Insect Protein Content Analysis in Handcrafted Fitness Bars by NIR Spectroscopy. Gaussian Process Regression and Data Fusion for Performance Enhancement of Miniaturized Cost-Effective Consumer-Grade Sensors

Future food supply will become increasingly dependent on edible material extracted from insects. The growing popularity of artisanal food products enhanced by insect proteins creates particular needs for establishing effective methods for quality control. This study focuses on developing rapid and e...

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Autores principales:	Krzysztof B. Beć, Justyna Grabska, Nicole Plewka, Christian W. Huck
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	near-infrared (NIR) spectroscopy miniaturized sensor handheld protein analysis partial least squares regression (PLSR) Gaussian process regression (GPR) Organic chemistry QD241-441
Acceso en línea:	https://doaj.org/article/f785ca77a54f4a23b949e19ced899114
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spelling	oai:doaj.org-article:f785ca77a54f4a23b949e19ced8991142021-11-11T18:24:53ZInsect Protein Content Analysis in Handcrafted Fitness Bars by NIR Spectroscopy. Gaussian Process Regression and Data Fusion for Performance Enhancement of Miniaturized Cost-Effective Consumer-Grade Sensors10.3390/molecules262163901420-3049https://doaj.org/article/f785ca77a54f4a23b949e19ced8991142021-10-01T00:00:00Zhttps://www.mdpi.com/1420-3049/26/21/6390https://doaj.org/toc/1420-3049Future food supply will become increasingly dependent on edible material extracted from insects. The growing popularity of artisanal food products enhanced by insect proteins creates particular needs for establishing effective methods for quality control. This study focuses on developing rapid and efficient on-site quantitative analysis of protein content in handcrafted insect bars by miniaturized near-infrared (NIR) spectrometers. Benchtop (Büchi NIRFlex N-500) and three miniaturized (MicroNIR 1700 ES, Tellspec Enterprise Sensor and SCiO Sensor) in hyphenation to partial least squares regression (PLSR) and Gaussian process regression (GPR) calibration methods and data fusion concept were evaluated via test-set validation in performance of protein content analysis. These NIR spectrometers markedly differ by technical principles, operational characteristics and cost-effectiveness. In the non-destructive analysis of intact bars, the root mean square error of cross prediction (RMSEP) values were 0.611% (benchtop) and 0.545–0.659% (miniaturized) with PLSR, and 0.506% (benchtop) and 0.482–0.580% (miniaturized) with GPR calibration, while the analyzed total protein content was 19.3–23.0%. For milled samples, with PLSR the RMSEP values improved to 0.210% for benchtop spectrometer but remained in the inferior range of 0.525–0.571% for the miniaturized ones. GPR calibration improved the predictive performance of the miniaturized spectrometers, with RMSEP values of 0.230% (MicroNIR 1700 ES), 0.326% (Tellspec) and 0.338% (SCiO). Furthermore, Tellspec and SCiO sensors are consumer-oriented devices, and their combined use for enhanced performance remains a viable economical choice. With GPR calibration and test-set validation performed for fused (Tellspec + SCiO) data, the RMSEP values were improved to 0.517% (in the analysis of intact samples) and 0.295% (for milled samples).Krzysztof B. BećJustyna GrabskaNicole PlewkaChristian W. HuckMDPI AGarticlenear-infrared (NIR) spectroscopyminiaturized sensorhandheldprotein analysispartial least squares regression (PLSR)Gaussian process regression (GPR)Organic chemistryQD241-441ENMolecules, Vol 26, Iss 6390, p 6390 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	near-infrared (NIR) spectroscopy miniaturized sensor handheld protein analysis partial least squares regression (PLSR) Gaussian process regression (GPR) Organic chemistry QD241-441
spellingShingle	near-infrared (NIR) spectroscopy miniaturized sensor handheld protein analysis partial least squares regression (PLSR) Gaussian process regression (GPR) Organic chemistry QD241-441 Krzysztof B. Beć Justyna Grabska Nicole Plewka Christian W. Huck Insect Protein Content Analysis in Handcrafted Fitness Bars by NIR Spectroscopy. Gaussian Process Regression and Data Fusion for Performance Enhancement of Miniaturized Cost-Effective Consumer-Grade Sensors
