Explore the vast range of titles available.

The evaluation of meat and fish quality is crucial to ensure that products are safe and meet the consumers’ expectation. The present work aims at developing a new low-cost, portable, and simplified electronic nose system, named Mastersense, to assess meat and fish freshness. Four metal oxide semiconductor sensors were selected by principal component analysis and were inserted in an “ad hoc” designed measuring chamber. The Mastersense system was used to test beef and poultry slices, and plaice and salmon fillets during their shelf life at 4 °C, from the day of packaging and beyond the expiration date. The same samples were tested for Total Viable Count, and the microbial results were used to define freshness classes to develop classification models by the K-Nearest Neighbours’ algorithm and Partial Least Square–Discriminant Analysis. All the obtained models gave global sensitivity and specificity with prediction higher than 83.3% and 84.0%, respectively. Moreover, a McNemar’s test was performed to compare the prediction ability of the two classification algorithms, which resulted in comparable values (p > 0.05). Thus, the Mastersense prototype implemented with the K-Nearest Neighbours’ model is considered the most convenient strategy to assess meat and fish freshness.

Saffron ( Crocus sativus L.) is the most expensive spice in the World, and Italy is among the major European producers. The study aims to improve value and recognition of high quality saffron by proposing a subdivision within the first quality category, according to ISO 3632 standards. The analysis of 125 saffron samples, collected from different Italian regions in 2021–2022 harvesting seasons, revealed that 95% of the samples met the first quality criteria, following ISO guidelines. Consequently, for the first quality samples, a differentiation into “premium”, “superior” and “high-quality” subcategories was proposed. Along with traditional methods, FT-NIR spectroscopy combined with chemometrics was employed for a comprehensive quality assessment. Discriminant and class-modelling approaches were developed to predict saffron quality subcategory. Best results were obtained by linear discriminant analysis models with accuracy in calibration (92.7%), cross-validation (86.4%) and prediction (87.0%). However, SIMCA modelling resulted more appropriate for class-modelling, confirming that none of the “premium” samples were misclassified as “high-quality” and vice-versa. The results support the inclusion of subcategories within ISO 3632 standards, thus refining the classification of saffron quality. Furthermore, the study emphasises the effectiveness of FT-NIR spectroscopy as a valuable tool for saffron quality assessment, with potential implications for industry standards and practices.

Adulteration and fraud are amongst the wrong practices followed nowadays due to the attitude of some people to gain more money or their tendency to mislead consumers. Obviously, the industry follows stringent controls and methodologies in order to protect consumers as well as the origin of the food products, and investment in these technologies is highly critical. In this context, chemometric techniques proved to be very efficient in detecting and even quantifying the number of substances used as adulterants. The extraction of relevant information from different kinds of data is a crucial feature to achieve this aim. However, these techniques are not always used properly. In fact, training is important along with investment in these technologies in order to cope effectively and not only reduce fraud but also advertise the geographical origin of the various food and drink products. The aim of this paper is to present an overview of the different chemometric techniques (from clustering to classification and regression applied to several analytical data) along with spectroscopy, chromatography, electrochemical sensors, and other on-site detection devices in the battle against milk adulteration. Moreover, the steps which should be followed to develop a chemometric model to face adulteration issues are carefully presented with the required critical discussion.

Industry 4 [...]

The inspection of strategic works such as dams is of vital importance both for their maintenance and for the safety of downstream populations. The reduced accessibility, both for uptake needs and for their strategic nature, and the large time needed for an inspection by traditional method do not facilitate the investigation of this type of structures. The new unmanned aerial vehicle (UAV) technology, equipped with high-performance cameras, allows for rapid photographic coverage of the whole dam system. Apart from the placement on the structure of a high number of markers, the correct geo-referencing and validation of the model also requires an important terrestrial topographic campaign by total station, Global Positioning System and laser scanner. Punctual, linear and surface analysis shows the high accuracy of the drone acquiring technique. The product is suitable for a detailed survey of the conservation status of the materials and the complete metric reconstruction of the dam system and the adjacent land. The present work explains firstly a UAV acquisition and then the first dense point cloud validation procedure of a concrete arch gravity dam. The Ridracoli dam is the object of the survey, located in the village of Santa Sofia in central Italy.

