title: Recent developments and applications of hyperspectral imaging for quality evaluation of agricultural products: a review.
abstract: Food quality and safety is the foremost issue for consumers, retailers as well as regulatory authorities. Most quality parameters are assessed by traditional methods, which are time consuming, laborious, and associated with inconsistency and variability. Non-destructive methods have been developed to objectively measure quality attributes for various kinds of food. In recent years, hyperspectral imaging (HSI) has matured into one of the most powerful tools for quality evaluation of agricultural and food products. HSI allows characterization of a sample's chemical composition (spectroscopic component) and external features (imaging component) in each point of the image with full spectral information. In order to track the latest research developments of this technology, this paper gives a detailed overview of the theory and fundamentals behind this technology and discusses its applications in the field of quality evaluation of agricultural products. Additionally, future potentials of HSI are also reported.
authors.affiliation: a College of Light Industry and Food Sciences , South China University of Technology , Guangzhou , 510641 , P. R. China.
authors.forename: Dan
authors.initials: D
authors.lastname: Liu
authors.forename: Xin-An
authors.initials: XA
authors.lastname: Zeng
authors.forename: Da-Wen
authors.initials: DW
authors.lastname: Sun
mesh_terms: D002138:Calibration; D002523:Edible Grain / Q000737:chemistry / Q000382:microbiology; D005506:Food Contamination / Q000032:analysis; D005516:Food Microbiology; D063427:Food Quality; D005638:Fruit / Q000737:chemistry / Q000382:microbiology; D007091:Image Processing, Computer-Assisted; D008962:Models, Theoretical; D015999:Multivariate Analysis; D015203:Reproducibility of Results; D013057:Spectrum Analysis; D014675:Vegetables / Q000737:chemistry / Q000382:microbiology
keywords: NIR; Non-destructive methods; chemometrics; computer vision; data mining; food quality; hyperspectral imaging