Quality Assessment of Pork and Turkey Hams Using FT-IR Spectroscopy, Colorimetric, and Image Analysis

The implementation of fast and nondestructive methods in meat products and colds cuts have become increasingly important to evaluate their quality in relation to different factors such as origin, type of processing, freshness, adulteration, and authenticity. In this study, Fourier Transform Infrared Spectroscopy (FT-IR), colorimetric, and image-analysis methods were implemented to characterize and classify ham cold cuts in terms of meat type, processing, and shelf life during refrigerated storage. Two types of commercial hams (made from pork and turkey) and three types of processing (boiled, smoked, and roasted) were selected. By using the most appropriate color parameters, a*, h, and C*, as well as the textural features’ angular second moment, long-running emphasis, and standard deviation of image intensity from the hams’ images, high-classification values for the different ham samples were achieved. The FT-IR analysis revealed the presence of absorbance bands of proteins, triglycerides, fatty acids, and carbohydrates with different intensities according to meat type and processing. Refrigeration storage caused significant alterations of color parameters and a partial degradation of triglycerides and proteins. Moreover, the image-analysis findings indicated that storage period caused significant degradation of ham images relating to local linearity, and structural and textural continuum

Quality Assessment of Pork and Turkey Hams Using FT-IR Spectroscopy, Colorimetric, and Image Analysis

The implementation of fast and nondestructive methods in meat products and colds cuts have become increasingly important to evaluate their quality in relation to different factors such as origin, type of processing, freshness, adulteration, and authenticity. In this study, Fourier Transform Infrared Spectroscopy (FT-IR), colorimetric, and image-analysis methods were implemented to characterize and classify ham cold cuts in terms of meat type, processing, and shelf life during refrigerated storage. Two types of commercial hams (made from pork and turkey) and three types of processing (boiled, smoked, and roasted) were selected. By using the most appropriate color parameters, a*, h, and C*, as well as the textural features’ angular second moment, long-running emphasis, and standard deviation of image intensity from the hams’ images, high-classification values for the different ham samples were achieved. The FT-IR analysis revealed the presence of absorbance bands of proteins, triglycerides, fatty acids, and carbohydrates with different intensities according to meat type and processing. Refrigeration storage caused significant alterations of color parameters and a partial degradation of triglycerides and proteins. Moreover, the image-analysis findings indicated that storage period caused significant degradation of ham images relating to local linearity, and structural and textural continuum

Improving Microarray Spots Segmentation by K-Means driven Adaptive Image Restoration.

are used to study human genome. However, microarray images are corrupted by spatially inhomogeneous noise that deteriorates image and consequently gene expression. An adaptive microarray image restoration technique is developed by suitably combining unsupervised clustering with the restoration filters for boosting the performance of microarray spots segmentation and for improving the accuracy of subsequent gene expression. Microarray images comprised a publicly available dataset of seven images, obtained from the database of the MicroArray Genome Imaging & Clustering Tool website. Each image contained 6400 spots investigating the diauxic shift of Saccharomyces cerevisiae. The adaptive microarray image restoration technique combined 1/a griding algorithm for locating individual cell images, 2/a clustering algorithm, for assessing local noise from the spot’s background, and 3/a wiener restoration filter, for enhancing individual spots. The effect of the proposed technique quantified using a well-known boundary detection algorithm (Gradient Vector Flow snake) and the information theoretic metric of Jeffrey’s divergence. The proposed technique increased the Jeffrey’s metric from 0.0194 bits to 0.0314 bits, while boosted the performance of the employed boundary detection algorithm. Application of the proposed technique on cDNA microarray images resulted in noise suppression and facilitated spot edge detection. M I