Hyperspectral imaging for nondestructive determination of some quality attributes for strawberry

Hyperspectral imaging for nondestructive determination of some quality attributes for strawberry
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DOI:
10.1016/j.jfoodeng.2006.10.016
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发表时间:
2007-07-01
影响因子:
5.5
通讯作者:
Ngadi, Michael
Ngadi, Michael
中科院分区:
农林科学1区
文献类型:
--
作者:
ElMasry, Garnal;Wang, Ning;Ngadi, Michael

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采用可见光和近红外(400-1000 nm)区域的高光谱成像技术,对草莓中的水分含量(MC)、总可溶性固形物(TSS)和酸度(以pH表示)进行了无损检测。光谱数据分析采用偏最小二乘(PLS)分析,一种多变量校准技术。预测MC、TSS和pH在4001000 nm全光谱范围内的相关系数(r)分别为0.90、0.80和0.87,SEC分别为6.085、0.233和0.105,SEP分别为3.874、0.184和0.129。利用PLS模型的β系数选择最佳波长。采用最优波长建立多元线性回归(MLR)模型预测质量属性。MLR模型预测MC、TSS和pH的相关系数(r)分别为0.87、0.80和0.92,SEC分别为6.72、0.220和0.084,SEP分别为5.786、0.211和0.091。此外,基于灰度共生矩阵(GLCM)对图像进行纹理分析,实现草莓的成熟期分类。在0度角水平方向上使用GLCM参数,分类准确率达到89.61%。(c) 2006 Elsevier Ltd.版权所有。
Hyperspectral imaging in the visible and near-infrared (400-1000 nm) regions was tested for nondestructive determination of moisture content (MC), total soluble solids (TSS), and acidity (expressed as pH) in strawberry. The spectral data were analyzed using the partial least squares (PLS) analysis, a multivariate calibration technique. The correlation coefficients (r) with the whole spectral range (4001000 nm) for predicting MC, TSS, and pH were 0.90, 0.80, and 0.87 with SEC of 6.085, 0.233, and 0.105 and SEP of 3.874, 0.184, and 0.129, respectively. Optimal wavelengths were selected using beta-coefficients from PLS models. Multiple linear regression (MLR) models were established using only the optimal wavelengths to predict the quality attributes. The correlation coefficients (r) for predicting MC, TSS, and pH using MLR models were 0.87, 0.80, and 0.92 with SEC of 6.72, 0.220, and 0.084 and SEP of 5.786, 0.211, and 0.091, respectively. Moreover, for classifying strawberry based on ripeness stage, a texture analysis was conducted on the images based on grey-level co-occurrence matrix (GLCM). The higher classification accuracy of 89.61% was achieved using the GLCM parameters at horizontal direction at angle of 0 degrees. (c) 2006 Elsevier Ltd. All rights reserved.