Determination of Leaf Water Content by Visible and Near-Infrared Spectrometry and Multivariate Calibration in Miscanthus

Leaf water content is one of the most common physiological parameters limiting efficiency of photosynthesis and biomass productivity in plants including Miscanthus. Therefore, it is of great significance to determine or predict the water content quickly and non-destructively. In this study, we explored the relationship between leaf water content and diffuse reflectance spectra in Miscanthus. Three multivariate calibrations including partial least squares (PLS), least squares support vector machine regression (LSSVR), and radial basis function (RBF) neural network (NN) were developed for the models of leaf water content determination. The non-linear models including RBF_LSSVR and RBF_NN showed higher accuracy than the PLS and Lin_LSSVR models. Moreover, 75 sensitive wavelengths were identified to be closely associated with the leaf water content in Miscanthus. The RBF_LSSVR and RBF_NN models for predicting leaf water content, based on 75 characteristic wavelengths, obtained the high determination coefficients of 0.9838 and 0.9899, respectively. The results indicated the non-linear models were more accurate than the linear models using both wavelength intervals. These results demonstrated that visible and near-infrared (VIS/NIR) spectroscopy combined with RBF_LSSVR or RBF_NN is a useful, non-destructive tool for determinations of the leaf water content in Miscanthus, and thus very helpful for development of drought-resistant varieties in Miscanthus.

[1]  Chunzhao Liu,et al.  Genotypic variation of cell wall composition and its conversion efficiency in Miscanthus sinensis, a potential biomass feedstock crop in China , 2014 .

[2]  Martin Kermit,et al.  Rapid Nondestructive Determination of Edible Meat Content in Crabs (Cancer Pagurus) by Near-Infrared Imaging Spectroscopy , 2010, Applied spectroscopy.

[3]  J. Roger,et al.  Application of LS-SVM to non-linear phenomena in NIR spectroscopy: development of a robust and portable sensor for acidity prediction in grapes , 2004 .

[4]  T. Fearn.  Assessing Calibrations: SEP, RPD, RER and R2 , 2002 .

[5]  Guo-ping Zhang,et al.  Genotypic differences in physiological characteristics in the tolerance to drought and salinity combined stress between Tibetan wild and cultivated barley. , 2013, Plant physiology and biochemistry : PPB.

[6]  Silvia Arazuri,et al.  Maturity, Variety and Origin Determination in White Grapes (Vitis Vinifera L.) Using near Infrared Reflectance Technology , 2005 .

[7]  E. Nevo,et al.  Biology, pretreatment and genetic development of Miscanthussinensis, a biomass crop with great potential in China , 2015 .

[8]  W. Cai,et al.  An improved boosting partial least squares method for near-infrared spectroscopic quantitative analysis. , 2010, Analytica chimica acta.

[9]  Xiaoli Li,et al.  Non-destructive measurement of acidity of Chinese bayberry using Vis/NIRS techniques , 2006 .

[10]  A correlative study on data from pork carcass and processed meat (Bauernspeck) for automatic estimation of chemical parameters by means of near-infrared spectroscopy. , 2013, Meat science.

[11]  Stefan K Arndt,et al.  Apoplastic water fraction and rehydration techniques introduce significant errors in measurements of relative water content and osmotic potential in plant leaves. , 2015, Physiologia plantarum.

[12]  Determination of grain protein content by near-infrared spectrometry and multivariate calibration in barley. , 2014, Food chemistry.

[13]  J. Lammertyn,et al.  Visible and near-infrared bulk optical properties of raw milk. , 2015, Journal of dairy science.

[14]  C. Tran,et al.  Determination of water contents in leaves by a near-infrared multispectral imaging technique , 2004 .

[15]  V. Maier,et al.  Abscisic acid accumulation and carotenoid and chlorophyll content in relation to water stress and leaf age of different types of citrus , 1990 .

[16]  Mapping of Leaf Water Content Using Near-Infrared Hyperspectral Imaging , 2013, Applied spectroscopy.

[17]  F Despagne,et al.  Neural networks in multivariate calibration. , 1998, The Analyst.

[18]  Trygve Helgerud,et al.  Towards on-line prediction of dry matter content in whole unpeeled potatoes using near-infrared spectroscopy. , 2015, Talanta.

[19]  Yunqian Ma,et al.  Practical selection of SVM parameters and noise estimation for SVM regression , 2004, Neural Networks.

[20]  Junhua Peng,et al.  Ecological characteristics and in situ genetic associations for yield-component traits of wild Miscanthus from eastern Russia. , 2016, Annals of botany.

[21]  Junhua Peng,et al.  Transferability of Genomic Simple Sequence Repeat and Expressed Sequence Tag‐Simple Sequence Repeat Markers from Sorghum to Miscanthus sinensis, a Potential Biomass Crop , 2013 .

[22]  B. Rock,et al.  Detection of changes in leaf water content using Near- and Middle-Infrared reflectances , 1989 .

[23]  Strategies for multivariate modeling of moisture content in freeze-dried mannitol-containing products by near-infrared spectroscopy. , 2012, Journal of pharmaceutical and biomedical analysis.

[24]  Johan A. K. Suykens,et al.  Least Squares Support Vector Machine Classifiers , 1999, Neural Processing Letters.

[25]  E. Nevo,et al.  Drought and salt tolerances in wild relatives for wheat and barley improvement. , 2010, Plant, cell & environment.

[26]  S. Palmer,et al.  Near-infrared and mid-IR spectroscopy of selected humite minerals , 2007 .

[27]  Martin Škrlep,et al.  Accuracy of near infrared spectroscopy for prediction of chemical composition, salt content and free amino acids in dry-cured ham. , 2011, Meat science.

[28]  E. V. Thomas,et al.  Partial least-squares methods for spectral analyses. 1. Relation to other quantitative calibration methods and the extraction of qualitative information , 1988 .

[29]  Kevin McDonnell,et al.  Prediction of moisture, calorific value, ash and carbon content of two dedicated bioenergy crops using near-infrared spectroscopy. , 2011, Bioresource technology.