Georgouli Konstantia, Martinez Del Rincon Jesus, Koidis Anastasios
Queens University Belfast, Institute for Global Food Security, Belfast, Northern Ireland, UK.
Queens University Belfast, Institute of Electronics, Communications and Information Technology, Belfast, Northern Ireland, UK.
Food Chem. 2017 Feb 15;217:735-742. doi: 10.1016/j.foodchem.2016.09.011. Epub 2016 Sep 8.
The main objective of this work was to develop a novel dimensionality reduction technique as a part of an integrated pattern recognition solution capable of identifying adulterants such as hazelnut oil in extra virgin olive oil at low percentages based on spectroscopic chemical fingerprints. A novel Continuous Locality Preserving Projections (CLPP) technique is proposed which allows the modelling of the continuous nature of the produced in-house admixtures as data series instead of discrete points. The maintenance of the continuous structure of the data manifold enables the better visualisation of this examined classification problem and facilitates the more accurate utilisation of the manifold for detecting the adulterants. The performance of the proposed technique is validated with two different spectroscopic techniques (Raman and Fourier transform infrared, FT-IR). In all cases studied, CLPP accompanied by k-Nearest Neighbors (kNN) algorithm was found to outperform any other state-of-the-art pattern recognition techniques.
这项工作的主要目标是开发一种新颖的降维技术，作为集成模式识别解决方案的一部分，该解决方案能够基于光谱化学指纹识别低百分比掺假物，如特级初榨橄榄油中的榛子油。提出了一种新颖的连续局部保持投影（CLPP）技术，该技术允许将内部生产的混合物的连续性质建模为数据序列而非离散点。数据流形连续结构的保持能够更好地可视化这个被研究的分类问题，并有助于更准确地利用流形来检测掺假物。所提出技术的性能通过两种不同的光谱技术（拉曼光谱和傅里叶变换红外光谱，FT-IR）进行了验证。在所研究的所有情况下，发现CLPP与k近邻（kNN）算法相结合的性能优于任何其他现有最先进的模式识别技术。
