Integration of hyperspectral imaging, non-targeted metabolomics and machine learning for vigour prediction of naturally and accelerated aged sweetcorn seeds
Integration of hyperspectral imaging, non-targeted metabolomics and machine learning for vigour prediction of naturally and accelerated aged sweetcorn seeds
复制标题
DOI：
10.1016/j.foodcont.2023.109930
复制
发表时间：
2023
期刊：
影响因子：
6
通讯作者：
Tingting Zhang;Long Lu;N. Yang;Ian D Fisk;Wensong Wei;Li Wang;Jing Li;Qun Sun;Rensen Zeng
中科院分区：
文献类型：
--
作者：
Tingting Zhang;Long Lu;N. Yang;Ian D Fisk;Wensong Wei;Li Wang;Jing Li;Qun Sun;Rensen Zeng
了解和预测甜玉米种子的储存稳定性对于有效的供应链管理至关重要，然而，预测能力在很大程度上依赖于加速陈化(AA)研究，这并不总是直接适用于自然陈化(NA)。本研究采用高光谱成像(HSI)和非靶向代谢组学(LC-MS/MS)相结合的方法，利用基于AA种子的数据，分别采用最小二乘法、支持向量机和最小二乘法对NA种子活力损失进行预测。首次发现了AA和NA处理的种子之间光谱变异模式的不一致。然后使用回归系数选择的所有波长和有效波长(EW)建立基于AA的活力预测模型。这些模型分别由独立的AA和NA种子数据集进行了外部验证。结果对AA种子(R2≥(0.814))的预测结果令人满意，但对NA种子(R2≤(0.696))的预测精度较低。代谢组分析鉴定出54种差异代谢物，其中含有较大比例的氨基酸、二肽及其衍生物，是反映AA和NA种子老化机理差异的重要物质。随后，N-H键相关波段被认为是影响模型实用性的一个可能的干扰因素。去除与N-H键相关的EWS后，基于AA的模型在NA种子上取得了更好的性能，R2v-2值从0.696增加到0.720，中天300从0.668增加到0.727。综上所述，将HSI、LC-MS/MS和机器学习相结合是一种较好的无损检测和预测贮藏甜玉米种子活力的方法。
Understanding and predicting the storage stability of sweetcorn seeds is critical for effective supply chain management, however, prediction ability relies heavily on accelerated ageing (AA) studies and this is not always directly applicable to natural ageing (NA). In this study, hyperspectral imaging (HSI) and non-targeted metabolomics (LC-MS/MS) were integrated using PLS-R, SVM-R and OPLS-DA to predict loss of seed vigour in NA seeds, using data based on AA seeds. The inconsistencies in the pattern of spectral variation between seeds undergoing AA and NA were first identified. AA-based vigour prediction models were then built using all wavelengths and effective wavelengths (EWs) selected by regression coefficients. These models were externally validated by independent AA and NA seed datasets, respectively. The results yielded satisfactory predictions for AA seeds (R2≥ 0.814), but low precision for NA seeds (R2≤ 0.696). Metabolome analysis identified 54 differential metabolites, containing a large proportion of amino acids, dipeptides and their derivatives, which were important substances reflecting discrepancies between the ageing mechanisms of AA and NA seeds. Subsequently, N-H bond-related wavebands were deemed to be a possible interference factor in the models' practicability. After removing the N-H bond-related EWs, the AA-based models achieved better performance on NA seeds, with R2v-2value increasing from 0.696 to 0.720 for Lvsechaoren and from 0.668 to 0.727 for Zhongtian 300. In summary, coupling HSI, LC-MS/MS and machine learning was shown as an appropriate approach for non-destructive monitoring and predicting the vigour of stored sweetcorn seeds.