Abstract
In this study, a hyperspectral imaging system of 866.4–1701.0 nm, combined with a variety of spectral processing methods were adopted to identify shrimp freshness. To gain the optimal model combination, three preprocessing methods (Savitzky-Golay first derivative (SG1), multivariate scatter correction (MSC), and standard normal variate (SNV)), three characteristic wavelength extraction algorithms (random frog algorithm (RFA), uninformative variables elimination (UVE), and competitive adaptive reweighted sampling (CARS)), and four discriminant models (partial least squares discrimination analysis (PLS-DA), least squares support vector machine (LSSVM), random forest (RF), and extreme learning machine (ELM)) were employed for experimental study. First of all, due to the full wavelength modeling analysis, three preprocessing methods were utilized to preprocess the original spectral data. The analysis showed that the spectral data processed by the SNV method had the best performance among the four discriminant models. Secondly, due to the characteristic wavelength modeling analysis, three characteristic wavelength extraction algorithms were utilized to extract the characteristic wavelength of the SNV-processed spectral data. It was found that the CARS algorithm achieved the best performance among the three characteristic wavelength extraction algorithms, and the combining adoption of the ELM model and different characteristic wavelength extraction algorithms obtained the best results. Therefore, the model based on SNV-CARS-ELM obtained the best performance and was elected as the optimal model. Lastly, for accurately and explicitly displaying the refrigeration days of shrimps, the original hyperspectral images of shrimps were substituted into the SNV-CARS-ELM model, thus obtaining the general classification accuracy of 97.92%, and the object-wise method was used to visualize the classification results. As a result, the method proposed in this study can effectively detect the freshness of shrimps.
Highlights
Shrimp is welcomed by consumers for its richness in zinc, selenium, copper, minerals, etc., high content of vitamin B12, low content of fat and sugar, delicious taste, fine texture, and high digestibility.Tissues of the shrimp are soft and rich in high-quality protein (20.6 g of protein per 100 g of shrimp meat)
This as study, random frog were taken as the set, and 105 samples wereIn taken the prediction set.algorithm (RFA), uninformative variables elimination (UVE), and competitive adaptive reweighted sampling (CARS)
It is essential to find out those characteristics that are most effective forrobustness classification identification among many characteristics, which should have athat large quantity of identification information to make the discriminant models easy to classify and arequantity most effective for classification identification among many characteristics, which should have ashould large of identification information to to make the the discriminant models easy to classify and have reliability and independence, so as achieve compression of the characteristic space a largehave quantity of identification information thethe discriminant models easy to classify and should reliability and independence, asto to make achieve compression of the characteristic space dimension
Summary
Shrimp is welcomed by consumers for its richness in zinc, selenium, copper, minerals, etc., high content of vitamin B12, low content of fat and sugar, delicious taste, fine texture, and high digestibility. Tissues of the shrimp are soft and rich in high-quality protein (20.6 g of protein per 100 g of shrimp meat). Astaxanthin in shrimp has high nutritional value [1,2]. Shrimp is rich in multiple microelements and vitamins necessary for the human body. Shrimp is the main ingredient of many.
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