Authentication of olive oils is of great importance, not only because they command a high price but also because of the health implications of adulteration with seed oils. A method for predicting the level of adulteration in a set of virgin and extra-virgin olive oils adulterated with corn oil, sunflower oil, and raw olive residue oil by near-infrared spectroscopy is presented. The best result was a correct prediction for 98% of the samples. Principal component analysis was used to predict the type of adulterant. The best result was a 75% prediction rate. From these results, it is concluded that it is possible to design a quality control system, which uses near-infrared technology to measure the level of adulteration. In the case where the only test is whether the sample is adulterated or not, a simple calibration for adulteration can be used. The results suggest that principal component analysis may offer a means of identifying the adulterant, although more work is required to give an acceptable level of accuracy.JAOCS 72, 289-292 (1995). KEY WORDS:Near-infrared spectroscopy, olive oil, principal component analysis.

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The application of discriminant analysis for identifying and quantifying adulterants in extra virgin olive oils is presented. Three adulterants were used (sunflower oil, rapessed oil, and soybean oil) and were present in the range 5–95%. Near‐infrared spectroscopy and principal components analysis were used to develop a discriminant analysis equation that could identify correctly the type of seed oil present in extra virgin olive oil in 90% of cases. Partial least squares analysis was used to develop a calibration equation that could predict the level of adulteration. Cross validation suggested that it was possible to measure the level of adulteration to an accuracy of ±0.9%. External validation of the derived calibation equation gave a standard error of performance of ±2.77%.

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