期刊
ANALYTICAL METHODS
卷 14, 期 31, 页码 2989-2999出版社
ROYAL SOC CHEMISTRY
DOI: 10.1039/d2ay00875k
关键词
-
资金
- Key R&D Program of Jiangsu Province [BE2020379]
- Selfinnovation Fund Project of Agricultural Science and Technology in Jiangsu Province [CX (20)2005]
This study successfully predicted the content of free amino acids and crude protein in raw peanut seeds using near-infrared spectroscopy combined with variable selection algorithms, with RF-PLS model achieving the best outcomes.
Given the nutritional importance of peanuts, this study examined the free amino acid (FAA) and crude protein (CP) content in raw peanut seeds. Near-infrared spectroscopy (NIRS) was employed in combination with variable selection algorithms after successful reference data analysis using colorimetric and Kjeldahl methods. Ensuing the application of partial least squares (PLS) as a full spectral model, the genetic algorithm (GA), bootstrapping soft shrinkage (BOSS), uninformative variable elimination (UVE), and random frog (RF) models were tested and assessed. A comparison of correlation coefficients of prediction (R-p), root mean square error of prediction (RMSEP), and residual predictive deviation (RPD) was performed to appraise the performance of the built models. Using RF-PLS, an unsurpassed outcome was achieved for FAA (R-p = 0.937, RPD = 3.38) and CP (R-p = 0.9261, RPD = 3.66). These findings demonstrated that NIR in combination with RF-PLS could be utilized for quantitative, rapid, and nondestructive prediction of FAA and CP in raw peanut seed samples.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据