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In recent years, research on the qualitative analysis of aquatic product using ultrasonic or biological impedance has received increasing attention. Unfortunately, aquatic product safety incidents occur now and then, thus new kinds of analytical techniques that are fast, nondestructive, and simple to operate would be advantageous. This article addressed a machine olfactory system for recognition of the storage life of a whole living mitten crab. A type of deep learning algorithm, namely stacked denoising auto‐encoders algorithm (SdA), was applied to extract effective features of the machine olfactory system sensors' responses. The traditional feature extraction methods such as principal component analysis and linear discriminant analysis were used for the purposes of comparison. Then the support vector machine (SVM) classifier was implemented for further qualitative classification. In addition, the crabs were analyzed for total volatile basic nitrogen and total viable counts during storage process. Validation experiments showed that the highest recognition rate (96.67%) was achieved by using SdA with SVM. This study may present a promising instruction for the storage life recognition of the living crab. Practical Application Evaluation of the freshness of mitten crab is of considerable importance to guarantee mitten crab quality. However, to our best knowledge, few reports on the development of an electronic‐nose for nondestructive evaluation of the quality of whole living crab are available. A machine olfactory system for recognition of the storage life of a whole living mitten crab was present in this study. Validation experiments showed the highest recognition rate based on the system. In summary, the metal‐semiconductor sensor‐based machine olfactory system presents some advantages including fast detecting, repeatability, and nondestructive. In the future, with the improvement of sensor performance and development of pattern recognition, it will be a favorable weapon for protecting the interests of consumers in aquatic market.

This paper focuses on presenting a new identification algorithm to estimate the parameters and state variables for two-input two-output dynamic systems with time delay based on canonical state space models. First, the related input-output equation is determined and transformed into an identification oriented model, which does not involve in the unmeasurable states, and then a residual based least squares identification algorithm is presented for the estimations. After the parameters being estimated, the system states are subsequently estimated by using the estimated parameters. Through theoretical analysis, the convergence of the algorithm is derived to provide assurance for applicability. Finally, a selected simulation example is given for a meaningful case study to show the effectiveness of the proposed algorithm.

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