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Using visible and near infrared diffuse transmittance technique to predict soluble solids content of watermelon in an on-line detection system

机译:在线检测系统中使用可见和近红外漫透射技术预测西瓜中的可溶性固形物含量

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摘要

Sugar content is one of the most important factors determining the eating quality of watermelon fruit. In order to detect the fruit soluble solids content (SSC) on-line, this work develops a nondestructive on-line detection prototype system using visible and near-infrared (Vis/NIR) technology. For the acquisition of the diffuse transmittance spectrum of watermelon, the conveyor was set at a speed of 0.3 m/s and ten 150W tungsten halogen lamps were used as the light source. The crucial model for SSC value prediction was optimized by chemometrics. Partial least squares regression (PLSR), stepwise multiple linear regressions (SMLR), Monte-Carlo uninformative variable elimination (MC-UVE) and genetic algorithms (GA) were applied to the spectra in the range of 687-920 nm. The data pre-processing methods were optimized to transmittance spectra with baseline offset correction (BOC), and the BOC-MC-UVE-SMLR calibration model was the best with a correlation coefficient (r(pre)) of 0.70, root mean square error of prediction (RMSEP) of 0.33 degrees Brix for the prediction set. In on-line testing of 30 samples, the r(pre) was 0.66 and RMSEP was 0.39 degrees Brix. The results showed that a nondestructive on-line SSC value determination prototype based on Vis/NIR technology was feasible
机译:含糖量是决定西瓜果实食用质量的最重要因素之一。为了在线检测水果中的可溶性固形物含量(SSC),这项工作开发了一种使用可见和近红外(Vis / NIR)技术的无损在线检测原型系统。为了获取西瓜的漫透射光谱,将传送带的​​速度设置为0.3 m / s,并使用十个150W卤素钨灯作为光源。化学计量学优化了SSC值预测的关键模型。将偏最小二乘回归(PLSR),逐步多元线性回归(SMLR),蒙特卡洛非信息变量消除(MC-UVE)和遗传算法(GA)应用于687-920 nm范围内的光谱。数据预处理方法针对基线偏移校正(BOC)的透射光谱进行了优化,并且BOC-MC-UVE-SMLR校准模型是最佳的,相关系数(r(pre))为0.70,均方根误差预测集的0.33度白利糖度的预测值(RMSEP)。在30个样品的在线测试中,r(pre)为0.66,RMSEP为0.39度白利糖度。结果表明,基于Vis / NIR技术的无损在线SSC值确定原型是可行的

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