Abstract
AbstractIn order to investigate the ability of NIR spectroscopy coupled with baseline correction to detect quality‐related changes of thawed tuna during the storage and the transportation. First, adaptive iteratively reweighted penalized least squares (airPLS) method was taken to eliminate the baseline and estimate spectra feature peaks of 168 samples (bigeye = 82, yellowfin = 86), and the Monte Carlo method was used to determine the optimal adjustment parameter λ. Second, predictive models based on the fitted features were established by the partial least squares regression (PLSR) to estimate docosahexaenoic acid (DHA), eicosapentaenoic acid (EPA), fat, and protein content. Compared with the standard normal variate (SNV), the proposed airPLS achieved good performance in a comparative experimental test. When the original spectra was preprocessed by airPLS, the number of feature peaks (nf) and the mean value of R2 () achieved 6 and 0.89 respectively, which is greater than that of the preprocessing algorithm SNV (nf = 4 and ). The proposed airPLS effectively reduces the interfering variability and reserves much more NIR features, which is a feasible and stable method for quality‐related analysis of the thawed tuna.Practical application: During the storage and the transportation, the rich nutrients in tuna are easily oxidized and degraded when they are exposed to the air. In order to develop effective methods to determine the quality of tuna and avoid selling thawed products as the fresh tuna, a quality‐related analysis method for thawed tuna based on the baseline correction is proposed to improve the prediction accuracy.
Published Version
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