西瓜检测部位差异对近红外光谱可溶性固形物预测模型的影响

doi:10.3964/j.issn.1000-0593(2016)06-1700-06

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Abstract Non-destructive detection for soluble solids content (SSC) is important to improve watermelon’s internal quality, which attracts more and more attention from consumers. In order to realize the precise detection for SSC of mini watermelon’s whole surface by using Near-infrared (NIR) spectroscopy and reduce the influence of detective position variability on the accuracy of NIR prediction model for SSC, the diffused transmission spectra and soluble solids content were collected from three different detective positions of ‘jingxiu’ watermelon, including the equator, calyx and stem. The prediction models of single detective position and mixed three detective positions for SSC were established with Partial least square (PLS). Successive projections algorithm (SPA) and competitive adaptive reweighted sampling (CARS) were adopted to select effective variables of NIR spectroscopy for SSC of watermelon as well. The results showed that the prediction model of mixed three detective positions was better than the model of single detective position. Meanwhile, 42 characteristic variables of NIR spectroscopy selected with CARS were used to establish PLS prediction model for SSC. The prediction model was simplified significantly and the prediction accuracy for SSC was improved greatly. The correlation coefficient of prediction (RP) and root mean square error of prediction (RMSEP) by CARS-PLS were 0.892, 0.684 °Brix for the equator, 0.905, 0.621 °Brix for the calyx, 0.899, 0.721 °Brix for the stem, respectively. However, the prediction result of SPA-PLS established by 19 characteristic wavelength variables of NIR spectroscopy was bad for the equator, calyx and stem detective positions. The correlation coefficient of prediction (RP) is less than 0.752 and root mean square error of prediction (RMSEP) is relatively high. It was proposed that the PLS prediction model established by mixed three different detective positions with effective characteristic wavelength variables selected by CARS can improve the prediction accuracy for SSC. And the CARS-PLS prediction model can achieve fast and precise detection for SSC of mini watermelon’s whole surface. The influence of detective position variability on the accuracy of NIR prediction model could be reduced simultaneously. Thispaper could provide theoretical basis for calibrating NIR prediction model for SSC of mini watermelon. It also could provide reference for developing the portable and non-destructive detection equipment for soluble solids content of mini watermelon’s whole surface.

Key words：Mini watermelon;Detective position;Near-infrared spectroscopy;Soluble solids content

Received: 2015-04-17 Accepted: 2015-08-20

ZTFLH:

O657.3

Corresponding Authors: CHEN Li-ping E-mail: chenlp@nercita.org.cn

Cite this article:

QIAN Man,HUANG Wen-qian,WANG Qing-yan, et al. Assessment of Influence Detective Position Variability on Precision of Near Infrared Models for Soluble Solid Content of Watermelon[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2016, 36(06): 1700-1705.

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http://www.gpxygpfx.com/EN/10.3964/j.issn.1000-0593(2016)06-1700-06 OR http://www.gpxygpfx.com/EN/Y2016/V36/I06/1700

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