Non-destructive follow-up of ‘Jintao’ kiwifruit ripening through VIS-NIR spectroscopy – individual vs. average calibration model’s predictions

• Autor(a) principal: Afonso, Andreia M.
• Data de Publicação: 2022
• Outros Autores: Antunes, Maria Dulce, Cruz, Sandra, Cavaco, A. M., Guerra, Rui Manuel Farinha das Neves
• Tipo de documento: Artigo
• Idioma: eng
• Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
• Texto Completo: http://hdl.handle.net/10400.1/18468
• topic: Noninvasive; Internal quality; Optimal harvest date; Signal-to-noise ratio; Flesh color; Soluble solids content

Visible/near infrared spectroscopy (Vis-NIRS) was used to monitor the yellow-fleshed kiwifruit (Actinidia chinensis Planch 'Jintao') ripening on two selected orchards along 13 weeks, from pre-harvest to the late harvest. Calibration models for several Internal Quality Attibutes (IQA) were built from the spectral data of 375 individual kiwifruit. The analyzed IQA were L*, a* and b* from the CIELAB color space, hue angle, chroma, firmness, dry matter (DM), soluble solids content (SSC), juice pH and titratable acidity (TA). Different pre-processing methods were tested for the construction of PLS calibration models. SSC and Hue were the best performing models with a correlation coefficient of 0.81 and 0.88, and root mean square error of prediction (RMSEP) of 1.27% and 1.95 degrees, respectively. The interpretation of the models in terms of the known absorption bands and the impact of signal to noise ratio (SNR) in them is discussed. The calibration models were used to perform average predictions of the IQA on orchard subareas, for each day of the experiment. These average predictions were compared with the IQA's average reference values on the same subareas and days. The model's metrics improved significantly through the averaging procedure, with RMSEP = 0.26-0.36% and R-2 = 0.99 for SSC; and RMSEP = 0.42 degrees - 0.56 degrees and R-2 = 1 for Hue. Since orchard management is done essentially through averages and not individual values, this result reinforces the applicability of the NIR technology for follow-up of fruit ripening in the tree.