The influence of vacuum impregnation on the fortification of apple parenchyma with quercetin derivatives in combination with pore structures X-ray analysis

The study aim was to investigate the vacuum impregnation (VI) technique for apple enrichment with quercetin glycosides from apple peel. Beside the determination of the quercetin content, structural effects on the apple parenchyma were analyzed by computerized microtomography (μCT). VI was an efficient method to enrich apples with quercetin derivatives which was affected by the apple cultivar, the vacuum pressure, the soluble solid concentration (SSC) and the viscosity of VI solution. After VI of 13 apple cultivars the quercetin content varied between 368 and 604 μg/g dry mass and correlated with the firmness of the native apple and the increased apple weight. The use of low SSC solution resulted in increased quercetin enrichment in contrast to apple pectin solutions with elevated viscosity. The μCT analyses demonstrate that VI was more effective in the inner apple sections than in the outer parts. The study indicates that differences of pore size and microstructure within the apple cortex substantially affected the enrichment process.     

Multifractal analysis application to the characterization of fatty infiltration in Iberian and White pork sirloins

This paper applies the multifractal analysis based on the sandbox method to describe the distribution of fatty infiltration in Iberian and White pork meat with the aim of characterization and classification. This work was carried out by making photographs of sirloin cuts of both breeds and then treated with image analysis software. The obtained image data were stored in text format and constituted the input for multifractal analysis. The results obtained show that pork sirloin connective fatty tissue exhibits a multifractal type of scaling. Significant correlations were found between some of the parameters governing the multifractal behavior and fat percentage, especially in the case of Iberian sirloin. The differences found for the relationships between the generalized fractal dimensions and fat percentage provide information for the categorization of the studied meat pieces.     

Microstructural characterization of multiphase chocolate using X-ray microtomography

In this study, X-ray microtomography (μCT) was used for the image analysis of the microstructure of 12 types of Italian aerated chocolate chosen to exhibit variability in terms of cocoa mass content. Appropriate quantitative 3-dimensional parameters describing the microstructure were calculated, for example, the structure thickness (ST), object structure volume ratio (OSVR), and the percentage object volume (POV). Chemical analysis was also performed to correlate the microstructural data to the chemical composition of the samples. Correlation between the μCT parameters acquired for the pore microstructure evaluation and the chemical analysis revealed that the sugar crystals content does not influence the pore structure and content. On the other hand, it revealed that there is a strong correlation between the POV and the sugar content obtained by chemical analysis. The results from this study show that μCT is a suitable technique for the microstructural analysis of confectionary products such as chocolates and not only does it provide an accurate analysis of the pores and microstructure but the data obtained could also be used to aid in the assessment of its composition and consistency with label specifications. PRACTICAL APPLICATION: X-ray microtomography (μCT) is a noninvasive and nondestructive 3-D imaging technique that has several advantages over other methods, including the ability to image low-moisture materials. Given the enormous success of μCT in medical applications, material science, chemical engineering, geology, and biology, it is not surprising that in recent years much attention has been focused on extending this imaging technique to food science as a useful technique to aid in the study of food microstructure. X-ray microtomography provides in-depth information on the microstructure of the food product being tested; therefore, a better understanding of the physical structure of the product and from an engineering perspective, knowledge about the microstructure of foods can be used to identify the important processing parameters that affect the quality of a product.     

Visual detection of apple bruises using AdaBoost algorithm and hyperspectral imaging

Hyperspectral imaging technique (400–1000 nm) was used for rapid and nondestructive recognition of bruises of apples. A total of 324 hyperspectral images were collected from 108 Fuji apples and the average spectral reflectance was extracted from the region of interest (ROI) of each image. The classification results of AdaBoost for the data pretreated by various existing methods were compared. Then, the correlation-based feature selection (CFS) algorithm was used to obtain characteristic wavelengths for reducing data redundancy. After pretreating with multiplicative scatter correction (MSC) and CFS, the average accuracy of the selected wavelengths was 97.63%. Then, an image processing algorithm based on the characteristic wavelengths selected before was proposed for the visual discrimination of bruises. This algorithm performed independent component analysis (ICA) transformation of the selected wavelengths, and chose the third component image of the ICA transform, then used adaptive threshold segmentation to obtain the bruise region of apples. The results showed that hyperspectral imaging technology could discriminate apple bruise, and this study can help to develop an online apple bruises detection system.     

