Current methods for shrimp and salmon quality evaluations rely on sensory methods. These procedures are subjective, prone to error and difficult to quantify. Chemical analyses are seldom used by the seafood industry due to the complexity and length of these methods. Automated evaluation of color and odor is desirable to reduce subjectivity and discrepancies, and assist with the creation of standards for inspectors worldwide. The objectives of this study were to develop color machine vision techniques for visual evaluation, and to test electronic nose sensors for odor assessment of raw shrimp and fresh raw salmon.

A color machine vision system was developed to analyze color of seafood samples. Hardware consisted of a light box, a video camera and a frame grabber. Software developed for the Windows environment was able to determine the color of food samples by discretizing the RGB color system into 64, 512 or 4096 colors, and by giving color information in different color scales (RGB, Lab, XYZ or Munsell). The system was able to analyze the color of samples with non-uniform color surfaces, predict the amount of melanosis (black spots) in shrimp, and measure color changes of shrimp and salmon during storage.

An electronic nose with twelve conducting polymer sensors was used to measure odors of shrimp and salmon stored at different temperatures, with different levels of spoilage and treated with different chemicals. Discriminant function analysis was used as the pattern recognition technique to differentiate samples based on odors. Results showed that the electronic nose could discriminate differences in odor due to species, storage time, spoilage levels and food additives present in shrimp or salmon. Results also showed good correlation of sensor readings with sensory scores and chemical concentrations. Overall, the electronic nose showed good sensitivity and accuracy.