Establishment of a comprehensive indicator to nondestructively analyze watermelon quality at different ripening stages

文献类型： 外文期刊

第一作者： Qi, Shuye

作者： Qi, Shuye;Jiang, Shengnan;Chen, Yingrui;Han, Donghai;Song, Shuhui;Li, Wu

作者机构：

关键词： Visible and near-infrared spectroscopy; X-ray imaging; maturity process; factor analysis; comprehensive indicator

期刊名称：JOURNAL OF INNOVATIVE OPTICAL HEALTH SCIENCES （ 影响因子：1.77； 五年影响因子：1.436 ）

ISSN： 1793-5458

年卷期： 2014 年 7 卷 4 期

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收录情况： SCI

摘要： Two nondestructive methods based on visible and near-infrared (VIS-NIR) spectroscopy and X-ray image have been used for the evaluation of watermelon quality. The prediction performance based on partial least squares (PLS) by diffuse transmittance measurement (500-1010 nm) was evaluated for chemical quality attributes SSC (Rc = 0.903; RMSEC = 0.572% Brix; Rp = 0.862; RMSEP = 0.717% Brix; RPD = 1.83), lycopene (Rc = 0.845; RMSEC = 0.266 mg/100gFW; Rp = 0.751; RMSEP = 0.439 mg/100gFW; RPD = 1.13) and moisture (Rc = 0.917; RMSEC = 0.280%; Rp = 0.937; RMSEP = 0.276%; RPD = 2.79). The X-ray calibration linear equations developed by extracting the appropriate gray threshold were sufficiently precise for volume (R-2 = 0.986) and weight (R-2 = 0.993). In order to optimize prediction model of watermelon quality in growth period, multivariate multi-block technique factor analysis enabled integration of these traits.chemical information is related to physical information. Applying principle component analysis to extract common factors and varimax with Kaiser normalization to improve explanatory, the comprehensive indicator based on variances was established satisfactorily with Rc = 0.94, RMSEC = 0.244, Rp = 0.93, RMSEP = 0.344 and RPD = 2.00. A comparison of these models indicates that the comprehensive indicator determined only by portable VIS-NIR spectrometer appears as a suitable method for appraising watermelon quality nondestructively on the plant at different ripen stages. This method contributes to infer the picking date of watermelon with higher accuracy and bigger economic benefits than that by experience.

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