Teye Ernest, Amuah Charles L Y, Yeh Tai-Sheng, Nyorkeh Regina
Department of Agricultural Engineering, School of Agriculture, College of Agriculture and Natural Sciences, University of Cape Coast, Cape Coast, Ghana.
Department of Physics, Laser and Fibre Optics Centre, School of Physical Sciences, University of Cape Coast, Cape Coast, Ghana.
J Anal Methods Chem. 2023 Jan 31;2023:3364720. doi: 10.1155/2023/3364720. eCollection 2023.
Rapid and nondestructive measurement of moisture content in crude palm oil is essential for promoting the shelf-stability and quality. In this research, micro NIR spectrometer coupled with a multivariate calibration model was used to collect and analyse fingerprinted information from palm oil samples at different moisture contents. Several preprocessing methods such as standard normal variant (SNV), multiplicative scatter correction (MSC), Savitzky-Golay first derivative (SGD1), Savitzky-Golay second derivative (SGD2) together with partial least square (PLS) regression techniques, full PLS, interval PLS (iPLS), synergy interval PLS (SiPLS), genetic algorithm PLS (GAPLS), and successive projection algorithm PLS (SPA-PLS) were comparatively employed to construct an optimum quantitative prediction model for moisture content in crude palm oil. The models were evaluated according to the coefficient of determination and root mean square error in calibration (Rc and RMSEC) and prediction (Rp and RMSEC) set, respectively. The model SGD1 + SiPLS was the optimal novel algorithm obtained among the others with the performance of Rc = 0.968 and RMSEC = 0.468 in the calibration set and Rp = 0.956 and RMSEP = 0.361 in the prediction set. The results showed that rapid and nondestructive determination of moisture content in palm oil is feasible and this would go a long way to facilitating quality control of crude palm oil.
快速无损测量粗棕榈油中的水分含量对于提高其货架稳定性和质量至关重要。在本研究中，采用微型近红外光谱仪结合多元校准模型来收集和分析不同水分含量棕榈油样品的指纹信息。使用了几种预处理方法，如标准正态变量变换（SNV）、多元散射校正（MSC）、Savitzky-Golay一阶导数（SGD1）、Savitzky-Golay二阶导数（SGD2），并结合偏最小二乘（PLS）回归技术、全PLS、区间PLS（iPLS）、协同区间PLS（SiPLS）、遗传算法PLS（GAPLS）和连续投影算法PLS（SPA-PLS），比较构建粗棕榈油水分含量的最佳定量预测模型。分别根据校准集（Rc和RMSEC）和预测集（Rp和RMSEP）中的决定系数和均方根误差对模型进行评估。模型SGD1 + SiPLS是其他模型中获得的最优新算法，在校准集中Rc = 0.968，RMSEC = 0.468，在预测集中Rp = 0.956，RMSEP = 0.361。结果表明，快速无损测定棕榈油中的水分含量是可行的，这将大大有助于粗棕榈油的质量控制。
