Quantitative analysis (chemistry)

Statistics

Chromatography

Principal component analysis

Acetic acid

Alum

Biochemistry

Calibration

Computer science

Food science

Qualitative research

Organic chemistry

Pattern recognition (psychology)

Mathematics

Filter paper

Analyte

Partial least squares regression

Sociology

Artificial intelligence

Chemistry

Qualitative analysis

Social science

Sensors and Actuators B Chemical

A simple and low cost method was presented for detection and determination of two major toxic materials including alum and synthetic acetic acid in fraud pickles based on a novel and sensitive colorimetric sensor array. This sensor was composed of a (4 × 5) array of pH and redox indicators. The color change profiles were individual fingerprints for each specifics analytes and can be monitored with an ordinary flatbed scanner followed by unsupervised pattern recognition method such as principal component analysis (PCA) and hierarchical clustering analysis (HCA). The produced color patterns were dependent on the type of fruit used for producing of pickle and hence they used for discrimination of the vinegar based on the type of fruits they originated. Also, the responses of the sensors were dependent on the amounts of alum and synthetic acetic acid added to the pickles. Partial least square (PLS) regression as a multivariate calibration method was used to estimate the content of alum and synthetic acetic acid in pickle samples through image analysis. A root mean square error for calibration and prediction of 0.469 and 0.446 for alum and also 1.34 and 0.933 for acetic acid were obtained, respectively. This colorimetric sensor array demonstrates excellent potential for qualitative and quantitative control of fruit pickle samples.

Qualitative and quantitative analysis of toxic materials in adulterated fruit pickle samples by a colorimetric sensor array