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

Abstract Black soldier fly ( Hermetia illucens ) larvae are gaining recognition as a sustainable farmed protein source for animal feed and human nutrition. Amino acid (AA) composition critically determines protein quality and overall nutritional value, necessitating rapid, cost-effective methods to ensure consistency in insect-based products. This study establishes near-infrared (NIR) spectroscopy coupled with partial least squares (PLS) regression as a robust tool for predicting AA profiles in live black soldier fly (BSF) larvae. Larvae were reared on seventeen diets varying in protein content (2.3–89.8%) and digestible carbohydrate content (8.5–96.0%). We analyzed 204 pooled batch samples, each comprising between 111 and 337 larvae. Samples were scanned twice using a FOSS DS2500 spectrometer (400–2500 nm) to capture NIR-spectral data, followed by freeze-drying and AA extraction via liquid chromatography-tandem mass spectrometry. The PLS prediction models were optimized via repeated ten-fold cross-validation. We observed distinct AA profiles, with alanine (6.50 ± 3.52 mg/g) and proline (5.61 ± 4.45 mg/g) as the most dominant components. Key NIR absorption bands at 1695–1745 nm, 1875–1895 nm, and 2300–2366 nm were critical for predictions. The prediction models demonstrated high accuracy for individual AAs (e.g. glutamate: ; asparagine: ; histidine: ) and the total AA content ( ). These findings illustrate NIR’s utility in BSF production, offering a rapid, scalable solution for real-time quality control as well as accurate phenotyping of live BSF larvae.