Correction of Temperature Variation with Independent Water Samples to Predict Soluble Solids Content of Kiwifruit Juice Using NIR Spectroscopy.

Harpreet Kaur,Rainer Künnemeyer,Andrew Mcglone

doi:10.3390/molecules27020504

Abstract

Using the framework of aquaphotomics, we have sought to understand the changes within the water structure of kiwifruit juice occurring with changes in temperature. The study focuses on the first (1300–1600 nm) and second (870–1100 nm) overtone regions of the OH stretch of water and examines temperature differences between 20, 25, and 30 °C. Spectral data were collected using a Fourier transform–near-infrared spectrometer with 1 mm and 10 mm transmission cells for measurements in the first and second overtone region, respectively. Water wavelengths affected by temperature variation were identified. Aquagrams (water spectral patterns) highlight slightly different responses in the first and second overtone regions. The influence of increasing temperature on the peak absorbance of the juice was largely a lateral wavelength shift in the first overtone region and a vertical amplitude shift in the second overtone region of water. With the same data set, we investigated the use of external parameter orthogonalisation (EPO) and extended multiple scatter correction (EMSC) pre-processing to assist in building temperature-independent partial least square regression models for predicting soluble solids concentration (SSC) of kiwifruit juice. The interference component selected for correction was the first principal component loading measured using pure water samples taken at the same three temperatures (20, 25, and 30 °C). The results show that the EMSC method reduced SSC prediction bias from 0.77 to 0.1 °Brix in the first overtone region of water. Using the EPO method significantly reduced the prediction bias from 0.51 to 0.04 °Brix, when applying a model made at one temperature (30 °C) to measurements made at another temperature (20 °C) in the second overtone region of water.

Highlights

Water is the major constituent of fruits, typically more than 80% [1,2], and absorbs near-infrared (NIR) radiation [3,4]
Acharya et al [12] have studied the effect of temperature on prediction models of fruit quality, observing that a calibration equation developed at one fixed temperature could not reliably predict on samples measured at a different temperature
Roger et al [10] removed the temperature-induced bias in solids content (SSC) predictions on apples by applying the external parameter orthogonalisation (EPO) algorithm, as a pre-processing step, to remove the part of the spectral data matrix most affected by temperature

Summary

Introduction

Water is the major constituent of fruits, typically more than 80% [1,2], and absorbs near-infrared (NIR) radiation [3,4]. NIR spectroscopy (NIRS) models for predicting dry matter (DM) and soluble solids content (SSC) of fruit (apples) have been developed using the narrow spectral range from 800 to 1100 nm around this absorption peak [5]. Acharya et al [12] have studied the effect of temperature on prediction models of fruit quality, observing that a calibration equation developed at one fixed temperature could not reliably predict on samples measured at a different temperature. Roger et al [10] found a model offset bias of 8 ◦Brix for a temperature variation of 20 ◦C (range 5–25 ◦C) for SSC prediction in apples. Peirs et al [13] developed robust calibration models for a wide range of apple cultivars, incorporating samples at all temperature ranges expected in future measurements. Several new techniques have been reported recently in the literature, indicating the problem is far from solved for all circumstances [14]

Methods

Results

Conclusion