Feasibility study on fingerprinting organic and conventional mango fruits, chips, and juice using portable near-infrared spectroscopy

Abstract

This research examined the distinction between organic and conventional mango fruits, chips, and juice using portable near-infrared (NIR) spectroscopy. A comprehensive analysis was conducted on a sample of 100 mangoes (comprising 50 organic and 50 conventional) utilising a portable NIR spectrometer that spans a wavelength range from 900 to 1700 nm. The mangoes were assessed in their entirety and their juice and chip forms. The spectral data underwent pre-processing through methodologies such as multiplicative scatter correction (MSC), standard normal variate (SNV), and derivatives to enhance the precision of the models. Principal component analysis (PCA) and various multivariate classification algorithms, including linear discriminant analysis (LDA), random forest (RF), k-nearest neighbors (kNN), and partial least squares discriminant analysis (PLSDA), were utilised to categorise the samples effectively. The findings indicated that the random forest method and specific pre-processing techniques achieved the highest classification accuracy for distinguishing organic and conventional mango products. For mango fruit and chips, it achieved 88.76% and 77.98% accuracy, respectively, when pre-processed using the second derivative, while for juice, it achieved 87.53% accuracy without pre-processing. This investigation demonstrates the efficacy of portable NIR spectroscopy as a dependable and non-invasive method for verifying organic mango products, thereby enhancing the integrity of food labelling and fostering consumer confidence.