A novel approach to Predict WTI crude spot oil price: LSTM-based feature extraction with Xgboost Regressor

Simsek, Ahmed Ihsan; Bulut, Emre; Gur, Yunus Emre; Gültekin Tarla, Esma

doi:10.1016/j.energy.2024.133102

Energy

2024

DOI: 10.1016/j.energy.2024.133102

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A novel approach to Predict WTI crude spot oil price: LSTM-based feature extraction with Xgboost Regressor

Ahmed Ihsan Simsek,

Emre Bulut,

Yunus Emre Gur

et al.

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2024

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Detection of Apple Sucrose Concentration Based on Fluorescence Hyperspectral Image System and Machine Learning

Zhan,

Mao,

Fan

et al. 2024

Foods

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China ranks first in apple production worldwide, making the assessment of apple quality a critical factor in agriculture. Sucrose concentration (SC) is a key factor influencing the flavor and ripeness of apples, serving as an important quality indicator. Nondestructive SC detection has significant practical value. Currently, SC is mainly measured using handheld refractometers, hydrometers, electronic tongues, and saccharimeter analyses, which are not only time-consuming and labor-intensive but also destructive to the sample. Therefore, a rapid nondestructive method is essential. The fluorescence hyperspectral imaging system (FHIS) is a tool for nondestructive detection. Upon excitation by the fluorescent light source, apples displayed distinct fluorescence characteristics within the 440–530 nm and 680–780 nm wavelength ranges, enabling the FHIS to detect SC. This study used FHIS combined with machine learning (ML) to predict SC at the apple’s equatorial position. Primary features were extracted using variable importance projection (VIP), the successive projection algorithm (SPA), and extreme gradient boosting (XGBoost). Secondary feature extraction was also conducted. Models like gradient boosting decision tree (GBDT), random forest (RF), and LightGBM were used to predict SC. VN-SPA + VIP-LightGBM achieved the highest accuracy, with Rp2, RMSEp, and RPD reaching 0.9074, 0.4656, and 3.2877, respectively. These results underscore the efficacy of FHIS in predicting apple SC, highlighting its potential for application in nondestructive quality assessment within the agricultural sector.

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Detection of Apple Sucrose Concentration Based on Fluorescence Hyperspectral Image System and Machine Learning

Zhan,

Mao,

Fan

et al. 2024

Foods

View full text Add to dashboard Cite

show abstract

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A novel approach to Predict WTI crude spot oil price: LSTM-based feature extraction with Xgboost Regressor

Cited by 1 publication

References 54 publications

Detection of Apple Sucrose Concentration Based on Fluorescence Hyperspectral Image System and Machine Learning

Detection of Apple Sucrose Concentration Based on Fluorescence Hyperspectral Image System and Machine Learning

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