Abstract
Near infrared spectroscopy is widely used to rapidly and cost-effectively collect chemical information from plant samples. Large datasets with hundreds to thousands of spectra and reference values are increasingly becoming more common as researchers accumulate data over many years or across research groups. These datasets potentially contain great spectral and chemical variation and could produce a broadly-applicable calibration model. In this study, partial least squares regression was used to model relationships between near infrared spectra and the foliar concentration of two ecologically-important chemical traits, available nitrogen and total formylated phloroglucinol compounds in Eucalyptus leaves. The nested spatial structure within the extensive dataset of spectra and reference values from 80 species of Eucalyptus was taken into account during calibration development and model validation. Geographic variation amongst samples influenced how well available nitrogen could be predicted. Predictive error of the model was greatest when tested against samples from different Australian states and local government areas to the calibration set. In addition, the results showed that simply relying on spectral variation (assessed by Mahalanobis distance) may mislead researchers into how many reference values are needed. The prediction accuracy of the model of available nitrogen differed little whether 300 or up to 987 calibration samples were included, which indicated that an excessive number of reference values were obtained. Lastly, a suitable multi-species calibration for formylated phloroglucinol compounds was produced and the difficulties associated with predicting complex chemical traits were discussed. Directing effort towards broadly applicable models will encourage sharing of calibration models across projects and research groups and facilitate the integration of near infrared spectroscopy in many research fields.
Highlights
Near infrared (NIR) spectroscopy is a nondestructive, fast and accurate technology, which is used to answer research questions in many fields
Identifying the underlying causes of variation in large datasets can help build broad-based calibration models, facilitating the integration of NIR spectroscopy into many fields
This study proposes the use of different cross-validation techniques for model fitting and selection
Summary
Near infrared (NIR) spectroscopy is a nondestructive, fast and accurate technology, which is used to answer research questions in many fields. As this technology has become more readily available, large datasets are increasingly becoming more common. As a project accumulates ever more samples with more diversity, the dataset intrinsically contains greater chemical and spectral variation that must be represented in a robust calibration model. Researchers can develop a broadly applicable “global” calibration model that encapsulates chemical diversity.[4] Reducing time and cost for NIR calibration development would benefit many fields such as forestry, agriculture, pharmaceutics and ecology where large datasets are becoming more common. It is still unclear how best to explore, capture and utilise the variation inherent within a large dataset
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