离子迁移谱技术用于橄榄油分类鉴定

离子迁移谱技术用于橄榄油分类鉴定

Introduction
Olive oil is a fundamental component of the Mediterranean diet because of its nutritional values. It is also rich in antioxidants, and therefore consumption of olive oil can help prevent cellular damage caused by free radicals. However, not all olive oils are equally beneficial for the human body because they do not contain the same amount of antioxidants and nutrients [1].

The quality of olive oil depends on the technological processes of its production and natural factors, including the type of soil and its composition or climate.

According to the regulations from European Union standards, there are three categories of olive oil, ”extra virgin” (EVOO), ”virgin” (VOO), and ”lampante” (LOO) [2]. The quality of olive oils is evaluated by a panel test, which assesses taste, appearance, and aroma. In the panel test, the experts assign a score to the oils. Subsequently, the statistical analysis is applied to the score, which the experts gave to the sample. Finally, they classify olive oil into one of three classes. It is a demanding, expensive, and time-consuming task, so there is a need to automate this process.

We propose the system for automatic spectrum recognition, which is using the methods of machine learning. In comparison to the other approaches, the application can also visualize the olive oil samples.

橄榄油是地中海饮食的基本组成部分，因为它的营养价值。它还富含抗氧化剂，因此食用橄榄油有助于防止自由基引起的细胞损伤。然而，并非所有的橄榄油对人体都同样有益，因为它们不包含相同数量的抗氧化剂和营养素[1]。

橄榄油的质量取决于其生产工艺和自然因素，包括土壤类型及其成分或气候。

根据欧盟标准的规定，橄榄油分为三类，“特级纯橄榄油”（EVOO）、“纯橄榄油”（VOO）和“兰潘特橄榄油”（LOO）[2]。橄榄油的质量通过小组测试进行评估，该测试评估味道、外观和香气。在小组测试中，专家为这些油打分。随后，对专家给样本的分数进行统计分析。最后，他们将橄榄油分为三类。这是一项要求高、成本高、耗时长的任务，因此需要将此过程自动化。

我们提出了使用机器学习方法的自动离子迁移谱识别系统。与其他方法相比，该应用程序还可以可视化橄榄油样本.

Our approach
The main goal of our project is to create a system to analyze the quality of olive oils. To achieve that goal, we propose the architecture of the application named ASR for automatic spectrum recognition, which can process data from AIMS (Advanced Ion Mobility Spectrometer). The AIMS allows us to analyze not only gaseous substances but also liquid and solid substances. The application is developed in cooperation with research company MaSaTech¹, which provides the data from the spectrometer. The proposed application has three main functions, i.e., data processing, classification, and data visualization. The system architecture is shown in Figure 1.

我们项目的主要目标是建立一个系统来分析橄榄油的质量。为了实现这一目标，我们提出了自动离子迁移光谱识别应用程序ASR的体系结构，该应用程序可以处理来自AIMS（高级离子迁移谱仪）的数据。AIMS使我们不仅可以分析气体物质，还可以分析液体和固体物质。该应用程序是与提供离子迁移谱数据的研究公司MaSaTech¹合作开发的。该应用程序具有三个主要功能，即数据处理、分类和数据可视化。系统架构如图1所示。

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Conclusion
Nowadays, the panel tests for olive oil classification are expensive and timeconsuming because the oil is assessed by 10-20 panelists. In contrary to the panel test, the proposed application provides a more accurate, faster, and cheaper way to predict the olive oil grade. Our solution includes not only the precise classification method but also a user-friendly and easy-to-use application. The application allows users to visualize the oil samples as images for a better understanding of measured data. The users also can train new models.

In future work, we want to devote improving the accuracy of proposed models.

We believe that there are some possibilities to give other classification methods a try. The convolutional neural network could be used not only for classification but also for feature extraction, which can improve the accuracy of this approach. Simultaneously, there is an opportunity for scaling up an application for use in other areas of spectrum recognition, such as wine classification.

如今，橄榄油分类的小组测试既昂贵又耗时，因为橄榄油是由10-20名小组成员评估的。与传统测试相反，拟议的应用程序提供了一种更准确、更快和更便宜的方法来预测橄榄油等级。我们的解决方案不仅包括精确的分类方法，而且还包括一个用户友好且易于使用的应用程序。该应用程序允许用户将油样可视化为图像，以便更好地理解测量数据。用户还可以培训新样品模型。

在未来的工作中，我们希望致力于提高所提出模型的准确性。

我们认为有一些可能性可以尝试其他分类方法。卷积神经网络不仅可以用于分类，还可以用于特征提取，这可以提高该方法的准确性。同时，有机会扩大应用程序，以用于光谱识别的其他领域，如葡萄酒分类。