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Predicting the performance of handheld near infrared photonic sensors from a master benchtop device



Many industries see a shifting focus towards performing on/site analysis using handheld spectroscopic devices. A determining factor for decision/making on the commissioning of these devices is available information on the potential performance of the device for specific applications. By now, myriad handheld solutions with very different specifications and pricing are available on the market. Although specifications are generally available for new devices, this does not directly quantify or predict how available devices will perform for targeted cases. We present a novel chemometric method to estimate the prediction performance of handheld NIR hardware and apply it to estimate the performance of two commercially available handheld NIR technologies in predicting protein content (ranging 120-180 g kg?1) in pig feed from existing data of a benchtop device. Adjusting benchtop data to the wavelength range and resolution of the handheld device lead to over/optimistic estimates of the handheld performances. Our method additionally utilizes information on the error structure of the handheld devices for the estimation. It yielded performance estimates differing less than 1 g kg?1 from the experimentally determined handheld performances and similar model parameters. Our method was effective for linear and nonlinear calibration algorithms, also when estimating performance after averaging multiple scans. Replicate spectra of twenty samples recorded using the handheld were required for replication error estimation to obtain an accurate performance estimation. The error structure could be reported by manufacturers in the future for this approach to be universally employed for predictive quantitative technology assessment. Overall, our method provides estimates of the performance of a handheld device for a specific task with minimal testing required and can thus be used as a device or application screening tool before committing to develop calibrations.

Published in: 
Animal Feed
Date of Publication: 
March 10, 2022
Mark Schoot / Martin Alewijn / Yannick Weesepoel / Judith Mueller/Maatsch / Christiaan Kapper / Geert Postma / Lutgarde Buydens / Jeroen Jansen
Nutricontrol / Radboud University / Wageningen University
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