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Spectrometry Evaluation of B10 Averrhoa carambola L. and Sala Mango Physical Attributes

机译:B10杨桃和萨拉芒果物理特性的光谱分析评价

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The physical quality attributes of B10 carambola and Sala mango are normally graded based on its colour index and size (weight). For colour classification, the indexing process is conducted by trained staffs from Federal Agricultural Marketing Authority (FAMA). However, this method is highly subject to error since the evaluation is made through human visual perception. This paper presents an innovative application of visible and near infrared (NIR) spectroscopy in quantitative measurement of carambola and mango physical quality, two of the Malaysian most popular tropical fruits. For comparative analysis, the spectroscopy measurement was conducted using reflectance and interactance techniques. The best algorithm for predicting carambola and mango index has been generated by MLR + SG (reflectance: R~2 = 0.934; RMSEP = 0.489) and MLR + MSC (reflectance: R~2 = 0.931; RMSEP = 0.504) respectively. On the other hand, the best algorithm for predicting carambola and mango weight has been generated by MLR + SG (interactance: R~2 = 0.754; RMSEP = 17.454 g) and MLR + SG (reflectance: R~2 = 0.769; RMSEP = 89.122 g) respectively.
机译:B10杨桃和萨拉芒果的物理质量属性通常根据其颜色指数和大小(重量)进行分级。对于颜色分类,索引过程由联邦农业市场管理局(FAMA)训练有素的人员进行。但是,由于该评估是通过人的视觉感知进行的,因此该方法极易出错。本文介绍了可见光和近红外(NIR)光谱技术在杨桃和芒果物理质量(马来西亚两种最受欢迎​​的热带水果)的定量测​​量中的创新应用。为了进行比较分析,使用反射率和相互作用技术进行了光谱测量。预测杨桃和芒果指数的最佳算法分别是MLR + SG(反射率:R〜2 = 0.934; RMSEP = 0.489)和MLR + MSC(反射率:R〜2 = 0.931; RMSEP = 0.504)。另一方面,通过MLR + SG(相互作用:R〜2 = 0.754; RMSEP = 17.454 g)和MLR + SG(反射率:R〜2 = 0.769; RMSEP =分别为89.122克)。

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