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Quantitative Biology > Quantitative Methods

arXiv:2503.02569v1 (q-bio)
[Submitted on 4 Mar 2025 ]

Title: Prediction of amino acid content in live black soldier fly larvae using near infrared spectroscopy

Title: 利用近红外光谱预测活体黑水虻幼虫中的氨基酸含量

Authors:R.M. Zaalberg, L.B. Andersen, S.J. Noel, A.J. Buitenhuis, K. Jensen, G. Gebreyesus
Abstract: Black soldier fly (Hermetia illucens) larvae are emerging as a sustainable protein source for animal feed and human nutrition. Ensuring consistent amino acid composition is crucial for quality control, necessitating rapid, non-destructive assessment methods, particularly for selective breeding. This study validates near-infrared (NIR) spectroscopy with partial least squares (PLS) regression as a reliable tool for predicting free amino acid (FAA) profiles in live larvae. Using a FOSS DS2500 spectrometer (400-2500 nm), 204 live larval samples reared on 17 diets with varying protein (2.3-89.8 percent) and sugar (8.5-96.0 percent) levels were scanned, followed by FAA extraction. Alanine (6.50 +/- 3.52 mg/g) and proline (5.61 +/- 4.45 mg/g) were the most abundant FAAs, with key NIR absorption bands at 1670-1786 nm and 2300-2366 nm enabling accurate predictions. PLS models showed strong predictive power for individual FAAs (e.g., glutamate: R^2 = 0.88; asparagine: R^2 = 0.78; alanine: R^2 = 0.65) and total FAAs (R^2 = 0.64). These results highlight NIR's potential for real-time quality control and phenotyping in BSF production and breeding.
Abstract: 黑水虻(Hermetia illucens)幼虫正作为可持续的蛋白质来源,用于动物饲料和人类营养。确保氨基酸组成的一致性对于质量控制至关重要,需要快速、非破坏性的评估方法,特别是在选择性育种中。本研究验证了近红外(NIR)光谱与偏最小二乘法(PLS)回归结合,作为预测活体幼虫游离氨基酸(FAA)谱的可靠工具。使用FOSS DS2500光谱仪(400-2500 nm),对204个在17种不同蛋白质(2.3-89.8%)和糖(8.5-96.0%)水平的饲料中饲养的活体幼虫样本进行了扫描,随后提取了FAA。丙氨酸(6.50 +/- 3.52 mg/g)和脯氨酸(5.61 +/- 4.45 mg/g)是含量最高的FAA,关键的NIR吸收波段在1670-1786 nm和2300-2366 nm范围内,能够实现准确预测。PLS模型对个别FAA(例如谷氨酸:R^2 = 0.88;天冬酰胺:R^2 = 0.78;丙氨酸:R^2 = 0.65)和总FAA(R^2 = 0.64)表现出强大的预测能力。这些结果突显了NIR在BSF生产与育种中的实时质量控制和表型分析潜力。
Comments: 28 pages, 2 figures, 3 supplementary files
Subjects: Quantitative Methods (q-bio.QM)
Cite as: arXiv:2503.02569 [q-bio.QM]
  (or arXiv:2503.02569v1 [q-bio.QM] for this version)
  https://doi.org/10.48550/arXiv.2503.02569
arXiv-issued DOI via DataCite

Submission history

From: Grum Gebreyesus Teklewold [view email]
[v1] Tue, 4 Mar 2025 12:47:19 UTC (1,440 KB)
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