ArticleFood science & nutrition2025
An Optimized Bidirectional Long Short-Term Memory Model Based on Hyperspectral Analysis of Protein Content in Milk Powder.
Article in Food science & nutrition, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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 (
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.