Non-invasive hyperspectral imaging approach for fruit quality control application and classification: case study of apple, chikoo, guava fruits

作者：Vetrekar N T; Gad R S^*; Fernandes I; Parab J S; Desai A R; Pawar J D; Naik G M; Umapathy S

来源：Journal of Food Science and Technology-Mysore, 2015, 52(11): 6978-6989.

DOI：10.1007/s13197-015-1838-8

• Hyperspectral imaging
• Grey Level Co-occurrence Matrix (GLCM)
• Feed forward-back propagation network
• Principal Component Analysis (PCA)

Mechanical injuries to fruits are often caused due to hidden internal damages that results in bruising of fruit. This is a serious cause of concern to the fruit industry, as spoiled or bruised fruits directly impact the producers profit. Hyperspectral imaging method can provide the ability to identify these internal bruises to classify these fruits as normal and injured (bruised), reducing time and increasing efficiency over the sorting line in marketing chain. In this paper, we have used three types of fruits i.e., apple, chikoo & guava for experiments. The mechanical injury is introduced by manual impact on surface of the fruits sample and hyperspectral images were captured over nine narrow band pass filters to produce hyperspectral cubes for a fruit. Three types of methods were used for the data processing. First two are non-invasive in nature i.e., pixel signatures over hyperspectral cubes and second is prediction model for classification of fruits quality into normal and bruised using feed forward back propagation neural network. Finally, invasive method is used to confirm the said prediction model using parameters like firmness, Total Soluble Solid (TSS) and weight with Principal Component Analysis. Results obtained by hyperspectral imaging method indicate scope for non-invasive quality control over spectral wavelength range of 400-1000 nm.

• 出版日期2015-11