Commercially sterilized mussel meats (Mytilus chilensis): a study on process yield.

The processing steps most responsible for yield loss in the manufacture of canned mussel meats are the thermal treatments of precooking to remove meats from shells, and thermal processing (retorting) to render the final canned product commercially sterile for long-term shelf stability. The objective of this study was to investigate and evaluate the impact of different combinations of process variables on the ultimate drained weight in the final mussel product (Mytilu chilensis), while verifying that any differences found were statistically and economically significant. The process variables selected for this study were precooking time, brine salt concentration, and retort temperature. Results indicated 2 combinations of process variables producing the widest difference in final drained weight, designated best combination and worst combination with 35% and 29% yield, respectively. Significance of this difference was determined by employing a Bootstrap methodology, which assumes an empirical distribution of statistical error. A difference of nearly 6 percentage points in total yield was found. This represents a 20% increase in annual sales from the same quantity of raw material, in addition to increase in yield, the conditions for the best process included a retort process time 65% shorter than that for the worst process, this difference in yield could have significant economic impact, important to the mussel canning industry.

Detection of abnormal fermentations in wine process by multivariate statistics and pattern recognition techniques.

Three multivariate statistical techniques (Multiway Principal Component Analysis, Multiway Partial Least Squares, and Stepwise Linear Discriminant Analysis) and one artificial intelligence method (Artificial Neural Networks) were evaluated to detect and predict early abnormal behaviors of wine fermentations. The techniques were tested with data of thirty-two variables at different stages of fermentation from industrial wine fermentations of Cabernet Sauvignon. All the techniques studied considered a pre-treatment to obtain a homogeneous space and reduce the overfitting. The results were encouraging; it was possible to classify at 72h 100% of the fermentation correctly with three variables using Multiway Partial Least Squares and Artificial Neural Networks. Additional and complementary results were obtained with Stepwise Linear Discriminant Analysis, which found that ethanol, sugars and density measurements are able to discriminate abnormal behavior.