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目的探索定量评价大黄鱼新鲜度的方法。方法在整鱼背部采集近红外光谱,将原始光谱预处理后分别与挥发性盐基氮(TVB-N)、菌落总数建立偏最小二乘(PLS)模型、区间偏最小二乘(iPLS)模型、向后区间偏最小二乘(biPLS)模型和联合区间偏最小二乘(siPLS)模型。结果 biPLS模型的精度最高、预测性能最佳。TVB-N的biPLS模型的校正集和预测集相关系数分别为0.8371和0.7652;菌落总数的biPLS模型的校正集和预测集相关系数分别为0.878和0.7009。结论大黄鱼的近红外光谱信息与其TVB-N、菌落总数间都存在较高的相关性,所建模型可以快速、无损地定量评价大黄鱼的新鲜度。
Objective To explore a method for quantitative evaluation of freshness of large yellow croaker. Methods The near-infrared spectrum was collected on the back of whole fish. Partial least squares (PLS) model, partial least squares (iPLS) model, and TVB-N , Backward interval partial least squares (biPLS) models and joint interval partial least squares (siPLS) models. Results The biPLS model has the highest accuracy and the best prediction performance. TVB-N biPLS model calibration set and the predicted set of correlation coefficients were 0.8371 and 0.7652; the total colony biPLS model calibration set and prediction set correlation coefficients were 0.878 and 0.7009. Conclusion There is a high correlation between near-infrared spectral information of big yellow croaker and its TVB-N and the total number of colonies. The established model can evaluate the freshness of large yellow croaker quickly and non-destructively.