Department of Agricultural Technology, Lampung State Polytechnic, Jl. Soekarno Hatta No. 10, Rajabasa, Bandar Lampung 35141, Indonesia.
Department of Agricultural Engineering, Faculty of Agriculture, The University of Lampung, Jl. Soemantri Brojonegoro No.1, Bandar Lampung 35145, Indonesia.
Molecules. 2021 Oct 9;26(20):6091. doi: 10.3390/molecules26206091.
In this present research, a spectroscopic method based on UV-Vis spectroscopy is utilized to quantify the level of corn adulteration in peaberry ground roasted coffee by chemometrics. Peaberry coffee with two types of bean processing of wet and dry-processed methods was used and intentionally adulterated by corn with a 10-50% level of adulteration. UV-Vis spectral data are obtained for aqueous samples in the range between 250 and 400 nm with a 1 nm interval. Three multivariate regression methods, including partial least squares regression (PLSR), multiple linear regression (MLR), and principal component regression (PCR), are used to predict the level of corn adulteration. The result shows that all individual regression models using individual wet and dry samples are better than that of global regression models using combined wet and dry samples. The best calibration model for individual wet and dry and combined samples is obtained for the PLSR model with a coefficient of determination in the range of 0.83-0.93 and RMSE below 6% (/) for calibration and validation. However, the error prediction in terms of RMSEP and bias were highly increased when the individual regression model was used to predict the level of corn adulteration with differences in the bean processing method. The obtained results demonstrate that the use of the global PLSR model is better in predicting the level of corn adulteration. The error prediction for this global model is acceptable with low RMSEP and bias for both individual and combined prediction samples. The obtained RPD and RER in prediction for the global PLSR model are more than two and five for individual and combined samples, respectively. The proposed method using UV-Vis spectroscopy with a global PLSR model can be applied to quantify the level of corn adulteration in peaberry ground roasted coffee with different bean processing methods.
在本研究中，利用基于紫外可见分光光度法的光谱方法，通过化学计量学定量测定了平豆研磨烘焙咖啡中的玉米掺杂物含量。使用了两种豆加工类型的平豆咖啡，即湿法和干法加工，并故意用 10-50%掺杂物水平的玉米进行掺假。在 250 到 400nm 之间，间隔为 1nm，获得水相样品的紫外可见光谱数据。使用三种多元回归方法，包括偏最小二乘回归（PLSR）、多元线性回归（MLR）和主成分回归（PCR），来预测玉米掺杂物的含量。结果表明，使用单个湿样和干样的所有个体回归模型均优于使用湿样和干样组合的全局回归模型。对于个体湿样和干样以及组合样品，PLSR 模型获得了最佳的校准模型，其决定系数范围为 0.83-0.93，校准和验证的 RMSE 低于 6% (/ )。然而，当个体回归模型用于预测具有豆加工方法差异的玉米掺杂物含量时，误差预测在 RMSEP 和偏差方面显著增加。研究结果表明，使用全局 PLSR 模型预测玉米掺杂物含量更好。对于该全局模型，个体和组合预测样品的 RMSEP 和偏差的误差预测是可以接受的。对于全局 PLSR 模型，在预测中获得的 RPD 和 RER 分别大于 2 和 5。使用紫外可见光谱和全局 PLSR 模型的方法可以应用于定量测定不同豆加工方法的平豆研磨烘焙咖啡中的玉米掺杂物含量。
