Prediction of Chemical Composition of Alfalfa Blocks Using FTIR Spectroscopy and Machine Learning Models.

Haijun Du, Yanhua Ma, Liying Cao, Hongzhou Liu, He Su, Xiangbo Liu

Abstract read

In one paragraph

Article in Foods (Basel, Switzerland), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed

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1 · What the graph read from it

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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.

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5 · Who and what money

Authors and funding

6 authors.

Haijun DuCollege of Mechanical Engineering, Inner Mongolia University of Science and Technology, Baotou 014010, China.ORCID 0000-0002-3428-8845

Yanhua MaCollege of Mechanical and Electrical Engineering, Inner Mongolia Agricultural University, Hohhot 010018, China.

Liying CaoCollege of Mechanical Engineering, Inner Mongolia University of Science and Technology, Baotou 014010, China.

Hongzhou LiuCollege of Mechanical and Electrical Engineering, Inner Mongolia Agricultural University, Hohhot 010018, China.

He SuCollege of Mechanical and Electrical Engineering, Inner Mongolia Agricultural University, Hohhot 010018, China.

Xiangbo LiuCollege of Mechanical and Electrical Engineering, Inner Mongolia Agricultural University, Hohhot 010018, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Alfalfa blocks are an important commercial roughage, and their crude protein (CP), starch, fat, neutral detergent fibre (NDF), and acid detergent fibre (ADF) contents are key indicators of nutritional quality. This study investigated Fourier-transform infrared (FTIR) spectroscopy combined with machine learning for rapid quantitative prediction of these constituents. Different spectral preprocessing strategies and variable-selection methods were compared using partial least squares regression (PLSR), random forest regression (RFR), and back-propagation (BP) neural networks. The effects of spectral preprocessing and preceding baseline correction varied among constituents, indicating that baseline correction did not uniformly improve predictive performance. Competitive adaptive reweighted sampling (CARS) reduced spectral dimensionality, and the resulting PLSR models outperformed the RFR and BP models for all five constituents in the reported comparisons, with values reported as mean ± standard deviation across 20 repeated data partitions. For CP, the PLSR model combining first-derivative Savitzky-Golay preprocessing with CARS (SG1D-CARS-PLSR) achieved a prediction-set coefficient of determination (R

Indexed as

alfalfa blocksCARSchemical compositionFTIRprediction

Identifiers

PMID42883482

DOI10.3390/foods15193375

PMCPMC13664120

What OpenQuestion holds

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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.