scholarly journals Comparison of prediction power of three multivariate calibrations for estimation of leaf anthocyanin content with visible spectroscopy in Prunus cerasifera

PeerJ ◽  
2019 ◽  
Vol 7 ◽  
pp. e7997
Author(s):  
Xiuying Liu ◽  
Chenzhou Liu ◽  
Zhaoyong Shi ◽  
Qingrui Chang
Keyword(s):  
Neural Network ◽  
Latent Variables ◽  
Near Infrared ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Coefficient Of Determination ◽  
Physiological Status ◽  
Anthocyanin Content ◽  
Visible Spectroscopy ◽  
Prunus Cerasifera

The anthocyanin content in leaves can reveal valuable information about a plant’s physiological status and its responses to stress. Therefore, it is of great value to accurately and efficiently determine anthocyanin content in leaves. The selection of calibration method is a major factor which can influence the accuracy of measurement with visible and near infrared (NIR) spectroscopy. Three multivariate calibrations including principal component regression (PCR), partial least squares regression (PLSR), and back-propagation neural network (BPNN) were adopted for the development of determination models of leaf anthocyanin content using reflectance spectra data (450–600 nm) in Prunus cerasifera and then the performance of these models was compared for three multivariate calibrations. Certain principal components (PCs) and latent variables (LVs) were used as input for the back-propagation neural network (BPNN) model. The results showed that the best PCR and PLSR models were obtained by standard normal variate (SNV), and BPNN models outperformed both the PCR and PLSR models. The coefficient of determination (R2), the root mean square error of prediction (RMSEp), and the residual prediction deviation (RPD) values for the validation set were 0.920, 0.274, and 3.439, respectively, for the BPNN-PCs model, and 0.922, 0.270, and 3.489, respectively, for the BPNN-LVs model. Visible spectroscopy combined with BPNN was successfully applied to determine leaf anthocyanin content in P. cerasifera and the performance of the BPNN-LVs model was the best. The use of the BPNN-LVs model and visible spectroscopy showed significant potential for the nondestructive determination of leaf anthocyanin content in plants.

scholarly journals Application of VIS/NIR Spectroscopy and SDAE-NN Algorithm for Predicting the Cold Storage Time of Salmon

2018 ◽  
Vol 2018 ◽  
pp. 1-9 ◽  
Author(s):  
Ting Wu ◽  
Nan Zhong ◽  
Ling Yang
Keyword(s):  
Neural Network ◽  
Cold Storage ◽  
Storage Time ◽  
Near Infrared ◽  
High Efficiency ◽  
Back Propagation ◽  
Nir Spectroscopy ◽  
Back Propagation Neural Network ◽  
Least Squares Regression ◽  
Test Sets

The cold storage time of salmon has a significant impact on its freshness, which is an important factor for consumers to evaluate the quality of salmon. The efficient, accurate, and convenient protocol is urgent to appraise the freshness for quality checking. In this paper, the ability of visible/near-infrared (VIS/NIR) spectroscopy was evaluated to predict the cold storage time of salmon meat and skin, which were stored at low-temperature box for 0~12 days. Meanwhile, a double-layer stacked denoising autoencoder neural network (SDAE-NN) algorithm was introduced to establish the prediction model without spectral pre-preprocessing. The results showed that, compared with the common methods such as partial least squares regression (PLSR) and back propagation neural network (BP-NN), the SDAE-NN method had a better performance due to its high efficiency in decreasing noise and optimizing the initial weights. The determination coefficient of test sets (R2test) and root mean square error of test sets (RMSEP) have been calculated based on SDAE-NN, for the salmon meat (skin), the R2test can reach 0.98 (0.92), and the RMSEP can reach 0.93 (1.75), respectively. It is highlighted that the algorithm is efficient and accurate and that the salmon meat would be more suitable for predicting freshness than the salmon skin. VIS/NIR spectroscopy combined with the SDAE-NN algorithm can be widely used to predict the freshness of various agricultural products.

