A Review of Data Analysis Techniques Used in Near-Infrared Spectroscopy

Abstract

Although the analysis of the structure of objects and the components that makeup them has been done for decades, it is one of today's research topics to do this analysis quickly and without damaging the sample. Near-infrared spectroscopy is used in many areas due to its non-contact measurement, fast analysis, and high accuracy features. Near-infrared spectroscopy is used in the classification or quality analysis of products, especially in the agriculture and food sector, due to the chemical bonds interacting in this region. The most critical part of achieving a successful result in near-infrared spectroscopy is pre-processing and analyzing the spectral data using the correct method. In this review, we perform a survey of recent studies that use near-infrared spectroscopy in food production and agriculture. Since there are many studies in this field in the literature, the survey is limited to cover works in the last five years. The review's main question is the pre-processing and data analysis methods used in these studies and the main features of these methods. Among the examined studies, the most frequently used pre-processing method was standard normal variate, and the most frequently used analysis method was partial least squares regression. In addition, the software tools and the spectrum range were also examined within the scope of the study.