ArticleHeliyon2024
Near-infrared spectroscopy for analysing livestock diet quality: A systematic review.
Article in Heliyon, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
What it found
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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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Who cites it
4 citing papers in PubMed.
- Convolutional Neural Network-Based Models for Near-Infrared Prediction of Nutritional Quality in Multi-Product Animal Feeds.Animals : an open access journal from MDPI · 2026Article
- Non-Destructive Detection of Fruit Quality: Technologies, Applications and Prospects.Foods (Basel, Switzerland) · 2025Review
- Construction and optimization of quantitative analysis models for pigments in broccoli (Food chemistry: X · 2025Article
- Increasing Cattle Herd Size or Density in CAnimals : an open access journal from MDPI · 2025Article
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Near-infrared spectroscopy (NIRS) is a non-invasive and fast technology that has been increasingly used to analyse livestock diet quality. The objective of this study was to conduct a systematic review of the literature to examine the utilisation of NIRS technology for analysing livestock diet quality, with a focus on identifying trends, methodologies, and challenges in recent research. We conducted a systematic search of the literature on five electronic databases and retrieved 718 studies that have been published on the subject. Fifty-four studies were subsequently selected and investigated in depth. These studies were categorised into two groups, namely benchtop and portable, based on the types of NIRS devices utilised, with a majority employing the reflectance spectra mode. Our analysis found that standard normal variate (SNV), detrend (DT), and multiplicative scatter correction (MSC) are the most commonly used spectral data processing methods. The findings indicate that NIRS technology can provide accurate and reliable measurements of key livestock diet quality parameters such as crude protein, fibre, and moisture content. Additionally, we discuss the challenges associated with NIRS technology and provide recommendations for future research directions to further advance the use of NIRS technology in the livestock industry.
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Registered trials
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