Departamento de Ingeniería Química, Facultad de Química, Universidad de Sevilla, 41012 Seville, Spain.
University Institute of Research on Olive Groves and Olive Oils, GEOLIT Science and Technology Park, University of Jaén, 23620 Mengíbar, Spain.
Sensors (Basel). 2022 Apr 7;22(8):2831. doi: 10.3390/s22082831.
The analysis of the physico-chemical parameters of quality of olive oil is still carried out in laboratories using chemicals and generating waste, which is relatively costly and time-consuming. Among the various alternatives for the online or on-site measurement of these parameters, the available literature highlights the use of near-infrared spectroscopy (NIRS). This article intends to comprehensively review the state-of-the-art research and the actual potential of NIRS for the analysis of olive oil. A description of the features of the infrared spectrum of olive oil and a quick explanation of the fundamentals of NIRS and chemometrics are also included. From the results available in the literature, it can be concluded that the four most usual physico-chemical parameters that define the quality of olive oils, namely free acidity, peroxide value, K232, and K270, can be measured by NIRS with high precision. In addition, NIRS is suitable for the nutritional labeling of olive oil because of its great performance in predicting the contents in total fat, total saturated fatty acids, monounsaturated fatty acids, and polyunsaturated fatty acids in olive oils. Other parameters of interest have the potential to be analyzed by NIRS, but the improvement of the mathematical models for their determination is required, since the errors of prediction reported so far are a bit high for practical application.
橄榄油理化参数的分析仍在实验室中使用化学物质进行,会产生废物,这种方法相对昂贵且耗时。在这些参数的在线或现场测量的各种替代方案中,现有文献强调了近红外光谱(NIRS)的使用。本文旨在全面回顾 NIRS 用于橄榄油分析的最新研究现状和实际潜力。还包括对橄榄油红外光谱特征的描述以及 NIRS 和化学计量学基本原理的快速解释。根据文献中的结果,可以得出结论,NIRS 可以高精度地测量橄榄油的四个最常用的理化参数,即游离酸度、过氧化物值、K232 和 K270。此外,由于 NIRS 在预测橄榄油中总脂肪、总饱和脂肪酸、单不饱和脂肪酸和多不饱和脂肪酸的含量方面表现出色,因此它也适用于橄榄油的营养标签。其他感兴趣的参数也有可能通过 NIRS 进行分析,但需要改进其测定的数学模型,因为迄今为止报告的预测误差对于实际应用来说有点高。