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首页> 外文期刊>Australian Journal of Crop Science >Estimating maturity by measuring pH, sugar, dry matter, water and vitamin C content of cashew apple (Anacardium occidentale) from remote spectral reflectance data using neural network
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Estimating maturity by measuring pH, sugar, dry matter, water and vitamin C content of cashew apple (Anacardium occidentale) from remote spectral reflectance data using neural network

机译:使用神经网络从远程光谱反射数据测量pH,糖,干物质,水和维生素C含量来估计成熟度,从远程光谱反射数据使用神经网络

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摘要

In agricultural sector, maturity is the main decision criterion for starting the harvest. This criterion is usually revealed by a number of parameters such as pH, sugar, dry matter, water and vitamin C, which are informative but technically tedious to measure. The cashew apple is the hypertrophied peduncle which is attached to the cashew nut. It is a nutritious (very juicy fruit (85 to 90% water), sweet (7 to 13% carbohydrates), acidic and vitamin C content) fruit with high therapeutic and medicinal properties. The cashew apple is used as a raw material for many industrial applications (juice and alcohol). This research was conducted as a preliminary step towards the development of a real-time remote sensing technique for assessing the quality of tropical fruits. Spectral acquisitions were carried out from intact cashew apple using optical system composed reflector coupled with spectrometer USB 4000 FL from Ocean Optics (350-1100 nm). Immediately after spectral acquisition, the samples were analyzed by using chemical methods (sugar content, dry matter content, water content, vitamin C and pH). Preprocessing treatment method, bootstrap method was required to create statistical new samples and to increase the number of samples required. This method was used to improve the predictive performance of calibration model. Statistical models of prediction were developed using an artificial neural network (ANN) method. The results obtained from the models built by ANN showed strong relationships between predicted and experimental values: (Rsquare = 0.9870, RMSE= 0.0262) for pH, (Rsquare=0.9869, RMSE=0.1392) for Sugar, (Rsquare=0.9726, RMSE=0.3333) for water content, (Rsquare=0.9703, RMSE=0.3464) for vitamin C and (Rsquare=0.9922, RMSE= 5.0304, RMSE=5.0304) for dry matter. These results confirm the potential of visible spectroscopy to predict quality parameters of cashew apples remotely and make decisions about best harvest time.
机译:在农业部门,成熟是开始收获的主要决策标准。该标准通常由多种参数揭示,例如pH,糖,干物质,水和维生素C,这是有效的但技术上令人疑惑的测量。腰果是肥大的花梗,附着在腰果上。它是一种营养丰富的(非常多汁的水果(85至90%的水),甜(7至13%的碳水化合物),酸性和维生素C含量),具有高治疗和药用性能。腰果用作许多工业应用(果汁和酒精)的原料。这项研究是促进发展热带水果质量的实时遥感技术的初步步骤。使用光学系统的Intact腰果Apple进行光谱采集,该光学系统由来自海洋光学(350-1100nm)的光谱仪USB 4000 FL耦合。通过使用化学方法(糖含量,干物质含量,含水含量,维生素C和pH),分析样品后立即进行分析。预处理处理方法,需要引导方法来创建统计新样本,并增加所需的样本数量。该方法用于改善校准模型的预测性能。使用人工神经网络(ANN)方法开发了预测统计模型。从Ann构建的模型获得的结果显示了预测和实验值之间的强大关系:(RSQUARE = 0.9870,RMSE = 0.0262)用于糖的(RSQUARE = 0.9869,RMSE = 0.1392),(RSQUARE = 0.9726,RMSE = 0.3333 )对于水含量(RSQUARE = 0.9703,RMSE = 0.3464),用于维生素C和(RSQUARE = 0.9922,RMSE = 5.0304,RMSE = 5.0304)进行干物质。这些结果证实了可见光光学的潜力,以远程预测腰果苹果的质量参数,并做出关于最佳收获时间的决定。

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