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利用偏最小二乘回归和紫外可见区域光谱数据对印尼麝香猫咖啡掺假进行定量分析

The Use of Partial Least Square Regression and Spectral Data in UV-Visible Region for Quantification of Adulteration in Indonesian Palm Civet Coffee.

作者信息

Suhandy Diding, Yulia Meinilwita

机构信息

Laboratory of Bioprocess and Postharvest Engineering, Department of Agricultural Engineering, The University of Lampung, Jl. Soemantri Brojonegoro No. 1, Gedong Meneng, Bandar Lampung, Lampung 35145, Indonesia.

Department of Agricultural Technology, Lampung State Polytechnic, Jl. Soekarno Hatta No. 10, Rajabasa, Bandar Lampung, Lampung, Indonesia.

出版信息

Int J Food Sci. 2017;2017:6274178. doi: 10.1155/2017/6274178. Epub 2017 Aug 20.

Abstract

Asian palm civet coffee or kopi luwak (Indonesian words for coffee and palm civet) is well known as the world's priciest and rarest coffee. To protect the authenticity of luwak coffee and protect consumer from luwak coffee adulteration, it is very important to develop a robust and simple method for determining the adulteration of luwak coffee. In this research, the use of UV-Visible spectra combined with PLSR was evaluated to establish rapid and simple methods for quantification of adulteration in luwak-arabica coffee blend. Several preprocessing methods were tested and the results show that most of the preprocessing spectra were effective in improving the quality of calibration models with the best PLS calibration model selected for Savitzky-Golay smoothing spectra which had the lowest RMSECV (0.039) and highest RPD value (4.64). Using this PLS model, a prediction for quantification of luwak content was calculated and resulted in satisfactory prediction performance with high both RPD and RER values.

摘要

亚洲棕榈猫咖啡或麝香猫咖啡(“咖啡”和“棕榈猫”的印尼语词汇)作为世界上最昂贵、最稀有的咖啡而闻名。为了保护麝香猫咖啡的纯正性并保护消费者免受麝香猫咖啡掺假的影响,开发一种强大而简单的方法来确定麝香猫咖啡的掺假情况非常重要。在本研究中,评估了紫外可见光谱结合偏最小二乘回归(PLSR)来建立快速、简单的方法,用于定量检测麝香猫-阿拉比卡混合咖啡中的掺假情况。测试了几种预处理方法,结果表明,大多数预处理光谱有效地提高了校准模型的质量,为Savitzky-Golay平滑光谱选择了最佳的PLS校准模型,其具有最低的交叉验证均方根误差(RMSECV,0.039)和最高的相对预测偏差(RPD)值(4.64)。使用该PLS模型,计算了麝香猫含量定量的预测结果,预测性能令人满意,RPD和相对误差比(RER)值都很高。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0fe9/5585669/ca236212b793/IJFS2017-6274178.001.jpg

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