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Functional Nonparametric Predictions in Food Industry Using Near-Infrared Spectroscopy Measurement

Computers, Materials & Continua 2023年第3期74卷 6307-6319页

作者： Ibrahim M.Almanjahie omar fetitah Mohammed Kadi Attouch Tawfik Benchikh Department of Mathematics College of ScienceKing Khalid UniversityAbha62529Saudi Arabia Statistical Research and Studies Support Unit King Khalid UniversityAbha62529Saudi Arabia Laboratory of Statistics and Stochastic Processes University of Djillali Liabes BP 89 Sidi Bel Abbes22000Algeria Medical Faculty Djillali Liabes University BP 89Sidi Bel Abbes22000Algeria

Functional statistics is a new technique for dealing with data thatcan be viewed as curves or images. Parallel to this approach, the Near-InfraredReflectance (NIR) spectroscopymethodology has been used in modern chemistryas a rapid, low-cost, and exact means of assessing an object’s chemicalproperties. In this research, we investigate the quality of corn and cookiedough by analyzing the spectroscopic technique using certain cutting-edgestatistical models. By analyzing spectral data and applying functional modelsto it, we could predict the chemical components of corn and cookie *** Functional Classical Estimation (KFCE), Kernel Functional QuantileEstimation (KFQE), Kernel Functional Expectile Estimation (KFEE),Semi-Partial Linear Functional Classical Estimation (SPLFCE), Semi-PartialLinear Functional Quantile Estimation (SPLFQE), and Semi-Partial LinearFunctional Expectile Estimation (SPLFEE) are models used to accuratelyestimate the different quantities present in Corn and Cookie dough. Theselection of these functional models is based on their ability to constructa forecast region with a high level of confidence. We demonstrate that theconsidered models outperform traditional models such as the partial leastsquaresregression and the principal component regression in terms of predictionaccuracy. Furthermore, we show that the proposed models are morerobust than competing models such as SPLFQE and SPLFEE in the sensethat data heterogeneity has no effect on their efficiency.

关键词： Functional statistics NIR chemical component kernel estimation

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Industrial Food Quality Analysis Using New k-Nearest-Neighbour methods

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Computers, Materials & Continua 2021年第5期67卷 2681-2694页

作者： omar fetitah Ibrahim M.Almanjahie Mohammed Kadi Attouch Salah Khardani Laboratory of Statistics and Stochastic Processes University of Djillali LiabesSidi Bel Abbes22000Algeria Department of Mathematics College of ScienceKing Khalid UniversityAbha62529Saudi Arabia Statistical Research and Studies Support Unit King Khalid UniversityAbha62529Saudi Arabia Facultédes sciences de Tunis Laboratoire des Réseaux Intelligents et NanotechnologieTunisTunisia

The problem of predicting continuous scalar outcomes from functional predictors has received high levels of interest in recent years in many fields,especially in the food *** k-nearest neighbor(k-NN)method of Near-Infrared Reflectance(NIR)analysis is practical,relatively easy to implement,and becoming one of the most popular methods for conducting food quality based on NIR *** k-NN is often named k nearest neighbor classifier when it is used for classifying categorical variables,while it is called k-nearest neighbor regression when it is applied for predicting noncategorical *** objective of this paper is to use the functional Near-Infrared Reflectance(NIR)spectroscopy approach to predict some chemical components with some modern statistical models based on the kernel and k-Nearest Neighbour *** this paper,three NIR spectroscopy datasets are used as examples,namely Cookie dough,sugar,and tecator ***,we propose three models for this kind of data which are Functional Nonparametric Regression,Functional Robust Regression,and Functional Relative Error Regression,with both kernel and k-NN approaches to compare between *** experimental result shows the higher efficiency of k-NN predictor over the kernel *** predictive power of the k-NN method was compared with that of the kernel method,and several real data sets were used to determine the predictive power of both methods.

关键词： Functional data analysis classical regression robust regression relative error regression kernel method k-NN method near-infrared spectroscopy

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