Cold storage of ‘Palmer’ mangoes sorted based on dry matter content using portable near infrared (VIS-NIR) spectrometer

Abstract

The objective of this study was to use dry matter (DM) calibration models to sort ‘Palmer’ mangoes prior cold storage and to evaluate the physiological and chemical changes during the storage period. PLS model developed with fruit from 2015/2016 season was not adequate to predict DM content in fruit from 2016/2017 (not adjusted R2). Therefore, VIS‐NIR spectra from 2016/2017 season were incorporated into data set and a new model was developed (RMSEcv of 10.5 g.kg−1, RnormalP2 of 0.75). With the new model, ‘Palmer’ mangoes were sorted into two maturity stages (150 g.kg−1 and 110 g.kg−1) which resulted in quality differences mainly in relation to DM and SSC. Portable VIS‐NIR spectrometer can be used to sort fruit according to maturity stages based on DM content and this classification affect fruit quality during cold storage as fruit with higher DM (150 g.kg−1) presented better quality than fruit with lower DM (110 g.kg−1). Practical applications Although results can be found regarding the use of portable NIR spectrometers to estimate maturity in mango fruit, there are no studies stating the use of this method to sort fruit prior cold storage. Our results highlight that portable VIS‐NIR spectrometer can be used to sort fruit according to maturity stages based on dry matter (DM) content and this classification affects fruit quality during cold storage as fruit with higher DM (150 g.kg−1) presented better quality than fruit with lower DM (110 g.kg−1) at the end of the storage period.

Sample size for postharvest quality traits of ‘Palmer’ mangoes

“…DM is a physicochemical trait that has been widely studied in mangoes, especially through its non-destructive evaluation using portable near infrared spectrometers (ANDERSON et al, 2017;MARQUES et al, 2016;SANTOS NETO et al, 2019;SUN et al, 2020). Since the DM content at harvest is highly and positively correlated with the sugar content in ready-to-eat mangoes, the higher the DM content at harvest, the higher will be the readyto-eat mango quality in the market (SANTOS NETO et al, 2018).…”

Flexible wireless in situ optical sensing system for banana ripening monitoring

“…However, the current traditional monitoring technology is time‐consuming and causes great damage to bananas. A fast and nondestructive method is needed to monitor the quality of bananas (Mubarok et al, 2021; Nazarloo et al, 2021; Santos Neto et al, 2018). Among many nondestructive monitoring methods, the visible/near‐infrared (Vis/NIR) optical monitoring method stands out and is widely used in agriculture, food, and the environment, and has been proven to be feasible for fruits in the past 10 years.…”

Sample selection method using near‐infrared spectral information entropy as similarity criterion for constructing and updating peach firmness and soluble solids content prediction models

“…This is achieved by assigning different model coefficients to the samples for ridge regression modeling, thereby obtaining a reliable model 14 . For the dry matter model of frozen mangoes, the predictive power was enhanced by combining model updating with multi‐seasonal sample data 15,16 . Mishra et al 25 combined the model built on selected variables with the model updating strategy to reduce the prediction deviation of pear moisture and soluble solids.…”

“…14 For the dry matter model of frozen mangoes, the predictive power was enhanced by combining model updating with multi-seasonal sample data. 15,16 Mishra et al 25 combined the model built on selected variables with the model updating strategy to reduce the prediction deviation of pear moisture and soluble solids. Most previous studies have performed model updating methods directly combined with new data for regression modeling, and this approach can be ineffective even after consuming a large number of new samples to update the model.…”