Abstract
This research work was aimed at developing and applying methods based on fast, simple and non-destructive techniques in the food quality control and authenticity analysis on different foods. At this purpose, isothermal calorimetry, near and mid infrared spectroscopy (NIR and FTIR, respectively) and Proton Transfer ReactionMass Spectrometry (PTR-MS) were used. The suitability of these techniques were tested with four case studies. The first case study involved the investigation of monomolecular, bimolecular and termination periods of autoxidation process in fish, linseed, corn and lard oil samples. Results show different times for each oil, that is 40, 224, and 470 min for fish oil, 390, 1102 and 1680 min for linseed oil, 30, 104 and 1134 min for lard oil and 2060, 1121 and 1160 min for corn oil. The results were discussed in comparison with the results of fatty acids profile measured with NIR spectroscopy. The second case study investigates the effect of harvesting altitude, fermentation time and roasting degree on the volatile profile released by coffee (Coffea arabica, variety Catimor) powder. The experimental design consisted of three factors: altitude at two levels (900 m and 1500 m, milder and harsher climate, respectively), four different fermentation times (0, 24, 48 and 72 h) and roasting at three levels (light, medium and dark). Analysis of the released VOCs revealed that the intensity of m/z 45 and 59 dramatically decreases at higher harvesting altitude. Longer fermentation times induce a decrease of m/z 61 and 75, probably related to the activity of the isocitrate lyase (ICL). Finally, a prolonged roasting degree induce a rise of the m/z 61, 75, 81 and 97. The total count per second measured by PTR-MS was used as index of “overall coffee aroma intensity” to build a linear regression model. The validation of the model (adjusted R2 = 0.91 and normalized relative standard deviation in prediction of 7%) reveals that, although the degree of roasting clearly affects the resulting aroma intensity, also altitude and fermentation time play a role in the formation of the volatile profile. The third case study aims to demonstrate the potential use of NIR spectroscopy to determine the geographical origin of raw cow milk samples, collected from high mountain areas (in South Tyrol, at 1900 m, 1050 m and 950 m a.s.l) and from a valley xii (Milan, at 200 a.s.l.). Principal component analysis of the spectra revealed that the short-wave near infrared bands, respectively, 847, 1084, and 1095 nm, were the most important to distinguish milk between farms. The signal intensities of these wavelengths were used to build a multivariate control chart based on the Hotelling T2 statistic. The results showed that short-wave near infrared spectroscopy can be successfully used to monitor milk products in a fast, simple and on-line way. The fourth study involved the use of Fourier Transform Infrared (FT-IR) spectroscopy coupled with chemometrics to distinguish between pure butter samples and adulterated ones. The quantification of the content of margarine in adulterated butter samples was investigated. Fingerprint region (1400-800 cm–1 ) was used to develop unsupervised pattern recognition (Principal Component Analysis, PCA), supervised modeling (Soft Independent Modelling by Class Analogy, SIMCA) and classification (Partial Least Squares Discriminant Analysis, PLS-DA) and regression (Partial Least Squares Regression, PLS-R) models. PCA of the fingerprinting region shows a clustering of the two sample types. All samples were classified in their rightful class by SIMCA approach, however, nine adulterated samples (between 1% and 30% w/w of margarine) were classified as belonging both at the butter class and at the non-butter one. In the two-class PLS-DA model’s (R2 = 0.73, RMSEP, Root Mean Square Error of Prediction = 0.26% w/w) sensitivity was 71.4 % and Positive Predictive Value (PPV) 100 %. Its threshold was calculated at 7% w/w of margarine in adulterated butter samples. Finally, PLS-R model (R2 = 0.84, RMSEP = 16.54%) was developed. PLSDA was a suitable classification tool and PLS-R a proper quantification approach. Results demonstrate that FT-IR spectroscopy combined with PLS-R can be used as a rapid, simple and safe method to identify pure butter samples from adulterated ones and to determine the grade of adulteration of margarine in butter samples.