X-ray computed tomography to study processed meat microstructure

Multivariate Statistical Analysis for the Classification of Sausages Based on Physicochemical Attributes, Using Attenuated Total Reflectance-Fourier Transform Infrared (ATR-FTIR) and Inductively Coupled Plasma-Mass Spectrometry (ICP-MS)

Sausage is a convenient food that is widely consumed in the world and in Vietnam. Due to the rapid development of this product, the authenticity of many famous brands has faded by the rise of adulteration. Therefore, in this study, principal component analysis (PCA) was combined with chemical analysis to identify 6 sausage brands. Sausage samples were dried and then ground to a fine powder for both instrumental analyses of attenuated total reflectance-Fourier transform infrared (ATR-FTIR) and inductively coupled plasma-mass spectrometry (ICP-MS). Dried measurements of ATR-FTIR was performed directly on the ZnSe crystal, while elemental data were obtained through microwave digestion before the ICP-MS analysis. Principal component analysis (PCA) within the framework software of XLSTAT and STATISTICA 12 was performed on the spectroscopy and elemental dataset of sausage samples. PCA visualized the distinction of 6 sausage brands on both datasets of ATR-FTIR and ICP-MS. The classification on the spectroscopy profile showed that although more than 90% variation of the dataset was explained on the first two PCs, the difference between several brands was not detected as the distribution of data overlapped with one another. The PCA observation of the elemental composition on PC1 and PC3 has separated the sausage brands into 6 distinctive groups. Besides, several key elements contributed to the brands' identification have been detected, and the most distinctive elements are Na, K, Ca, and Ba. PCA visualization showed the feasibility of the classification of sausage samples from different brands when combined with the results of FT-IR and ICP-MS methods. The experiment was able to differentiate the sausages from the 5 brands using multivariate statistics.

Sugar-free aerated chocolate: Production, investigation of bubble features using X-ray computed tomography and image processing

In contrast to other imaging techniques, X-ray imaging does not destruct the internal structure of the sample being imaged. Furthermore, this technique is able to capture numerous images of the sample at a low slice thickness, which is almost impossible in other imaging techniques. In this study, sugar was replaced with inulin:maltodextrin mixtures at ratios of 25:75 (i25), 50:50 (i50), and 75:25 (i75). Then, nitrogen (N2 ) and carbon dioxide (CO2 ) were injected into the three mixtures as well as the sugar-containing sample (control) at pressures of 3, 4.5, and 6 bar to produce aerated chocolate. The images of the samples were captured using X-ray computed tomography (XCT). After processing, they were segmented using the Chan-Vese model. Image segmentation showed that the Chan-Vese method, compared with adaptive thresholding, was more able to segment the images and remove the noise. The bubble total volume (10440 ± 9206 mm3 ) and average diameter (1.30 ± 0.10 mm) of the control were larger than those of the other samples. The results also demonstrated that the sugar-free aerated samples had lower hardness than the corresponding unaerated ones. However, it was reversed in the case of the control. This research sheds light on the industrialization of the production of aerated chocolate and the application of XCT and image processing in the analysis of the microstructure of aerated products.

An Optimized Method for Microcomputed Tomography Analysis of Trabecular Parameters of Metal Scaffolds for Bone Ingrowth

Owing to its superior mechanical and biological properties, titanium metal is widely used in dental implants, orthopedic devices, and bone regenerative materials. Advances in 3D printing technology have led to more and more metal-based scaffolds being used in orthopedic applications. Microcomputed tomography (μCT) is commonly applied to evaluate the newly formed bone tissues and scaffold integration in animal studies. However, the presence of metal artifacts dramatically hinders the accuracy of μCT analysis of new bone formation. To acquire reliable and accurate μCT results that reflect new bone formation in vivo, it is crucial to lessen the impact of metal artifacts. Herein, an optimized procedure for calibrating μCT parameters using histological data was developed. In this study, the porous titanium scaffolds were fabricated by powder bed fusion based on computer-aided design. These scaffolds were implanted in femur defects created in New Zealand rabbits. After 8 weeks, tissue samples were collected to assess new bone formation using μCT analysis. Resin-embedded tissue sections were then used for further histological analysis. A series of deartifact two-dimensional (2D) μCT images were obtained by setting the erosion radius and the dilation radius in the μCT analysis software (CTan) separately. To get the μCT results closer to the real value, the 2D μCT images and corresponding parameters were subsequently selected by matching the histological images in the particular region. After applying the optimized parameters, more accurate 3D images and more realistic statistical data were obtained. The results demonstrate that the newly established method of adjusting μCT parameters can effectively reduce the influence of metal artifacts on data analysis to some extent. For further validation, other metal materials should be analyzed using the process established in this study.

