Skip to main content

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

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

This is a preview of subscription content, access via your institution.

Fig. 1
Fig. 2
Fig. 3
Fig. 4

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

References

  1. Grundon NJ, Robson AD, Lambert MJ, Snowball K. Nutrient deficiency and toxicity symptoms. In: Reuter DJ, Robinson JB, Dutkiewicz C, editors. Plant analysis: an interpretation manual. Collingwood: CSIRO; 1997. p. 37–51.

    Google Scholar 

  2. Marschner H. Mineral nutrition of higher plants. 2nd ed. London: Academic; 1995.

    Google Scholar 

  3. Edwards IK, Kalra YP, Radford FG. Chloride determination and levels in the soil-plant environment. Environ Pollut Series B Chem Phys. 1981;2:109–17.

    CAS  Article  Google Scholar 

  4. White PJ, Broadley MR. Chloride in soils and its uptake and movement within the plant: a review. Ann Bot. 2001;88:967–88.

    CAS  Article  Google Scholar 

  5. Xu G, Magen H, Tarchitzky J, Kafkafi V. Advances in chloride nutrition. Adv Agron. 2000;68:96–150.

    Google Scholar 

  6. Ayers RS, Westcot DW. Water Quality for Agriculture. Irrig Drain Paper Paper 29, Rev. 1. Rome: FAO; 1985.

  7. Visconti F, de Paz JM, Bonet L, Jordà M, Quiñones A, Intrigliolo DS. Effects of a commercial calcium protein hydrolysate on the salt tolerance of Diospyros kaki L. cv. “Rojo Brillante” grafted on Diospyros lotus. Sci Hortic. 2015;185:129–38.

    CAS  Article  Google Scholar 

  8. George A, Nissen B, Broadley R. Persimmon nutrition: a practical guide to improving fruit quality and production. Camberwell: Queensland Horticulture Institute; 2005.

    Google Scholar 

  9. de Paz JM, Visconti F, Tudela L, Quiñones A, Intrigliolo D, Jordà M, Bonet L. La fitotoxicidad por cloruro en el cultivo del caqui: Descripción del problema. Agrícola Vergel (in press). 2016.

  10. Besada C, Gil R, Bonet L, Quiñones A, Intrigliolo D, Salvador A. Chloride stress triggers maturation and negatively affects the postharvest quality of persimmon fruit. Involvement of calyx ethylene production. Plant Physiol Biochem. 2016;100:105–12.

    CAS  Article  Google Scholar 

  11. FAO (2015) FAOSTAT database—Food and Agriculture Organization of the United Nations (FAO), Rome. http://faostat3.fao.org/home/E. Accessed 6 Apr 2015.

  12. Ercisli S, Akbulut M. Persimmon cultivation and genetic resources in Turkey. Acta Horticulturae (ISHS). 2009;833:35–8.

    Article  Google Scholar 

  13. Miller RO. Extractable chloride, nitrate, orthophosphate, potassium, and sulfate-sulfur in plant tissue: 2% acetic acid extraction. In: Kalra YP, editor. Handbook of reference methods for plant analysis. Boca Raton: CRC Press; 1998. p. 115–8.

    Google Scholar 

  14. Chang CW, Laird DA. Near-infrared reflectance spectroscopic analysis of soil C and N. Soil Sci. 2002;167:110–6.

    CAS  Article  Google Scholar 

  15. Morra MJ, Hall MH, Freeborn LL. Carbon and nitrogen analysis of soil fractions using near-infrared reflectance spectroscopy. Soil Sci Soc Am J. 1991;55:288–91.

    CAS  Article  Google Scholar 

  16. Cozzolino D. Near infrared spectroscopy in natural products analysis. Planta Med. 2009;75:746–56.

    CAS  Article  Google Scholar 

  17. Begley TH, Lanza E, Norris KH, Hruschka WR. Determination of sodium chloride in meat by near-infrared diffuse reflectance spectroscopy. J Agric Food Chem. 1984;32:984–7.

    CAS  Article  Google Scholar 

  18. Shao XG, Ning Y, Liu FX, Li JH, Cai WS. Application of near-infrared spectroscopy in micro inorganic analysis. Acta Chim Sin. 2012;70:2109–14.

    CAS  Article  Google Scholar 

  19. van Maarschalkerweerd M, Bro R, Egebo M, Husted S. Diagnosing latent copper deficiency in intact barley leaves (Hordeum vulgare, L.) using near infrared spectroscopy. J Agric Food Chem. 2013;61:10901–10.

