Sweet and nonsweet taste discrimination of nectarines using visible and near-infrared spectroscopy

Abstract

The feasibility of using visible and near-infrared spectroscopy technology combined with multivariate analysis to discriminate cv. 'Big Top' and cv. 'Diamond Ray' nectarines has been studied. These varieties are very difficult to differentiate visually on the production line but show important differences in taste that affects the acceptance by final consumers. The relationship between the diffuse reflectance spectra and the two nectarine varieties was established. Five hundred nectarine samples (250 of each variety) were used for the study. Tests were performed by using a spectrometer capable of measuring in two different spectral ranges (600-1100 nm and 900-1700 nm). These spectral ranges were used to develop two accurate classification models based on linear discriminate analysis (LDA) and partial least squares discriminate analysis (PLS-DA). Later, selection techniques were applied to select the most effective wavelengths. The results showed that the PLS-DA model achieved better accuracy and less latent variables than LDA model, and specifically, good results with 100 % classification accuracy were obtained using only the 600-1100 nm spectral range for the two models and eight selected wavelengths. These results places visible and near-infrared spectrocopy as an accurate classification tool for nectarine varieties with a very similar appearance but different tastes that could be potentially used in an automated inspection system.

Chemometrics in analytical chemistry – an overview of applications from 2014 to 2018

“…Kimuli et al 60 also used SWIR hyperspectral imaging system (1000 -2500 nm) to detect aflatoxin B1 combined with PCA, PLS-DA and FDA techniques to explore and classify maize kernels of four varieties from different States of the USA. Cortés et al 61 discriminated two nectarine varieties in different spectral ranges of NIR and Vis-NIR using PLS-DA and LDA models. Shrestha et al 62 used NIR hyperspectral imaging data from 975 to 2500 nm and PLS-DA to investigate seed quality parameters such as year of production and variety in tomato seed lots.…”

Partial least squares with discriminant analysis and UV– visible spectroscopy for qualitative evaluation of Arabica and Robusta coffee in Lampung

“…The developed PLS-DA model was then used to predict the class membership for the prediction sample set using ± 0.5 as a threshold value to delimit the classes. 16,18,23,28 Then, the overall rate of correct classification (accuracy) was calculated to evaluate the performance of the PLS-DA model. 29 The PLS-DA and all spectral transformations were done using the multivariate analysis software of The Unscrambler® X (30 day trial version -CAMO Software, Oslo, Norway).…”

“…Using ±0.5 as a threshold value, there was only one Arabica sample adulterated with Robusta which failed to be correctly recognized (19 samples were correctly classified from a total of 20 samples). 16,18,23,28 Thus, a total rate of correct classification of 97.5% was obtained in the prediction set.…”

A method of two-dimensional correlation spectroscopy combined with residual neural network for comparison and differentiation of medicinal plants raw materials superior to traditional machine learning: a case study on Eucommia ulmoides leaves

“…As an alternative technology, spectroscopic technologies have the characteristics of sample pretreatment is simple, routine analysis is fast, operation is simple and easy, no reagents are required, and economical technique, which allows simultaneous multi-component analysis. It has been widely used in the fields of textile, environment, petroleum, agriculture, medicine, and so on [ 9 , 10 ]. Although some spectroscopic technology (Raman spectra, etc.)…”