A review on material analysis of food safety based on fluorescence spectrum combined with artificial neural network technology

Abstract

Aiming at the problem that it is difficult to achieve rapid and accurate detection of pesticide residues, the artificial neural network method is used to separate the mixed fluorescence spectra in the measurement of acetamiprid pesticide residues, and a fluorescence spectrum that can quickly detect the pesticide residues of acetamiprid on solid surfaces is designed. According to the back-propagation algorithm, the three-layer artificial neural network principle is used to detect the acetamiprid residue in the mixed system of acetamiprid and filter paper with severely overlapping fluorescence spectra. In the range of 340nm~400nm, using the fluorescence intensity values at 20 characteristic wavelengths as the characteristic network parameters, after network training and testing, the recovery rates of acetamiprid concentrations of 40mg/kg and 90mg/kg are 102% and 97%, respectively. The relative standard deviations of the determination results were 1.4% and 1.9%, respectively. The experimental results show that the BP neural network-assisted fluorescence spectroscopy method for the determination of acetamiprid pesticide residues on filter paper has the characteristics of fast network training, short detection period, and high measurement accuracy.

Intrinsic Fluorescence Markers for Food Characteristics, Shelf Life, and Safety Estimation: Advanced Analytical Approach

“…The introduction of new methods such as artificial neural network technology in fluorescence spectral data processing is promising. This was recently documented in the detection of pesticide residues of acetamiprid on solid surfaces based on fluorescence spectral data [ 87 ]. Also, the use of artificial intelligence, such as machine learning, in chemometric techniques may considerably accelerate the analysis of big data collected using fluorescence methods.…”

A rapid identification method of rice protein in rice starch based on synchronous fluorescence spectroscopy

“…Rice contains four proteins: albumin, globulin, prolamin, and glutelin, the latter of which contains the most gluten (Chen et al, 2018).Since rice protein contains lysine with high-quality amino acid composition, it is hypoallergenic and, therefore, favorable for human consumption (Roa-Acosta et al, 2022). Food safety is one of the major challenges of countries in underdeveloped countries and developed and developing countries (Mahmudiono et al, 2022). With the extensive research on rice protein, there is an urgent need to develop a rapid detection method for rice protein.…”

“…Because of advances in spectroscopy and the development of novel sensor technologies such as spectrophotometers and cameras, non-destructive and rapid assessments of quality-related factors in foods are becoming more common (Pranoto et al, 2022). In the past few decades, a good number of rapid nondestructive detection technologies have been developed for the safety and quality detection of various agricultural products, one of which is the Synchronous fluorescence spectrum (Xin et al, 2022;Mahmudiono et al, 2022). Synchronous fluorescence spectroscopy is a change at the same time under the condition of excitation wavelength and emission wavelength scanning some with fluorescent effect of substances such as amino acid, Practical Application: These outcomes highlighted that fluorescence spectroscopy coupled with chemometrics can be used as quick method to predict the amount of rice protein.…”

A Fuzzy Intelligent Computing Approach for Energy/Voltage Control of Microgrids

“…Tis method is called AC. In recent years, signifcant research has been done to increase the stability of power networks [1,2].…”