    Article  Google Scholar 

  20. Guo ZM, Zhao CJ, Huang WQ, Wang YA, Guo JH. Nondestructive quantification of foliar chlorophyll in an apple orchard by visible/near-infrared reflectance spectroscopy and partial least squares. Spectr Lett. 2014;47:481–7.

    CAS  Article  Google Scholar 

  21. Tobias RD. An introduction to partial least squares regression. SAS Global Forum Proceedings/SUGI 95. 1995. pp 1250–1257.

  22. Wold S, Martens H, Wold H. The multivariate calibration problem in chemistry solved by the PLS method. Matrix Pencils. 1983;973:286–93.

    Article  Google Scholar 

  23. Viscarra-Rossel RA, Behrens T. Using data mining to model and interpret soil diffuse reflectance spectra. Geoderma. 2010;158:46–54.

    Article  Google Scholar 

  24. Viscarra-Rossel RA. ParLeS: software for chemometric analysis of spectroscopic data. Chemom Intell Lab Syst. 2008;90:72–83.

    CAS  Article  Google Scholar 

  25. Jolliffe IT. Principal component analysis. London: Springer; 2002.

    Google Scholar 

  26. Martens H, Næs T. Multivariate calibration. Chichester: Wiley; 1989.

    Google Scholar 

  27. Gilliam JW. Rapid measurement of chlorine in plants materials. Soil Sci Soc Am Proc. 1971;35:512–3.

    Article  Google Scholar 

  28. Cotlove E. Determination of the true chloride content of biological fluids and tissues. II. Analysis by simple, nonisotopic methods. Anal Chem. 1963;35:101–5.

    Article  Google Scholar 

  29. Williams PC. Implementation of near-infrared technology. In: Williams PC, Norris KH, editors. Near-infrared technology in the agricultural and food industry. 2nd ed. St. Paul: American Association of Cereal Chemists; 2001. p. 8.

    Google Scholar 

  30. Fearn T. Assessing calibrations: SEP, RPD, RER and R2. NIR News. 2002;13:12–4.

    Article  Google Scholar 

  31. Elvidge DE. Visible and near infrared reflectance characteristics of dry plant materials. Int J Remote Sens. 1990;11:1775–95.

    Article  Google Scholar 

  32. Rotbart N, Schmilovitch Z, Cohen Y, Alchanatis V, Erel R, Ignat T, et al. Estimating olive leaf nitrogen concentration using visible and near-infrared spectral reflectance. Biosyst Eng. 2013;114:426–34.

    Article  Google Scholar 

  33. Dalal RC, Henry RJ. Simultaneous determination of moisture, organic carbon, and total nitrogen by near infrared reflectance spectrophotometry. Soil Sci Soc Am J. 1986;50:120–3.

    CAS  Article  Google Scholar 

  34. Shi T, Cui L, Wang J, Fei T, Chen Y, Wu G. Comparison of multivariate methods for estimating soil total nitrogen with visible/near-infrared spectroscopy. Plant Soil. 2013;366:363–75.

    CAS  Article  Google Scholar 

  35. Slama I, Abdelly C, Bouchereau A, Flowers T, Savouré A. Diversity, distribution and roles of osmoprotective compounds accumulated in halophytes under abiotic stress. Ann Bot. 2015;115:433–47.

    Article  Google Scholar 

  36. Workman J, Weyer L. Practical guide and spectral atlas for interpretive near-infrared spectroscopy. Boca Raton: CRC Press; 2012.

    Book  Google Scholar 

  37. Jadhav NH, Kashid DN, Kulkarni SR. Subset selection in multiple linear regression in the presence of outlier and multicollinearity. Stat Methodol. 2014;19:44–59.

    Article  Google Scholar 

Download references

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.

Author information

Authors and Affiliations

Authors

Corresponding authors

Correspondence to José Miguel de Paz or Fernando Visconti.

Ethics declarations

Conflict of interest

The authors do not have any conflict of interest regarding the results and interpretations communicated in this article. Besides, product identifications in this chapter have been provided for the benefit of the readers and do not imply any endorsement of the authors, nor their institutions.

Electronic supplementary material

Below is the link to the electronic supplementary material.

ESM 1

(PDF 8 kb)

ESM 2

(XLS 45 kb)

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

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

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00216-016-9430-2

Keywords

  • Chloride
  • Agriculture
  • Chemometrics/statistics
  • IR spectroscopy
  • Persimmon
  • Partial least squares