偏最小二乘回归
校准
近红外光谱
化学
分析化学(期刊)
交叉验证
化学计量学
光谱学
色谱法
数学
统计
光学
量子力学
物理
作者
Jin‐Kui Ma,Han Zhang,Tomohiro Tuchiya,Yelian Miao,Jie Yu Chen
出处
期刊:Food Science and Technology Research
[Japanese Society for Food Science and Technology]
日期:2014-01-01
卷期号:20 (2): 217-223
被引量:23
摘要
The degradation of frying oil was determined using near-infrared (NIR) spectroscopy and partial least-squares (PLS) regression. One hundred and fifty six samples of frying oil (104 in a calibration set and 52 in a validation set) were obtained after use in an actual potato frying process. NIR transmission spectra of the samples were acquired directly using glass test tubes (13 mm dia.) and a NIR spectrometer. Calibration models with very high accuracy were developed for predicting acid value (AV) and total polar compounds (TPC) using PLS regression with full cross-validation. The coefficients of determination for calibration (R2) and standard error of cross-validation (SECV) were 0.99 (SECV: 0.17 mgKOH/g) and 0.98 (SECV: 1.25%) for AV and TPC, respectively. The accuracy of the NIR calibration models was tested using the validation set, yielding values for the root mean square of the prediction (SEP) of 0.17 mgKOH/g and 1.04% for AV and TPC, respectively. The results demonstrate that frying oils can be successfully monitored to a very high accuracy using NIR spectroscopy combined with glass tubes of 13 mm diameter as cells.
科研通智能强力驱动
Strongly Powered by AbleSci AI