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. 2021 Jul 13;17(1):76.
doi: 10.1186/s13007-021-00777-8.

A systematic high-throughput phenotyping assay for sugarcane stalk quality characterization by near-infrared spectroscopy

Maoyao Wang  1 , Xinru Li  1 , Yinjuan Shen  1 , Muhammad Adnan  1 , Le Mao  1 , Pan Lu  1 , Qian Hu  1 , Fuhong Jiang  1 , Muhammad Tahir Khan  2 , Zuhu Deng  1   3 , Baoshan Chen  1 , Jiangfeng Huang  4 , Muqing Zhang  5
Affiliations

A systematic high-throughput phenotyping assay for sugarcane stalk quality characterization by near-infrared spectroscopy

Maoyao Wang et al. Plant Methods. .

Abstract

Background: Sugarcane (Saccharum officinarum L.) is an economically important crop with stalks as the harvest organs. Improvement in stalk quality is deemed a promising strategy for enhancing sugarcane production. However, the lack of efficient approaches for systematic evaluation of sugarcane germplasm largely limits improvements in stalk quality. This study is designed to develop a systematic near-infrared spectroscopy (NIRS) assay for high-throughput phenotyping of sugarcane stalk quality, thereby providing a feasible solution for precise evaluation of sugarcane germplasm.

Results: A total of 628 sugarcane accessions harvested at different growth stages before and after maturity were employed to take a high-throughput assay to determine sugarcane stalk quality. Based on high-performance anion chromatography (HPAEC-PAD), large variations in sugarcane stalk quality were detected in terms of biomass composition and the corresponding fundamental ratios. Online and offline NIRS modeling strategies were applied for multiple purpose calibration with partial least square (PLS) regression analysis. Consequently, 25 equations were generated with excellent determination coefficients (R2) and ratio performance deviation (RPD) values. Notably, for some observations, RPD values as high as 6.3 were observed, which indicated their exceptional performance and predictive capability.

Conclusions: This study provides a feasible method for consistent and high-throughput assessment of stalk quality in terms of moisture, soluble sugar, insoluble residue and the corresponding fundamental ratios. The proposed method permits large-scale screening of optimal sugarcane germplasm for sugarcane stalk quality breeding and beyond.

Keywords: Biomass; Culm sugar content; HPAEC; NIRS; Sugarcane.

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Conflict of interest statement

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Figures

Fig. 1
Fig. 1
High-performance anion chromatography assay for sugar determination in sugarcane. A Chromatogram of sugar determination in sugarcane; B chromatogram of standard mixtures at different concentrations; C standard curves; D sugar determination in different batches of sugarcane
Fig. 2
Fig. 2
Variations in dry biomass composition in sugarcane stalks. Sugar mass content (A) and frequency distribution (B) in sugarcane stalks; insoluble residue content (C) and frequency distribution (D) in sugarcane stalks; ratio (E) between biomass composition and its frequency distribution (F). Various genotypes of sugarcane were collected at five different times, and the numbers for the collections were 164, 162, 184, 70 and 48. Samples in different collections were merged together (n = 628) to calculate the distribution frequency of biomass component composition in B, D and F. Sug/Res, total soluble sugar/residues; Suc/Total, sucrose/total soluble sugar; Fru/Glc, ratio of fructose/glucose in soluble sugar
Fig. 3
Fig. 3
Variations in fresh biomass composition in sugarcane stalks. Moisture content (A) and frequency distribution (B) in sugarcane stalks; sugar content (C) and frequency distribution (D) in sugarcane stalks; insoluble residue content (E) and frequency distribution (F) in sugarcane stalks. Various genotypes of sugarcane were collected at five different times, and the numbers for each collection were 164, 162, 184, 70 and 48. Samples in different collections were merged together (n = 628) to calculate the distribution frequency of biomass component composition in B, D and F
Fig. 4
Fig. 4
Variations of NIRS absorbance spectra for sugarcane samples. Original spectra of fresh (A) and dry samples (C); PCA scores of near-infrared spectra for fresh (B) and dry samples D
Fig. 5
Fig. 5
Correlation analysis between predicted and true values for biomass component content (% dry matter) in sugarcane stalks, using offline NIRS calibration. A Sugar; B insoluble residues; C ratio between biomass components. The red and blue dots represent internal cross validation and external validation, respectively. R2, coefficient determination; RMSE, root mean square error; RPD, ratio performance deviation
Fig. 6
Fig. 6
Correlation between predicted and true values for biomass component content (% dry matter) in sugarcane stalks, using online NIRS calibration. A Sugar; B insoluble residues; and C ratios between biomass components. The red and blue dots represent internal cross validation and external validation, respectively. R2, coefficient determination; RMSE, root mean square error; RPD, ratio performance deviation
Fig. 7
Fig. 7
Correlation analysis between the predicted and true values for biomass component content (% fresh weight) in sugarcane stalks upon online NIRS calibration. A Sugar; B moisture; C insoluble residues. The red and blue dots represent internal cross validation and external validation, respectively. R2, coefficient determination; RMSE, root mean square error; RPD, ratio performance deviation
Fig. 8
Fig. 8
Correlation analysis between the fit (predicted) and true values for biomass component content in sugarcane stalks. Offline NIRS calibration for dry biomass of sugarcane stalks upon sugar content (A), residues (B) and ratio between them (C); D–F online NIRS calibration for dry biomass of sugarcane stalks upon sugar content (D), residues (E) and the ratio (F); online NIRS calibration for fresh biomass of sugarcane stalks based on moisture content (G), sugar content (H) and residues (I). The red and black colors represent calibration and internal cross validation, respectively
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