Combining experimental design and artificial neural networks for the determination of chlorinated compounds in fish using matrix solid-phase dispersion

Comparison of Chemometric Problems in Food Analysis Using Non-Linear Methods

Food analysis is a challenging analytical problem, often addressed using sophisticated laboratory methods that produce large data sets. Linear and non-linear multivariate methods can be used to process these types of datasets and to answer questions such as whether product origin is accurately labeled or whether a product is safe to eat. In this review, we present the application of non-linear methods such as artificial neural networks, support vector machines, self-organizing maps, and multi-layer artificial neural networks in the field of chemometrics related to food analysis. We discuss criteria to determine when non-linear methods are better suited for use instead of traditional methods. The principles of algorithms are described, and examples are presented for solving the problems of exploratory analysis, classification, and prediction.

Evaluation of Antibacterial Enrofloxacin in Eggs by Matrix Solid Phase Dispersion-Flow Injection Chemiluminescence

The study based on the chemiluminescence (CL) reaction of potassium ferricyanide and luminol in sodium hydroxide medium, enrofloxacin (ENRO) could dramatically enhance CL intensities and incorporated with matrix solid-phase dispersion (MSPD) technique (Florisil used as dispersant, dichloromethane eluted the target compounds). A simple flow injection chemiluminescence (FL-CL) method with MSPD technique for determination of ENRO in eggs was described. Under optimal conditions, the CL intensities were linearly related to ENRO concentration ranging from4.0×10-8 g.L−1to5.0×10-5 g.L−1, with a correlation coefficient of 0.9989 and detection limit of5.0×10-9 g.L−1. The relative standard deviation was 3.6% at an ENRO concentration of2.0×10-6 g.L−1. Our testing technique can help ensure food safety, and thus, protect public health.