Hyperspectral imaging as an effective tool for prediction the moisture content and textural characteristics of roasted pistachio kernels
Hyperspectral imaging as an effective tool for prediction the moisture content and textural characteristics of roasted pistachio kernels
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DOI：
10.1007/s11694-018-9764-x
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发表时间：
2018-02
影响因子：
3.4
通讯作者：
Toktam Mohammadi-Moghaddam;S. Razavi;M. Taghizadeh;B. Pradhan;A. Sazgarnia;Ahmad Shaker-Ardekani
中科院分区：
文献类型：
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作者：
Toktam Mohammadi-Moghaddam;S. Razavi;M. Taghizadeh;B. Pradhan;A. Sazgarnia;Ahmad Shaker-Ardekani
本研究的目的是建立预测水分含量和质地特征的校准模型（断裂力、硬度、表观弹性模量和压缩能）（温度90、120和150 °C;时间20、35和50分钟，空气速度0.5、1.5和2.5 m/s）使用维斯/NIR高光谱成像和多变量分析。不同的预处理方法和光谱处理，如归一化[乘法散射校正（MSC），标准正态变量变换（SNV）]，平滑（中值滤波，Savitzky-Golay和小波）和微分（一阶导数，D1和二阶导数，D2）对所获得的数据的影响进行了研究。采用偏最小二乘回归（PLSR）和人工神经网络（ANN）建立预测模型。结果表明，人工神经网络模型具有更高的潜力，预测含水率和质构特性的烤阿月浑子仁相比PLSR模型。在反射率数据和断裂力之间观察到高度相关性（R2= 0.957和RMSEP = 3.386），使用MSC、Savitzky-Golay和D1，压缩能量（R2= 0.907和RMSEP = 15.757）使用MSC、小波和D1的组合，采用SNV、Wavelet和D1的组合方法分别对含水率（R2 = 0.907和RMSEP = 0.179）和表观弹性模量（R2= 0.921和RMSEP = 2.366）进行了预测。此外，可见-近红外光谱数据与硬度相关性良好（R2= 0.876和RMSEP = 5.216），使用SNV，小波和D2。这些结果表明，维斯/近红外高光谱成像的能力和多变量分析的核心作用，在开发准确的模型预测的水分含量和质地特性的烤阿月浑子仁。
The objective of this study was to develop calibration models for prediction of moisture content and textural characteristics (fracture force, hardness, apparent modulus of elasticity and compressive energy) of pistachio kernels roasted in different conditions (temperatures 90, 120 and 150 °C; times 20, 35 and 50 min and air velocities 0.5, 1.5 and 2.5 m/s) using Vis/NIR hyperspectral imaging and multivariate analysis. The effects of different pre-processing methods and spectral treatments such as normalization [multiplicative scatter correction (MSC), standard normal variate transformation (SNV)], smoothing (median filter, Savitzky–Golay and Wavelet) and differentiation (first derivative, D1and second derivative, D2) on the obtained data were investigated. The prediction models were developed by partial least square regression (PLSR) and artificial neural network (ANN). The results indicated that ANN models have higher potential to predict moisture content and textural characteristics of roasted pistachio kernels comparing to PLSR models. High correlation was observed between reflectance data and fracture force (R2= 0.957 and RMSEP = 3.386) using MSC, Savitzky–Golay and D1, compressive energy (R2= 0.907 and RMSEP = 15.757) using the combination of MSC, Wavelet and D1, moisture content (R2= 0.907 and RMSEP = 0.179) and apparent modulus of elasticity (R2= 0.921 and RMSEP = 2.366) employing combination of SNV, Wavelet and D1, respectively. Moreover, Vis–NIR data correlated well with hardness (R2= 0.876 and RMSEP = 5.216) using SNV, Wavelet and D2. These results showed the capability of Vis/NIR hyperspectral imaging and the central role of multivariate analysis in developing accurate models for prediction of moisture content and textural properties of roasted pistachio kernels.