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Postharvest monitoring of organic potato (cv. Anuschka) during hot-air drying using visible-NIR hyperspectral imaging.
Moscetti, Roberto; Sturm, Barbara; Crichton, Stuart Oj; Amjad, Waseem; Massantini, Riccardo.
Afiliación
  • Moscetti R; Department for Innovation in Biological, Agro-food and Forest Systems, Tuscia University, Viterbo, Italy.
  • Sturm B; Postharvest Technologies and Processing Group, Department of Agricultural and Biosystems Engineering, University of Kassel, Witzenhausen, Germany.
  • Crichton SO; School of Agriculture, Food and Rural Development, Newcastle University, Newcastle upon Tyne, UK.
  • Amjad W; Postharvest Technologies and Processing Group, Department of Agricultural and Biosystems Engineering, University of Kassel, Witzenhausen, Germany.
  • Massantini R; Department of Energy Systems Engineering, University of Agriculture, Faisalabad, Pakistan.
J Sci Food Agric ; 98(7): 2507-2517, 2018 May.
Article en En | MEDLINE | ID: mdl-29023753
BACKGROUND: The potential of hyperspectral imaging (500-1010 nm) was evaluated for monitoring of the quality of potato slices (var. Anuschka) of 5, 7 and 9 mm thickness subjected to air drying at 50 °C. The study investigated three different feature selection methods for the prediction of dry basis moisture content and colour of potato slices using partial least squares regression (PLS). RESULTS: The feature selection strategies tested include interval PLS regression (iPLS), and differences and ratios between raw reflectance values for each possible pair of wavelengths (R[λ1 ]-R[λ2 ] and R[λ1 ]:R[λ2 ], respectively). Moreover, the combination of spectral and spatial domains was tested. Excellent results were obtained using the iPLS algorithm. However, features from both datasets of raw reflectance differences and ratios represent suitable alternatives for development of low-complex prediction models. Finally, the dry basis moisture content was high accurately predicted by combining spectral data (i.e. R[511 nm]-R[994 nm]) and spatial domain (i.e. relative area shrinkage of slice). CONCLUSIONS: Modelling the data acquired during drying through hyperspectral imaging can provide useful information concerning the chemical and physicochemical changes of the product. With all this information, the proposed approach lays the foundations for a more efficient smart dryer that can be designed and its process optimized for drying of potato slices. © 2017 Society of Chemical Industry.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Solanum tuberosum / Espectroscopía Infrarroja Corta Tipo de estudio: Evaluation_studies / Prognostic_studies Idioma: En Revista: J Sci Food Agric Año: 2018 Tipo del documento: Article País de afiliación: Italia Pais de publicación: Reino Unido
Texto completo
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Solanum tuberosum / Espectroscopía Infrarroja Corta Tipo de estudio: Evaluation_studies / Prognostic_studies Idioma: En Revista: J Sci Food Agric Año: 2018 Tipo del documento: Article País de afiliación: Italia Pais de publicación: Reino Unido