Quantitative detection of apple watercore and soluble solids content by near infrared transmittance spectroscopy
Quantitative detection of apple watercore and soluble solids content by near infrared transmittance spectroscopy
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DOI：
10.1016/j.jfoodeng.2020.109955
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发表时间：
2020-08-01
影响因子：
5.5
通讯作者：
Zou, Xiaobo
中科院分区：
文献类型：
--
作者：
Guo, Zhiming;Wang, MingMing;Zou, Xiaobo
近红外（NIR）光谱作为一种新兴的分析技术，首次用于定量检测苹果的水核度和可溶性固形物含量（SSC）。为了减少数据处理时间并满足实际应用的需要，采用协同区间（SI）、逐次投影算法（SPA）、遗传算法（GA）和竞争自适应重加权采样（CARS）等变量选择方法来识别特征变量并简化模型。利用与苹果生物活性成分密切相关的光谱变量建立偏最小二乘（PLS）模型。预测相关系数 (R-p)、预测均方根误差 (RMSEP) 和残差预测偏差 (RPD) 用于估计模型的性能。 CARS-PLS模型使用600-1000 nm光谱显示出最佳的预测性能，苹果水核度的R-p、RMSEP和RPD值为0.9562、1.340%和3.720；苹果 SSC 分别为 0.9808、0.327 (o)Bx 和 4.845。这些结果证明了近红外透射光谱技术在定量检测苹果果实中SSC和水核度方面的潜力。
Near-infrared (NIR) spectroscopy as an emerging analytical technique was used for the first time to quantitatively detect the watercore degree and soluble solids content (SSC) in apple. To reduce the data processing time and meet the needs of practical application, the variable selection methods including synergy interval (SI), successive projections algorithm (SPA), genetic algorithm (GA) and competitive adaptive reweighted sampling (CARS) were used to identify the characteristic variables and simplify the models. The spectral variables closely related to the apple bioactive components were used for the establishment of the partial least squares (PLS) models. The predictive correlation coefficient (R-p), root mean square error of prediction (RMSEP), and residual predictive deviation (RPD) were used to estimate the performance of the models. The CARS-PLS models displayed the best prediction performance using 600-1000 nm spectra with R-p, RMSEP, and RPD values of 0.9562, 1.340% and 3.720 for apple watercore degree; 0.9808, 0.327 (o)Bx and 4.845 for apple SSC, respectively. These results demonstrate the potential of the NIR transmittance spectroscopy technology for quantitative detection of SSC and watercore degree in apple fruit.