This paper presents an overview of recent developments in smartphone spectrometers focusing on light collection, dispersion, detection and spectral calibration. These spectrometers potentially offer unprecedented field diagnostics that can exploit real-time IoT edge data transfer into the cloud and back. However, there are technical challenges if these are to perform as well, if not better, than laboratory-based instruments. Limitations of traditional spectral calibration are addressed by considering an accurate non-linear wavelength increment over the spatial region of the detector pixels and dispersion associated with the effective refractive index (n) variation of a sample and its holder. To evaluate the performance of novel wavelength calibration, the algorithm was applied to the measurement of absorption spectra of vegetable oils (olive and soybean) using a double beam smartphone spectrometer. In the presence of a significant variation of sample n, the spectrometer measures spectra that overlap well with the spectra obtained using a standard spectrometer. The results also show a significant variation in absorbance spectrum between oil types with distinct absorption bands. This allows one to use the instrument as a straightforward tool for determining the adulteration level of olive oils in the fields and a means of addressing global food fraud more generally.

中文翻译：

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智能手机光谱仪样品分析的最新发展


本文概述了智能手机光谱仪的最新发展，重点关注光收集、色散、检测和光谱校准。这些光谱仪可能提供前所未有的现场诊断，可以利用实时物联网边缘数据传输到云端并返回。然而，如果这些仪器的性能与实验室仪器一样好，甚至更好，则存在技术挑战。通过考虑探测器像素空间区域内的精确非线性波长增量以及与样品及其支架的有效折射率 (n) 变化相关的色散，解决了传统光谱校准的局限性。为了评估新型波长校准的性能，该算法被应用于使用双光束智能手机光谱仪测量植物油（橄榄油和大豆）的吸收光谱。在样品 n 存在显着变化的情况下，光谱仪测量的光谱与使用标准光谱仪获得的光谱很好地重叠。结果还表明，具有不同吸收带的油类型之间的吸收光谱存在显着差异。这使得人们可以将该仪器用作确定田间橄榄油掺假水平的直接工具，也是更广泛地解决全球食品欺诈问题的一种手段。