A Research on Methane Prediction Model Based on Improved BP-GA Network

Hao, Zhao Ming; Du, Jing Yi; Zheng, Zhi Chen

doi:10.4028/www.scientific.net/amm.239-240.1382

Cited by 2 publications

(1 citation statement)

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“…It does not easily fall into a local optimum, and can be used for parallel distributed processing (Hoseinian, Rezai, & Kowsari, 2017). Since BP neural network is prone to get into local minimal points, we combined the GA and BP algorithms to optimize the original weights and thresholds of the neural network (Hao, Du, & Zheng, 2012). The population size was 100, crossover probability was .6, genetic iteration time was set to 50, and mutation probability was .05.…”

Section: Methodsmentioning

confidence: 99%

Detecting total acid content quickly and accurately by combining hyperspectral imaging and an optimized algorithm method

Jiang

Huang

et al. 2021

J Food Process Engineering

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The total acid content of Daqu during fermentation directly influences the quality of the Daqu. Hyperspectral imaging (HSI) was used to rapidly and accurately detect the total acid content of Daqu. The performances of two spectral ranges obtained by two hyperspectral cameras for Daqu total acid content were compared. Different pretreatment methods were used to build the partial least squares regression (PLSR) model, and the optimal pretreatment for the corresponding spectra was determined. A combination of principal component analysis (PCA) and successive projection algorithm (SPA) was used to select the feature wavelengths. The PCA algorithm optimized by SPA was more accurate than the PCA algorithm alone when extracting the feature wavelengths. PLSR, backpropagation neural network (BP), and the backpropagation‐genetic algorithm (BP‐GA) model were developed to predict the total acid content of the Daqu samples by using the full wavelengths and feature wavelengths, respectively. The predictive performance of each model was compared and analyzed. The optimal model was the MSC + PCA‐SPA+BP‐GA model (Rc2 = 0.8832, RMSEC = 0.1163, Rp20.25em = 0.9724, RMSEP = 0.0593, RPD = 4.2860, AB_RMSE = 0.0570). In addition, the distribution of total acid content within the region of interest (ROI) of Daqu samples was visualized at different fermentation stages using the optimized model. These results show that HSI technology can rapidly and precisely detect the total acid content of Daqu and visualize the content distribution, which provides important technical support for upgrading and transforming the liquor. Practical Applications Daqu is the saccharifying agent, leavening agent, and aroma‐generating agent of liquor brewing. During the fermentation process, the total acid content is an important indicator for the quality evaluation of Daqu. Therefore, it is highly significant to detect the total acid content in Daqu quickly and accurately. The HSI technique is widely used to detect the contents of various substances in food. In this study, the HSI technique combining the PCA‐SPA and BP‐GA algorithms was established to detect the total acid content of Daqu, and a satisfactory result was obtained, showing that HSI technology combined with an optimized algorithm could be used to realize rapid and accurate detection of the total acid content of Daqu. In addition, the predictive model proposed in this study is not only applicable for detecting other food substances but also this method has great potential for real‐time indicators detection in winery for future work.

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Section: Methodsmentioning

confidence: 99%

Detecting total acid content quickly and accurately by combining hyperspectral imaging and an optimized algorithm method

Jiang

Huang

et al. 2021

J Food Process Engineering

View full text Add to dashboard Cite

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Development of a predictive model for assessing quality of winter jujube during storage utilizing hyperspectral imaging technology

Wei,

Liu,

Fan

et al. 2024

J Food Process Engineering

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The necessity for precise determination of fruit storage durations is paramount in both industrial and domestic spheres, with hyperspectral imaging (HSI) technology emerging as a pivotal tool for prognosticating the physicochemical attributes indicative of mature fruit quality. This investigation employed hyperspectral imaging to conduct a noninvasive analysis of variations in soluble solids content (SSC), hardness, and moisture content (MC) in jujube fruit over the course of storage at divergent temperatures. Throughout the storage intervals (0, 7, 14, and 21 days) at varying temperatures (4 and 20°C), both physicochemical and spectral data were amassed. The raw spectral information underwent preprocessing through multiple scattering correction (MSC), standard normal variation (SNV), and Savitzky–Golay (SG) algorithms to refine the methodologies for SSC, hardness, and moisture content. Subsequently, the competitive adaptive reweighted sampling (CARS) algorithm facilitated the discernment and elimination of extraneous variables, thereby enhancing feature wavelength extraction. This process underpinned the development of partial least squares regression (PLSR), back propagation (BP), and genetic algorithm‐back propagation (GA‐BP) models predicated on CARS‐derived features, culminating in the selection of an optimal model. The findings underscore the capability of hyperspectral imaging technology to swiftly and nondestructively ascertain the SSC, hardness, and MC of jujube throughout the storage phase, thereby enabling the assessment of quality attributes over varying storage durations and facilitating the surveillance of jujube quality maintenance during storage.Practical ApplicationsThe winter jujube garners appreciation from consumers owing to its exquisite flavor, yet its quality diminishes over time in storage due to various factors, thereby impacting its market value. Consequently, it is imperative to surveil the quality alterations of jujube throughout its storage to mitigate the degradation of its quality. Hyperspectral imaging technology offers a sophisticated means to forecast the physicochemical index changes indicative of the fruit's quality at maturity. This research delineates the development of predictive models for the soluble solids, hardness, and moisture content of jujube at divergent temperatures throughout the storage interval, selecting the paramount model through a holistic assessment, thereby fully harnessing the capabilities of hyperspectral imaging technology in monitoring jujube quality during storage. Moreover, the methodology employed herein is adaptable to other fruits in storage, harboring the potential for future application in the real‐time quality monitoring of fruits as they exit the storage facilities.

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A Research on Methane Prediction Model Based on Improved BP-GA Network

Cited by 2 publications

References 0 publications

Detecting total acid content quickly and accurately by combining hyperspectral imaging and an optimized algorithm method

Detecting total acid content quickly and accurately by combining hyperspectral imaging and an optimized algorithm method

Development of a predictive model for assessing quality of winter jujube during storage utilizing hyperspectral imaging technology

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