ArticleSensors (Basel, Switzerland)2022
Handheld NIR Spectral Sensor Module Based on a Fully-Integrated Detector Array.
Article in Sensors (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Convergence of Thermistor Materials and Focal Plane Arrays in Uncooled Microbolometers: Trends and Perspectives.Nanomaterials (Basel, Switzerland) · 2025Review
- Application-Specific Optimization of Integrated Spectral Sensors.ACS photonics · 2025Article
- Machine Learning-Based Identification of Plastic Types Using Handheld Spectrometers.Sensors (Basel, Switzerland) · 2025Article
- Quantum dot-enabled infrared hyperspectral imaging with single-pixel detection.Light, science & applications · 2024Article
- LED-Based Desktop Analyzer for Fat Content Determination in Milk.Sensors (Basel, Switzerland) · 2023Article
- Portable NIR spectroscopy: the route to green analytical chemistry.Frontiers in chemistry · 2023Review
- Using Fuzzy Logic to Increase Accuracy in Mango Maturity Index Classification: Approach for Developing a Portable Near-Infrared Spectroscopy Device.Sensors (Basel, Switzerland) · 2022Article
Corrections and comments
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Authors and funding
10 authors.
Funding
Abstract
For decades, near-infrared (NIR) spectroscopy has been a valuable tool for material analysis in a variety of applications, ranging from industrial process monitoring to quality assessment. Traditional spectrometers are typically bulky, fragile and expensive, which makes them unsuitable for portable and in-field use. Thus, there is a growing interest for miniaturized, robust and low-cost NIR sensors. In this study, we demonstrate a handheld NIR spectral sensor module, based on a fully-integrated multipixel detector array, sensitive in the 850-1700 nm wavelength range. Differently from a spectrometer, the spectral sensor measures a limited number of NIR spectral bands. The capabilities of the spectral sensor module were evaluated alongside a commercially available portable spectrometer for two application cases: to quantify the moisture content in rice grains and to classify plastic types. Both devices achieved the two sensing tasks with comparable performance. Moisture quantification was achieved with a root mean square error (RMSE) prediction of 1.4% and 1.1% by the spectral sensor and spectrometer, respectively. Classification of the plastic type was achieved with a prediction accuracy on unknown samples of 100% and 96.4% by the spectral sensor and spectrometer, respectively. The results from this study are promising and demonstrate the potential for the compact NIR modules to be used in a variety of NIR sensing applications.
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Registered trials
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