Massimo Mirisola

Use of Near-Infrared Spectroscopy for Fast Fraud Detection in Seafood: Application to the Authentication of Wild European Sea Bass (Dicentrarchus labrax)

The possibility of using near-infrared spectroscopy (NIRS) for the authentication of wild European sea bass ( Dicentrarchus labrax ) was investigated in this study. Three different chemometric techniques to process the NIR spectra were developed, and their ability to discriminate between wild and farmed sea bass samples was evaluated. One approach used spectral information to directly build the discrimination model in a latent variable space; the second approach first used wavelets to transform the spectral information and subsequently derived the discrimination model using the transformed spectra; in the third approach a cascaded arrangement was proposed whereby very limited chemical information was first estimated from spectra using a regression model, and this estimated information was then used to build the discrimination model in a latent variable space. All techniques showed that NIRS can be used to reliably discriminate between wild and farmed sea bass, achieving the same classification performance as classification methods that use chemical properties and morphometric traits. However, compared to methods based on chemical analysis, NIRS-based classification methods do not require reagents and are simpler, faster, more economical, and environmentally safer. All proposed techniques indicated that the most predictive spectral regions were those related to the absorbance of groups CH, CH(2), CH(3), and H(2)O, which are related to fat, fatty acids, and water content.

Comparison of Visible and Near-Infrared Reflectance Spectroscopy to Authenticate Fresh and Frozen-Thawed Swordfish (Xiphias gladius L)

This study evaluated near-infrared (NIR) and visible-NIR (Vis-NIR) spectroscopy as a way to distinguish fresh (F) from frozen-thawed (T) swordfish cutlets (Xiphias gladius). A total of 90 F and 60 T samples were used. The T samples were stored at a high and low frozen temperature (HT: −10°C; LT: −18°C). Spectra were collected using a Vis-NIR portable spectrophotometer (380–1080 nm) and a NIR monochromator (1100–2500 nm). The percentage of correctly classified samples obtained with Vis-NIR spectroscopy was ≥ 96.7%, whereas that for NIR was ≥ 90.0%. The best classification was observed comparing F and HT samples using Vis-NIR (100 vs. 96.7%, respectively). The more descriptive principal component scores (PCS) of NIR and Vis-NIR were used with a multivariate binary logistic regression. The model with the PCS of the first two Vis-NIR principal components accounted for 81.1% of the classification. Vis-NIR could be a strategic tool to screen the cold treatment of swordfish.

Prediction performances of portable near infrared instruments for at farm forage analysis

Abstract The objective of this study was to evaluate the use of Near Infrared Spectroscopy (NIRS) to analyze maize silage with a portable instrument. The instrument was a Zeiss Corona 45 working between 960 and 1700 nm which was used in Italy, Czech Republic and Poland. Best prediction performances were obtained using the Italian data set. Prediction error were 1.0, 0.16 and 0.4 respectively for DM, CP and NDF on a as is basis. With the instrument from Poland and Czech Republic there were lower accuracy of prediction compared to the Italian dataset, probably for their limited (less than 100 samples) calibration data set. Merging all the data set improved prediction accuracy for CP but not DM. It would appear that some form of instrument standardization is needed before merging data set.

Near infrared calibration transfer for undried whole maize plant between laboratory and on-site spectrometers

Abstract The analysis of the maize plant immediately after harvest is essential in order to check the composition and maturity of the plant to optimise the quality of silage. NIRS calibrations were carried out on chopped maize using three spectrophotometers: a laboratory instrument (FOSS NIRSystems 5000 scanning monochromator, FOSS, Silver Spring, MD) and two versions of new-generation portable instruments (poliSPECNIR, PL1 and PL2). The aim was to verify the quality of the transfer of the calibration curves between FOSS, PL1 and PL2 and between PL1 and PL2, obtained by three methods of spectra processing: pre-processing, piecewise direct standardisation (PDS) and direct standardisation (DS). Seventy-six samples of chopped whole maize plant were scanned with the three instruments and were analysed by wet chemistry for dry matter (DM), ash, crude protein (CP), neutral detergent fibre (NDF), acid detergent fibre (ADF), starch and total sugars, to develop calibration equations. Two more datasets of 15 samples each were used for the standardisation of equations and validation. The calibration transfer obtained, according to the values of R2, standard error of prediction and bias, can be considered satisfactory (0.72 > R2 <0.97) for DM, ash and NDF for both poliSPECNIR, while CP and ADF have shown a good accuracy of prediction (0.78 > R2 <0.82) with PL2. Using FOSS as a master instrument, the choice of method of standardisation varies depending on the slave instrument even though the best results are obtained using PDS with PL2. The most accurate predictions are reached using PDS even when PL1 is the master.

Near infrared spectroscopy (NIRS) as a tool to predict meat chemical composition and fatty acid profile in different rabbit genotypes

Abstract Two hundreds rabbits were obtained from 3 different maternal lines and 5 paternal lines, for a total of 11 combinations. After slaughtering the fresh hind legs (HL) and Longissimus dorsi muscles (LD) were scanned in the near infrared region by using a Foss NIRSystem 5000 (λ=1100-2498 nm). The WINISI software (v 1.50) was used for the spectra analysis and samples selection (49 HL and 11 LD). Selected samples were analyzed chemically for dry matter (DM), protein, lipid, ash and fatty acid profile (FA). The obtained results were used to expand and improve the existing calibration equations for fresh rabbit’s meat. Afterwards these equations were used to predict meat composition of the unselected samples. Discriminant analysis didn’t segregate genetic lines. The calibration results for the 400 meat samples were accurate in predicting DM, protein, lipid and some FA (R2<0.80). Poor results were obtained for ash and for physical properties of meat. It was demonstrated that NIRS is a reliable and affordable technology to predict fresh rabbit meat composition, but because of the small differences between genotypes, NIRS wasn’t able to discriminate samples according to their genetic belonging.