BACKGROUND: Peppers are a frequent object of food safety alerts in various member states of the European Union owing to the presence in some batches of unauthorised pesticide residues. This study assessed the viability of near-infrared reflectance spectroscopy (NIRS) for the measurement of pesticide residues in peppers. Commercially available spectrophotometers using different sample-presentation methods were evaluated for this purpose: a diode-array spectrometer for intact raw peppers and two scanning monochromators fitted with different sample-presentation accessories (transport and spinning modules) for crushed peppers and for dry extract system for infrared analysis (DESIR), respectively. RESULTS: Models developed using partial least squares-discriminant analysis (PLS2-DA) correctly classified between 62 and 68% of samples by presence/absence of pesticides, depending on the instrument used. At model validation, the highest percentage of correctly classified samples-75 and 82% for pesticide-free and pesticide-containing samples respectively-were obtained for intact peppers using the diode-array spectrometer. CONCLUSION: The results obtained confirmed that NIRS technology may be used to provide swift, non-destructive preliminary screening for pesticide residues; suspect samples may then be analysed by other confirmatory analytical methods.
BACKGROUND: Peppers are a frequent object of food safety alerts in various member states of the European Union owing to the presence in some batches of unauthorised pesticide residues. This study assessed the viability of near-infrared reflectance spectroscopy (NIRS) for the measurement of pesticide residues in peppers. Commercially available spectrophotometers using different sample-presentation methods were evaluated for this purpose: a diode-array spectrometer for intact raw peppers and two scanning monochromators fitted with different sample-presentation accessories (transport and spinning modules) for crushed peppers and for dry extract system for infrared analysis (DESIR), respectively. RESULTS: Models developed using partial least squares-discriminant analysis (PLS2-DA) correctly classified between 62 and 68% of samples by presence/absence of pesticides, depending on the instrument used. At model validation, the highest percentage of correctly classified samples-75 and 82% for pesticide-free and pesticide-containing samples respectively-were obtained for intact peppers using the diode-array spectrometer. CONCLUSION: The results obtained confirmed that NIRS technology may be used to provide swift, non-destructive preliminary screening for pesticide residues; suspect samples may then be analysed by other confirmatory analytical methods.