Prediction of pH and total soluble solids content of mango using biresponse multipredictor local polynomial nonparametric regression

IF 0.5 Q4 COMPUTER SCIENCE, INFORMATION SYSTEMS Communications in Mathematical Biology and Neuroscience Pub Date : 2023-01-01 DOI:10.28919/cmbn/7941
M. Ulya, N. Chamidah, T. Saifudin
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Abstract

: Mango's internal quality can be determined based on its acidity and sweetness in the form of pH and total soluble solids (TSS) content. Research on fruit internal quality prediction based on near-infrared spectroscopy generally uses parametric regression modeling such as linear and partial least square regression. The study proposed biresponse multipredictor local polynomial nonparametric regression to determine mango's internal quality. The study aims to apply the theory of biresponse multipredictor local polynomial nonparametric regression for predicting the mango's internal quality in the form of pH and TSS value. We created R code for estimating nonparametric
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用双响应多预测局部多项式非参数回归预测芒果的pH和总可溶性固形物含量
芒果的内在品质可以通过其酸碱度和甜度以及总可溶性固形物(TSS)含量来确定。基于近红外光谱的水果内部品质预测研究一般采用线性回归和偏最小二乘回归等参数回归模型。本研究提出双响应多预测局部多项式非参数回归来确定芒果的内在品质。本研究旨在应用双响应多预测局部多项式非参数回归理论,以pH和TSS值的形式预测芒果的内在品质。我们创建了R代码来估计非参数
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来源期刊
Communications in Mathematical Biology and Neuroscience
Communications in Mathematical Biology and Neuroscience COMPUTER SCIENCE, INFORMATION SYSTEMS-
CiteScore
2.10
自引率
15.40%
发文量
80
期刊介绍: Communications in Mathematical Biology and Neuroscience (CMBN) is a peer-reviewed open access international journal, which is aimed to provide a publication forum for important research in all aspects of mathematical biology and neuroscience. This journal will accept high quality articles containing original research results and survey articles of exceptional merit.
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