基于近红外光谱技术的腐竹脂肪定量分析

采用近红外光谱技术结合化学计量学方法，建立腐竹脂肪含量的快速分析方法。收集不同生产线、不同时间的腐竹样本180 份，利用积分球附件采集漫反射光谱（4 000～10 000 cm－1）。为消除颗粒散射影响和光谱基线漂移，二阶导数和卷积平滑用于光谱预处理。采用反向区间偏最小二乘法、组合区间偏最小二乘法、搜索组合移动窗口偏最小二乘法和遗传偏最小二乘法优化建模变量，最终构建了定量预测模型。结果显示，4 种方法均可有效地提取信息变量、降低模型维度、提高预测性能；遗传偏最小二乘法一次优选获得143 个变量，构建的模型性能最佳，其校正相关系数、校正均方根误差、预测相关系数、预测均方根误差分别为0.96、0.95、0.92和1.17。研究表明，经过信息变量提取后所构建的近红外模型简单、预测精度高，可用于腐竹脂肪含量的日常监测。

Determination of Fat Content in Yuba by Near Infrared Spectroscopy and Chemometrics

The objective of this study was to develop a method to determine the fat content in yuba by near infrared (NIR)
spectroscopy combined with chemometrics. A total of 180 yuba samples collected at different occasions from different
production lines were tested by NIR spectroscopy. The diffuse reflectance spectra (4 000?10 000 cm-1) were collected using
an integrating sphere attachment. In order to eliminate the particle scattering and baseline drift, the NIR reflectance spectra
were preprocessed by 2nd order derivative with Savitzky-Golay. Backward interval partial least squares (BiPLS), synergy
interval partial least squares (SiPLS), searching combination moving window partial least squares (SCMWPLS) and genetic
algorithms partial least squares (GAPLS) were employed to extract informative variables and construct quantitative models
for the fat content in yuba. After comparison, the best model was obtained by GAPLS method with 143 data points. The
correlation coefficient (r) was 0.96 and the root mean square error of cross-validation (RMSECV) was 0.95 in calibration set,
and the r was 0.92 and the root mean square error of prediction (RMSEP) was 1.17 in prediction set. This work demonstrates
that variables extraction methods not only allow selection of the NIR informative variables for the fat content of yuba and
simplify the models, but also highlight the potential of NIR technique for assessing the quality of yuba on-line.