Use of image analysis to determine fat and connective tissue in salmon muscle

 An attempt was made to measure connective tissue and fat in salmon fillets using image analysis. Farmed salmon fillets were digitized by video camera and transferred to an image analysis program. A Laplace transformation was performed, and the image was segmented into different scales of grey. Particle analysis was applied to the binary pictures to calculate the percentage of white stripes in the total surface of the fillet, which were related to the percentage of connective tissue (CT) and fat. Although low correlation coefficients were obtained, it could be assumed that the video image analysis method is sensitive enough to detect great variations in fat content, which could prove useful in industrial applications. In contrast, CT was found to have less influence on the visual appraisal of the fillet surface. Received: 17 July 1998 / Revised version: 20 October 1998     

Detection of fungal infection in five different pulses using near-infrared hyperspectral imaging

The five major pulse crops grown in Canada are: chick peas, green peas, lentils, pinto beans and kidney beans. Potential causes of fungal infection in these pulses are Aspergillus flavus and Penicillium commune. Early stages of fungal infections in pulses are not detectable with human eyes and traditional microbial methods require significant time to detect fungal infection. Near-infrared (NIR) hyperspectral imaging system is an advanced technique widely being assessed for detection of insect infestation and fungal infection in cereal grains and oilseeds. The primary objective of this study was to assess the feasibility of the NIR hyperspectral imaging system to identify fungal infections in pulses. Hyperspectral images of healthy and fungal infected chick peas, green peas, lentils, pinto beans and kidney beans were acquired and features (six statistical and 10 histogram) were used to develop classification models to identify fungal infection caused by A. flavus and P. commune. Images of healthy and fungal-infected kernels were acquired at 2 week intervals (0, 2, 4, 6, 8 and 10 weeks from artificial inoculation). Six-way (healthy vs the five different stages of infection) and two-way (healthy vs every stage of infection) models were developed and classifications were done using linear discriminant analysis (LDA) and quadratic discriminant analysis (QDA) classifiers. The LDA classifier identified both types of fungal infections with 90–94% accuracy while using the six-way model, and with 98–100% accuracy when using the two-way models for all five types of pulses. The QDA classifier also showed promising results as it gave 85–90% accuracy for the six-way model and 96–100% accuracy for the two-way models. The two fungal species could not be differentiated by the hyperspectral imaging.     

Assessment of intramuscular fat level and distribution in beef muscles using X-ray microcomputed tomography

In the present research, the X-ray microtomography (μCT) technique was used to quantify intramuscular fat content and to study fat distribution in different breeds and commercial meat joints.     

Direct analysis for the distribution of toxic glycoalkaloids in potato tuber tissue using matrix-assisted laser desorption/ionization mass spectrometric imaging

A simple and rapid analytical method for detection and spatial distribution of glycoalkaloids in potato tubers has been developed for the first time using matrix-assisted laser desorption/ionization mass spectrometric imaging (MALDI-MSI). For effective MALDI-MSI analysis, we have developed a uniform matrix coating method using 2,5-dihydroxybenzoic acid (2,5-DHB) as the preferred matrix which results in better sensitivity than 2,4,6-trihydroxyacetophenone (2,4,6-THAP) using MALDI-TOF. The relative concentrations of two major and two minor glycoalkaloids, α-chaconine and α-solanine, dehydrochaconine and dehydrosolanine, were clearly detected and distinguished in various parts of potato tuber and their relative amounts were directly compared. We also successfully showed the relative concentrations of glycoalkaloids that were accumulated by light exposure during storage using MALDI-MSI. Therefore, MALDI-MSI has been shown to be a useful technique for screening toxic and bioactive metabolites in foods and medicinal plants.     

Detection of granary weevil Sitophilus granarius (L.) eggs and internal stages in wheat grain using soft X-ray and image analysis

In order to prevent grain mass and quality losses, rapid methods for early detection of insect infestation of cereal grain during trade and storage are urgently needed. Amongst many options, the soft X-ray method using roentgenograms is one of the most frequently applied. It has been shown that when some corrections for working parameters of the equipment used are made and some modification of the digital image analysis introduced, the soft X-ray method is suitable for accurate detection of granary weevil eggs laid in wheat kernels if at least 5 days after oviposition have elapsed.