作物所在甘薯塊根高通量NIRS表型分析方麵取得新進展
近日，作物所甘薯團隊在國際學術期刊Food Chemistry-X（中科院大類一區，IF=6.1）發表題為“High-throughput phenotyping of nutritional quality components in sweet potato roots by near-infrared spectroscopy and chemometrics methods”的文章。作物所唐朝臣博士為論文第一作者，王章英研究員為通訊作者。
甘薯（Ipomoea batatasL.）是一種高產且營養豐富的塊根作物。提高塊根品質是促進甘薯產業高質量發展的重要舉措。然而，缺乏高效的甘薯品質分析方法嚴重製約了甘薯種質的品質鑒評和優質新品種的選育。為了解決這一問題，本研究選用了125份代表性的甘薯塊根樣品，采用了雙重優化的建模策略（即樣本子集劃分和光譜變量選擇），旨在構建一種基於NIRS的高通量分析塊根品質（總澱粉、直/支鏈澱粉、支直比、可溶性糖、粗蛋白、總黃酮和總酚）的方法，為快速篩選優質甘薯種質提供可行的解決方案。在本研究中，共建立了8個最優的NIRS定量預測模型，校正集決定係數（R2C）為0.95–0.99，交叉驗證決定係數（R2CV）為0.93–0.98，驗證集決定係數（R2V）為0.89–0.96，驗證集相對分析誤差（RPD）為6.33–11.35。總之，本研究開發的NIRS模型為高通量分析甘薯塊根品質提供了一種實用可行的方法，為實現高效、精準的甘薯品質育種奠定了基礎。
本研究得到國家重點研發計劃子課題、國家甘薯產業技術體係、廣東省現代農業產業技術體係、必威betways 作物研究所所長基金、必威betways 科技人才引進/培養專項等項目的資助。
原文鏈接：
https://www.sciencedirect.com/science/article/pii/S2590157523003590
Fig. 1. Variations in quality components among representative sweet potato samples. (a) Higher content group, including total starch (TS), amylose (AL), amylopectin (AP), and soluble sugar (SS). (b) Lower content group, including ratio of amylopectin to amylose (RAA), crude protein (CP), total flavonoid content (TFC), and total phenolic content (TPC).
Fig. 2. Variations in NIRS absorbance spectra among representative sweet potato samples. (a) Original near-infrared spectra of hot-air-dried samples. (b) PCA scores of near-infrared spectra for hot-air-dried samples. (c) Original near-infrared spectra of freeze-dried samples. (d) PCA scores of near-infrared spectra for freeze-dried samples.
Fig. 3. The selection of variables for quality components of sweet potato samples in the calibration set using competitive adaptive reweighted sampling (CARS), random frog (RF), and Monte Carlo-uninformative variable elimination (MC-UVE). (a) TS, total starch; (b) AL, amylose; (c) AP, amylopectin; (d) RAA, ratio of amylopectin to amylose; (e) SS, soluble sugar; (f) CP, crude protein; (g) TFC, total flavonoid content; (h)TPC, total phenolic content.
Fig. 4. Predictive performance of partial least squares models for the quality components. FS, full spectra; CARS, competitive adaptive reweighted sampling; RF, random frog;R2C, coefficient determination of calibration;R2CV, coefficient determination of cross-validations;R2V, coefficient determination of validations; RMSEC, root mean standard error of calibration; RMSECV, root mean standard error of cross-validation; RMSEP, root mean square error of prediction; RPD, the ratio of prediction to deviation; RER, the range error ratio. (a) TS, total starch; (b) AL, amylose; (c) AP, amylopectin; (d) RAA, ratio of amylopectin to amylose; (e) SS, soluble sugar; (f) CP, crude protein; (g) TFC, total flavonoid content; (h)TPC, total phenolic content.