基于全集成传感器芯片的手持近红外模块的光谱传感

IF 0.8 4区 化学 Q4 SPECTROSCOPY
F. Ou, A. van Klinken, K. Hakkel, M. Petruzzella, Don M. J. van Elst, P. Sevo, Chenhui Li, F. Pagliano, R. V. van Veldhoven, A. Fiore
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引用次数: 1

摘要

近红外光谱学广泛应用于材料的分类和材料性质的定量分析。今天,将这种技术的使用扩展到便携式应用程序,并最终与消费设备和智能手机集成的需求很高。为了实现这一目标,近红外传感器的总体尺寸、生产成本、稳健性和抗振动性尤为重要。本文描述了一种近红外(850-1700 nm)光谱传感的方法,该方法使用基于完全集成的多像素探测器阵列的手持传感器模块,其占地面积约为2×2 mm2。光谱传感器模块的功能最近在两个应用案例中进行了评估:原料牛奶中脂肪百分比的量化和塑料类型的分类。脂肪定量预测均方根误差(RMSE)为0.14%,塑料类型分类对未知样本的预测精度为100%。结果表明,集成传感器采用的直接近红外传感方法是可行的,具有广泛的应用前景。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Spectral Sensing Using a Handheld NIR Module Based on a Fully Integrated Sensor Chip
Near-infrared (NIR) spectroscopy is widely used for the classification of materials and the quantification of their properties. Today, there is a high demand for extending the use of this technique to portable applications, and eventually, the integration with consumer appliances and smartphones. To reach this goal, the overall size of the NIR sensor, its production cost, robustness, and resistance to vibrations are of particular importance. This paper describes an approach to spectral sensing in the NIR (850–1700 nm) using a handheld sensor module based on a fully integrated multipixel detector array with a footprint of around 2×2 mm2. The capabilities of the spectral sensor module were recently evaluated in two application cases: Quantification of the fat percentage in raw milk and the classification of plastic types. Fat quantification was achieved with a root mean square error (RMSE) of prediction of 0.14% and classification of plastic types was achieved with a prediction accuracy on unknown samples of 100%. The results demonstrate the feasibility of the direct NIR sensing approach used by the integrated sensor, which has potential to be used in a variety of applications.
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来源期刊
Spectroscopy
Spectroscopy 物理-光谱学
CiteScore
1.10
自引率
0.00%
发文量
0
审稿时长
3 months
期刊介绍: Spectroscopy welcomes manuscripts that describe techniques and applications of all forms of spectroscopy and that are of immediate interest to users in industry, academia, and government.
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