Govindarajan Konda Naganathan

Optical scattering with hyperspectral imaging to classify longissimus dorsi muscle based on beef tenderness using multivariate modeling

The objective of this study was to develop a non-destructive method for classifying cooked-beef tenderness using hyperspectral imaging of optical scattering on fresh beef muscle tissue. A hyperspectral imaging system (λ=922-1739 nm) was used to collect hyperspectral scattering images of the longissimus dorsi muscle (n=472). A modified Lorentzian function was used to fit optical scattering profiles at each wavelength. After removing highly correlated parameters extracted from the Lorentzian function, principal component analysis was performed. Four principal component scores were used in a linear discriminant model to classify beef tenderness. In a validation data set (n=118 samples), the model was able to successfully classify tough and tender samples with 83.3% and 75.0% accuracies, respectively. Presence of fat flecks did not have a significant effect on beef tenderness classification accuracy. The results demonstrate that hyperspectral imaging of optical scattering is a viable technology for beef tenderness classification.

Morphometric analysis of gastrocnemius muscle biopsies from patients with peripheral arterial disease: objective grading of muscle degeneration

Peripheral arterial disease (PAD), which affects ~10 million Americans, is characterized by atherosclerosis of the noncoronary arteries. PAD produces a progressive accumulation of ischemic injury to the legs, manifested as a gradual degradation of gastrocnemius histology. In this study, we evaluated the hypothesis that quantitative morphological parameters of gastrocnemius myofibers change in a consistent manner during the progression of PAD, provide an objective grading of muscle degeneration in the ischemic limb, and correlate to a clinical stage of PAD. Biopsies were collected with a Bergström needle from PAD patients with claudication (n = 18) and critical limb ischemia (CLI; n = 19) and control patients (n = 19). Myofiber sarcolemmas and myosin heavy chains were labeled for fluorescence detection and quantitative analysis of morphometric variables, including area, roundness, perimeter, equivalent diameter, major and minor axes, solidity, and fiber density. The muscle specimens were separated into training and validation data sets for development of a discriminant model for categorizing muscle samples on the basis of disease severity. The parameters for this model included standard deviation of roundness, standard deviation of solidity of myofibers, and fiber density. For the validation data set, the discriminant model accurately identified control (80.0% accuracy), claudicating (77.7% accuracy), and CLI (88.8% accuracy) patients, with an overall classification accuracy of 82.1%. Myofiber morphometry provided a discriminant model that establishes a correlation between PAD progression and advancing muscle degeneration. This model effectively separated PAD and control patients and provided a grading of muscle degeneration within clinical stages of PAD.

Quality Evaluation of Beef and Pork

The meat industry is the largest food industry in the United States. There exists a need for objective, non-invasive systems for sorting meat based on quality traits to facilitate marketing. Hyperspectral imaging has a great potential to fulfill the need, as it can collect both spatial (structural) and spectral (biochemical) information on the meat surface. This section will focus on hyperspectral imaging of beef and pork.

PREDICTING BEEF TENDERNESS USING HYPERSPECTRAL IMAGING

Beef tenderness is an important quality attribute for consumer satisfaction. The objective of this study was to implement hyperspectral imaging to predict 14 day aged, cooked beef tenderness. A pushbroom hyperspectral imaging system ( : 400 – 1000 nm) with diffuse-flood lighting system was developed; spatial, spectral and reflectance calibrations were performed. Hyperspectral images of beef steaks (n=111) at 14 day postmortem were acquired. Slice shear force (SSF) values were used as a tenderness reference. Principal component analysis was carried out on the hyperspectral images to reduce dimension along the spectral axis. The first five principal components explained over 90% variance of all bands in the image. On the principal component images, co-occurrence matrix analysis was conducted to statistically extract textural features. A canonical discriminant model was developed to predict three beef tenderness categories namely tender (SSF= =26 kg). With a leave-one-out crossvalidation procedure, the model predicted the three tenderness categories with 96.4% accuracy. All the tough samples were correctly identified. Hyperspectral imaging shows promise for predicting beef tenderness.