description	Future food supply will become increasingly dependent on edible material extracted from insects. The growing popularity of artisanal food products enhanced by insect proteins creates particular needs for establishing effective methods for quality control. This study focuses on developing rapid and efficient on-site quantitative analysis of protein content in handcrafted insect bars by miniaturized near-infrared (NIR) spectrometers. Benchtop (Büchi NIRFlex N-500) and three miniaturized (MicroNIR 1700 ES, Tellspec Enterprise Sensor and SCiO Sensor) in hyphenation to partial least squares regression (PLSR) and Gaussian process regression (GPR) calibration methods and data fusion concept were evaluated via test-set validation in performance of protein content analysis. These NIR spectrometers markedly differ by technical principles, operational characteristics and cost-effectiveness. In the non-destructive analysis of intact bars, the root mean square error of cross prediction (RMSEP) values were 0.611% (benchtop) and 0.545–0.659% (miniaturized) with PLSR, and 0.506% (benchtop) and 0.482–0.580% (miniaturized) with GPR calibration, while the analyzed total protein content was 19.3–23.0%. For milled samples, with PLSR the RMSEP values improved to 0.210% for benchtop spectrometer but remained in the inferior range of 0.525–0.571% for the miniaturized ones. GPR calibration improved the predictive performance of the miniaturized spectrometers, with RMSEP values of 0.230% (MicroNIR 1700 ES), 0.326% (Tellspec) and 0.338% (SCiO). Furthermore, Tellspec and SCiO sensors are consumer-oriented devices, and their combined use for enhanced performance remains a viable economical choice. With GPR calibration and test-set validation performed for fused (Tellspec + SCiO) data, the RMSEP values were improved to 0.517% (in the analysis of intact samples) and 0.295% (for milled samples).
format	article
author	Krzysztof B. Beć Justyna Grabska Nicole Plewka Christian W. Huck
author_facet	Krzysztof B. Beć Justyna Grabska Nicole Plewka Christian W. Huck
author_sort	Krzysztof B. Beć
title	Insect Protein Content Analysis in Handcrafted Fitness Bars by NIR Spectroscopy. Gaussian Process Regression and Data Fusion for Performance Enhancement of Miniaturized Cost-Effective Consumer-Grade Sensors
title_short	Insect Protein Content Analysis in Handcrafted Fitness Bars by NIR Spectroscopy. Gaussian Process Regression and Data Fusion for Performance Enhancement of Miniaturized Cost-Effective Consumer-Grade Sensors
title_full	Insect Protein Content Analysis in Handcrafted Fitness Bars by NIR Spectroscopy. Gaussian Process Regression and Data Fusion for Performance Enhancement of Miniaturized Cost-Effective Consumer-Grade Sensors
title_fullStr	Insect Protein Content Analysis in Handcrafted Fitness Bars by NIR Spectroscopy. Gaussian Process Regression and Data Fusion for Performance Enhancement of Miniaturized Cost-Effective Consumer-Grade Sensors
title_full_unstemmed	Insect Protein Content Analysis in Handcrafted Fitness Bars by NIR Spectroscopy. Gaussian Process Regression and Data Fusion for Performance Enhancement of Miniaturized Cost-Effective Consumer-Grade Sensors
title_sort	insect protein content analysis in handcrafted fitness bars by nir spectroscopy. gaussian process regression and data fusion for performance enhancement of miniaturized cost-effective consumer-grade sensors
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/f785ca77a54f4a23b949e19ced899114
work_keys_str_mv	AT krzysztofbbec insectproteincontentanalysisinhandcraftedfitnessbarsbynirspectroscopygaussianprocessregressionanddatafusionforperformanceenhancementofminiaturizedcosteffectiveconsumergradesensors AT justynagrabska insectproteincontentanalysisinhandcraftedfitnessbarsbynirspectroscopygaussianprocessregressionanddatafusionforperformanceenhancementofminiaturizedcosteffectiveconsumergradesensors AT nicoleplewka insectproteincontentanalysisinhandcraftedfitnessbarsbynirspectroscopygaussianprocessregressionanddatafusionforperformanceenhancementofminiaturizedcosteffectiveconsumergradesensors AT christianwhuck insectproteincontentanalysisinhandcraftedfitnessbarsbynirspectroscopygaussianprocessregressionanddatafusionforperformanceenhancementofminiaturizedcosteffectiveconsumergradesensors
_version_	1718431858568462336

Insect Protein Content Analysis in Handcrafted Fitness Bars by NIR Spectroscopy. Gaussian Process Regression and Data Fusion for Performance Enhancement of Miniaturized Cost-Effective Consumer-Grade Sensors

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