高光谱成像技术鉴别鱼新鲜度

doi:10.3964/j.issn.1000-0593(2018)02-0559-05

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Abstract This study investigated the feasibility of using near infrared hyperspectral imaging system (NIR-HIS) technique for non-destructiveidentification of fresh and frozen-thawed fish fillets. Hyperspectral images of freshness, storage time, and frozen-thawed times offillets for turbot flesh were obtained in the spectral region of 380~1 023 nm. Reflectance values were extracted from each region of interest (ROI) of each sample. Competitive adaptive reweighted sampling (CARS) algorithm, successive projections algorithm (SPA) and genetic algorithm (GA) were carried out to identify the most significant wavelengths. Based on the fifty-seven, thirty-one and sixty-six wavelengths suggested by CARS, SPA and GA, respectively, two classified models (least squares-support vector machine, LS-SVM and SIMCA) were established. Among the established models, SPA-LS-SVM model performed well withthe highest classification rate (100%) in calibration and 98% in prediction sets. SPA-LS-SVM and CARS-LS-SVM models obtainedbetter results 98% and 96% of classification rate in prediction set with thirty-one and fifty-seven effective wavelengths respectively. The CARS-SIMCA, GA-SIMCA and SPA-SIMCA models obtained poor results with 52% of classification rate in prediction set. The results showedthat NIR-HIS technique could be used to identify the varieties of fresh and frozen-thawed fish fillets rapidly and non-destructively, and SPA and CARS were effective wavelengths selection methods.

Key words：CARS; SPA; GA; LS-SVM; SIMCA

Received: 2017-03-12 Accepted: 2017-08-16

ZTFLH:

O433.5

Corresponding Authors: LUO Wei E-mail: 15270030556@163.com

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https://www.gpxygpfx.com/EN/10.3964/j.issn.1000-0593(2018)02-0559-05 OR https://www.gpxygpfx.com/EN/Y2018/V38/I02/559

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