Measurement of total anthocyanins content in flowering tea using near infrared spectroscopy combined with ant colony optimization models.

Flowering tea has become a popular beverage consumed across the world. Anthocyanins content is considered as an important quality index of flowering tea. The feasibility of using near infrared (NIR) spectra at the wavelength range of 10,000-4000 cm(-1) for rapid and nondestructive determination of total anthocyanins content in flowering tea was investigated. Ant colony optimization interval partial least squares (ACO-iPLS) and Genetic algorithm interval partial least squares (GA-iPLS) were used to develop calibration models for total anthocyanins content. Two characteristic wavelength regions (4590-4783, 5770-5,963 cm(-1)), which corresponding to the ultraviolet/visible absorption bands of anthocyanins, were selected by ACO-iPLS. The optimal ACO-iPLS model for total anthocyanins content (R=0.9856, RMSECV=0.1,198 mg/g) had better performance than full-spectrum PLS, iPLS, and GA-iPLS models. It could be concluded that NIR spectroscopy has significant potential in the nondestructive determination of total anthocyanins content in flowering tea.

Variables selection methods in near-infrared spectroscopy.

Near-infrared (NIR) spectroscopy has increasingly been adopted as an analytical tool in various fields, such as the petrochemical, pharmaceutical, environmental, clinical, agricultural, food and biomedical sectors during the past 15 years. A NIR spectrum of a sample is typically measured by modern scanning instruments at hundreds of equally spaced wavelengths. The large number of spectral variables in most data sets encountered in NIR spectral chemometrics often renders the prediction of a dependent variable unreliable. Recently, considerable effort has been directed towards developing and evaluating different procedures that objectively identify variables which contribute useful information and/or eliminate variables containing mostly noise. This review focuses on the variable selection methods in NIR spectroscopy. Selection methods include some classical approaches, such as manual approach (knowledge based selection), "Univariate" and "Sequential" selection methods; sophisticated methods such as successive projections algorithm (SPA) and uninformative variable elimination (UVE), elaborate search-based strategies such as simulated annealing (SA), artificial neural networks (ANN) and genetic algorithms (GAs) and interval base algorithms such as interval partial least squares (iPLS), windows PLS and iterative PLS. Wavelength selection with B-spline, Kalman filtering, Fisher's weights and Bayesian are also mentioned. Finally, the websites of some variable selection software and toolboxes for non-commercial use are given.