Agro-Food Robotics, Wageningen University & Research, the Netherlands.
Agro-Food Robotics, Wageningen University & Research, the Netherlands.
Anal Chim Acta. 2022 Jan 15;1190:339235. doi: 10.1016/j.aca.2021.339235. Epub 2021 Nov 2.
Spectral imaging (SI) in analytical chemistry is widely used for the assessment of spatially distributed physicochemical properties of samples. Although massive development in instrument and chemometrics modelling has taken place in the recent years, the main challenge with SI is that available sensors require extensive system integration and calibration modelling before their use for routine analysis. Further, the models developed during one experiment are rarely useful once the system is reintegrated for a new experiment. To avoid system reintegration and reuse calibrated models, this study presents an intelligent All-In-One SI (ASI) laboratory system allowing standardised automated data acquisition and real-time spectral model deployment. The ASI system supplies a controlled standardised illumination environment, an in-built computing system, embedded software for automated image acquisition, and model deployment to predict the spatial distribution of sample properties in real-time. To show the capability of the ASI framework, exemplary cases of fruit property prediction in different fruits are presented. Furthermore, ASI is also benchmarked in performance against the current commercially available portable as well as high-end laboratory spectrometers.
光谱成像(SI)在分析化学中被广泛用于评估样品的空间分布物理化学性质。尽管近年来仪器和化学计量学建模取得了巨大的发展,但 SI 的主要挑战在于,可用传感器在用于常规分析之前需要进行广泛的系统集成和校准建模。此外,一旦系统重新集成用于新的实验,在一个实验中开发的模型很少有用。为了避免系统重新集成和重新使用校准模型,本研究提出了一种智能一体式 SI(ASI)实验室系统,允许标准化自动数据采集和实时光谱模型部署。ASI 系统提供受控的标准化照明环境、内置计算系统、用于自动图像采集的嵌入式软件以及模型部署,以实时预测样品属性的空间分布。为了展示 ASI 框架的能力,展示了不同水果中水果属性预测的示例案例。此外,还针对当前市售的便携式和高端实验室光谱仪对 ASI 进行了性能基准测试。