Towards fruit maturity estimation using NIR spectroscopy

Syed Sohaib Ali Shah

doi:10.1016/j.infrared.2020.103479

Towards fruit maturity estimation using NIR spectroscopy

2020-12-01

63

PRE

AI

DOI：10.1016/j.infrared.2020.103479

Original

Original request for help

Save

Abstract

En 中文

Maturity estimation is critical for harvesting fruit at the correct time which in turn results in good quality and longer shelf life of fruit. Researchers have explored several fruit maturity indicators over the past years including chronological, physical and biochemical (destructive) features. In recent years, various rapid non-destructive techniques along with different chemometric algorithms have been developed to estimate fruit maturity at the time of harvest. Among the non-destructive techniques, near infrared (NIR) spectroscopy has shown promising results for estimation of fruit harvest maturity. This paper reviews extensive work done on maturity estimation of various fruits such as apple, mango, grapes, peaches, pears, and melons using different NIR spectrometers in recent years. We observed that spectral information along with parameters measured through chemometric algorithms are used to develop NIR based fruit maturity indices estimator using machine learning techniques. The machine learning model for fruit maturity estimation for one variety may not directly applicable to other varieties of the same fruit and require tuning of the estimator or a new estimator with more sample readings of that variety. To validate, we used the pre-built predictive Kensington Pride (KP) mango model on Felix F-750 for local variety of mango. The results showed that the pre-build KP mango model is not valid for local varieties without re-calibration for these varieties. We observed that prediction models built using partial least squares (PLS) regression show promising results for apple, mango, grapes, melon and kiwifruit. Non-linear models such as least squares support vector machine (LSSVM), also give promising results for fruits such as pear and peach. We also observed that few dedicated portable instruments are developed and commercialized using NIR technology and have been tested in-field. The available devices are expensive for in-field fruit maturity estimation that constraint its use at large in developing countries. Democratization of NIR spectroscopy fruit maturity estimation requires the focus to be shifted from expensive handheld NIR device, which covers wide range of fruits, to a more economical photodiode-based fruit maturity estimation device that is calibrated for a particular fruit.

Keywords:

Fruit maturity estimation

NIR Spectroscopy

Chemometrics

Prediction models

Towards fruit maturity estimation using NIR spectroscopy

Abstract

AI Summary

Journal

Organization