Application of visible and near-infrared spectroscopy to classification of Miscanthus species

Abstract

The feasibility of visible and near infrared (NIR) spectroscopy as tool to classify Miscanthus samples was explored in this study. Three types of Miscanthus plants, namely, M. sinensis, M. sacchariflorus and M. fIoridulus, were analyzed using a NIR spectrophotometer. Several classification models based on the NIR spectra data were developed using line discriminated analysis (LDA), partial least squares (PLS), least squares support vector machine regression (LSSVR), radial basis function (RBF) and neural network (NN). The principal component analysis (PCA) presented rough classification with overlapping samples, while the models of Line_LSSVR, RBF_LSSVR and RBF_NN presented almost same calibration and validation results. Due to the higher speed of Line_LSSVR than RBF_LSSVR and RBF_NN, we selected the line_LSSVR model as a representative. In our study, the model based on line_LSSVR showed higher accuracy than LDA and PLS models. The total correct classification rates of 87.79 and 96.51% were observed based on LDA and PLS model in the testing set, respectively, while the line_LSSVR showed 99.42% of total correct classification rate. Meanwhile, the lin_LSSVR model in the testing set showed correct classification rate of 100, 100 and 96.77% for M. sinensis, M. sacchariflorus and M. fIoridulus, respectively. The lin_LSSVR model assigned 99.42% of samples to the right groups, except one M. fIoridulus sample. The results demonstrated that NIR spectra combined with a preliminary morphological classification could be an effective and reliable procedure for the classification of Miscanthus species.

Identification of waxy cassava genotypes using fourier‐transform near‐infrared spectroscopy

“…In the agricultural sciences, spectrometry has been used for soil classification (Mouazen, Kuang, De Baerdemaeker, & Ramon, 2010), determination of nutritional deficiencies in plants (Butler, Adams, McAinsh, & Martin, 2017), identification of plant species (Castillo, Contreras, Freer, Ruiz, & Valenzuela, 2008;Jin et al, 2017;Kosmowski & Worku, 2018;Lang, Almeida, & Costa, 2017), discrimination of transgenic plants (Lee & Choung, 2011;Xie, Ying, Ying, Yu, & Fu, 2007), and determination of the geographical origin of some varieties (Chen, Zhao, & Lin, 2009;Li, Wei, Duan, Xi, & Wu, 2012). To perform these analyses, it is necessary to identify a spectral pattern that is capable of discriminating the classes of interest.…”

“…As an analysis sample, classification of a portion of the branch bundles and three dry leaves scanned per specimen resulted in accuracies of .91 and .94, respectively. This same strategy was used to distinguish species with the genera Eschweilera and Corythophora, for which 99% of the species identifications were correct (Durgante, Higuchi, Almeida, & Vicentini, 2013), as well as in the prediction of three Miscanthus species with complete accuracy using a least squares support vector regression model (LSSVR) (Jin et al, 2017).…”

Forecasting Young Apple Tree Bud Status with a Visible/Near-Infrared Spectrometer

“…One of these technologies is nearinfrared spectroscopy that exposes material to near-infrared light (700-3000 nm). UVvisible spectroscopy is widely used to determine organic substances by exposing the objects to visible and ultraviolet light (200-800 nm) and measuring their absorbance [4,7,13].…”

Near Infrared Reflectance Spectroscopy and Multivariate Analyses for Fast and Non-Destructive Prediction of Corn Seed Germination

“…It is sensitive enough to detect the changes in the free and structure-bounded components within a sample (Salgó and Gergely, 2012). The measurements are based on the correlation among the molecular vibration of an organic molecule and their interactions with the amount of absorbed and reflected near infrared radiation by the chemical structure (Jin et al, 2017) containing OH, NH, CH and SH bounds within a sample (Qiu et al, 2018). The absorption bands are characterized by very broad and overlapped peaks (Kosmowski and Worku, 2018), which inhibit the visual assessment and direct interpretation of the spectra (Büchi, 2013).…”