Using Information-Based Classifications To Distinguish Characteristics Of Raw Agricultural Materials By Near-Infrared Spectroscopy

Near-infrared (NIR) spectroscopy is a promising technique for identifying raw agricultural materials. However, it is rarely used because of its poor discriminant rate. In this study, we took tobacco leaves from five origins as experimental materials. An origin discriminant model by the discriminant partial least squares (DPLS) was established, and the correct discriminant rate of internal cross validation was 76.54%. Origins were divided into three groups. The discriminant model of the three groups was improved, and the correct discriminant rate of internal cross validation was 98.77% along with a 100% external validation rate. We analyzed the characteristics of the three groups' average spectra under normal variable pretreatment and found they have different absorption characteristics in different regions, and that the classification is information-based. The results show that using information-based classifications can establish a better model, and that main chemical components and NIR spectra can determine whether the classification is information-based, and whether projection based on principal component and Fisher criterion (PPF), can be more effective.