偏最小二乘回归
含水量
校准
化学计量学
拉曼光谱
环境科学
化学
分析化学(期刊)
生物系统
统计
数学
环境化学
色谱法
工程类
物理
光学
岩土工程
生物
作者
Youqing Wen,Zhiyao Li,Ying Ning,Yong Yan,Zheng Li,Huidong Xie,Haixia Wang
标识
DOI:10.1016/j.saa.2024.123956
摘要
Portable Raman spectroscopy coupled with partial least squares regression (PLSR) model was performed for monitoring and predicting four quality indicators, moisture content, water activity, polysaccharide content and microbial content of the fresh-cut Chinese yam at different storage temperatures. The variations in the four key indicators were first depicted through a spider web diagram as the storage temperature changed. More importantly, the four key indicators can be accurately monitored and predicted through optimized PLSR models combining with Raman spectroscopy. Among all of the PLSR models for the four indicators, the regression model for moisture content was relatively the best. In addition, storage temperature played a significant role on the model performance of PLSR. The model performance for all indicators at room temperature and high temperature was better than the corresponding PLSR models at refrigeration and freezing conditions. Especially at 25 ℃, the R2 in the calibration set basically reached 0.9. These observations indicated that portable Raman spectroscopy, a simple and easy-to-use detection technique, can monitor and predict the multiple quality indicators of fresh-cut Chinese yam combined with effectively PLSR model, which would be conducive to their applications in food industry.
科研通智能强力驱动
Strongly Powered by AbleSci AI