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

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® Handheld™ 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 the working efficiency of the internal quality evaluation of the cabbage. The research provides a new approach for quality evaluation of head cabbage. © 2018, Editorial Department of the Transactions of the Chinese Society of Agricultural Engineering. All right reserved.