Prediction of runoff using BPNN, FFBPNN, CFBPNN algorithm in arid watershed: A case study

2020 ◽  
Vol 24 (3) ◽  
pp. 243-251
Author(s):  
Sandeep Samantaray ◽  
Abinash Sahoo
Keyword(s):  
Neural Network ◽  
Transfer Function ◽  
Mean Square Error ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Coefficient Of Determination ◽  
Hydraulic Structures ◽  
Mean Square ◽  
Feed Forward Back Propagation

Here, an endeavor has been made to predict the correspondence between rainfall and runoff and modeling are demonstrated using Feed Forward Back Propagation Neural Network (FFBPNN), Back Propagation Neural Network (BPNN), and Cascade Forward Back Propagation Neural Network (CFBPNN), for predicting runoff. Various indicators like mean square error (MSE), Root Mean Square Error (RMSE), and coefficient of determination (R2) for training and testing phase are used to appraise performance of model. BPNN performs paramount among three networks having model architecture 4-5-1 utilizing Log-sig transfer function, having R2 for training and testing is correspondingly 96.43 and 95.98. Similarly for FFBPNN, with Tan-sig function preeminent model architecture is seen to be 4-5-1 which possess MSE training and testing value 0.000483, 0.001025, RMSE training and testing value 0.02316, 0.03085 and R2 for training and testing as 0.9925, 0.9611, respectively. But for FFBPNN the value of R2 in training and testing is 0.8765 0.8976. Outcomes on the whole recommend that assessment of runoff is suitable to BPNN as contrasted to CFBPNN and FFBPNN. This consequence helps to plan, arrange and manage hydraulic structures of watershed.

scholarly journals Estimation of minimum miscibility pressure during CO2 flooding in hydrocarbon reservoirs using an optimized neural network

2020 ◽  
Vol 38 (6) ◽  
pp. 2485-2506
Author(s):  
Yapeng Tian ◽  
Binshan Ju ◽  
Yong Yang ◽  
Hongya Wang ◽  
Yintao Dong ◽  
...  
Keyword(s):  
Neural Network ◽  
Evolutionary Algorithms ◽  
Pearson Correlation ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Propagation Model ◽  
Coefficient Of Determination ◽  
Co2 Flooding ◽  
Dragonfly Algorithm ◽  
Minimum Miscibility Pressure

CO2 flooding recovery strongly depends on the minimum miscibility pressure (MMP). Conventional tests to determine gas–oil MMP such as rising bubble apparatus and slim tube displacement are either costly or time consuming. In order to propose a quick and accurate model to determine MMP, a back-propagation neural network is presented for MMP prediction during pure and impure CO2 injections. Five new variables were screened as input parameters to the network. Next, the network was optimized using five evolutionary algorithms, and this work highlights that three of these evolutionary algorithms (e.g. Mind Evolutionary, Artificial Bee Colony, and Dragonfly) are firstly used to predict MMP. Then, data from the literature were input to the optimized network to train it. Statistical evaluation and graphical analyses were used to evaluate the performance of the proposed models and for comparison with published MMP correlates to obtain the optimal model for predicting MMP. The back-propagation model optimized using the dragonfly algorithm exhibited the highest accuracy among all those considered and MMP correlates; its coefficient of determination, average absolute percent relative error, root mean square error, and standard deviation were 0.965, 5.79%, 206.1, and 0.08, respectively. In addition, reservoir temperature was determined as the strongest MMP predictor (Pearson correlation = 0.63) based on sensitivity analysis.

scholarly journals Artificial neural network and partial least square in predicting blood hemoglobin using near-infrared spectrum

2019 ◽  
Vol 16 (2) ◽  
pp. 701
Author(s):  
Mohd Nazrul Effendy Mohd Idrus ◽  
Kim Seng Chia
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Latent Variables ◽  
Near Infrared ◽  
Spectroscopic Analysis ◽  
Partial Least Square ◽  
Least Square ◽  
Coefficient Of Determination ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

<p>Predictive models is crucial in near-infrared (NIR) spectroscopic analysis. Partial least square - artificial neural network (PLS-ANN) is a hybrid method that may improve the performance of prediction in NIR spectroscopic analysis. This study investigates the advantage of PLS-ANN over the well-known modelling in spectroscopy analysis that is partial least square (PLS) and artificial neural network (ANN). The results show that ANN that coupled with first order SG derivatives achieved the best prediction with root mean square error of prediction (RMSEP) of 0.3517 gd/L and coefficient of determination ( ) of 0.9849 followed by PLS-ANN with RMSEP of 0.4368 gd/L and  of 0.9787, and PLS with RMSEP of 0.4669 gd/L and  of 0.9727. This suggests that the spectrum information may unable to be totally represented by the first few latent variables of PLS and a nonlinear model is crucial to model these nonlinear information in NIR spectroscopic analysis.</p>

scholarly journals Prediction of Ozone Concentration using Feed Forward Back Propagation Neural Network (FFBP-NN)

