Galindo-Luján Rocío, Pont Laura, Quispe Fredy, Sanz-Nebot Victoria, Benavente Fernando
Department of Chemical Engineering and Analytical Chemistry, Institute for Research on Nutrition and Food Safety (INSA·UB), University of Barcelona, 08028 Barcelona, Spain.
Serra Húnter Program, Generalitat de Catalunya, 08007 Barcelona, Spain.
Foods. 2024 Jun 17;13(12):1906. doi: 10.3390/foods13121906.
Quinoa is an Andean crop that stands out as a high-quality protein-rich and gluten-free food. However, its increasing popularity exposes quinoa products to the potential risk of adulteration with cheaper cereals. Consequently, there is a need for novel methodologies to accurately characterize the composition of quinoa, which is influenced not only by the variety type but also by the farming and processing conditions. In this study, we present a rapid and straightforward method based on matrix-assisted laser desorption ionization time-of-flight mass spectrometry (MALDI-TOF-MS) to generate global fingerprints of quinoa proteins from white quinoa varieties, which were cultivated under conventional and organic farming and processed through boiling and extrusion. The mass spectra of the different protein extracts were processed using the MALDIquant software (version 1.19.3), detecting 49 proteins (with 31 tentatively identified). Intensity values from these proteins were then considered protein fingerprints for multivariate data analysis. Our results revealed reliable partial least squares-discriminant analysis (PLS-DA) classification models for distinguishing between farming and processing conditions, and the detected proteins that were critical for differentiation. They confirm the effectiveness of tracing the agricultural origins and technological treatments of quinoa grains through protein fingerprinting by MALDI-TOF-MS and chemometrics. This untargeted approach offers promising applications in food control and the food-processing industry.
藜麦是一种安第斯作物,作为一种富含优质蛋白质且无麸质的食物而脱颖而出。然而,其日益普及使藜麦产品面临被更便宜谷物掺假的潜在风险。因此,需要新的方法来准确表征藜麦的成分,藜麦的成分不仅受品种类型影响,还受种植和加工条件影响。在本研究中,我们提出了一种基于基质辅助激光解吸电离飞行时间质谱(MALDI-TOF-MS)的快速简便方法,用于生成白藜麦品种的藜麦蛋白质全局指纹图谱,这些白藜麦品种采用传统和有机种植方式,并经过煮沸和挤压加工。使用MALDIquant软件(版本1.19.3)处理不同蛋白质提取物的质谱图,检测到49种蛋白质(其中31种初步鉴定)。然后将这些蛋白质的强度值视为用于多变量数据分析的蛋白质指纹图谱。我们的结果揭示了用于区分种植和加工条件的可靠偏最小二乘判别分析(PLS-DA)分类模型,以及对区分至关重要的检测蛋白质。它们证实了通过MALDI-TOF-MS和化学计量学的蛋白质指纹图谱追踪藜麦籽粒农业起源和加工处理的有效性。这种非靶向方法在食品控制和食品加工业中具有广阔的应用前景。