1. Non-Destructive Evaluation of Salmon and Tuna Freshness in a Room-Temperature Incubation Environment Using a Portable Visible/Near-Infrared Imaging Spectrometer
- Author
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Chengxun Cui and Jinshi Cui
- Subjects
Food industry ,business.industry ,Visible near infrared ,Biomedical Engineering ,Imaging spectrometer ,Soil Science ,Forestry ,Non destructive ,%22">Fish ,Environmental science ,Food quality ,Spectral data ,Tuna ,business ,Agronomy and Crop Science ,Food Science ,Remote sensing - Abstract
HighlightsWhile freshness is a critical value of food quality, its assessment requires complex methods, which are costly and time-consuming.In this work, it is demonstrated that spectral responses obtained from a portable VIS/NIR imaging spectrometer can be used to predict food freshness using a CNN-based machine learning algorithm.In the food industry, the method can assess real-time food freshness nondestructively and cost-effectively.Abstract. There has been strong demand for the development of accurate but simple methods to assess the freshness of foods. In this study, a system is proposed to determine the freshness of fish by analyzing the spectral response with a portable visible/near-infrared (VIS/NIR) imaging spectrometer and a convolution neural network (CNN) machine learning algorithm. Spectral response data from salmon and tuna, which were incubated at 25°C, were obtained every minute for 30 h and were categorized into three stages (fresh, likely spoiled, or spoiled) based on the time and pH. Using the obtained spectral data, a CNN-based machine learning algorithm was built to evaluate the freshness of the experimental samples. The accuracy of the spectral data in predicting the freshness was ~84% for salmon and ~88% for tuna. Keywords: CNN, Fish, Freshness, pH, Spectral data, VIS/NIR.
- Published
- 2021