The present paper describes novel low frequency (LF) ¹H NMR energy relaxation time signal analysis for mapping the different chemical and morphological domains in complex cattle manure (CM) and cattle forage (CF) biomass. Relaxation signals generated by different absorbed water pools and aliphatic chains are analyzed by specifically designed sparse representation methods and a convex optimization PDCO solver, for generating 2D T₁ (spin–matrix) vs T₂ (spin–spin) energy relaxation time spectrum graphics and 3D graphs that include ¹H population density. Using analytical spectral analyses and spiking assignment with material standards of the individual T₁ vs T₂ peaks in the generated CM graphics, a morphological and chemical domain dictionary was formulated demonstrating well resolved signal peaks and a better understanding of the different chemical and morphological structural organization within the complex biomass material. This benchtop proton LF-NMR relaxation sensor system and its signal generation into chemical-morphological spectrum graphics has the potential to significantly contribute to a rapid and accurate monitoring system for biobased industrial processes with significant applicability in, for example, biorefineries.

中文翻译：

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低场质子NMR能量弛豫信号分析技术对复杂生物质化学形态域的2D和3D光谱图

本文介绍了新颖的低频（LF）¹ H NMR能量弛豫时间信号分析，用于绘制复杂的牛粪（CM）和牛饲料（CF）生物量中的不同化学和形态域。通过专门设计的稀疏表示方法和凸优化PDCO解算器分析了由不同吸收的水池和脂肪族链产生的弛豫信号，以生成2D T ₁（自旋-矩阵）vs T ₂（自旋-自旋）能量弛豫时间谱图和包含¹ H人口密度的3D图形。使用分析光谱分析和尖峰分配以及单个T ₁与T _2的材料标准在生成的CM图形中找到峰，制定了形态和化学结构域词典，展示了良好分辨的信号峰，并更好地理解了复杂生物质材料中的不同化学和形态结构组织。这种台式质子LF-NMR弛豫传感器系统及其将信号生成为化学形态谱图的潜力，有可能极大地促进对基于生物的工业过程的快速，准确的监控系统的应用，例如在生物精炼厂中具有显着的适用性。