Guayulin content, resin and rubber fraction by near infrared spectroscopy in guayule stems (Parthenium argentatum, A. Gray)

García-Martínez, M. Mercedes; Latorre, Guayente; Jara, Francisco Miguel; Rozalén, Juana; Carrión, M. Engracia; Carmona, Manuel; Zalacaín, Amaya

doi:10.1016/j.indcrop.2022.114951

Cited by 3 publications

(2 citation statements)

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“…Currently, the majority of near-infrared spectroscopy-based NR content prediction models have predominantly focused on P. hysterophorus L. as the subject of study [ 22 – 25 ]. Notably, Chen et al [ 26 ] have contributed to the domain by generating a predictive model for NR content in TKS.…”

Section: Discussionmentioning

confidence: 99%

“…Luo et al [ 24 ] attempted preprocessing with smoothing, DT, SNV, and derivatives, creating a PLS model for NR content in P. hysterophorus L with a cross-validation set R 2 of 0.79. García-Martínez et al [ 25 ] preprocessed the spectra with smoothing, SNV, DT, and derivatives to establish a PLS model for NR content in P. hysterophorus L, achieving a cross-validation set R 2 of 0.9 and an relative percentage deviation (RPD) of 2.65. These findings confirm that preprocessing methods like smoothing, SNV, and derivatives can effectively remove some environmental errors in the spectra and enhance spectral features related to NR content.…”

Section: Introductionmentioning

confidence: 99%

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A prediction model of rubber content in the dried root of Taraxacum kok-saghyz Rodin based on near-infrared spectroscopy

Chen,

Yan,

Tuoheti

et al. 2024

Plant Methods

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Background Taraxacum kok-saghyz Rodin (TKS) is a highly potential source of natural rubber (NR) due to its wide range of suitable planting areas, strong adaptability, and suitability for mechanized planting and harvesting. However, current methods for detecting NR content are relatively cumbersome, necessitating the development of a rapid detection model. This study used near-infrared spectroscopy technology to establish a rapid detection model for NR content in TKS root segments and powder samples. The K445 strain at different growth stages within a year and 129 TKS samples hybridized with dandelion were used to obtain their near-infrared spectral data. The rubber content in the root of the samples was detected using the alkaline boiling method. The Monte Carlo sampling method (MCS) was used to filter abnormal data from the root segments of TKS and powder samples, respectively. The SPXY algorithm was used to divide the training set and validation set in a 3:1 ratio. The original spectrum was preprocessed using moving window smoothing (MWS), standard normalized variate (SNV), multiplicative scatter correction (MSC), and first derivative (FD) algorithms. The competitive adaptive reweighted sampling (CARS) algorithm and the corresponding chemical characteristic bands of NR were used to screen the bands. Partial least squares (PLS), random forest (RF), Lightweight gradient augmentation machine (LightGBM), and convolutional neural network (CNN) algorithms were employed to establish a model using the optimal spectral processing method for three different bands: full band, CARS algorithm, and chemical characteristic bands corresponding to NR. The model with the best predictive performance for high rubber content intervals (rubber content > 15%) was identified. Result The results indicated that the optimal rubber content prediction models for TKS root segments and powder samples were MWS–FD CASR–RF and MWS–FD chemical characteristic band RF, respectively. Their respective $${\text{R}}_{{\text{P}}}^{2}$$ R P 2 , RMSEP, and RPDP values were 0.951, 0.979, 1.814, 1.133, 4.498, and 6.845. In the high rubber content range, the model based on the LightGBM algorithm had the best prediction performance, with the RMSEP of the root segments and powder samples being 0.752 and 0.918, respectively. Conclusions This research indicates that dried TKS root powder samples are more appropriate for constructing a rubber content prediction model than segmented samples, and the predictive capability of root powder samples is superior to that of root segmented samples. Especially in the elevated rubber content range, the model formulated using the LightGBM algorithm has superior predictive performance, which could offer a theoretical basis for the rapid detection technology of TKS content in the future.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%