Application of the radial basis function neural networks to improve the nondestructive Vis/NIR spectrophotometric analysis of potassium in fresh lettuces

Xiong, Yujuan; Ohashi, Shintaroh; Nakano, K.; Jiang, Weizhong; Takizawa, Kenji; Iijima, Kazuhiro; Maniwara, Phonkrit

doi:10.1016/j.jfoodeng.2020.110417

Cited by 21 publications

(14 citation statements)

References 33 publications

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“…The results for the preprocessed spectra were improved compared to the results for the raw spectra. This result is the same as that of previous studies, in which the derivation [45,60,61], normalization, EMSC [59], and OSC [37] of the spectral data achieved better model performance than using raw spectra. Additionally, 1D offered better model performance than 2D and was much better than other methods, yielding an R 2 c of 0.92, R 2 p of 0.99, and RPD of 8.9.…”

Section: Pls Model Developmentsupporting

confidence: 89%

“…The 1D and 2D pretreatments spectra led to more evident and sharper peaks than the raw spectra and other methods at approximately 1400, 1800, and 2200 nm. The baseline drift can be eliminated, and the influence of background interference with NIR data can be reduced by using the derivation of preprocessed NIR data [61]. Xavier H. argued that first derivatives are used to remove baselines and second derivatives to removes slopes [64].…”

Section: Pls Model Developmentmentioning

confidence: 99%

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Prediction of the Carbon Content of Six Tree Species from Visible-Near-Infrared Spectroscopy

Meng

Zhang

et al. 2021

Forests

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This study aimed to measure the carbon content of tree species rapidly and accurately using visible and near-infrared (Vis-NIR) spectroscopy coupled with chemometric methods. Currently, the carbon content of trees used for calculating the carbon storage of forest trees in the study of carbon sequestration is obtained by two methods. One involves measuring carbon content in the laboratory (K2CrO7-H2SO4 oxidation method or elemental analyzer), and another involves directly using the IPCC (Intergovernmental Panel on Climate Change) default carbon content of 0.45 or 0.5. The former method is destructive, time-consuming, and expensive, while the latter is subjective. However, Vis-NIR detection technology can avoid these shortcomings and rapidly determine carbon content. In this study, 96 increment core samples were collected from six tree species in the Heilongjiang province of China for analysis. The spectral data were preprocessed using seven methods, including extended multiplicative scatter correction (EMSC), first derivative (1D), second derivative (2D), baseline correction, de-trend, orthogonal signal correction (OSC), and normalization to eliminate baseline drifting and noise, as well as to enhance the model quality. Linear models were established from the spectra using partial least squares regression (PLS). At the same time, we also compared the effects of full-spectrum and reduced spectrum on the model’s performance. The results showed that the spectral data processed by 1D with the full spectrum could obtain a better prediction model. The 1D method yielded the highest R2c of 0.92, an RMSEC (root-mean-square error of calibration) of 0.0056, an R2p of 0.99, an RMSEP (root-mean-square error of prediction) of 0.0020, and the highest RPD (residual prediction deviation) value of 8.9. The results demonstrate the feasibility of Vis-NIR spectroscopy coupled with chemometric methods in determining the carbon content of tree species as a simple, rapid, and non-destructive method.

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Section: Pls Model Developmentsupporting

confidence: 89%

Section: Pls Model Developmentmentioning

confidence: 99%

Prediction of the Carbon Content of Six Tree Species from Visible-Near-Infrared Spectroscopy

Meng

Zhang

et al. 2021

Forests

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“…− Four algorithms were demonstrated: linear regression, elastic net, k-nearest neighbor, and support vector regression to forecast potato yield from soil and crop data properties collected over proximal sensing. [25] 2021…”

Section: Ref Nomentioning

confidence: 99%

Exploration of Machine Learning Approaches for Paddy Yield Prediction in Eastern Part of Tamilnadu

2021

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Agriculture is the principal basis of livelihood that acts as a mainstay of any country. There are several changes faced by the farmers due to various factors such as water shortage, undefined price owing to demand–supply, weather uncertainties, and inaccurate crop prediction. The prediction of crop yield, notably paddy yield, is an intricate assignment owing to its dependency on several factors such as crop genotype, environmental factors, management practices, and their interactions. Researchers are used to predicting the paddy yield using statistical approaches, but they failed to attain higher accuracy due to several factors. Therefore, machine learning methods such as support vector regression (SVR), general regression neural networks (GRNNs), radial basis functional neural networks (RBFNNs), and back-propagation neural networks (BPNNs) are demonstrated to predict the paddy yield accurately for the Cauvery Delta Zone (CDZ), which lies in the eastern part of Tamil Nadu, South India. The performance of each developed model is examined using assessment metrics such as coefficient of determination (R2), root mean square error (RMSE), mean absolute error (MAE), mean squared error (MSE), mean absolute percentage error (MAPE), coefficient of variance (CV), and normalized mean squared error (NMSE). The observed results show that the GRNN algorithm delivers superior evaluation metrics such as R2, RMSE, MAE, MSE, MAPE, CV, and NSME values about 0.9863, 0.2295 and 0.1290, 0.0526, 1.3439, 0.0255, and 0.0136, respectively, which ensures accurate crop yield prediction compared with other methods. Finally, the performance of the GRNN model is compared with other available models from several studies in the literature, and it is found to be high while comparing the prediction accuracy using evaluation metrics.

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“…Similar to BPNN, the radial basis function (RBF) neural network is also a feedforward neural network (Chen et al., 2020; Deng et al., 2021); both have roughly the same structure, as shown in Figure 3b. RBF neural networks were introduced in a seminal paper by Broomhead and Lowe (Broomhead & Lowe, 1988; Xiong et al., 2021; Powell, 1987). RBF neural network has a strong global approximation ability and no local minimum problem (Xiong et al., 2021).…”

Section: Introduction and Mechanism Of Shelf Life Modelmentioning

confidence: 99%

Recent advances in shelf life prediction models for monitoring food quality

Cui

Zheng

Wang

et al. 2023

Comp Rev Food Sci Food Safe

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Each year, 1.3 billion tons of food is lost due to spoilage or loss in the supply chain, accounting for approximately one third of global food production. This requires a manufacturer to provide accurate information on the shelf life of the food in each stage. Various models for monitoring food quality have been developed and applied to predict food shelf life. This review classified shelf life models and detailed the application background and characteristics of commonly used models to better understand the different uses and aspects of the commonly used models. In particular, the structural framework, application mechanisms, and numerical relationships of commonly used models were elaborated. In addition, the study focused on the application of commonly used models in the food field. Besides predicting the freshness index and remaining shelf life of food, the study addressed aspects such as food classification (maturity and damage) and content prediction. Finally, further promotion of shelf life models in the food field, use of multivariate analysis methods, and development of new models were foreseen. More reliable transportation, processing, and packaging methods could be screened out based on real‐time food quality monitoring.

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Application of the radial basis function neural networks to improve the nondestructive Vis/NIR spectrophotometric analysis of potassium in fresh lettuces

Cited by 21 publications

References 33 publications

Prediction of the Carbon Content of Six Tree Species from Visible-Near-Infrared Spectroscopy

Prediction of the Carbon Content of Six Tree Species from Visible-Near-Infrared Spectroscopy

Exploration of Machine Learning Approaches for Paddy Yield Prediction in Eastern Part of Tamilnadu

Recent advances in shelf life prediction models for monitoring food quality

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