利用FTIR光谱和DRIFTS和Disk技术对两个不同采收年开心果(Pistacia vera,希腊‘Aegina’品种)的化学计量鉴别

L. Valasi, C. Pappas
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引用次数: 0

摘要

食品质量是一个非常重要的话题,因为越来越多的人强调产品的质量而不是数量。先前的研究指出了质量与收获年份的相互作用。在本研究中,使用傅里叶红外光谱(FTIR)、(a)漫反射红外傅里叶变换光谱(DRIFTS)和(b) KBr/样品盘技术对22个黄合欢(希腊“埃伊纳”品种)样品(11个来自2017年,11个来自2018年)进行了区分。在这两年,开心果树的生长遵循标准的种植方法和类似的农艺条件。采用偏最小二乘判别分析(PLS-DA)建立了两个化学计量学模型。DRIFTS证明不能对样本进行统计学区分(R2 = 0.96266, Q2 = 0.63152)。相反,圆盘技术完全区分开心果样品(R2 = 0.99705, Q2 = 0.97719)。1720 ~ 1800 cm−1区域是造成这种差异的主要区域。圆盘傅里叶红外化学计量模型是快速,稳健,经济,环保的确定开心果基质质量。
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Chemometric Differentiation of Pistachios (Pistacia vera, Greek ‘Aegina’ Variety) from Two Different Harvest Years Using FTIR Spectroscopy and DRIFTS and Disk Techniques
Food quality is a topic of utmost importance as more and more emphasis is placed on quality rather than quantity of products. Previous studies have pointed out the interaction of quality with the harvest year. In this study, 22 Pistacia vera (Greek ‘Aegina’ variety) samples (11 from 2017 and 11 from 2018) were differentiated using Fourier transform infrared spectroscopy (FTIR) and (a) diffuse reflectance infrared Fourier transform spectroscopy (DRIFTS) and (b) KBr/sample disk techniques. In both years, the pistachios trees’ growing followed standard cultivation methods and similar agronomic conditions. Two chemometric models were developed using partial least squares-discrimination analysis (PLS-DA). DRIFTS proved unable to statistically differentiate the samples (R2 = 0.96266, Q2 = 0.63152). On the contrary, the disk technique completely differentiated the pistachio samples (R2 = 0.99705, Q2 = 0.97719). The 1720–1800 cm−1 region mostly contributed to the discrimination. The disk-FTIR chemometric model is fast, robust, economical, and environmentally friendly for determining pistachio matrix quality.
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