Guo Jing, Jiang Sijia, Lu Bingjie, Zhang Wei, Zhang Yinyin, Hu Xiao, Yang Wanneng, Feng Hui, Xu Liang
College of Plant Science, Huazhong Agricultural University, Wuhan, Hubei, 430070, PR China.
National Key Laboratory of Crop Genetic Improvement, Hubei Hongshan Laboratory, National Center of Plant Gene Research (Wuhan), Huazhong Agricultural University, Wuhan, Hubei, 430070, PR China.
Plant Methods. 2025 Feb 21;21(1):25. doi: 10.1186/s13007-025-01345-0.
The enhancement of rice quality stands as a pivotal focus in crop breeding research, with spectral analysis-based non-destructive quality assessment emerging as a widely adopted tool in agriculture. A prevalent trend in this field prioritizes the assessment of effectiveness of individual spectral technologies while overlooking the influence of sample type on spectral quality testing outcomes. Thus, the present study employed Microscopic Hyperspectral Imaging, Raman, and Laser-Induced Breakdown Spectroscopy (LIBS) to acquire spectral data from paddy rice, brown rice, polished rice, and rice flour. The data were then modeled and analyzed with respect to the amylopectin and protein contents of the rice samples via regression methods. Correlation analysis revealed varying degrees of correlation, both positive and negative, among the three spectral techniques and the analytes of interest. LIBS and Raman spectroscopy demonstrated stronger correlations with the analytes compared to microscopic hyperspectral imaging. Based on the selected correlation variables, feature screening and regression modeling were conducted. The modeling results indicated that microscopic hyperspectral data modeling yielded the lowest coefficient of determination of R² = 0.2, followed by Raman data modeling result was higher than it, which was about 0.5. The modeling effect of polished rice is the best. LIBS data modeling performed best, with a coefficient of determination of 0.6. The influence of different sample types on the modeling results was less than that of Raman spectroscopy, and modeling results of grains were better. The feature matching analysis of Raman and libs spectroscopy techniques showed that there were spectral variables that could match amylopectin and protein in the features obtained by multiple modeling statistics, but some modeling variables failed to match. LIBS matched more variables than Raman. These findings provide valuable insights into the application effectiveness of different spectral techniques in detecting rice contents across diverse sample types.
水稻品质的提升是作物育种研究的关键重点，基于光谱分析的无损品质评估已成为农业中广泛采用的工具。该领域的一个普遍趋势是优先评估单个光谱技术的有效性，而忽视样品类型对光谱质量测试结果的影响。因此，本研究采用微观高光谱成像、拉曼光谱和激光诱导击穿光谱（LIBS）从水稻、糙米、精米和米粉中获取光谱数据。然后通过回归方法对数据进行建模，并针对水稻样品的支链淀粉和蛋白质含量进行分析。相关性分析揭示了三种光谱技术与目标分析物之间存在不同程度的正相关和负相关。与微观高光谱成像相比，LIBS和拉曼光谱与分析物的相关性更强。基于选定的相关变量，进行了特征筛选和回归建模。建模结果表明，微观高光谱数据建模的决定系数R²最低，为0.2，其次是拉曼数据建模结果，约为0.5，精米的建模效果最佳。LIBS数据建模表现最佳，决定系数为0.6。不同样品类型对建模结果的影响小于拉曼光谱，谷物的建模结果更好。拉曼光谱和LIBS光谱技术的特征匹配分析表明，在多次建模统计得到的特征中，存在能够与支链淀粉和蛋白质匹配的光谱变量，但部分建模变量无法匹配。LIBS匹配的变量比拉曼光谱更多。这些发现为不同光谱技术在检测不同样品类型水稻成分中的应用效果提供了有价值的见解。
