The Ability of Near Infrared (NIR) Spectroscopy to Predict Functional Properties in Foods: Challenges and Opportunities

Near infrared (NIR) spectroscopy is considered one of the main routine analytical methods used by the food industry. This technique is utilised to determine proximate chemical compositions (e.g., protein, dry matter, fat and fibre) of a wide range of food ingredients and products. Novel algorithms and new instrumentation are allowing the development of new applications of NIR spectroscopy in the field of food science and technology. Specifically, several studies have reported the use of NIR spectroscopy to evaluate or measure functional properties in both food ingredients and products in addition to their chemical composition. This mini-review highlights and discussed the applications, challenges and opportunities that NIR spectroscopy offers to target the quantification and measurement of food functionality in dairy and cereals.

Biomolecular, Histological, Clinical, and Radiological Analyses of Dental Implant Bone Sites Prepared Using Magnetic Mallet Technology: A Pilot Study in Animals

Background. A new instrumentation exploiting magneto-dynamic technology (mallet) proposed for implant site preparation was investigated. Methods. In the tibias of three minipigs, two sites were prepared by mallet and two by drill technique. Primary stability (ISQ) was detected after implant positioning (T0) and at 14 days (T14). X-rays and computed tomography were performed. At T14, bone samples were utilized for histological and biomolecular analyses. Results. In mallet sites, histological evaluations evidenced a significant increase in the newly formed bone, osteoblast number, and a smaller quantity of fibrous tissue. These results agree with the significant BMP-4 augmentation and the positive trend in other osteogenic factors (biological and radiological investigations). Major, albeit IL-10-controlled, inflammation was present. For both techniques, at T14 a significant ISQ increase was evidenced, but no significant difference was observed at T0 and T14 between the mallet and drill techniques. In mallet sites, lateral bone condensation was observed on computed tomography. Conclusions. Using biological, histological, clinical, and radiological analyses, this study first shows that the mallet technique is effective for implant site preparation. Based on its ability to cause osseocondensation and improve newly formed bone, mallet technology should be chosen in all clinical cases of poor bone quality.

Pterosaurs evolved a muscular wing–body junction providing multifaceted flight performance benefits: Advanced aerodynamic smoothing, sophisticated wing root control, and wing force generation

Pterosaurs were the first vertebrate flyers and lived for over 160 million years. However, aspects of their flight anatomy and flight performance remain unclear. Using laser-stimulated fluorescence, we observed direct soft tissue evidence of a wing root fairing in a pterosaur, a feature that smooths out the wing–body junction, reducing associated drag, as in modern aircraft and flying animals. Unlike bats and birds, the pterosaur wing root fairing was unique in being primarily made of muscle rather than fur or feathers. As a muscular feature, pterosaurs appear to have used their fairing to access further flight performance benefits through sophisticated control of their wing root and contributions to wing elevation and/or anterior wing motion during the flight stroke. This study underscores the value of using new instrumentation to fill knowledge gaps in pterosaur flight anatomy and evolution.

Tools for supporting solution scattering during the COVID-19 pandemic

During the COVID-19 pandemic, synchrotron beamlines were forced to limit user access. Performing routine measurements became a challenge. At the Life Science X-ray Scattering (LiX) beamline, new instrumentation and mail-in protocols have been developed to remove the access barrier to solution scattering measurements. Our efforts took advantage of existing instrumentation and coincided with the larger effort at NSLS-II to support remote measurements. Given the limited staff–user interaction for mail-in measurements, additional software tools have been developed to ensure data quality, to automate the adjustments in data processing, as users would otherwise rely on the experience of the beamline staff, and produce a summary of the initial assessments of the data. This report describes the details of these developments.

