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Non‐invasive quality analysis of thawed tuna using near infrared spectroscopy with baseline correction
Journal of Food Process Engineering ( IF 2.7 ) Pub Date : 2020-06-20 , DOI: 10.1111/jfpe.13445 Yuqiang Li 1, 2 , Tianhong Pan 1, 2 , Haoran Li 2 , Shan Chen 2
Journal of Food Process Engineering ( IF 2.7 ) Pub Date : 2020-06-20 , DOI: 10.1111/jfpe.13445 Yuqiang Li 1, 2 , Tianhong Pan 1, 2 , Haoran Li 2 , Shan Chen 2
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In 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.
中文翻译：
使用基线校正的近红外光谱技术对解冻金枪鱼进行非侵入式质量分析
为了研究近红外光谱结合基线校正在储存和运输过程中检测与质量相关的融化金枪鱼变化的能力。首先，采用自适应迭代加权加权最小二乘（airPLS）方法消除基线并估计168个样品（大眼= 82，黄鳍= 86）的光谱特征峰，然后使用蒙特卡洛方法确定最佳调整参数λ。第二，通过偏最小二乘回归（PLSR）建立基于拟合特征的预测模型，以估计二十二碳六烯酸（DHA），二十碳五烯酸（EPA），脂肪和蛋白质含量。与标准正态变量（SNV）相比，建议的airPLS在对比实验测试中取得了良好的性能。当用airPLS预处理原始光谱时，特征峰的数量（n f）和R 2（）的平均值分别达到6和0.89，这比预处理算法SNV（n f = 4和）。拟议中的airPLS有效降低了干扰变异性并保留了更多的NIR特征，这是一种用于解冻金枪鱼质量相关分析的可行且稳定的方法。
更新日期：2020-06-20
) 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.
中文翻译：
使用基线校正的近红外光谱技术对解冻金枪鱼进行非侵入式质量分析
为了研究近红外光谱结合基线校正在储存和运输过程中检测与质量相关的融化金枪鱼变化的能力。首先，采用自适应迭代加权加权最小二乘（airPLS）方法消除基线并估计168个样品（大眼= 82，黄鳍= 86）的光谱特征峰，然后使用蒙特卡洛方法确定最佳调整参数λ。第二，通过偏最小二乘回归（PLSR）建立基于拟合特征的预测模型，以估计二十二碳六烯酸（DHA），二十碳五烯酸（EPA），脂肪和蛋白质含量。与标准正态变量（SNV）相比，建议的airPLS在对比实验测试中取得了良好的性能。当用airPLS预处理原始光谱时，特征峰的数量（n f）和R 2（
）的平均值分别达到6和0.89，这比预处理算法SNV（n f = 4和）。拟议中的airPLS有效降低了干扰变异性并保留了更多的NIR特征，这是一种用于解冻金枪鱼质量相关分析的可行且稳定的方法。
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