Chemometric Labeling of Cereal Tissues in Multichannel Fluorescence Microscopy Images Using Discriminant Analysis

• Published in: Analytical chemistry (Washington) Vol. 69; no. 21; pp. 4339 - 4348
• Main Authors: Baldwin, Paul M, Bertrand, Dominique, Novales, Bruno, Bouchet, Brigitte, Collobert, Gaëlle, Gallant, Daniel J
• Format: Journal Article
• Language: English
• Published: Washington, DC American Chemical Society 01-11-1997
• Subjects: Analytical chemistry; Biological and medical sciences; Chemical Sciences; Chemistry; Diverse techniques; Fluorescence; Fundamental and applied biological sciences. Psychology; Identification; Molecular and cellular biology; Scientific imaging; Performance evaluation; Plant tissue; Discriminant analysis; Investigation method; Biological material; Chemometrics; Fluorescence microscopy; MICROSCOPIE EN FLUORESCENCE; CONTROLE DE QUALITE
• ISSN: 0003-2700; 1520-6882
• DOI: 10.1021/ac970145x

This paper presents a novel, semiautomatic method for microscopic identification of multicomponent samples, which allows the identification, location, and percentage quantity of each component to be determined. The method involves applying discriminant analysis to a sequence of multichannel fluorescence microscopy images via a supervised learning approach; by selecting groups of pixels that are representative for each component type in a “known” sample, a computer is “taught” how to recognize the behavior (i.e., fluorescence emission) of the various components when illuminated under different spectral conditions. The identity, quantity, and location of these components in “unknown” samples (i.e., samples with the same component types but in different ratios or distributions) can then be investigated. The technique therefore enables semiautomatic quantitative fluorescence microscopy and has potential as a quality control tool. This work demonstrates the application of the technique to artificial and natural samples and critically discusses its quality, potential, and limitations.