Abstract
The nutritional profile, especially amino acid profile, determines the quality and commercial value of insect protein products. Multiple previous studies have used spectroscopy technologies and machine learning algorithms to predict essential amino acid content in various foods and feeds. However, these approaches were not applied for predicting essential amino acid content in insects before. In this study, 200 insect samples containing 9 commercial insect species were collected. Machine learning methods were applied to build the prediction models to predict amino acid content using Fourier-transform infrared spectroscopy (FTIR) raw spectra and first derivative. For all amino acids, partial least square regression, decision tree and radial basis artificial neural network exhibited high performances to predict essential amino acids. Model performances were improved for some amino acids using first derivative than using raw spectra. The highest performance (coefficient of determination: 0.97, root mean square error of prediction: 0.05 g/100 g and ratio of performance: 4.07) was achieved for phenylalanine prediction using radial basis artificial neural network modeling. The high model performance indicates the potential of applying FTIR and subsequent machine learning modeling for fast and non-destructive prediction of amino acid of insect products.
Keywords:
mealworm; amino acid; FTIR; machine learning; prediction
Thumbnail
