Citation: WANG Fan, LI Yong-Yu, PENG Yan-Kun, SUN Hong-Wei, LI Long. Nondestructive Determination of Lycopene Content Based on Visible/Near Infrared Transmission Spectrum[J]. Chinese Journal of Analytical Chemistry, ;2018, 46(9): 1424-1431. doi: 10.11895/j.issn.0253-3820.181164 shu

Nondestructive Determination of Lycopene Content Based on Visible/Near Infrared Transmission Spectrum

College of Engineering, China Agricultural University, Beijing 100083, China

Received Date: 15 March 2018
Revised Date: 24 April 2018

Fund Project: This work was supported by the National Science and Technology Support Project of China (No.2014BAD04B05).

Lycopene is an important nutrient quality of tomato and has attracted people's attention in recent years. The traditional way of detecting lycopene damages tomatoes and takes a long time. So it is necessary to explore a method for nondestructive detection of lycopene. Due to the large differences in tomato composition, we chose transmission as a detection method and designed a visible/near infrared transmission spectrum detection system. Using this system, tomato transmission spectra were collected and then pretreated with Savitzky-Golay detrend (DT), standard normal variable transformation (SNV), muliplication scattering correction (MSC), normalize (NOR) and first derivative (FD). Finally, partial least squares model was established after processing. The model established by the SNV pre-processed spectrum had the best effect. The correlation coefficients between the calibration set and the verification set were 0.9771 and 0.9504, respectively. The root mean square errors of the calibration set and verification set were 0.9711 mg/kg and 1.0496 mg/kg. Considering that the original spectrum contained too many independent variables, uninformative variable elimination (UVE), successive projections algorithm (SPA) and competitive adaptive reweighted sampling (CARS) were used to optimize the variables. The three methods constituted five schemes (UVE, SPA, CARS, UVE-SPA, UVE-CARS). Among these five treatments, the lycopene prediction model was the best after UVE-CARS. The correlation coefficients of the calibration set and validation set increased to 0.9830 and 0.9741, respectively, and the root mean square errors decreased to 0.6919 mg/kg and 0.7680 mg/kg, respectively. In addition, 25 tomato samples were used to externally verify the established model. The correlation coefficient of the prediction set of the UVE-CARS-PLS model was 0.9812, the root mean square error of the prediction set was 0.7071 mg/kg, and the average relative error was 4.32%. As a comparison, the correlation coefficient of the prediction set of the full-spectrum model was 0.951, the root mean square error was 1.0610 mg/kg, and the average relative error was 5.981%. The results showed that compared with the full-spectrum PLS model, UVE-CARS-PLS could greatly simplify the model, improve the model accuracy, and reduce the detection error limit. The result showed that the method with the combination of visible/near-infrared transmission spectroscopy and spectral processing methods could be used for the rapid and non-destructive detection of lycopene content.
- Lycopene,
- Visible/near infrared transmission spectrum,
- Nondestructive testing
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