人工智能
拉曼光谱
卷积神经网络
深度学习
均方误差
模式识别(心理学)
计算机科学
残余物
特征(语言学)
校准
生物系统
机器学习
数学
统计
算法
物理
光学
生物
语言学
哲学
作者
Yingchao Xue,Hui Jiang
出处
期刊:Foods
[MDPI AG]
日期:2023-06-17
卷期号:12 (12): 2402-2402
被引量:4
标识
DOI:10.3390/foods12122402
摘要
This study presents a novel method for the quantitative detection of residual chlorpyrifos in corn oil through Raman spectroscopy using a combined long short-term memory network (LSTM) and convolutional neural network (CNN) architecture. The QE Pro Raman+ spectrometer was employed to collect Raman spectra of corn oil samples with varying concentrations of chlorpyrifos residues. A deep-learning model based on LSTM combined with a CNN structure was designed to realize feature self-learning and model training of Raman spectra of corn oil samples. In the study, it was discovered that the LSTM-CNN model has superior generalization performance compared to both the LSTM and CNN models. The root-mean-square error of prediction (RMSEP) of the LSTM-CNN model is 12.3 mg·kg-1, the coefficient of determination (RP2) is 0.90, and the calculation of the relative prediction deviation (RPD) results in a value of 3.2. The study demonstrates that the deep-learning network based on an LSTM-CNN structure can achieve feature self-learning and multivariate model calibration for Raman spectra without preprocessing. The results of this study present an innovative approach for chemometric analysis using Raman spectroscopy.
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