title: Non-destructive prediction of pigment content in lettuce based on visible-NIR spectroscopy.
abstract: BACKGROUND
  Lettuce (Lactuca sativa L.) is one of the most important salad vegetables in the world, with a number of head shapes, leaf types and colors. The lettuce pigments play important physiological functions, such as photosynthetic processes and light stress defense, but they also benefit human health because of their antioxidant action and anticarcinogenic properties. In this study three lettuce cultivars were grown under different farming systems, and partial least squares models were built to predict the leaf chlorophyll, carotenoid and anthocyanin content.
  
  
  RESULTS
  The three proposed models resulted in high coefficients of determination and variable importance for the projection values, as well as low estimative errors for calibration and external validation datasets. These results confirmed that it is possible to accurately predict chlorophyll, carotenoid and anthocyanin content of green and red lettuces, grown in different farming systems, based on the spectral reflectance from 500 to 1000 nm.
  
  
  CONCLUSIONS
  The proposed models were adequate for estimating lettuce pigments in a quick and non-destructive way, representing an alternative to conventional measurement methods. Prediction accuracies were improved by using the detrending, smoothing and first derivative pretreatments to the original spectral signatures prior to estimating lettuce chlorophyll, carotenoid and anthocyanin content, respectively. © 2016 Society of Chemical Industry.
authors.affiliation: Department of Agrarian Sciences, Sete Lagoas Campus, Federal University of São João del-Rei, Minas Gerais, 35701-970, Brazil.
authors.forename: Antonio José
authors.initials: AJ
authors.lastname: Steidle Neto
authors.affiliation: Department of Agrarian Sciences, Sete Lagoas Campus, Federal University of São João del-Rei, Minas Gerais, 35701-970, Brazil.
authors.forename: Lorena de Oliveira
authors.initials: LO
authors.lastname: Moura
authors.affiliation: Department of Agrarian Sciences, Sete Lagoas Campus, Federal University of São João del-Rei, Minas Gerais, 35701-970, Brazil.
authors.forename: Daniela de Carvalho
authors.initials: DC
authors.lastname: Lopes
authors.affiliation: Department of Food Engineering, Sete Lagoas Campus, Federal University of São João del-Rei, Minas Gerais, 35701-970, Brazil.
authors.forename: Lanamar de Almeida
authors.initials: LA
authors.lastname: Carlos
authors.affiliation: Department of Food Engineering, Sete Lagoas Campus, Federal University of São João del-Rei, Minas Gerais, 35701-970, Brazil.
authors.forename: Luma Moreira
authors.initials: LM
authors.lastname: Martins
authors.affiliation: Department of Agrarian Sciences, Sete Lagoas Campus, Federal University of São João del-Rei, Minas Gerais, 35701-970, Brazil.
authors.forename: Leila de Castro Louback
authors.initials: LC
authors.lastname: Ferraz
mesh_terms: D000872:Anthocyanins / Q000737:chemistry*; D002338:Carotenoids / Q000737:chemistry*; D002734:Chlorophyll / Q000737:chemistry*; D018545:Lactuca / Q000737:chemistry* / Q000254:growth & development; D018515:Plant Leaves / Q000737:chemistry; D019265:Spectroscopy, Near-Infrared / Q000379:methods*; D014675:Vegetables / Q000737:chemistry / Q000254:growth & development
chemical_list: D000872:Anthocyanins; D002734:Chlorophyll; D002338:Carotenoids
keywords: anthocyanin; carotenoids; chlorophyll; partial least squares regression; spectral reflectance