Near-infrared prediction of edible oil frying times based on Bayesian Ridge Regression

Liu, Yi; Sun, Laijun; Du, Chengsi; Wang, Xing

doi:10.1016/j.ijleo.2020.164950

Cited by 16 publications

(7 citation statements)

References 27 publications

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“…These methods above all need the participating of professionals and expensive instruments. Recent studies 17 , 24 construct regression models to automatically predict carbonyl value of frying oil given time value. Although the models of Liu et al 17 , 24 show great performance, their model can only function at a fixed temperature due to the limit of regression methods they used.…”

Section: Discussionmentioning

confidence: 99%

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A novel GAN-based regression model for predicting frying oil deterioration

Yang

Wang

Chen

et al. 2022

Sci Rep

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Frying is a common food processing method because fried food is popular with consumers for its attractive colour and crisp taste. What’s concerning is that the complex physical and chemical reactions occurring during deep frying are harmful to the well-being of people. For this reason, researchers proposed various detecting methods to assess frying oil deterioration. Some studies design sensor probe, others utilize spectroscopic related methods. However, these methods all need the participating of professionals and expensive instruments. Some of the methods can only function on a fixed temperature. To fix the defects of the above models, in this study, we make use of recent advances in machine learning, specifically generative adversarial networks (GAN). We propose a GAN-based regression model to predict frying oil deterioration. First, we conduct deep frying experiments and record the values of indexes we choose under different temperature and frying time. After collecting the data, we build a GAN-based regression model and train it on the dataset. Finally, we test our model on the test set and analyze the experimental results. Our results suggest that the proposed model can predict frying oil deterioration without experiments. Our model can be applied to other regression problems in various research areas, including price forecasting, trend analysis and so on.

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Section: Discussionmentioning

confidence: 99%

“…In order to determine these metrics, researchers have proposed various methods. Column chromatography 17 is frequently used to detect TPC content in frying oil. While being accurate, the detection process is time-consuming and has to be done by experts 18 .…”

Section: Introductionmentioning

confidence: 99%

A novel GAN-based regression model for predicting frying oil deterioration

Yang

Wang

Chen

et al. 2022

Sci Rep

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“…In addition to the LSTM neural network method, several other ML methods, i.e., Bayesian Ridge Regression (BRR), Gradient Boosted Decision Tree (GBDT), Linear Regression (LR) and Support Vector Regression (SVR), are used in this research as a comparison group. BRR, based on Bayesian knowledge, is aimed to solve the problem of multicollinearity in linear regression, and to serve the purpose of estimating regression coefficients and selecting variables [94]. GBDT is a suitable method for classification and regression problems, which uses decision stumps or regression tress as weak classifiers [95].…”

Section: Methodsmentioning

confidence: 99%

Storm Surge Prediction Based on Long Short-Term Memory Neural Network in the East China Sea

et al. 2021

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As an area frequently suffering from storm surge, the Yangtze River Estuary in the East China Sea requires fast and accurate prediction of water level for disaster prevention and mitigation. Due to storm surge process being affected by the long-term and short-term correlation of multiple factors, this study attempts to introduce a data-driven idea into the water level prediction during storm surge. By collecting the observed meteorological data and water level data of 12 typhoons from 1986 to 2016 at the Lusi tidal station of Jiangsu Province, China near the north branch of the Yangtze River Estuary, a Long Short-Term Memory (LSTM) neural network model was constructed by using multi-factor time series to predict the water level during the storm surge period. This study concludes that the LSTM model performs precisely for 1 h prediction of water level during the storm surge period and it can provide a 15 h prediction of water level within a limited error, and the prediction performance of the LSTM model is visibly superior to the four traditional ML models by 41% in terms of Accuracy Coefficient.

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“…This information can be revealed by a specific method which is called chemometrics. It is the use of mathematical and statistical methods like regression, normalization, and validation to correlate the spectral data with actual reference-quality parameters measured using standard laboratory procedures [8,35].…”

Section: Spectra Features Of Cocoa Beanmentioning

confidence: 99%

Near-Infrared Technology in Agriculture: Non-Destructive Determination of Inner Quality Parameters in Intact Cocoa Beans

Zulfahrizal

Munawar

2021

Trends Sci

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This present study aimed to apply the near-infrared technology based on reflectance spectroscopy or NIRS in determining 2 main quality attributes on intact cocoa beans namely fat content (FC) and moisture content (MC). Absorbance spectral data, in a wavelength range from 1000 to 2500 nm were acquired and recorded for a total of 110 bulk cocoa bean samples. Meanwhile, actual reference FC and MC were obtained using standard laboratory approaches and Soxhlet and Gravimetry methods. Samples were split onto calibration and validation datasets. The prediction models, used to determine both quality attributes were developed from the calibration dataset using 2 regression methods: Principal component regression (PCR) and partial least square regression (PLSR). To obtain more accurate and robust prediction performance, 4 different spectra correction methods namely baseline shift correction (BSC), mean normalization (MN), standard normal variate (SNV), and orthogonal signal correction (OSC) were employed. The results showed that PLSR was better than PCR for both quality parameters prediction. Moreover, spectra corrections enhanced the prediction accuracy and robustness from which OSC was found to be the best correction method for FC and MC determination. The prediction performance using validation dataset generated a correlation coefficient (r), ratio prediction to deviation (RPD), and ratio error to range (RER) indexes for FC were 0.93, 3.16 and 7.12, while for MC prediction, the r coefficient, RPD and RER indexes were 0.96, 3.43 and 9.25, respectively. Based on obtained results, it may conclude that NIRS combined with proper spectra correction and regression approaches can be used to determine inner quality attributes of intact cocoa beans rapidly and simultaneously. HIGHLIGHTS We study and apply NIRS technology as a fast and novel method to predict inner quality parameters of intact cocoa beans in form of moisture and fat content Prediction models, used to determine both quality attributes were developed using 2 regression methods: Principal component regression (PCR) and partial least square regression (PLSR) To obtain more accurate and robust prediction performance, 4 different spectra correction methods namely baseline shift correction (BSC), mean normalization (MN), standard normal variate (SNV), and orthogonal signal correction (OSC) The best prediction performance was obtained when the models are constructed using PLSR in combination with OSC correction approach The maximum correlation coefficient (r) and ratio prediction to deviation (RPD) indexes for Fat content were 0.93 and 3.16, while for moisture content prediction, the r coefficient and RPD indexes were 0.96 and 3.43, respectively GRAPHICAL ABSTRACT

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Near-infrared prediction of edible oil frying times based on Bayesian Ridge Regression

Cited by 16 publications

References 27 publications

A novel GAN-based regression model for predicting frying oil deterioration

A novel GAN-based regression model for predicting frying oil deterioration

Storm Surge Prediction Based on Long Short-Term Memory Neural Network in the East China Sea

Near-Infrared Technology in Agriculture: Non-Destructive Determination of Inner Quality Parameters in Intact Cocoa Beans

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