Hyperspectral Reflectance Imaging To Classify Lettuce Varieties By Optimum Selected Wavelengths And Linear Discriminant Analysis

Lettuce (Lactuca sativa L.) has a wide variation of pigment classes and content in its leaves, with a variety of colors, textures, and sizes, which impose difficulties for their classification. Currently, the search for varieties with higher levels of antioxidant compounds has led the consumer to seek those with a purple color. However, there are no classification systems on the part of the distributor sector that allow the product delivered to the market to be directed. Thus, the use of remote sensing has become essential for better classification, providing nondestructive, fast, and precise measurement. Based on this, the aim of this study was to examine the potential of hyperspectral imaging for classifying different lettuce varieties through multivariate statistical techniques. Hyperspectral images in 825 visible infrared wavelengths (from 350 to 1000 nm) were acquired for eleven varieties with different foliar pigments (chlorophyll and anthocyanin). Analysis of pigments, analysis of tissues by optical microscopy, principal component analysis (PCA) and linear discriminant analysis (LDA), based on STEPWISE wavelengths selection were made. The results showed that there are differences in the leaf thickness and the anthocyanin overlap under chlorophyll in purple varieties, resulting in different spectral responses. The PCA explained 83% of variability in the data in the first two components, separate clusters were formed between the purple and green varieties. The STEPWISE procedure selected 31 wavelengths: 12 in the red, 9 in the green, 1 in the blue, and the remainder in the near infrared spectrum. The LDA models achieved a classification success rate of 81.04%. The results show that the use of hyperspectral imaging has great potential to distinguish among lettuce varieties. In the future, these results can support the development of a quickly analysis with semi-automated procedures in a repeatable, fast, and objective manner that can be integrated in farms and distribution centers.