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<p>The objective of the study was to evaluate infrared (IR) spectroscopy in combination with pattern recognition analysis as a rapid technique to quantify the percentage of insect lipid added into the chickpea-based dough as well as the dough’s fatty acid profile. Several chickpea-based doughs were prepared with a variable amount of <i>Tenebrio molitor</i>, <i>Alphitobius diaperinus</i>, and <i>Acheta domesticus</i> lipid fraction (0, 2.9%, 5.8%, 8.7%, and 11.6%) replacing the same amount of olive and sunflower oil. The raw dough was analyzed using portable Fourier transform mid-infrared (FT-MIR) and handheld FT near (FT-NIR) spectrometers. The fatty acid profile was determined by using fatty acid methyl esters (FAME) methods. Partial least squares regression (PLSR) with cross-validation (leave-one-out) was used to build up a model to predict the percentage of insect lipid added showing a low standard error of cross-validation (SE<sub>CV</sub> ≤ 0.71%), strong correlation (<i>R</i><sub>CV</sub> ≥ 0.85), and great predictive ability (RPD, 5.21–5.53) with the external validation set. The saturated (SFA), monounsaturated (MUFA), and polyunsaturated (PUFA) fatty acids as well as the content of palmitic, oleic, and linoleic were correctly predicted with values of SE<sub>CV</sub> ≤ 5.64% and an <i>R</i><sub>CV</sub> ≥ 0.88. Nonetheless, the FT-MIR device tested showed higher performance to predict SFA, MUFA, PUFA, and fatty acids reaching values of 0.97 in coefficient of correlation (<i>R</i><sub>P</sub>) and 2.81% in standard error in prediction (SE<sub>P</sub>).</p>

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Rapid Determination of Insect Lipids and Their Fatty Acid Profile in Dough Using Handheld and Portable Infrared Spectrometers

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年2月6日

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  • Carmen Mendez-Sanchez,
  • Madushika K. Ranasinghe,
  • Carme Güell,
  • Montserrat Ferrando,
  • Luis Rodriguez-Saona,
  • Silvia de Lamo Castellvi

摘要

The objective of the study was to evaluate infrared (IR) spectroscopy in combination with pattern recognition analysis as a rapid technique to quantify the percentage of insect lipid added into the chickpea-based dough as well as the dough’s fatty acid profile. Several chickpea-based doughs were prepared with a variable amount of Tenebrio molitor, Alphitobius diaperinus, and Acheta domesticus lipid fraction (0, 2.9%, 5.8%, 8.7%, and 11.6%) replacing the same amount of olive and sunflower oil. The raw dough was analyzed using portable Fourier transform mid-infrared (FT-MIR) and handheld FT near (FT-NIR) spectrometers. The fatty acid profile was determined by using fatty acid methyl esters (FAME) methods. Partial least squares regression (PLSR) with cross-validation (leave-one-out) was used to build up a model to predict the percentage of insect lipid added showing a low standard error of cross-validation (SECV ≤ 0.71%), strong correlation (RCV ≥ 0.85), and great predictive ability (RPD, 5.21–5.53) with the external validation set. The saturated (SFA), monounsaturated (MUFA), and polyunsaturated (PUFA) fatty acids as well as the content of palmitic, oleic, and linoleic were correctly predicted with values of SECV ≤ 5.64% and an RCV ≥ 0.88. Nonetheless, the FT-MIR device tested showed higher performance to predict SFA, MUFA, PUFA, and fatty acids reaching values of 0.97 in coefficient of correlation (RP) and 2.81% in standard error in prediction (SEP).