In this work, attempts were made to estimate the total oil content (TOC) in single peanut kernels, using the CI meter (Chari’s Impedance meter, described below). Mature peanut kernels of selected varieties with a ran...In this work, attempts were made to estimate the total oil content (TOC) in single peanut kernels, using the CI meter (Chari’s Impedance meter, described below). Mature peanut kernels of selected varieties with a range of oil contents from 47% to 61% were placed one at a time, between the parallel-plate electrodes of the CI meter, and the impedance (Z) and phase angle (q) of the system were measured, and capacitance, C was computed at 1, 5 and 9 MHz. After the measurements, the TOC of each kernel was determined by Soxhlet method. Using the known TOC values, and the corresponding C, Z and q values, initially on a calibration group of kernels, calibration equations were developed. Using the model coefficients from the calibration, the TOCs of kernel samples of 31 diverse peanut genotypes grown in different environments in Australia were determined. The method predicted the TOC values of peanut kernels of 31 peanut genotypes, within 2% of the Soxhlet values, with an R2 of 0.87 (P 0.001).展开更多
文摘In this work, attempts were made to estimate the total oil content (TOC) in single peanut kernels, using the CI meter (Chari’s Impedance meter, described below). Mature peanut kernels of selected varieties with a range of oil contents from 47% to 61% were placed one at a time, between the parallel-plate electrodes of the CI meter, and the impedance (Z) and phase angle (q) of the system were measured, and capacitance, C was computed at 1, 5 and 9 MHz. After the measurements, the TOC of each kernel was determined by Soxhlet method. Using the known TOC values, and the corresponding C, Z and q values, initially on a calibration group of kernels, calibration equations were developed. Using the model coefficients from the calibration, the TOCs of kernel samples of 31 diverse peanut genotypes grown in different environments in Australia were determined. The method predicted the TOC values of peanut kernels of 31 peanut genotypes, within 2% of the Soxhlet values, with an R2 of 0.87 (P 0.001).
文摘页岩储层总有机碳(total organic carbon,TOC)含量的地震预测普遍采用密度回归拟合法,仅考虑了单因素的线性关系,预测结果误差较大。针对常规方法的不足,提出了基于深度学习的TOC含量预测方法。首先,从测井资料中优选出与TOC含量曲线相关度相对较高的多个弹性参数曲线作为样本集输入数据,TOC含量曲线作为样本集输出数据,构建针对TOC含量预测的深度前馈神经网络模型;然后,调试网络模型结构,并利用共轭梯度法进行网络参数寻优;最后,将叠前振幅随偏移距变化(amplitude versus offset,AVO)反演得到的弹性参数数据体输入深度前馈神经网络模型,预测得到最终的TOC含量数据体。通过四川盆地页岩储层实际测井、地震资料的应用,对比了该方法相对于常规回归拟合法的优越性,验证了方法的实用性和可行性,为页岩储层TOC含量预测提供了新思路。