Detectors and Cultural Heritage: The INFN-CHNet Experience

Detectors are a key feature of the contemporary scientific approach to cultural heritage (CH), both for diagnostics and conservation. INFN-CHNet is the network of the Italian National Institute of Nuclear Physics that develops and applies new instrumentation for the study of CH. This process results in both optimized traditional state-of-the-art and highly innovative detection setups for spectrometric techniques. Examples of the former are X-rays, gamma-rays, visible-light and particles spectrometers tailored for CH applications, with optimized performances, reliability, weight, transportability, cost, absorbed power, and complementarity with other techniques. Regarding the latter, examples are ARDESIA, the array of detectors at the DAΦNE-Light facility, the MAXRS detection setup at the Riken-RAL muon beamline and the imaging facilities at the LENA Laboratory. Paths for next-generation instruments have been suggested, as in the case of the X-ray Superconductive Detectors and X-ray Microcalorimeter Spectrometers, allowing astonishing improvement in energy resolution. Many issues in CH can now be addressed thanks to scientific techniques exploiting the existing detectors, while many others are still to be addressed and require the development of new approaches and detectors.

Taming Reflection

Android developers heavily use reflection in their apps for legitimate reasons. However, reflection is also significantly used for hiding malicious actions. Unfortunately, current state-of-the-art static analysis tools for Android are challenged by the presence of reflective calls, which they usually ignore. Thus, the results of their security analysis, e.g., for private data leaks, are incomplete, given the measures taken by malware writers to elude static detection. We propose a new instrumentation-based approach to address this issue in a non-invasive way. Specifically, we introduce to the community a prototype tool called DroidRA, which reduces the resolution of reflective calls to a composite constant propagation problem and then leverages the COAL solver to infer the values of reflection targets. After that, it automatically instruments the app to replace reflective calls with their corresponding Java calls in a traditional paradigm. Our approach augments an app so that it can be more effectively statically analyzable, including by such static analyzers that are not reflection-aware. We evaluate DroidRA on benchmark apps as well as on real-world apps, and we demonstrate that it can indeed infer the target values of reflective calls and subsequently allow state-of-the-art tools to provide more sound and complete analysis results.

A New Acoustic-Based Approach for Assessing Induced Adulteration in Bovine Milk

Milk is an important dietary requirement for many populations due to its high nutritional value. However, increased demand has also made it prone to fraudulent activity. In this sense, scientists have sought to develop simple, low-cost, and portable techniques to achieve quality control of milk in industry and farms as well. This work proposes a new instrumentation system based on acoustic propagation and advanced signal processing techniques to identify milk adulteration by industrial contaminants. A pair of transmitter-receiver low-cost piezoelectric transducers, configured in a pitch-catch mode, propagated acoustic waves in the bovine milk samples contaminated with 0.5% of sodium bicarbonate, urea, and hydrogen peroxide. Signal processing approaches such as chromatic technique and statistical indexes like the correlation coefficient, Euclidian norm and cross-correlation square difference were applied to identify the contaminants. According to the presented results, CCSD and RMSD metrics presented more effectiveness to perform the identification of milk contaminants. However, CCSD was 2.28 × 105 more sensitivity to distinguish adulteration in relation to RMSD. For chromatic clustering technique, the major selectivity was observed between the contamination performed by sodium bicarbonate and urea. Therefore, results indicate that the proposed approach can be an effective and quick alternative to assess the milk condition and classify its contaminants.

Machine Learning Based Surface Velocimetry

<p>Streamflow measurement is of great importance in hydrological research, water management and water infrastructure design. Traditional measurement methods typically employ intrusive techniques, and under certain conditions, obtaining accurate streamflow data with these techniques can be challenging because of safety concerns, especially in some critical circumstances, such as during flood flows. The advent of new instrumentation and technologies, and in particular advances in digital imagery, has led to the emergence of non-intrusive novel image-based technologies that can be used to estimate surface velocity, which in turn can be used to estimate streamflow. Image based technologies, most of which are based on correlation between consecutive images, have the potential for remote and on demand measurements and can provide data when the application of other traditional methods are not possible, reliable or safe. In this study, we present a novel machine learning based optical flow algorithm for streamflow surface velocimetry estimation. The developed algorithm is tested in different flow conditions and using drone and fixed photogrammetry. This method appears to outperform all the other available image-based surface velocimetry approaches (i.e. correlation based and classical optical flow methods). Moreover, this method requires the least user involvement for velocity estimation and thus reduces the impact or arbitrary choices linked to user expertise.</p>