Near-Infrared Spectroscopy and Mode Cloning (NIR-MC) for In-Situ Analysis of Crude Protein in Bamboo

ORCID

Vance: https://orcid.org/0000-0001-8466-6077

MSU Affiliation

College of Agriculture and Life Sciences; Department of Biochemistry, Molecular Biology, Entomology and Plant Pathology; College of Forest Resources; Department of Wildlife, Fisheries and Aquaculture

Creation Date

2026-10-30

Abstract

This study develops Near-Infrared Spectroscopy (NIRS) and Mode-Cloning (MC) for the rapid assessment of the nutritional quality of bamboo leaves, the primary diet of giant pandas (Ailuropoda melanoleuca) and red pandas (Ailurus fulgens). To test the NIR-MC approach, we evaluated three species of bamboo (Phyllostachys bissetii, Phyllostachys rubromarginata, Phyllostachys aureosulcata). Mode-Cloning incorporated a Slope and Bias Correction (SBC) transform to crude protein prediction models built with NIR spectra taken from Fine–Ground leaves (master mode). The modified models were then applied to spectra from leaves in the satellite minimal processing modes (Course–Ground, Dry–Whole, and Fresh–Whole). The NIR-MC using the SBC yielded a residual prediction deviation (RPD) = 2.73 and 1.84 for Course–Ground and Dry–Whole sample modes, respectively, indicating a good quantitative prediction of crude protein for minimally processed samples that could be easily acquired under field conditions using a portable drier and grinder. The NIR-MC approach also improved the model of crude protein for spectra collected from Fresh–Whole bamboo leaves in the field. Thus, NIR-MC has the potential to provide a real-time prediction of the macronutrient distribution in bamboo in situ, which affects the foraging behavior and dispersion of giant and red pandas in their natural habitats.

Keywords

bamboo leaves, calibration transfer, forage grasses, giant panda, nutrition, red panda, sampling

Publication Date

3-8-2022

Publication Title

Remote Sensing

Publisher

MDPI

Creative Commons License

Creative Commons Attribution 4.0 International License
This work is licensed under a Creative Commons Attribution 4.0 International License.

Rights

© 2022 by the authors

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Digital Object Identifier (DOI)

https://doi.org/10.3390/rs14061302