water environment, and the vegetation purification became less obvious in heavily polluted areas. In addition, the rising temperature and precipitation disturbed water quality under the background of global warming. This study is helpful for water pollution management and it will give enlightenment for the water environment protection under the combining effects of human disturbance, economic development and climate change.

Implementation of The Backpropagation Method and The Kohonen Network to Predict Blood Availability: Case Study in PMI Kota Magelang

The availability of blood stocks at the Indonesian Red Cross or Palang Merah Indonesia (PMI) is a must and absolute for institutions that organize the procurement and distribution of blood for medical purposes. The problem is that the blood stock in PMI Magelang City Branch is not ideally available in each blood type, especially blood type AB, which in recent years has been very minimal and difficult to obtain. The purpose of this study was to predict blood stock of type AB with software based on artificial neural networks backpropagation and Coherent tissues. Artificial Neural Network (JST) backpropagation is used to predict the stock supply of blood type AB. Meanwhile, Coherent is a network used to divide input patterns into groups. The application has a network structure consisting of 2 input neurons, 10 neurons in the hidden layer, and 1 and 1 neuron in the output layer. The total amount of data is 3 years (2015-2017), 2 years of data are used for training data, and 1 year of data is used for testing data. The engine predicts using a maximum iteration of 1,000 epochs, an expletive constant of 0.5, a momentum of 0.9, and a minimum error rate of 0.001. With variations in the value of the backpropagation component, a prediction of less than 140 bags per year is generated. Meanwhile, the resulting weight is predicted by the Coherent method and produces a prediction of the production of type AB blood stocks per month. Based on the results against 3 years of test data, the percentage of the system accuracy rate is 100%. The reduction of learning constants and the addition of training data systems may affect the accuracy of the system in making predictions.

Predicting the Self-Diffusion Coefficient of Liquids Based on Backpropagation Artificial Neural Network: A Quantitative Structure–Property Relationship Study

Predicting the Self-Diffusion Coefficient of Liquids Based on Backpropagation Artificial Neural Network: A Quantitative Structure–Property Relationship Study

Mathematical Modelling for Predicting Thermal Properties of Selected Limestone

Due to a lack of geotechnical and geothermal studies on Jordanian limestone, this paper aims to provide the thermal properties, including thermal conductivity, thermal diffusivity, and specific heat, using the Hot Disk Transient Plane Source (TPS) 2200 method. It also aims to provide a set of mathematical models through which the thermal properties can be indirectly predicted from the rocks’ physical and engineering properties. One hundred cylindrical rock specimens with a height of 20 cm and a diameter of 10 cm were extracted and prepared. The results showed that the thermal conductivity values ranged between (1.931–3.468) (W/(m*k)), thermal diffusivity (1.032–1.81) (mm2/s), and specific heat (1.57–2.563) ((MJ)/(m3*K)). The results also suggest a direct relationship between conductivity and diffusivity and an inverse relationship between conductivity and specific heat. On the other hand, the results indicate the direct relationship between the conductivity and diffusivity, and the inverse relationship between the specific heat and density, hardness, sound velocity, and rock strength; the opposite happens when the rock’s porosity is considered. Simple regression, multivariate regression, and the backpropagation–artificial neural network (BP–ANN) approach were utilized to predict the thermal properties of limestone. Results indicated that the ANN model provided superior prediction performance compared to other models.

A Versatile Method for Quantitative Analysis of Total Iron Content in Iron Ore Using Laser-Induced Breakdown Spectroscopy.

Focus in quality assessment of iron ore is the content of total iron (TFe). Laser-induced breakdown spectroscopy (LIBS) technology possesses the merits of rapid, in situ, real-time multielement analysis for iron ore, but its application to quantitative TFe content is subject to interference of the iron matrix effect and the lack of suitable data mining tools. Here, a new method of LIBS-based variable importance back propagation artificial neural network (VI-BP-ANN) for quantitative TFe content in iron ore was first proposed. After the LIBS spectra of 80 representative iron samples were obtained, random forest (RF) was optimized by out-of-bag (OOB) error and then used to measure and rank variable importance. The variable importance thresholds and the number of neurons were optimized with five-fold cross-validation (CV) with correlation coefficient (R2) and root mean square error (RMSE). With using only 1.40% of full spectral variables to construct BP-ANN model, the resulted R2, the root mean squared error of prediction (RMSEP) and the modeling time of the final VI-BP-ANN model was 0.9450, 0.3174 wt%, and 24 s, respectively. Compared with full spectrum-based model, for example, BP-ANN, RF, support vector machine (SVM), and PLS and VI-RF model, the VI-BP-ANN model reduced overfitting and obtained the highest R2 and the lowest RMSE both for calibration and prediction. Meanwhile, the characteristics of variables selected by VI were analyzed. In addition to the elemental emission lines of Ca, Al, Na, K, Mn, Si, Mg, Ti, Zr, and Li, partial spectral baselines of 540-610 nm and 820-970 nm were also selected as characteristic variables, which indicated that VI can take into full consideration the elemental interactions and the spectral baselines. Our approach shows that LIBS combined with VI-BP-ANN is able to quantify TFe content rapidly and accurately in iron ore.