Near Infrared (NIR) Spectroscopy

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The accuracy of prediction models based on spectroscopic measurements can be influenced by a variety of factors, including aging equipment. Continuous monitoring is key to managing the PAT model lifecycle and ensuring that changes over time do not negatively affect prediction model performance.

A novel approach to NIR spectral sensing, using a miniaturized fully-integrated multipixel array of resonant-cavity-enhanced InGaAs photodetectors, enables sensors with a millimeter-scale footprint and wafer-scale fabrication. This multipixel sensor does not measure the full spectrum, but rather a limited number of spectral regions with limited resolution (50–100 nm).

grape seed oil in a bottle with grapes surrounding it

Given that grape seed oil has shown beneficial effects for consumers, there is a interest in measuring oil quality and potential adulteration. This study demonstrates an effective near-infrared (NIR) spectroscopy method, using a series of machine learning approaches for wavelength variable selection, to rapidly discriminate grape seed oil adulteration.

leaf with raindrops on it

Spectral reflectance is a non-destructive method that is applicable to remote sensing and may be used to measure the chlorophyll content in a crop, which indicates the photosynthetic capacity, growth cycles, and degrees of stress (such as disease, insect infestation, and heavy metal stress) on plant ecosystems. This vis-NIR spectral reflectance method measures leaf chlorophyll using a wavelet analysis algorithm approach.

mine carts on tracks heading into a mine

Depletion of modern mineral resources due to continuous exploitation and utilization makes it economically necessary to quickly identify the locate sources of low-grade ore. Here, we propose a vis-NIR remote sensing method to determine copper content in mining areas as well as to measure the environmental impact of surface mining methods.