三聚氰酸
三聚氰胺
偏最小二乘回归
傅里叶变换红外光谱
主成分分析
人工智能
主成分回归
无损检测
模式识别(心理学)
分析化学(期刊)
数学
计算机科学
化学
材料科学
机器学习
色谱法
物理
光学
复合材料
量子力学
作者
Rahul Joshi,Lakshmi Priya Gg,Mohammad Akbar Faqeerzada,Tanima Bhattacharya,Moon Sung Kim,Insuck Baek,Byoung‐Kwan Cho
出处
期刊:Sensors
[MDPI AG]
日期:2023-05-24
卷期号:23 (11): 5020-5020
被引量:4
摘要
Melamine and its derivative, cyanuric acid, are occasionally added to pet meals because of their nitrogen-rich qualities, leading to the development of several health-related issues. A nondestructive sensing technique that offers effective detection must be developed to address this problem. In conjunction with machine learning and deep learning technique, Fourier transform infrared (FT-IR) spectroscopy was employed in this investigation for the nondestructive quantitative measurement of eight different concentrations of melamine and cyanuric acid added to pet food. The effectiveness of the one-dimensional convolutional neural network (1D CNN) technique was compared with that of partial least squares regression (PLSR), principal component regression (PCR), and a net analyte signal (NAS)-based methodology, called hybrid linear analysis (HLA/GO). The 1D CNN model developed for the FT-IR spectra attained correlation coefficients of 0.995 and 0.994 and root mean square error of prediction values of 0.090% and 0.110% for the prediction datasets on the melamine- and cyanuric acid-contaminated pet food samples, respectively, which were superior to those of the PLSR and PCR models. Therefore, when FT-IR spectroscopy is employed in conjunction with a 1D CNN model, it serves as a potentially rapid and nondestructive method for identifying toxic chemicals added to pet food.
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