Hyperspectral Imaging Detection

Traditional optical sensing techniques, such as imaging and spectroscopy introduced in Chaps. 2 and 3, have limitations to acquire adequate spatial and spectral information for nondestructive evaluation of food and agricultural products. Generally, conventional imaging cannot acquire spectral information and spectroscopy measurement cannot cover large sample areas. In recent years, hyperspectral imaging has emerged as a powerful process analytical tool for nondestructive food analysis. It is an emerging platform technology that integrates conventional imaging and spectroscopy to attain both spatial and spectral information from an object. Therefore, hyperspectral imaging has the capability to rapidly and noninvasively monitor both physical and morphological characteristics and intrinsic chemical and molecular information of a food product for the purpose of quality and safety analyses and assessments. Some fundamental knowledge about hyperspectral imaging is introduced at first in this chapter, which includes the relationship between spectroscopy, imaging and chemical imaging, instruments for hyperspectral imaging, and image acquisition and processing. Two commonly used chemometric methods for obtaining useful features from hyperspectral images, principal component analysis (PCA) and independent component analysis (ICA), are discussed for dimension reduction and band selection. The reader is provided with a detailed overview of how to use chemometrics in hyperspectral data, along with a critical discussion on their respective advantages and potential pitfalls. The examples that we use for this purpose are the detection of chlorophyll in cucumber leaves, and the mapping of the total flavonoid distributions in fresh ginkgo leaves.