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

Abstract

Functional statistics is a new technique for dealing with data that can be viewed as curves or images. Parallel to this approach, the Near-Infrared Reflectance (NIR) spectroscopy methodology has been used in modern chemistry as a rapid, low-cost, and exact means of assessing an object's chemical properties. In this research, we investigate the quality of corn and cookie dough by analyzing the spectroscopic technique using certain cutting-edge statistical models. By analyzing spectral data and applying functional models to it, we could predict the chemical components of corn and cookie dough.

Local linear-$k$NN smoothing for semi-functional partial linear regression

“…The dataset contains NIR spectroscopy measurements resulting from an experiment to determine the composition of 72 samples of cookie dough pieces (formed but unbaked cookies) studied by [17] which had been analysed by [2]. The main objective is to predict the percentage of fat content Y for cookie dough datasets from the corresponding content, namely, sucrose (X 1 ), dry flour (X 2 ) and water (X 3 ) as well as from the spectra of nearinfrared absorbance ξ, using SFPLR.…”