基于可见/短波近红外光谱检测结球甘蓝维生素C含量

维生素C是人类必需的营养素,结球甘蓝作为主要蔬菜品种之一富含维生素C。该试验利用可见/短波近红外光谱分析技术,开展结球甘蓝维生素C含量的快速检测方法研究。首先通过Kennard-Stone(K-S)法将样本按照6:1划分为校正集(60个样本)和验证集(11个样本),分别利用反射率和吸光度的原始光谱、一阶导数(first derivative,FD)和二阶导数(second derivative,SD)光谱预处理后对应的6个数据集,建立偏最小二乘(partial least squares,PLS)回归模型。针对最优光谱预处理方法处理后的光谱,设置5个置信水平(0.95,0.975,0.99,0.995,0.999 5),利用逐步回归(stepwise regression,SR)进行建模波长选择,以各置信水平对应的各组优选波长进行多元线性回归建模。结果表明:利用FD光谱预处理方法可以提高PLS回归模型精度,验正集R~2从处理前的0.85提高到0.96,是该研究的最佳光谱数据预处理方法。利用降维后的7个主成分继续建立PLS回归模型,校正集R~2为0.92,交互验证均方根误差(root mean squared error of cross validation,RMSECV)为0.658 0 mg/100 g,验证集R~2为0.96,预测均方根误差(root mean squared error of prediction,RMSEP)为1.620 4 mg/100 g。PLS回归模型预测维生素C含量,检测精度高,可以代替传统检测方法,为结球甘蓝的品质评定提供一种新的途径。进一步分别应用8,6,5个优选波长进行多元线性回归建模,校正集R~2平均为0.78,RMSECV平均为3.760 9 mg/100 g,验证集R~2平均为0.73,RMSEP平均为2.879 2 mg/100 g,虽然R~2有所降低,但波长点少,利用较少的波长变量来预测维生素C含量,降低模型复杂度,可以为便携式检测仪器开发提供技术支持,以提高结球甘蓝内部品质评定作业效率。

Detection of vitamin C content in head cabbage based on visible/near-infrared spectroscopy

Abstract: The experiment was conducted to study the rapid detection method of soluble sugar content in head cabbage by near infrared spectroscopy, partial least squares (PLS) regression and stepwise regression (SR). The experiment collected a total of 71 samples of head cabbage. The spectral data were measured by the spectrometer of ASD FieldSpec(r) Handheld(tm) 2 made in ASD Company, USA, and the vitamin C (Vc) was measured by the 2, 6 two chloro indigo titration method. The kennard-stone (K-S) method was used to divide all samples into a calibration set and a validation set according to the given ratio. First derivative (FD) and second derivative (SD) pretreatment methods were used to improve the S/N ratio so as to find the better one from them. The coefficient of determination (R2), root mean squared error of cross validation (RMSECV), and root mean squared error of prediction (RMSEP) were used to evaluate models. The samples were divided into calibration set (60 samples) and validation set (11 samples) according to the ratio of 6:1 by K-S method. Six PLS models were established by using original, FD, and SD preprocessed spectral data of preflectance and absorbance, respectively. The modeling results showed that the spectral preprocessing method using FD could well improve modeling accuracy, the R2 of the validation set with which increased from 0.85 to 0.93, and this method was thought to be the better spectral data pretreatment method in this experiment. For the spectrum after the FD treatment, 5 confidence levels (0.95, 0.975, 0.99, 0.995, 0.999 5) were set up, and SR method was used to select modeling wavelength. With the increase of confidence level, the number of selected wavelength variables decreased from 14 to 3, and then 5 multiple linear regression models were established by 5 sets of preferred wavelengths corresponding to 5 confidence levels. The R2 values were 0.91, 0.83, 0.77, 0.7 and 0.61, respectively, and the RMSECV values were 2.362 2, 3.316 3, 3.841 1, 4.125 2 and 4.962 5 mg/100 g respectively for calibration set, while the R2 values were 0.82, 0.71, 0.72, 0.75 and 0.69, respectively, and the RMSEP values were 2.121 9, 2.983 6, 2.902 4, 2.751 6 and 3.021 5 mg/100 g respectively for validation set. According to the determination coefficient and the root mean square error analysis of the calibration set and the validation set, the multiple linear regression models corresponding to 8, 6 and 5 wavelength variables were established, and the model performance was consistent for the calibration set and the validation set. The average R2 was 0.78, the average RMSECV was 3.760 9 mg/100 g for calibration set, and the average R2 was 0.73, the average RMSEP was 2.879 2 mg/100 g for validation set, which meant that using fewer wavelength variables to predict Vc content in head cabbage was practical. So the visible / short wave near infrared spectra could be used for quantitative detection of vitamin C content in head cabbage. For the PLS model, it had many wavelength variables, the information was large, and so the detection precision was high. For the multiple linear regression models with 8, 6 or 5 selected wavelength variables, with the reduction of number of wavelength variables, the R2 decreased, which could yet provide technical support for the development of portable testing instrument. Using near infrared spectroscopy analysis technique, the prediction model for the content of Vc in cabbage was established, and it could improve