2019 ◽  
Vol 8 (4) ◽  
pp. 9257-9260
Keyword(s):  
Neural Network ◽  
Air Quality ◽  
Ozone Concentration ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
City Council ◽  
Mitigation Measures ◽  
Coefficient Of Determination ◽  
Feed Forward ◽  
Feed Forward Back Propagation

Air pollution has been an ongoing problem in Malaysia. One of the major air quality issue in Malaysia is high concentrations of ozone in urban area. Rapid increase in vehicles number and fossil fuel consumption in Malaysia cause the emission of ozone and their precursors especially nitrogen oxides increasing sharply. This research focus on daytime and nighttime ozone concentration at Kuala Terengganu, Malaysia. The aim of this study is to predict ozone concentration using feed forward back propagation neural network (FFBP-NN) with two hidden layers. Five performance indicators were used to evaluate the models performances which are normalized absolute error (NAE), root mean squared error (RMSE), index of agreement (IA), prediction accuracy (PA) and coefficient of determination (R2 ). Result show that FFBP-NN with 2 hidden layers model gives good performance for prediction of ozone concentration with high accuracy measures (IA=0.9551, PA=0.8453, R2 =0.8402) and small error measures (NAE=0.1642, RMSE=4.4958) for daytime and nighttime (IA=0.9541, PA=0.8429, R2 =0.8358, NAE=0.2160, RMSE=3.2485). The result from this study provides a reference for city council to improve the existing guidelines and to plan an effective mitigation measures to monitor the status of air quality towards a sustainable environment.

scholarly journals Size Estimation of Tomato Fruits Based on Spectroscopic Analysis

2011 ◽  
Vol 225-226 ◽  
pp. 1254-1257 ◽  
Author(s):  
Hai Qing Yang ◽  
Bo Yan Kuang ◽  
Abdul M. Mouazen
Keyword(s):  
Latent Variables ◽  
Near Infrared ◽  
Back Propagation ◽  
Nir Spectroscopy ◽  
Coefficient Of Determination ◽  
Least Squares Regression ◽  
Size Estimation ◽  
Tomato Fruits ◽  
Leave One Out ◽  
Plsr Model

This study used visible and near-infrared (VIS-NIR) spectroscopy for size estimation of tomato fruits of three cultivars. A mobile, fibre-type, VIS-NIR spectrophotometer (AgroSpec, Tec 5, Germany) with spectral range of 350-2200 nm, was used to measure reflectance spectra of on-vine tomatoes growing from July to September 2010. Spectra were divided into a calibration set (75%) and an independent validation set (25%). A partial least squares regression (PLSR) with leave-one-out cross validation was adopted to establish calibration models between fruit diameter and spectra. Furthermore, the latent variables (LVs) obtained from PLS regression was used as input to back-propagation artificial neural network (BPANN) analysis. Result shows that the prediction of PLSR model can produce good performance with coefficient of determination (R2) of 0.82, root-mean-square error of prediction (RMSEP) of 4.87 mm and residual prediction deviation (RPD) of 2.35. Compared to the PLSR model, the PLS-BPANN model provides considerably higher prediction performance withR2of 0.88, RMSEP of 3.98 mm and RPD of 2.89. It is concluded that VIS-NIR spectroscopy coupled with PLS-BPANN can be adopted successfully for size estimation of tomato fruits.

scholarly journals Groundwater Level Prediction based on Neural Networks: A case study in Linze, Northwestern China

2021 ◽  
Vol 266 ◽  
pp. 09005
Author(s):  
Hui Zhang ◽  
Jixuan Zhao ◽  
Chong Chen
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Groundwater Level ◽  
Mean Squared Error ◽  
Rbf Neural Network ◽  
Groundwater Resources ◽  
Back Propagation ◽  
Theoretical Models ◽  
Back Propagation Neural Network ◽  
Coefficient Of Determination

Groundwater level is an important factor in evaluating groundwater resources. Due to numerous non-linear factors, establishing theoretical models is difficult.. Therefore, this paper proposesthe BP (Back Propagation) neural network and the Radial Basis Function (RBF) neural network. The study area is divided into two zones. The R2 (coefficient of determination) and RMSE (Root Mean Squared Error) are used to evaluate the performance. The BP neural network is used to predict groundwater level in the two zones with the R2of0.57 and 0.54, with the RMSE of 0.0804 meters and 0.1864 meters respectively. The RBF neural network is implemented with R2of 0.65 and 0.61, with RMSE of 0.0720 meters and 0.1519 meters, respectively. The results show the RBF neural network performs better than the BP neural network in the accuracy of predicting groundwater level. This study shows the feasibility and superiority of groundwater simulation using neural network.

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