Yogurt fortification with purslane (Portulaca oleracea L.) can improve its health benefits, but it may alter the fermentation step and its final properties. Thus, the current study investigated the suitability of Fourier Transform-Near Infrared (FT-NIR) spectroscopy for in-line monitoring of lactic acid fermentation of purslane-fortified yogurt compared with fundamental rheology. Changes in the yogurt properties during storage were also assessed. Set-type yogurts without and with lyophilized purslane leaves (0.55%) were produced and stored at 4 °C for up to 18 days. Lactic acid bacteria concentrations before and after fermentation at 43 °C for 2.5 h showed that the presence of purslane did not interfere with bacterial growth. The purslane addition increased the milk viscosity, resulting in a yogurt with complex modulus values higher than those of the reference sample (360 vs. 172 Pa). The elaboration of spectral data with Principal Component Analysis and the Gompertz equation enabled calculation of the kinetic critical points. Applying the Gompertz equation to the rheological data, it was evident that FT-NIR spectroscopy detected earlier the fermentation progression (the critical times were about 18% earlier on average), thus enabling better control of yogurt production. No significant changes in microbial or textural properties were noted during yogurt storage, demonstrating that purslane addition did not affect the product stability.

Failures in milk coagulation during cheese manufacturing can lead to decreased yield, anomalous behaviour of cheese during storage, significant impact on cheese quality and process wastes. This study proposes a Process Analytical Technology approach based on FT-NIR spectroscopy for milk renneting control during cheese manufacturing. Multivariate Curve Resolution optimized by Alternating Least Squares (MCR-ALS) was used for data analysis and development of Multivariate Statistical Process Control (MSPC) charts. Fifteen renneting batches were set up varying temperature (30, 35, 40 °C), milk pH (6.3, 6.5, 6.7), and fat content (0.1, 2.55, 5 g/100 mL). Three failure batches were also considered. The MCR-ALS models well described the coagulation processes (explained variance ≥99.93%; lack of fit <0.63%; standard deviation of the residuals <0.0067). The three identified MCR-ALS profiles described the main renneting phases. Different shapes and timing of concentration profiles were related to changes in temperature, milk pH, and fat content. The innovative implementation of MSPC charts based on T2 and Q statistics allowed the detection of coagulation failures from the initial phases of the process.

Rising challenges for food innovation and environmental issues have led to an increased interest in bio-economy and more sustainable food production [...].Rising challenges for food innovation and environmental issues have led to an increased interest in bio-economy and more sustainable food production [...].

Quantitative analysis of carotenoids has been extensively reported using UV-Vis spectrophotometry and chromatography, instrumental techniques that require complex extraction protocols with organic solvents. Fourier transform infrared spectroscopy (FTIR) is a potential alternative for simplifying the analysis of food constituents. In this work, the application of FTIR with attenuated total reflectance (ATR) was evaluated for the determination of total carotenoid content (TCC) in Cucurbita spp. samples. Sixty-three samples, belonging to different cultivars of butternut squash (C. moschata) and pumpkin (C. maxima), were selected and analyzed with FTIR- ATR (attenuated total reflectance). Three different preparation protocols for samples were followed: homogenization (A), freeze-drying (B), and solvent extraction (C). The recorded spectra were used to develop regression models by Partial Least Squares (PLS), using data from TCC, determined by UV-Vis spectrophotometry. The PLS regression model obtained with the FTIR data from the freeze-dried samples, using the spectral range 920–3000 cm−1, had the best figures of merit (R2CAL of 0.95, R2PRED of 0.93 and RPD of 3.78), being reliable for future application in agriculture. This approach for carotenoid determination in pumpkin and squash avoids the use of organic solvents. Moreover, these results are a rationale for further exploring this technique for the assessment of specific carotenoids in food matrices.

The milling industry envisions solutions to become fully compatible with the industry 4.0 technology where sensors interconnect devices, machines and processes. In this contest, the work presents an integrated solution merging a deeper understanding and control of the process due to real-time data collection by MicroNIR sensors (VIAVI, Santa Rosa, CA)—directly from the manufacturing process—and data analysis by Chemometrics. To the aim the sensors were positioned at wheat cleaning and at the flour blends phase and near infrared spectra (951–1608 nm) were collected online. Regression models were developed merging the spectra information with the results obtained by reference analyses, i.e., chemical composition and rheological properties of dough by Farinograph® (Brabender GmbH and Co., Duisburg, Germany), Alveograph® (Chopin, NG Villeneuve-la-Garenne Cedex, France) and Extensograph®.(Brabender GmbH and Co., Duisburg, Germany) The model performance was tested by an external dataset obtaining, for most of the parameters, RPRED higher than 0.80 and Root Mean Squares Errors in prediction lower than two-fold the value of the reference method errors. The real-time implementation resulted in optimal (100% of samples) or really good (99.9%–80% of samples) prediction ability. The proposed work succeeded in the implementation of a process analytical approach with Industrial Internet of Things near infrared (IIoT NIR) devices for the prediction of relevant grain and flour characteristics of common wheat at the industrial level.