人工智能
化学计量学
偏最小二乘回归
线性判别分析
主成分分析
化学
支持向量机
数学
模式识别(心理学)
残差神经网络
近红外光谱
质量评定
分析化学(期刊)
色谱法
生物系统
人工神经网络
计算机科学
统计
评价方法
物理
工程类
生物
量子力学
可靠性工程
作者
Gang He,Qi Lin,Shaobing Yang,Yuanzhong Wang
标识
DOI:10.1016/j.jfca.2023.105199
摘要
Amomum tsao-ko (A. tsao-ko), is a medicine and food homology plant with high economic value. Different drying temperature affects flavor and quality of A. tsao-ko, which is closely related to its economic value and consumer acceptance. This research investigated the feasibility of combining Fourier-transform near infrared spectroscopy (FT-NIR) with chemometrics and machine learning to the identification of drying temperatures of A. tsao-ko. A total of 626 fresh samples from Honghe Prefecture, Yunnan, were dried to constant weight at 40 ℃ (62 h), 50 ℃ (54 h), 60 ℃ (43 h), 70 ℃ (35 h) and 80 ℃ (25 h) using an electric thermostatic drying oven. Principal component analysis (PCA) was used to observe the clustering of samples treated at different drying temperatures. Partial least squares-discriminant analysis (PLS-DA), support vector machines (SVM), and Residual Neural Network (ResNet) are used to build discriminant models. The accuracy of GS-SVM, GA-SVM, and ResNet models was more than 99% in the test set for identifying the drying temperature of A. tsao-ko. In contrast, the ResNet model based on synchronous two-dimensional correlation spectroscopy (2DCOS) images obtained the most satisfactory discrimination results. The use of appropriate data pre-processing (SD) can optimize the performance of the model to a certain extent and improve the accuracy of discrimination. This study demonstrates the feasibility of FT-NIR in the identification of the drying temperature of A. tsao-ko, providing a rapid and nondestructive method for the assessment and control of A. tsao-ko quality.
科研通智能强力驱动
Strongly Powered by AbleSci AI