Hyperspectral data for predicting moisture content and distribution in scallops during continuous and intermittent drying
Hyperspectral data for predicting moisture content and distribution in scallops during continuous and intermittent drying
复制标题
用于预测连续和间歇干燥过程中扇贝水分含量和分布的高光谱数据
DOI：
10.1080/07373937.2020.1837153
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发表时间：
2020-10-24
影响因子：
3.3
通讯作者：
Wang, Huihui
中科院分区：
文献类型：
--
作者：
Sun, Jialiang;Zhang, Xueyu;Wang, Huihui
随着光学和光谱传感器的发展，高光谱技术在食品加工质量监测中得到了广泛的应用。干燥是食品加工中最常用的工艺之一。水分含量（MC）和水分分布（MD）是评价干燥工艺和干燥成品质量的重要指标。本研究旨在探讨利用高光谱数据（387.1-1024.7 nm）预测连续干燥和间歇干燥过程中水分密度的可行性，实现水分密度的可视化，从而以无损和方便的方式确定最佳干燥方式。建立了两种特征波长选择方法：回归系数法（RC）和二维相关光谱法（2d - cos），分别得到4个（405.7、513.2、606.9和967.1 nm）和3个（402.3、511.5和965.2 nm）的特征波长。采用FW和CW分别建立了预测MC的偏最小二乘回归模型（FW-PLSR、RC-PLSR和2 D-COS-PLSR）。比较模型的性能，确定RC-PLSR模型最优(变量数= 4,R-C (2)=0.9731, R-CV （2)= 0.9703, R-P (2)=0.9662, RMSEC = 0.0335, RMSECV = 0.0355, RMSEP = 0.0353, RPD = 5.5198）。通过优化后的模型生成了扇贝的MC和MD的可视化图，结果表明，干燥4 h回火1 h的间歇干燥工艺可以提高扇贝的产品质量。本研究提出了高光谱技术在扇贝连续干燥和间歇干燥监测中的应用前景，并对高光谱技术在扇贝连续干燥和间歇干燥监测中的应用前景进行了展望。
With the development of optical and spectroscopy sensors, hyperspectral technology has been wildly used in the quality monitoring of food processing. Drying is one of the most commonly used processes in food processing. Moisture content (MC) and moisture distribution (MD) are very important in evaluating a drying technique and the quality of dried final products. This study aimed to investigate the feasibility of using hyperspectral data (387.1-1024.7 nm) to predict MC and realize visualization of MD during constant and intermittent drying, leading to determine the optimal drying in a nondestructive and convenient way. Two characteristic wavelength (CW) selected methods were established: regression coefficients (RC) and two-dimensional correlation spectroscopy (2 D-COS), resulting in 4 (405.7, 513.2, 606.9 and 967.1 nm) and 3 (402.3, 511.5 and 965.2 nm) CWs, respectively. The partial least-square regression (PLSR) models (FW-PLSR, RC-PLSR, and 2 D-COS-PLSR) for MC prediction were built using FW and CW. Comparing the performance of the models, RC-PLSR model was determined to be optimal (variable number = 4, R-C (2)=0.9731, R-CV (2) = 0.9703, R-P (2)=0.9662, RMSEC = 0.0335, RMSECV = 0.0355, RMSEP = 0.0353, and RPD = 5.5198). The visualization maps for MC and MD were generated by the optimized model, which revealed that the intermittent drying process of drying for 4 h with 1 h tempering could improve the product quality of scallops. This study proposed the method for predicting MC and MD and highlighted the potential of hyperspectral technology in the monitoring of constant and intermittent drying in scallop.