首页> 外文期刊>Talanta: The International Journal of Pure and Applied Analytical Chemistry >Differentiation of organic and non-organic ewe's cheeses using main mineral composition or near infrared spectroscopy coupled to chemometric tools: A comparative study
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Differentiation of organic and non-organic ewe's cheeses using main mineral composition or near infrared spectroscopy coupled to chemometric tools: A comparative study

机译:使用主要矿物成分或近红外光谱结合化学计量学工具对有机和非有机母羊奶酪进行区分的比较研究

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摘要

Two independent methodologies were investigated to achieve the differentiation of ewes' cheeses from different systems of production (organic and non-organic). Eighty cheeses (40 organic and 40 non-organic) from two systems of production, two different breeds of ewe, different sizes, seasons (summer and winter) and ripening times up to 9 months were elaborated. Their mineral composition or the information provided by their spectra in the near infrared zone (NIR) coupled to chemometric tools were used in order to differentiate between organic and non-organic cheeses. Main mineral composition (Ca, K, Mg, Na and P) of cheeses and stepwise lineal discriminant analysis were used to develop a discriminant model. The results from canonical standardised coefficients indicated that the most important mineral was Mg (1.725) followed by P (0.764) and K (0.742). The percentage of correctly classified samples was 88% in internal validation and 90% in external validation, selecting Mg, K and P as variables.Spectral information in the NIR zone was used coupled to a discriminant analysis based on a regression by partial least squares in order to obtain a model which allowed a rate of samples correctly classified of 97% in internal validation and 85% in external validation.
机译:研究了两种独立的方法,以实现母羊奶酪与不同生产系统(有机和非有机)的区别。精心制作了八种奶酪,分别来自两种生产体系,两种不同的母羊品种,不同的大小,季节(夏季和冬季)以及成熟期长达9个月的奶酪(40种有机奶酪和40种非有机奶酪)。为了区分有机奶酪和非有机奶酪,使用了它们的矿物成分或由近红外区(NIR)中的光谱提供的信息以及化学计量工具。奶酪的主要矿物质成分(Ca,K,Mg,Na和P)和逐步线性判别分析用于建立判别模型。规范化标准系数的结果表明,最重要的矿物是Mg(1.725),其次是P(0.764)和K(0.742)。选择Mg,K和P作为变量,在内部验证中正确分类的样本的百分比为88%,在外部验证中为90%。将NIR区域中的光谱信息与基于偏最小二乘回归的回归判别分析结合使用为了获得一个模型,该模型允许内部验证中正确分类的样本率为97%,外部验证中为85%。

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