高光谱成像
卵清蛋白
内容(测量理论)
生物系统
化学
计算机科学
生物
人工智能
数学
免疫学
免疫系统
数学分析
作者
Kunshan Yao,Jun Sun,Jiehong Cheng,Min Xu,Chen Chen,Xin Zhou,Chunxia Dai
出处
期刊:Foods
[MDPI AG]
日期:2022-07-08
卷期号:11 (14): 2024-2024
被引量:9
标识
DOI:10.3390/foods11142024
摘要
S-ovalbumin content is an indicator of egg freshness and has an important impact on the quality of processed foods. The objective of this study is to develop simplified models for monitoring the S-ovalbumin content of eggs during storage using hyperspectral imaging (HSI) and multivariate analysis. The hyperspectral images of egg samples at different storage periods were collected in the wavelength range of 401-1002 nm, and the reference S-ovalbumin content was determined by spectrophotometry. The standard normal variate (SNV) was employed to preprocess the raw spectral data. To simplify the calibration models, competitive adaptive reweighted sampling (CARS) was applied to select feature wavelengths from the whole spectral range. Based on the full and feature wavelengths, partial least squares regression (PLSR) and least squares support vector machine (LSSVM) models were developed, in which the simplified LSSVM model yielded the best performance with a coefficient of determination for prediction (R2P) of 0.918 and a root mean square error for prediction (RMSEP) of 7.215%. By transferring the quantitative model to the pixels of hyperspectral images, the visualizing distribution maps were generated, providing an intuitive and comprehensive evaluation for the S-ovalbumin content of eggs, which helps to understand the conversion of ovalbumin into S-ovalbumin during storage. The results provided the possibility of implementing a multispectral imaging technique for online monitoring the S-ovalbumin content of eggs.
科研通智能强力驱动
Strongly Powered by AbleSci AI