Wavelength-Selective Partial Least Squares Regression of Near-Infrared Spectra for Predicting Chlorophylls and Carotenoids in Leaves

Mohammad Uzair Ali, Saurav Bharadwaj

Abstract


This study investigates the quantification of leaf pigments using near-infrared (NIR) spectroscopy, with an emphasis on the application of wavelength-selective partial least squares regression (WSPLSR) for improved predictive modelling. While conventional PLSR yields varying prediction accuracies—chlorophyll a (R² = 0.922), chlorophyll b (R² = 0.853), chlorophyll a+b (R² = 0.910), and carotenoids (R² = 0.787)—the incorporation of wavelength selection significantly enhances model performance. The WSPLSR approach identifies optimal spectral regions strongly correlated with pigment concentrations, achieving higher coefficients of determination: chlorophyll a (R² = 0.978), chlorophyll b (R² = 0.953), chlorophyll a+b (R² = 0.974), and carotenoids (R² = 0.908). Model robustness is confirmed by the corresponding RPD values: chlorophyll a (RPD = 6.761), chlorophyll b (RPD = 4.593), chlorophyll a+b (RPD = 6.2), and carotenoids (RPD = 3.29). These findings demonstrate the efficacy of WSPLSR models in NIR spectroscopy as a rapid, non-destructive, and non-invasive tool for farmers, facilitating the early detection and management of pigment-related stress in plants.

Full Text:

PDF

References


Gitelson AA, Gritz Y, Merzlyak MN. Relationships between leaf chlorophyll content and spectral re-flectance and algorithms for non-destructive chlorophyll assessment in higher plant leaves. Journal of plant physiology. 2003 Jan 1;160(3):271-82.

Croft H, Chen JM, Luo X, Bartlett P, Chen B, Staebler RM. Leaf chlorophyll content as a proxy for leaf photosynthetic capacity. Global change biology. 2017 Sep;23(9):3513-24.

Daughtry CS, Walthall CL, Kim MS, De Colstoun EB, McMurtrey Iii JE. Estimating corn leaf chlo-rophyll concentration from leaf and canopy reflectance. Remote sensing of Environment. 2000 Nov 1;74(2):229-39.

Fritschi FB, Ray JD. Soybean leaf nitrogen, chlorophyll content, and chlorophyll a/b ratio. Photosyn-thetica. 2007 Mar;45:92-8.

Dhami N, Cazzonelli CI. Environmental impacts on carotenoid metabolism in leaves. Plant Growth Regulation. 2020 Dec;92(3):455-77.

Zhou X, Huang W, Kong W, Ye H, Dong Y, Casa R. Assessment of leaf carotenoids content with a new carotenoid index: Development and validation on experimental and model data. International Journal of Applied Earth Observation and Geoinformation. 2017 May 1;57:24-35.

Gitelson AA, Zur Y, Chivkunova OB, Merzlyak MN. Assessing carotenoid content in plant leaves with reflectance spectroscopy¶. Photochemistry and photobiology. 2002 Mar;75(3):272-81.

Zarco-Tejada PJ, Guillén-Climent ML, Hernández-Clemente R, Catalina A, González MR, Martín P. Estimating leaf carotenoid content in vineyards using high resolution hyperspectral imagery acquired from an unmanned aerial vehicle (UAV). Agricultural and forest meteorology. 2013 Apr 15;171:281-94.

Wickliff JL, Aronoff S. Quantitative measurement of leaf chlorophylls by spectrophotometry of their pheophytins in aqueous alcoholic extracts. Plant physiology. 1962 Sep;37(5):584.

Henriques FS. Leaf chlorophyll fluorescence: background and fundamentals for plant biologists. The Botanical Review. 2009 Sep;75:249-70.

Das A, Guyer L, Hörtensteiner S. Chlorophyll and chlorophyll catabolite analysis by HPLC. Plant Se-nescence: Methods and Protocols. 2018:223-35.

Quach HT, Steeper RL, Griffin GW. An improved method for the extraction and thin-layer chroma-tography of chlorophyll a and b from spinach. Journal of Chemical Education. 2004 Mar;81(3):385.

Rivera S, Vilaró F, Canela R. Determination of carotenoids by liquid chromatography/mass spectrom-etry: effect of several dopants. Analytical and bioanalytical chemistry. 2011 May;400:1339-46.

Bharadwaj S, Kumar R. The assessment of reflectance ratios for determination of water content in leaves using shorter end of near-infrared spectroscopy. InBiomedical Spectroscopy, Microscopy, and Imaging II 2022 May 27 (Vol. 12144, pp. 135-144). SPIE.

Rasooli Sharabiani V, Soltani Nazarloo A, Taghinezhad E, Veza I, Szumny A, Figiel A. Prediction of winter wheat leaf chlorophyll content based on VIS/NIR spectroscopy using ANN and PLSR. Food Science & Nutrition. 2023 May;11(5):2166-75.

Liu W, Li Y, Tomasetto F, Yan W, Tan Z, Liu J, Jiang J. Non-destructive measurements of toona sinensis chlorophyll and nitrogen content under drought stress using near infrared spectroscopy. Fron-tiers in Plant Science. 2022 Jan 21;12:809828.

Ge Y, Atefi A, Zhang H, Miao C, Ramamurthy RK, Sigmon B, Yang J, Schnable JC. High-throughput analysis of leaf physiological and chemical traits with VIS–NIR–SWIR spectroscopy: a case study with a maize diversity panel. Plant methods. 2019 Dec;15:1-2.

Ji-Yong S, Xiao-Bo Z, Jie-Wen Z, Kai-Liang W, Zheng-Wei C, Xiao-Wei H, De-Tao Z, Holmes M. Nondestructive diagnostics of nitrogen deficiency by cucumber leaf chlorophyll distribution map based on near infrared hyperspectral imaging. Scientia Horticulturae. 2012 May 1;138:190-7.

S. Jacquemound L. Bidel C. Francois G. Pavan. 2003. ANGERS Leaf Optical Properties Database (2003). Data set. Available on-line [http://ecosis.org] from the Ecological Spectral Information System (EcoSIS)

Afseth NK, Kohler A. Extended multiplicative signal correction in vibrational spectroscopy, a tutorial. Chemometrics and Intelligent Laboratory Systems. 2012 Aug 1;117:92-9.


Refbacks

  • There are currently no refbacks.


------------------------------------------------------------------------------------------------------------------------

The ADBU Journal of Engineering Technology (AJET)" ISSN:2348-7305

This journal is published under the terms of the Creative Commons Attribution (CC-BY) (http://creativecommons.org/licenses/)

Number of Visitors to this Journal: