Wang Fan, Zhao Chunjiang, Yang Guijun
Beijing Research Center for Information Technology in Agriculture, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China.
National Engineering Research Center for Information Technology in Agriculture, Beijing 100097, China.
Foods. 2020 Nov 30;9(12):1778. doi: 10.3390/foods9121778.
Juiciness is a primary index of pear quality and freshness, which is also considered as important as sweetness for the consumers. Development of a non-destructive detection method for pear juiciness is meaningful for producers and sellers. In this study, visible-near-infrared (VIS/NIR) spectroscopy combined with different spectral preprocessing methods, including normalization (NOR), first derivative (FD), detrend (DET), standard normal variate (SNV), multiplicative scatter correction (MSC), probabilistic quotient normalization (PQN), modified optical path length estimation and correction (OPLECm), linear regression correction combined with spectral ratio (LRC-SR) and orthogonal spatial projection combined with spectral ratio (OPS-SR), was used for comparison in detection of pear juiciness. Partial least squares (PLS) regression was used to establish the calibration models between the preprocessing spectra (650-1100 nm) and juiciness measured by the texture analyzer. In addition, competitive adaptive reweighted sampling (CARS) was used to identify the characteristic wavelengths and simplify the PLS models. All obtained models were evaluated via Monte Carlo cross-validation (MCCV) and external validation. The PLS model established by 19 characteristic variables after LRC-SR preprocessing displayed the best prediction performance with external verification determination coefficient () of 0.93 and root mean square error () of 0.97%. The results demonstrate that VIS/NIR coupled with LRC-SR method can be a suitable strategy for the quick assessment of juiciness for pears.
多汁性是梨品质和新鲜度的一个主要指标，对消费者来说，它与甜度同样重要。开发一种梨多汁性的无损检测方法对生产者和销售者来说具有重要意义。在本研究中，将可见 - 近红外（VIS/NIR）光谱与不同的光谱预处理方法相结合，包括归一化（NOR）、一阶导数（FD）、去趋势（DET）、标准正态变量变换（SNV）、多元散射校正（MSC）、概率商归一化（PQN）、改进的光程长度估计与校正（OPLECm）、结合光谱比的线性回归校正（LRC - SR）以及结合光谱比的正交空间投影（OPS - SR），用于比较检测梨的多汁性。采用偏最小二乘（PLS）回归建立预处理光谱（650 - 1100 nm）与通过质构分析仪测量的多汁性之间的校准模型。此外，使用竞争性自适应重加权采样（CARS）来识别特征波长并简化PLS模型。所有得到的模型均通过蒙特卡罗交叉验证（MCCV）和外部验证进行评估。经过LRC - SR预处理后由19个特征变量建立的PLS模型显示出最佳的预测性能，外部验证决定系数（）为0.93，均方根误差（）为0.97%。结果表明，VIS/NIR与LRC - SR方法相结合可以作为快速评估梨多汁性的一种合适策略。
