Modelling Volume Change and Deformation in Food Products/Processes: An Overview.

Volume change and large deformation occur in different solid and semi-solid foods during processing, e.g., shrinkage of fruits and vegetables during drying and of meat during cooking, swelling of grains during hydration, and expansion of dough during baking and of snacks during extrusion and puffing. In addition, food is broken down during oral processing. Such phenomena are the result of complex and dynamic relationships between composition and structure of foods, and driving forces established by processes and operating conditions. In particular, water plays a key role as plasticizer, strongly influencing the state of amorphous materials via the glass transition and, thus, their mechanical properties. Therefore, it is important to improve the understanding about these complex phenomena and to develop useful prediction tools. For this aim, different modelling approaches have been applied in the food engineering field. The objective of this article is to provide a general (non-systematic) review of recent (2005-2021) and relevant works regarding the modelling and simulation of volume change and large deformation in various food products/processes. Empirical- and physics-based models are considered, as well as different driving forces for deformation, in order to identify common bottlenecks and challenges in food engineering applications.

Freshness assessment of European hake (Merluccius merluccius) through the evaluation of eye chromatic and morphological characteristics.

The most commonly used method for fish freshness determination is the sensory inspection; alternative sensory methods such as the Quality Index Method (QIM), based on the significant sensory parameters of one specific species, have been recently suggested. Considering that most of the sensory parameters are based on chromatic and morphological visual impression, the set-up of an objective method using computer vision techniques is very promising. The objective of this research was to characterize the changes in eye chromatic and morphological characteristics of European hake (Merluccius merluccius) during 13 days of storage on ice, using a tailored computer vision technique and a 3D scanner. Results obtained by multivariate statistical analysis of the colour spectra of eye images and by the eye concavity index using a 3D scanner permitted to estimate fish unacceptability after 7 days of storage, in agreement with results obtained by QIM sensory analysis. Moreover, 1H NMR was used to evaluate the production of trimethylamine (TMA) and the Ki index, confirming a good correlation with eye chromatic and morphological features. This preliminary study showed the high potentiality of the developed method as a non-destructive technique for raw fish freshness characterization / prediction, being a promising approach to create a robust portable instrument for the evaluation of fish freshness in real transport and marketing conditions.

Potential use of ricotta cheese whey for the production of lactobionic acid by Pseudomonas taetrolens strains.

Lactobionic acid (LBA) is a fine chemical largely applied in the food, chemical, cosmetics and pharmaceutical industries. Here, its production from ricotta cheese whey (RCW), or scotta, the main by-product obtained from ricotta cheese production process and currently employed mainly for cattle feed, was evaluated. Among seven bacterial species tested, only two Pseudomonas taetrolens strains were selected after preliminary screening in shake-flasks. When autoclaved RCW was used, a lactobionic acid titer of 34.25 ±â€¯2.86 g/l, with a conversion yield (defined as mol LBA/mol of consumed lactose%) of up to 85 ±â€¯7.0%, was obtained after 48 h of batch fermentation in 3 L stirred tank bioreactor. This study is a preliminary investigation on the potential industrial use of scotta as a substrate for bacterial growth and lactobionic acid production that details the possible biotechnological valorization pathways and feasibility of the process.

Analysis of oligomer proanthocyanidins in different barley genotypes using high-performance liquid chromatography-fluorescence detection-mass spectrometry and near-infrared methodologies.

Proanthocyanidins are a class of polyphenols present in many foodstuffs (i.e., tea, cocoa, berries, etc.) that may reduce the risk of several chronic diseases. Barley, with sorghum, rice, and wheat, are the only cereals that contain these compounds. Because of that, two barley genotypes, named waxy and non-waxy, were analyzed by normal-phase high-performance liquid chromatography-fluorescence detection-mass spectrometry (NP-HPLC-FLD-MS). Total proanthocyanidin content ranged between 293.2 and 652.6 µg/g of flour. Waxy samples reported the highest content (p < 0.05) of proanthocyanidins. Dimer compounds were the principal proanthocyanidin constituents of barley samples. Moreover, the possibility to use near-infrared (NIR) spectroscopy as a rapid method to discriminate between waxy and non-waxy samples and to predict quantitatively proanthocyanidins in barley samples was evaluated. Partial least squares (PLS) models were built to predict the proanthocyanidin constituent, obtaining determination coefficients (R(2)) ranging from 0.92 to 0.97, in test set validation. Because of that, this study highlights that NIR spectroscopy technology with multivariate calibration analysis could be successfully applied as a rapid method to determine proanthocyanidin content in barley.

Evaluation of coffee roasting degree by using electronic nose and artificial neural network for off-line quality control.

UNLABELLED: An electronic nose (EN) based on an array of 10 metal oxide semiconductor sensors was used, jointly with an artificial neural network (ANN), to predict coffee roasting degree. The flavor release evolution and the main physicochemical modifications (weight loss, density, moisture content, and surface color: L*, a*), during the roasting process of coffee, were monitored at different cooking times (0, 6, 8, 10, 14, 19 min). Principal component analysis (PCA) was used to reduce the dimensionality of sensors data set (600 values per sensor). The selected PCs were used as ANN input variables. Two types of ANN methods (multilayer perceptron [MLP] and general regression neural network [GRNN]) were used in order to estimate the EN signals. For both neural networks the input values were represented by scores of sensors data set PCs, while the output values were the quality parameter at different roasting times. Both the ANNs were able to well predict coffee roasting degree, giving good prediction results for both roasting time and coffee quality parameters. In particular, GRNN showed the highest prediction reliability. PRACTICAL APPLICATION: Actually the evaluation of coffee roasting degree is mainly a manned operation, substantially based on the empirical final color observation. For this reason it requires well-trained operators with a long professional skill. The coupling of e-nose and artificial neural networks (ANNs) may represent an effective possibility to roasting process automation and to set up a more reproducible procedure for final coffee bean quality characterization.

FT-NIR spectroscopy for the quality characterization of apricots (Prunus armeniaca L.).

UNLABELLED: The nondestructive assessment of apricot fruit quality (Bora cultivar) was carried out by means of FT-NIR reflectance spectroscopy in the wavenumber range 12000 to 4000 cm⁻¹. Samples were harvested at four different ripening stages and scanned by a fiber optical probe immediately after harvesting and after a storage of 3 d (2 d at 4 °C and 1 d at 18 °C); the flesh firmness (FF), the soluble solids content (SSC), the acidity (A), and the titratable acidity (malic and citric acids) were then measured by destructive methods. Soft independent modeling of class analogy (SIMCA) analysis was used to classify spectra according to the ripening stage and the storage: partial least squares regression (PLS) models to predict FF, SSC, A, and the titratable acidity were also set-up for both just harvested and stored apricots. Spectral pretreatments and wavenumber selections were conducted on the basis of explorative principal component analysis (PCA). Apricot spectra were correctly classified in the right class with a mean classification rate of 87% (range: 80% to 100%). Test set validations of PLS models showed R2 values up to 0.620, 0.863, 0.842, and 0.369 for FF, SSC, A, and the titratable acidity, respectively. The best models were obtained for the SSC and A and are suitable for rough screening; a lower power prediction emerged for the other maturity indices and the relative predictive models are not recommended. PRACTICAL APPLICATION: The results of the study could be used as a tool for the assessment of the ripening stage during the harvest and the quality during the postharvest storage of apricot fruits.