A Rapid Prediction Method of Moisture Content for Green Tea Fixation Based on WOA-Elman

Lan, Tianmeng; Shen, Shuai; Yuan, Haibo; Jiang, Yongwen; Tong, Huarong; Yang, Yong

doi:10.3390/foods11182928

Cited by 6 publications

(1 citation statement)

References 43 publications

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“…This is advantageous when dealing with data where the number of time steps may change from one example to another. Elman neural network is used as a rapid prediction method of moisture content for green tea fixation (Lan et al, 2022).…”

Section: Machine Learning Methodsmentioning

confidence: 99%

Use of HSI Coupled with Machine Learning for the Identification of Water Stress in Pear Seedling Leaves

An,

Wang

2024

Preprint

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Water plays a vital role in the healthy growth of pears. Early detection of water stress can play a significant role in the timely management of water deficiency for pear yield. Most current methods are labour-intensive, time-consuming, only provide point measurements, and could be destructive. In this research, a real-time non-destructive method using a push-broom hyperspectral system (400-1000nm) was used to collect hyperspectral image data and detect the water stress of the pear seedling leaves. To build a reliable prediction model, machine learning techniques were used. The Successive Projections Algorithm (SPA) was applied for optimal wavelength selection. In particular, CNN was applied to obtain the features of key wavelengths. Both the CNN features and key wavelengths were put into RR-MLR, BLR and ENN for analysis. The training accuracy of the three modellings all reach the accuracy above 70% after about 100 epochs, while combination of CNN features outperformed the mere main spectra analysis. This research demonstrated that hyperspectral imaging coupled with machine learning techniques could be applied to predict the water content of pear leaves predict non-destructively.

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Section: Machine Learning Methodsmentioning

confidence: 99%

Use of HSI Coupled with Machine Learning for the Identification of Water Stress in Pear Seedling Leaves

An,

Wang

2024

Preprint

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An Optimized Bidirectional Long Short‐Term Memory Model Based on Hyperspectral Analysis of Protein Content in Milk Powder

Zhang,

Xue,

Jiang

et al. 2024

Food Science & Nutrition

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Protein content is an important index in the assessment of dairy nutrition. As a crucial source of protein absorption in people's daily life, the quality of milk powder products not only has a deep impact on the development of the dairy industry, but also seriously damages the health of consumers. It is of great significance to find a faster and more accurate method for detecting milk protein content. This paper utilizes the chemical content of milk powder and hyperspectral data as independent variables. By comparing 14 kinds of preprocessing algorithms, the mean‐centered (MC) method is selected to preprocess the data, and then the combined method of competitive adaptive reweighted sampling (CARS) and uninformative variable elimination (UVE) is used to screen the feature wavelength, so as to establish the model and learn the internal dynamic change law of the feature. Furthermore, the Attention mechanism was introduced to assign different weights to bidirectional long short‐term memory (BiLSTM) hidden states through mapping weighting and learning parameter matrix. To reduce the loss of information and strengthen the influence of important information, at the same time, in order to solve the difficult problem of hyperparameter selection of the model, the whale optimization algorithm (WOA) is proposed to optimize the hyperparameter selection of the model. The test results showed that with WOA‐BiLSTM‐Attention model algorithm, the coefficient of determination (R2) of 0.9975 and root mean square error (RMSEP) of 0.0337 in comparison with R2 and RMSEP values obtained from BiLSTM‐Attention model algorithm, which were higher by 0.799% lower by 56.5%, respectively. This study provides algorithm support and theoretical basis for fast non‐destructive testing based on deep learning algorithm to predict protein content in milk powder.

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Revealing the mechanism of post-harvest processing on rose quality based on dynamic changes in water content, enzyme activity, volatile and non-volatile metabolites

Xu,

Sutar,

Ren

et al. 2024

Food Chemistry

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A Rapid Prediction Method of Moisture Content for Green Tea Fixation Based on WOA-Elman

Cited by 6 publications

References 43 publications

Use of HSI Coupled with Machine Learning for the Identification of Water Stress in Pear Seedling Leaves

Use of HSI Coupled with Machine Learning for the Identification of Water Stress in Pear Seedling Leaves

An Optimized Bidirectional Long Short‐Term Memory Model Based on Hyperspectral Analysis of Protein Content in Milk Powder

Revealing the mechanism of post-harvest processing on rose quality based on dynamic changes in water content, enzyme activity, volatile and non-volatile metabolites

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