Detection of age and insect damage incurred by wheat, with an electronic nose

Wheats of five storage ages and with 15 degrees of insect damage were evaluated and classified by the static-headspace sampling method using an electronic nose (E-nose). A commercial E-nose (PEN2) comprising 10 metal-oxide semiconductor (MOS) sensors was used to generate a typical chemical fingerprint of the volatile compounds present in the samples. Principal-component analysis (PCA) and linear-discriminant analysis (LDA) were applied to the generated patterns to achieve classification into the five groups of different storage-age wheats and the 15 groups of different degrees of insect-damaged wheat. The results obtained indicated that the E-nose could discriminate successfully among wheats of different age and with different degrees of insect damage.