Abstract The aim of the present study is to test the ability of a low-cost and portable middle infrared spectrometer based on a linear array of 1 × 128 of PbSe, coupled with a linear variable optical filter in the wavelength range of 3–4.5 μm, for the differentiation of pure chemical substances and quality control of fuels. Potential additives and adulterants for gasoline were tested, considering the alcohols ethanol, n-butanol, n-propanol and n-hexanol as potential additives and methanol and diesel oils as adulterants. Multivariate analysis of variance (MANOVA) applied to the scores obtained in the Principal Component Analysis (PCA) was conducted to analyze the spectral data and distinguish between the individual components. For the purposes of classifying anonymous samples, the centroid of each pure substance in the canonical variables was calculated, followed by the distance calculated between new samples to such centroids, assigning the individual to the most proximate category. The results demonstrated that the technique was able to discriminate between gasoline, diesel oils and the alcohols methanol, ethanol, n-propanol, n-butanol and n-hexanol and that it had the potential to be applied in the fuel industry.

中文翻译：

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中红外非制冷传感器，用于识别汽油中的纯燃料、添加剂和掺假物

摘要 本研究的目的是测试基于 1 × 128 PbSe 线性阵列与 3-4.5 μm 波长范围内的线性可变光学滤波器的低成本便携式中红外光谱仪的能力。 ，用于纯化学物质的鉴别和燃料的质量控制。以乙醇、正丁醇、正丙醇和正己醇为潜在添加剂，甲醇和柴油为掺杂物，对汽油的潜在添加剂和掺杂物进行了测试。多变量方差分析 (MANOVA) 应用于主成分分析 (PCA) 中获得的分数，以分析光谱数据并区分各个成分。为了对匿名样本进行分类，计算标准变量中每种纯物质的质心，然后计算新样本与这些质心之间的距离，将个体分配到最接近的类别。结果表明，该技术能够区分汽油、柴油和醇类甲醇、乙醇、正丙醇、正丁醇和正己醇，具有在燃料工业中应用的潜力。