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利用常规和快速分析方法检测和量化番茄酱掺假。

Detection and Quantification of Tomato Paste Adulteration Using Conventional and Rapid Analytical Methods.

机构信息

Institute of Bioengineering and Process Control, Department of Measurements and Process Control, Faculty of Food Science, Szent István University, 1118 Budapest, Hungary.

Institute of Food Technology, Department of Postharvest, Commercial and Sensory Science, Faculty of Food Science, Szent István University, 1118 Budapest, Hungary.

出版信息

Sensors (Basel). 2020 Oct 24;20(21):6059. doi: 10.3390/s20216059.

Abstract

Tomato, and its concentrate are important food ingredients with outstanding gastronomic and industrial importance due to their unique organoleptic, dietary, and compositional properties. Various forms of food adulteration are often suspected in the different tomato-based products causing major economic and sometimes even health problems for the farmers, food industry and consumers. Near infrared (NIR) spectroscopy and electronic tongue (e-tongue) have been lauded as advanced, high sensitivity techniques for quality control. The aim of the present research was to detect and predict relatively low concentration of adulterants, such as paprika seed and corn starch (0.5, 1, 2, 5, 10%), sucrose and salt (0.5, 1, 2, 5%), in tomato paste using conventional (soluble solid content, consistency) and advanced analytical techniques (NIR spectroscopy, e-tongue). The results obtained with the conventional methods were analyzed with univariate statistics (ANOVA), while the data obtained with advanced analytical methods were analyzed with multivariate methods (Principal component analysis (PCA), linear discriminant analysis (LDA), partial least squares regression (PLSR). The conventional methods were only able to detect adulteration at higher concentrations (5-10%). For NIRS and e-tongue, good accuracies were obtained, even in identifying minimal adulterant concentrations (0.5%). Comparatively, NIR spectroscopy proved to be easier to implement and more accurate during our evaluations, when the adulterant contents were estimated with R above 0.96 and root mean square error (RMSE) below 1%.

摘要

番茄及其浓缩物是重要的食品成分,具有独特的感官、饮食和组成特性,在烹饪和工业方面具有重要意义。由于各种形式的食品掺假,不同的番茄制品经常受到怀疑,这给农民、食品工业和消费者带来了重大的经济问题,有时甚至是健康问题。近红外(NIR)光谱和电子舌(e-tongue)已被称赞为先进的、高灵敏度的质量控制技术。本研究的目的是使用常规(可溶性固形物含量、稠度)和先进的分析技术(近红外光谱、电子舌)检测和预测相对低浓度的掺杂物,如辣椒粉和玉米淀粉(0.5%、1%、2%、5%、10%)、蔗糖和盐(0.5%、1%、2%、5%)在番茄酱中的存在。用常规方法得到的结果用单变量统计(ANOVA)进行分析,而用先进的分析方法得到的数据用多元方法(主成分分析(PCA)、线性判别分析(LDA)、偏最小二乘回归(PLSR)进行分析。常规方法只能在较高浓度(5-10%)下检测到掺假。对于 NIRS 和电子舌,即使在识别最小掺杂物浓度(0.5%)时,也能获得良好的准确性。相对而言,在我们的评估中,当用 R 大于 0.96 和均方根误差(RMSE)小于 1%来估计掺杂物含量时,NIR 光谱法被证明更容易实施,也更准确。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3540/7663517/91a5de8e2c23/sensors-20-06059-g001.jpg

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