Antonio Fernández, Ismael Montero-Fernández, Olga Monago-Maraña, E. Martín-Tornero, D. Martín-Vertedor
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引用次数: 0
Abstract
Californian-style is one of the most important black table olive elaborations. During its processing, table olives produce acrylamide, a potential carcinogen compound generated during sterilization. In the present study, total fat and acrylamide content in Californian-style table olives were determined and a regression between them was performed (acrylamide concentration range: below limit of detection—2500 ng g−1 and 8–22% for total fat). Nowadays, there are fast and efficient new techniques, such as Near-Infrared Spectroscopy (NIRS) to measure fat content parameters. In that sense, NIRS was used to perform a fat content quantification model in olives in order to indirectly determine acrylamide content. Calibration models for fat quantification were obtained in defatted olive pastes from a unique variety and for olive pastes from different varieties. In the first case, best results were obtained since only one variety was used (R2 = 0.9694; RMSECV = 1.31%; and REP = 8.4%). However, in the second case, results were still acceptable R2 = 0.678, RMSECV = 2.3%, REP = 17.7% and RMSEV = 2.17%. Regression coefficients showed the most influence variables corresponded with fat. The determination coefficient for the fat and acrylamide correlation was high (r = 0.877), being an efficient approach to find out the contribution of fat degradation to acrylamide synthesis in table olives.
加州风格是最重要的黑餐桌橄榄精心设计之一。食用橄榄在加工过程中会产生丙烯酰胺,这是一种在杀菌过程中产生的潜在致癌化合物。在本研究中,测定了加州风味餐桌橄榄中的总脂肪和丙烯酰胺含量,并对其进行了回归分析(丙烯酰胺浓度范围:低于检测限——2500 ng g−1,总脂肪含量为8-22%)。目前,有一些快速有效的新技术,如近红外光谱(NIRS)来测量脂肪含量参数。从这个意义上说,近红外光谱用于对橄榄中的脂肪含量进行量化模型,以间接测定丙烯酰胺含量。从一个独特品种的脱脂橄榄酱和不同品种的橄榄酱中获得了脂肪定量的校准模型。在第一种情况下,由于只使用了一个品种,因此获得了最好的结果(R2=0.9694;RMSECV=1.31%;REP=8.4%)。然而,在第二种情况中,结果仍然可以接受R2=0.678,RMSECV=2.3%,REP=17.7%和RMSEV=2.17%。回归系数显示,与脂肪相对应的影响变量最多。脂肪和丙烯酰胺相关性的测定系数较高(r=0.877),是了解食用橄榄中脂肪降解对丙烯酰胺合成贡献的有效方法。
期刊介绍:
Chemosensors (ISSN 2227-9040; CODEN: CHEMO9) is an international, scientific, open access journal on the science and technology of chemical sensors published quarterly online by MDPI.The journal is indexed in Scopus, SCIE (Web of Science), CAPlus / SciFinder, Inspec, Engineering Village and other databases.