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

Abstract

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).

Analysis of wheat flour-insect powder mixtures based on their near infrared spectra

“…The applicability of NIRS has been demonstrated by previous publications on the detection of various insect pests and insect fragments in different cereals such as wheat, rice and sorghum ( Biancolillo et al, 2019 , Johnson, 2020 ). NIRS has also been used successfully to analyse the protein content of insect-based energy bars ( Beć et al, 2021 ). NIRS is usually applied with different classification and prediction methods to extract the relevant information from the obtained signals ( Benes et al, 2020 ).…”

Determination of Moisture and Protein Content in Living Mealworm Larvae (Tenebrio molitor L.) Using Near-Infrared Reflectance Spectroscopy (NIRS)

“…There are also some applications that could be used for the detection of insect pests in stored grains [ 36 , 37 , 38 ], but without analyzing the chemical composition of these insects. It is also possible to analyze the protein content of insect-based energy bars [ 39 ]. Some NIR models were built to classify edible insect powders using Fourier-transform infrared spectroscopy (FTIR) [ 40 ].…”

Near-Infrared Spectroscopy and Mode Cloning (NIR-MC) for In-Situ Analysis of Crude Protein in Bamboo

“…Importantly, the determination of the nutritional value of forage grasses can be used to understand the nutritional drivers of wild-animal movement in situ [5]. For such studies, miniaturized NIR spectrometers designed for in-field spectral collection are more practical for real-time assessment than bench-top systems [6][7][8].…”