Measurement of pesticide residues in peppers by near-infrared reflectance spectroscopy.

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.

A methodology based on NIR-microscopy for the detection of animal protein by-products.

This study develops a methodology based on NIR-microscopy analysis and chemometric tools for the detection of animal protein by-products in mixtures, such as compound feeds and mixtures of ingredients, using a library of animal meal by-products only. The proposed methodology is a two-step strategy which worked better than the SIMCA approach it was compared with. In the first step, animal particles are identified using one of two methods, a global or a local distance measure. In the second, K-nearest-neighbours (KNN) is used to discriminate between terrestrial and fish particles. The models were developed using a training set comprising 11,727 spectra of pure terrestrial meals and 5843 of fish meals. KNN using second derivative spectra and five neighbours correctly classifies 98.5% of these samples under cross-validation. The procedure was validated using two external datasets, one made up of mixtures of species (fish and bovine), and a second of commercial compound feeds. The results obtained confirm that the procedure is able to reliably detect the presence of animal meals, although further work would be needed to develop it into an accurate quantitative method.

Use of near-infrared reflectance spectroscopy for shelf-life discrimination of green asparagus stored in a cool room under controlled atmosphere.

This study sought to evaluate the ability of near-infrared reflectance spectroscopy (NIRS) to classify intact green asparagus, in refrigerated storage under controlled atmosphere, by storage time and post-harvest treatments applied. A total of 468 green asparagus (Asparagus officinalis, L., cultivar UC-157) were sampled after 7, 14, 21 and 28 days of refrigerated storage (2 degrees C, 95% R.H.) under three controlled atmosphere (CA) treatments: air (21 kPa O(2)+0.3 kPa CO(2)), CA(1) (5 kPa O(2)+5 kPa CO(2)) and CA(2) (10 kPa O(2)+10kPa CO(2)). Two commercially available spectrophotometers were evaluated for this purpose: a scanning monochromator (SM) of 400-2500 nm and a combination of diode array and scanning monochromator (DASM) of 350-2500 nm. Models developed using partial least squares 2-discriminant analysis (PLS2-DA) correctly classified between 81-100% of samples by post-harvest storage time, depending on the instrument used. Using similar models, the DASM instrument correctly classified 85% of samples by post-harvest treatment, compared with 72% using the SM. These results confirmed that NIR spectroscopy, coupled with the use of chemometric techniques, provides a reliable, accurate method of predicting the shelf-life of asparagus under different storage conditions and as a function of post-harvest treatment applied; the method can be readily applied at industrial level.

Nondestructive determination of total soluble solid content and firmness in plums using near-infrared reflectance spectroscopy.

The fruit industry requires rapid, economical, and nondestructive methods for classifying fruit by internal quality, which can be built into the processing line. Total soluble solid content and firmness are the two indicators of plum internal quality that most affect consumer acceptance. These parameters are routinely evaluated using methods which involve destruction of the fruit; as a result, only control batches can be analyzed. The development of nondestructive analytical methods would enable the quality control of individual fruits. Near-IR spectroscopy (NIRS) was used to assess total soluble solid content (SSC, degrees Brix) and firmness (N) in intact plums. A total of 720 plums (Prunus salicina L. cv. 'African Pride', 'Black Diamond', 'Fortune', 'Laetitia', 'Larry Anne', 'Late Royal', 'Prime Time', 'Sapphire', and 'Songold') were used to obtain calibration models based on reference data and near-IR spectral data. Standard errors of cross-validation (SECV) and coefficients of determination for cross-validation (r(2)) were (0.77 degrees Brix; 0.83) for total soluble solids content and (2.54 N; 0.52) for firmness. Results suggest that NIRS technology enables fruit to be classified in terms of total soluble solid content and firmness, thus allowing increased sampling of each production batch and ensuring a given quality with greater precision and accuracy.