橄榄油因其高营养等特点, 成为植物油中日常消费量逐渐增大的主要品类。 橄榄油按照加工工艺分为初榨、 精炼和混合等不同质量等级。 由于不同等级橄榄油价格差异较大, 导致橄榄油市场存在以次充好等问题。 同时, 涉及等级鉴定的指标繁杂, 对应的理化检测方法大部分涉及大型实验室设备, 检测成本高、 效率低且工作量繁重。 我国是橄榄油的主要进口国, 采用产品标准中逐项指标确认后判定的模式, 无法满足目前急速增长的进口产品快速通关要求。 该研究聚焦进口橄榄油在口岸监管现场的快速质量评价需求, 开发了多光谱信息同时采集和降维融合成像的方法, 将紫外-可见光谱与拉曼光谱进行特征数据融合, 构建拉曼-紫外可见2D谱图, 通过二维成像进行指纹特征判断, 构建特级初榨橄榄油、 精炼橄榄油以及果渣油的标准2D融合成像源图, 作为等级区分标准对照二维谱, 进行橄榄油等级可视化判定； 结合空间角度值转化算法对橄榄油进行等级定性评判, 通过角度值计算得到特级初榨橄榄油与精炼橄榄油的夹角范围在0.794 7～1.094 7之间, 与油橄榄果渣油其值在1.157 0～1.319 8之间, 而特级初榨橄榄油之间角度值均小于0.1, 由此可进行不同橄榄油的等级判定； 采用角度决策模型进行橄榄油掺杂样品定量分析。 制备不同等级橄榄油的混合样本计算得到特级初榨混合精炼橄榄油、 果渣油的模型相关系数r分别为0.994 2和0.991 0, 代入不同样本进行验证, 相对误差在-4.48%~2.58%之间。 采用拉曼-紫外可见融合光谱结合化学计量学建立二维标准谱图对橄榄油等级可视化判定, 并建立初榨橄榄油掺伪检测模型进行橄榄油含量的定量分析, 实现口岸食品质量和安全风险信息的多维度、 高精度和高准确度直观展示。 通过采用进口橄榄油质量等级的快速筛查方法, 能有效提高口岸关注风险的监测效率, 提高进口食品监管的精准度, 为口岸食品风险监测方式模式的智慧转换提供技术支撑。

Olive oil has become the main category with increasing daily consumption of vegetable oils due to its high nutritional characteristics. According to the processing technology, olive oil is divided into different quality grades such as virgin, refining and blending. Because the prices of different grades of olive oil are quite different, the olive oil market has a certain degree of real attribute problems, such as substandard quality. At the same time, there are complex indicators related to grade identification, and most of the corresponding physical and chemical testing methods involve large-scale laboratory equipment with high testing costs, low efficiency and heavy workload. Our country is a major importer of olive oil, and adopting the model of product standard confirmation and determination of indicators one by one, it cannot meet the rapidly increasing requirements for rapid customs clearance of imported products. This research focuses on the rapid quality assessment requirements of imported olive oil at the port supervision site. It develops a method of simultaneous collection of multi-spectral information and dimensionality reduction fusion imaging, which combines the characteristic data of the visible-ultraviolet spectrum and the Raman spectrum to construct the Raman-Ultraviolet, visible 2D spectrum. Then the fingerprint feature is judged by two-dimensional imaging. The standard 2D fusion imaging source map of extra virgin olive oil, refined olive oil and pomace oil is constructed, which is used as the grade discrimination standard to compare the two-dimensional spectrum to determine the olive oil grade visually. Finally, combined with the spatial angle value conversion algorithm, the olive oil grade is qualitatively judged. Through the angle value calculation, the angle between extra virgin olive oil and refined olive oil is between 0.794 7 and 1.094 7, and that of olive-pomace oil is between 1.157 0 and 1.319 8. The angle values between the extra virgin olive oils are all less than 0.1, which can be used to determine the grades of different olive oils, and the angle decision model is used for quantitative analysis of olive oil adulterated samples. Preparing mixed samples of different olive oil grades and calculating the model correlation coefficients of extra virgin mixed refined olive oil and pomace oil to be 0.994 2 and 0.991 0, respectively. Different samples are taken for verification, and the relative error is between -4.48%% and 2.58%. This study uses Raman-UV-Vis fusion spectroscopy combined with chemometrics to establish a standard two-dimensional spectrum to visually determine the grade of olive oil and establish a virgin olive oil adulteration detection model for quantitative analysis of olive oil content to achieve food quality and safety at the port Multi-dimensional, high-precision and high-accuracy visual display of risk information. Adopting the rapid screening method of imported olive oil quality grade can effectively improve the monitoring efficiency of the port’s attention to risks, improve the accuracy of imported food supervision, and provide technical support for the intelligent transformation of the port food risk monitoring mode.