Ernestina Casiraghi

Beer fermentation: monitoring of process parameters by FT-NIR and multivariate data analysis.

This work investigates the capability of Fourier-Transform near infrared (FT-NIR) spectroscopy to monitor and assess process parameters in beer fermentation at different operative conditions. For this purpose, the fermentation of wort with two different yeast strains and at different temperatures was monitored for nine days by FT-NIR. To correlate the collected spectra with °Brix, pH and biomass, different multivariate data methodologies were applied. Principal component analysis (PCA), partial least squares (PLS) and locally weighted regression (LWR) were used to assess the relationship between FT-NIR spectra and the abovementioned process parameters that define the beer fermentation. The accuracy and robustness of the obtained results clearly show the suitability of FT-NIR spectroscopy, combined with multivariate data analysis, to be used as a quality control tool in the beer fermentation process. FT-NIR spectroscopy, when combined with LWR, demonstrates to be a perfectly suitable quantitative method to be implemented in the production of beer.

Chemical, physical and sensory attributes for the characterization of an Italian dry-cured sausage

This work was designed to characterize Felino salami, an Italian dry-cured sausage. For this purpose, a wide range of chemical, physical and sensory parameters were studied in 29 samples. Seven chemical and physical variables were selected by using principal components analysis: namely, pH, NaCl/moisture, moisture/protein, soluble N/protein, lactic acid, elasticity index and sample luminosity (L(∗)). The first two principal components were significant according to double-cross validation and accounted for 79% of variance. The seven-variable chemometric model shows that variability in the first principal component is determined by variables expressing the acidity and the extent of lactic fermentation, while the second component is determined by variables expressing the degree of ripening and is related to the sensory scores maturation and hardness, and to salami age (p < 0.001). Sensory scores were evaluated by a multivariate method to verify the consonance among assessors as to the different attributes. The semi-trained panel was consonant and reliable for five of the nine sensory attributes evaluated. The predictive ability of the chemometric model for the sensory attributes was assessed with cross-validation.

Characterisation and classification of Italian virgin olive oils by near- and mid-infrared spectroscopy

Virgin olive oil quality is the result of complex interactions between olive variety, environment and cultivar practice. Evaluation of its quality is based on chemical and sensory analyses (ECC Regulation) that are time-consuming, expensive and destructive of the sample. Spectroscopic techniques present significant advantages in terms of speed and cost of analysis per sample. Italian extra virgin olive oils from Lombardy, Tuscany and Calabria were analysed by conventional analytical and spectroscopic methods. The sample set was composed of 60 single-cultivar (Casaliva, Leccino and Frantoio) extra virgin olive oils (monovarietal extra virgin olive oils) and 59 extra virgin olive oils produced from a mixture of cultivars from each geographical area (industrial extra virgin olive oils). Free acid content, peroxide value and spectrophotometric indices (K232, K270 and ΔK) were measured. Olive oils were also analysed by near infrared (NIR) and mid-infrared (MIR) spectroscopy in transmission and attenuated total reflectance, respectively, to classify oils on the basis of their geographical origin. Principal component analysis was applied both to chemical and spectral data as an exploratory technique. Classification methods studied were linear discriminant analysis, partial least squares discriminant analysis and soft independent modelling of class analogy (SIMCA). Both FT-NIR and FT-IR allowed sample classification of oils on the basis of geographical origin. NIR spectroscopy was able to classify better the industrial extra virgin olive oils producing a correct classification of about 90% of the samples, while the MIR technique was able to classify both monovarietal and industrial olive oils, allowing a higher correct classification of samples (>95%). SIMCA applied to MIR spectra classified about 70% of samples correctly on the basis of geographical origin.

Evaluation of freshness decay of minced beef stored in high-oxygen modified atmosphere packaged at different temperatures using NIR and MIR spectroscopy

Meat freshness has been monitored by various microbiological, chemical and sensorial indices. However, these methods are slow and not suited to automation. Infrared spectroscopy is one of the most convenient analytical tools which could be used to monitor the evolution of food quality. The aim of this work was to investigate the ability of both NIR (Near Infrared) and MIR (Mid Infrared) spectroscopy to follow meat freshness decay. The minced beef was packaged in high-oxygen modified atmosphere (30% CO2 and 70% O2) and stored at three temperatures. Spectra were collected by Fourier-Transformation (FT)-NIR and FT-IR instruments. PCA, applied to the data, was able to discriminate samples on the basis of storage time and temperature. The modelling of PC scores versus time allowed the setting of the time of initial freshness decay for the samples (6-7 days at 4.3°C, 2-3 days at 8.1°C and less than 1 day at 15.5°C).

Comparing the analytical performances of Micro-NIR and FT-NIR spectrometers in the evaluation of acerola fruit quality, using PLS and SVM regression algorithms

The main goal of this study was to investigate the analytical performances of a state-of-the-art device, one of the smallest dispersion NIR spectrometers on the market (MicroNIR 1700), making a critical comparison with a benchtop FT-NIR spectrometer in the evaluation of the prediction accuracy. In particular, the aim of this study was to estimate in a non-destructive manner, titratable acidity and ascorbic acid content in acerola fruit during ripening, in a view of direct applicability in field of this new miniaturised handheld device. Acerola (Malpighia emarginata DC.) is a super-fruit characterised by a considerable amount of ascorbic acid, ranging from 1.0% to 4.5%. However, during ripening, acerola colour changes and the fruit may lose as much as half of its ascorbic acid content. Because the variability of chemical parameters followed a non-strictly linear profile, two different regression algorithms were compared: PLS and SVM. Regression models obtained with Micro-NIR spectra give better results using SVM algorithm, for both ascorbic acid and titratable acidity estimation. FT-NIR data give comparable results using both SVM and PLS algorithms, with lower errors for SVM regression. The prediction ability of the two instruments was statistically compared using the Passing-Bablok regression algorithm; the outcomes are critically discussed together with the regression models, showing the suitability of the portable Micro-NIR for in field monitoring of chemical parameters of interest in acerola fruits.

