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Determination of persimmon leaf chloride contents using near-infrared spectroscopy (NIRS)

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Abstract

Early diagnosis of specific chloride toxicity in persimmon trees requires the reliable and fast determination of the leaf chloride content, which is usually performed by means of a cumbersome, expensive and time-consuming wet analysis. A methodology has been developed in this study as an alternative to determine chloride in persimmon leaves using near-infrared spectroscopy (NIRS) in combination with multivariate calibration techniques. Based on a training dataset of 134 samples, a predictive model was developed from their NIR spectral data. For modelling, the partial least squares regression (PLSR) method was used. The best model was obtained with the first derivative of the apparent absorbance and using just 10 latent components. In the subsequent external validation carried out with 35 external data this model reached r 2 = 0.93, RMSE = 0.16 % and RPD = 3.6, with standard error of 0.026 % and bias of −0.05 %. From these results, the model based on NIR spectral readings can be used for speeding up the laboratory determination of chloride in persimmon leaves with only a modest loss of precision. The intermolecular interaction between chloride ions and the peptide bonds in leaf proteins through hydrogen bonding, i.e. N–H···Cl, explains the ability for chloride determinations on the basis of NIR spectra.

The NIRS-PLSR alternative to the wet reference analytical method for chloride determination in persimmon leaves saves lab work in exchange of a modest loss of precissionᅟ

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Abbreviations

A :

Apparent absorbance

DW:

Dry weight

K-S:

Kolmogorov-Smirnov

LC:

Latent component

MLR:

Multiple linear regression

NIR:

Near-infrared

NIRS:

Near-infrared spectroscopy

PC:

Principal component

PCA:

Principal components analysis

PCR:

Principal components regression

PLSR:

Partial least squares regression

r :

Pearson’s product-moment correlation coefficient

r 2 :

Coefficient of determination

R :

Diffuse reflectance

RMSE:

Root mean square error

RPD:

Ratio of standard error of performance to standard deviation

SD:

Standard deviation

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Acknowledgments

The funding for this work was provided by the Spanish “Ministerio de Ciencia e Innovación” through project CGL2012-39725-C02, and locally funding by the “San Bernat” agricultural cooperative of Carlet (Valencia-Spain). F. Visconti thanks the financial support received from the “Ministerio de Economía y Competitividad” through grant “Juan de la Cierva” (JCI-2011-11254). We would also like to thank the comments, indications and suggestions of the anonymous reviewers and editor that were of much help to improve the article.

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Authors and Affiliations

  1. Instituto Valenciano de Investigaciones Agrarias–IVIA (GV), Centro para el Desarrollo de la Agricultura Sostenible–CDAS, Carretera CV-315, Km 10.7, 46113, Moncada, València, Spain

    José Miguel de Paz, Fernando Visconti & Mara Chiaravalle

  2. Departamento de Citricultura y Producción Vegetal, Instituto Valenciano de Investigaciones Agrarias–IVIA (GV), Carretera CV-315, Km 10.7, 46113, Moncada, València, Spain

    Ana Quiñones

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  1. José Miguel de Paz
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  2. Fernando Visconti
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Correspondence to José Miguel de Paz or Fernando Visconti.

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de Paz, J.M., Visconti, F., Chiaravalle, M. et al. Determination of persimmon leaf chloride contents using near-infrared spectroscopy (NIRS). Anal Bioanal Chem 408, 3537–3545 (2016). https://doi.org/10.1007/s00216-016-9430-2

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