Near-Infrared Reflectance Spectroscopy for Quantitative Analysis of Fat and Fatty Acid Content in Living Tenebrio molitor Larvae to Detect the Influence of Substrate on Larval Composition

Author:

Kröncke Nina1ORCID,Neumeister Monique1,Benning Rainer1

Affiliation:

1. Institute of Food Technology and Bioprocess Engineering, University of Applied Sciences Bremerhaven, 27568 Bremerhaven, Germany

Abstract

Several studies have shown that mealworms (Tenebrio molitor L.) could provide animals and humans with valuable nutrients. Tenebrio molitor larvae were studied to determine whether their rearing diets affected their fat and fatty acid content and to ascertain if it is possible to detect the changes in the larval fat composition using near-infrared reflectance spectroscopy (NIRS). For this reason, a standard control diet (100% wheat bran) and an experimental diet, consisting of wheat bran and the supplementation of a different substrate (coconut flour, flaxseed flour, pea protein flour, rose hip hulls, grape pomace, or hemp protein flour) were used. The results showed lesser weight gain and slower growth rates for larvae raised on diets with a high fat content. A total of eight fatty acids were identified and quantified, where palmitic, oleic, and linoleic acids were the most prevalent and showed a correlation between larval content and their content in the rearing diets. There was a high content of lauric acid (3.2–4.6%), myristic acid (11.4–12.9%), and α-linolenic acid 8.4–13.0%) in mealworm larvae as a result of the high dietary content of these fatty acids. NIR spectra were also influenced by the fat and fatty acid composition, as larval absorbance values differed greatly. The coefficient of the determination of prediction (R2P) was over 0.97, with an RPD value of 8.3 for the fat content, which indicates the high predictive accuracy of the NIR model. Furthermore, it was possible to develop calibration models with great predictive efficiency (R2P = 0.81–0.95, RPD = 2.6–5.6) for all fatty acids, except palmitoleic and stearic acids which had a low predictive power (R2P < 0.5, RPD < 2.0). The detection of fat and fatty acids using NIRS can help insect producers to quickly and easily analyze the nutritional composition of mealworm larvae during the rearing process.

Funder

Federal Ministry for Economic Affairs and Energy

Publisher

MDPI AG

Subject

Insect Science

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