Identification and quantification of turkey meat adulteration in fresh, frozen-thawed and cooked minced beef by FT-NIR spectroscopy and chemometrics.

This work aims at the development of a method based on FT-NIR spectroscopy and multivariate analysis for the identification and quantification of minced beef meat adulteration with turkey meat. Samples were analyzed as raw, frozen-thawed and cooked. Different multivariate regression and class-modeling strategies were evaluated. PLS regression models with R(2) in prediction higher than 0.884 and RMSEP lower than 10.8% were developed. PLS-DA applied to discriminate each type of sample in two classes (adulteration threshold=20%) showed values of sensitivity and specificity in prediction higher than 0.84 and 0.76, respectively. Thus, the study demonstrates that FT-NIR spectroscopy coupled with suitable chemometric strategies is a reliable tool for the identification and quantification of minced beef adulteration with turkey meat not only in fresh products, but also in frozen-thawed and cooked samples. This achievement is of crucial importance in the meat industry due to the increasing number of processed meat products, in which technological treatments can mask a possible inter-species adulteration.

Studies on proofing of yeasted bread dough using near- and mid-infrared spectroscopy.

Dough proofing is the resting period after mixing during which fermentation commences. Optimum dough proofing is important for production of high quality bread. Near- and mid-infrared spectroscopies have been used with some success to investigate macromolecular changes during dough mixing. In this work, both techniques were applied to a preliminary study of flour doughs during proofing. Spectra were collected contemporaneously by NIR (750-1100 nm) and MIR (4000-600 cm(-1)) instruments using a fiberoptic surface interactance probe and horizontal ATR cell, respectively. Studies were performed on flours of differing baking quality; these included strong bakers flour, retail flour, and gluten-free flour. Following principal component analysis, changes in the recorded spectral signals could be followed over time. It is apparent from the results that both vibrational spectroscopic techniques can identify changes in flour doughs during proofing and that it is possible to suggest which macromolecular species are involved.

Ripening of salami: Assessment of colour and aspect evolution using image analysis and multivariate image analysis

During ripening of salami, colour changes occur due to oxidation phenomena involving myoglobin. Moreover, shrinkage due to dehydration results in aspect modifications, mainly ascribable to fat aggregation. The aim of this work was the application of image analysis (IA) and multivariate image analysis (MIA) techniques to the study of colour and aspect changes occurring in salami during ripening. IA results showed that red, green, blue, and intensity parameters decreased due to the development of a global darker colour, while Heterogeneity increased due to fat aggregation. By applying MIA, different salami slice areas corresponding to fat and three different degrees of oxidised meat were identified and quantified. It was thus possible to study the trend of these different areas as a function of ripening, making objective an evaluation usually performed by subjective visual inspection.

Monitoring of lactic acid fermentation process using Fourier transform near infrared spectroscopy

The aim of this paper was to evaluate the suitability of Fourier transform near infrared (FT-NIR) spectroscopy, combined with multivariate data analysis, to monitor milk lactic acid fermentation as an indication of possible deviations in quality parameters. Fermentation trials performed with different inocula (Streptococcus thermophilus and Lactobacillus delbrueckii subsp. bulgaricus as single or in mixed cultures) at three incubation temperatures (37°C, 41°C and 45°C) were monitored by FT-NIR spectroscopy. Rheological and conventional quality parameters (microbial counts, pH, titratable acidity, lactose, galactose and lactic acid concentrations) were used as reference values to assess the findings with FT-NIR spectroscopy. Principal component analysis was applied to spectra to uncover molecular modifications. PC1 scores, rheological data and conventional quality parameter values were modelled as a function of fermentation time to designate critical points all along the process. Results showed that FT-NIR spectroscopy is a useful tool for real-time assessment of curd development during fermentation, offering crucial information in agreement with rheology and conventional quality parameters.

Effects of housing system and age of laying hens on egg performance in fresh pasta production: pasta cooking behaviour

BACKGROUND Very few studies concern the effects of layer housing systems and age on egg technological properties. Thus the aim of this work was to study the influence of these two factors on egg performance in fresh pasta production, focusing on pasta cooking behaviour. Samples of pasta subjected to analysis were prepared with eggs laid by Hy-Line Brown hens (from 27 to 68 weeks old) housed in cage, barn and organic systems. RESULTS Higher average values of weight increase and matter loss during pasta cooking were observed for samples prepared with eggs laid by older hens. Such cooking behaviour indicated the development of a weaker pasta protein network, resulting from a decrease in the quantity of albumen protein and an increase in fat content, which is due to the reduction in albumen/yolk ratio during hen aging. The housing system had a significant effect only on matter loss in cooking water, but differences between samples were so small as to be unlikely perceived by consumers. CONCLUSION Both hen age and housing system significantly affected pasta cooking behaviour, but the greatest effect was exerted by the hen age.