Non-Destructive Spectroscopic and Imaging Techniques for the Detection of Processed Meat Fraud

In recent years, meat authenticity awareness has increased and, in the fight to combat meat fraud, various analytical methods have been proposed and subsequently evaluated. Although these methods have shown the potential to detect low levels of adulteration with high reliability, they are destructive, time-consuming, labour-intensive, and expensive. Therefore, rendering them inappropriate for rapid analysis and early detection, particularly under the fast-paced production and processing environment of the meat industry. However, modern analytical methods could improve this process as the food industry moves towards methods that are non-destructive, non-invasive, simple, and on-line. This review investigates the feasibility of different non-destructive techniques used for processed meat authentication which could provide the meat industry with reliable and accurate real-time monitoring, in the near future.

X-ray computed tomography for quality inspection of agricultural products: A review

The quality of agricultural products relates to the internal structure, which has long been a matter of interest in agricultural scientists. However, inspection methods of the opaque nature of internal information on agricultural products are usually destructive and require sample separation or preparation. X-ray computed tomography (X-ray CT) technology is one of the important nondestructive testing (NDT) technologies without sample separation and preparation. In this study, X-ray CT technology is used to obtain two-dimensional slice images and three-dimensional tomographic images of samples. The purpose of the review was to provide an overview of the working principle of X-ray CT technology, image processing, and analysis. This review aims to focus on the development of the agricultural products (e.g., wheat, maize, rice, apple, beef) and its applications (e.g., internal quality evaluation, microstructure observation, mechanical property measurement, and others) using CT scanner. This paper covers the aspects regarding the advantages and disadvantages of NDT technology, especially the unique advantages and limitations of X-ray CT technology on the quality inspection of agricultural products. Future prospects of X-ray CT technology are also put forward to become indispensable to the quality evaluation and product development on agricultural products.

Non-destructive techniques for the detection of fungal infection in cereal grains

Infection of cereal grains by fungi is a serious problem worldwide. Depending on the environmental conditions, cereal grains may be colonised by different species of fungi. These fungi cause reduction in yield, quality and nutritional value of the grain; and of major concern is their production of mycotoxins which are harmful to both humans and animals. Early detection of fungal contamination is an essential control measure for ensuring storage longevity and food safety. Conventional methods for detection of fungal infection, such as culture and colony techniques or immunological methods are either slow, labour intensive or difficult to automate. In recent years, there has been an increasing need to develop simple, rapid, non-destructive methods for early detection of fungal infection and mycotoxins contamination in cereal grains. Methods such as near infrared (NIR) spectroscopy, NIR hyperspectral imaging, and electronic nose were evaluated for these purposes. This paper reviews the different non-destructive techniques that have been considered thus far for detection of fungal infection and mycotoxins in cereal grains, including their principles, application and limitations.

Soft matter food physics--the physics of food and cooking

This review discusses the (soft matter) physics of food. Although food is generally not considered as a typical model system for fundamental (soft matter) physics, a number of basic principles can be found in the interplay between the basic components of foods, water, oil/fat, proteins and carbohydrates. The review starts with the introduction and behavior of food-relevant molecules and discusses food-relevant properties and applications from their fundamental (multiscale) behavior. Typical food aspects from 'hard matter systems', such as chocolates or crystalline fats, to 'soft matter' in emulsions, dough, pasta and meat are covered and can be explained on a molecular basis. An important conclusion is the point that the macroscopic properties and the perception are defined by the molecular interplay on all length and time scales.

Image Analysis of Foods

The structure of foods, both natural and processed ones, is controlled by many variables ranging from biology to chemistry and mechanical forces. The structure also controls many of the properties of the food, including consumer acceptance, taste, mouthfeel, appearance, and so on, and nutrition. Imaging provides an important tool for measuring the structure of foods. This includes 2-dimensional (2D) images of surfaces and sections, for example, viewed in a microscope, as well as 3-dimensional (3D) images of internal structure as may be produced by confocal microscopy, or computed tomography and magnetic resonance imaging. The use of images also guides robotics for harvesting and sorting. Processing of images may be needed to calibrate colors, reduce noise, enhance detail, and delineate structure and dimensions. Measurement of structural information such as volume fraction and internal surface areas, as well as the analysis of object size, location, and shape in both 2- and 3-dimensional images is illustrated and described, with primary references and examples from a wide range of applications.

A Method to Determine the Density of Foods using X-ray Imaging

Density of foods is an important physical property, which depends on structural properties of food. For porous foods such as baked foods, accurate measurement of density is challenging since traditional density measurement techniques are tedious, operator-dependent and incapable of precise volume measurement of foods. To overcome such limitations, a methodology was developed using both digital radiography(DR) and computed tomography(CT) x-ray imaging to directly determine density of foods. Apparent density was determined directly from x-ray linear attenuation coefficients by scanning at 40, 60, 80 kVp on DR and 45, 55, 70 kVp on CT. The apparent density can be directly determined using CT however sample thickness is needed to determine density using DR. No significant difference (p<0.05) was observed between density obtained from traditional methods, with density determined from x-ray linear attenuation coefficients. Density determined on CT for all foods with mean 0.579 g/cm3 had a standard deviation, SD = 0.0367 g/cm3. Density determination using x-ray linear attenuation was found to be a more efficient technique giving results comparable with conventional techniques.