Fourier-transform infrared spectroscopy and machine learning to predict amino acid content of nine commercial insects

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.

Rapid determination of water content in potato tubers based on hyperspectral images and machine learning algorithms

“…In recent years, machine learning methods have been found to be effective in predicting the content of food ingredients using spectral data (Hou et al, 2022). Machine learning is an extension of mathematical statistics and computer science and includes many statistical models and computer program algorithms.…”

“…Zheng et al (2018) established a model for estimating chlorophyll content in potato leaves at the red edge position, with an R 2 of 0.87. Hou et al (2022) used Fourier transform infrared spectroscopy and machine learning to predict the amino acid content of insects, and the analysis of insect spectral data through machine learning proved to be able to predict amino acid content. Therefore, this study provides guidance for nondestructive testing of potato water content.…”

Combination of machine learning and intelligent sensors in real-time quality control of alcoholic beverages

“…DL algorithms such as BP-NN, CNN, and PNN have shown outstanding advancement when dealing with a large set of samples with high accuracy and efficiency (Pan et al, 2014;Ha et al, 2020). ML has been widely used in dealing with complex sensory data in multiple food science fields such as wine, beer, milk, and apples (Farah et al, 2021;Hou et al, 2022;Zou et al, 2022).…”

Identification of pesticide residues on black tea by fluorescence hyperspectral technology combined with machine learning

“…In recent years, machine learning is often been used in the field of food safety detection, such as the classification of different quality crops (Li et al, 2020;Chen & Yu, 2022;Hou et al, 2022), and the identification and classification of pesticide residues on the surface of crops (Zhu et al, 2021). A one-dimensional convolutional neural network (1D CNN) is a widely used machine learning algorithm, and its biggest advantage is that it can automatically extract features from the input without manual selection (Chatzidakis & Botton, 2019;Zhang et al, 2019).…”