Non-destructive prediction of total soluble solids in strawberry using near infrared spectroscopy

• Autor(a) principal: Agulheiro-Santos, A.C.
• Data de Publicação: 2022
• Outros Autores: Laranjo, M., Ricardo-Rodrigues, S., Melgão, C., Velasquez, R.
• Tipo de documento: Artigo
• Idioma: por
• Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
• Texto Completo: http://hdl.handle.net/10174/34736; https://doi.org/Agulheiro-Santos, A.C., Ricardo-Rodrigues, S., Laranjo, M., Melgão, C. and Velázquez, R. (2022), Non-destructive prediction of total soluble solids in strawberry using near infrared spectroscopy. J Sci Food Agric, 102: 4866-4872. https://doi.org/10.1002/jsfa.11849; https://doi.org/10.1002/jsfa.11849
• topic: Fragaria × ananassa Duch.; NIRS; Quality; Ripeness; Total Soluble Solids

BACKGROUND Near-infrared spectroscopy (NIRS) is considered to be a fast and reliable non-destructive technique for fruit analysis. Considering that consumers are looking for strawberries with good sweetness, texture, and appearance, producers need to effectively measure the ripeness stage of strawberries to guarantee their final quality. Therefore, the use of this technique can contribute to decreasing the high level of waste and delivering good ripe strawberries to consumers. The present study aimed to evaluate the predictive capacity of NIRS technology, as a possible alternative to conventional methodology, for the analysis of the main organoleptic parameters of strawberries (Fragaria × ananassa Duch.) RESULTS Spectroscopic measurements and physicochemical analyses [total soluble solids (TSS), titratable acidity, colour, texture] of ‘Victory’ strawberries were carried out. The predictive models developed for titratable acidity, colour and texture were not good enough to quantify those parameters. By contrast, in the NIRS quantitative prediction analysis of TSS, it was observed that the spectral pre-treatment with the highest predictive capacity was the first derivative 1-5-5. The coefficients of determination were: 0.9277 for the calibration model; 0.5755 for the validation model; and 0.8207 for the prediction model, using a seven-factor partial least squares multivariate regression analysis CONCLUSION Therefore, these results demonstrate that NIR analysis could be used to predict the TSS in strawberry, and further work on sampling is desirable to improve the prediction obtained in the present study. It is shown that NIRS technology is a suitable tool for determining quality attributes of strawberry in a fast, economic, and environmentally friendly way. © 2022 Society of Chemical Industry.