Industrial applications of flow injection analysis

Abstract

Flow injection analysis (FIA) is characterized by its ease of automation, high throughput, simple instrumental requirements, and wide applicability. These unique features make FIA a very useful analytical tool in industrial applications. In this dissertation, FIA systems, operating both in liquid and gas phase, were used to solve some challenging industrial analytical problems.

At first, multi-channel FIA systems with thermometric, conductivity, and photometric detectors were designed to simultaneously determine hydroxide, chloride, hypochlorite, and chlorate ions that exist in Chlor-Alkali cell effluents in concentrations ranging from sub-millimolar to several molar concentrations. A negative bleaching method and a positive absorbance iodiometric method were studied and optimized for this purpose. The methods are reliable, simple, fast, and capable of a throughput rate of -100 samples per hour.

A liquid phase FIA system was developed for automated measurement of hydroperoxides in oil, fat, and polyol samples. Lipid peroxidation has received much attention because of toxicities, bitter tastes, and off flavors produced during lipid oxidation. The method is based on the oxidation of Fe(II) to Fe(III) by peroxides in organic medium, followed by the colorimetric detection of the latter as the thiocyanate complex. The system exhibits a wide dynamic range and good linearity (e.g., linear r^2, 0.9943 for 0.1 -120 meq/kg cottonseed oil hydroperoxides) with a good throughput rate (up to 60 samples/h).

Oxidative stability determination of various materials containing fats and oils is an important process in food, feed, and related industries. It is also a difficult parameter to measure. An attractive gas phase FIA method was developed for the stability evaluation. In the method, the rate of oxygen consumption of samples are measured at discrete temperatures. For all samples studied, log(oxygen consumption) is linearly related to the reciprocal of the absolute temperature. This makes it possible to extrapolate the temperature-dependent data to predict the stability of the samples at other temperatures, e.g., typical ambient storage temperatures, at which the direct determination of oxidative stability would be too slow for most samples. Compared with existing methods, not only is the developed method reliable, but also its sample throughput rate is an order of magnitude faster.