Preliminary study on the use of near infrared hyperspectral imaging for quantitation and localisation of total glucosinolates in freeze-dried broccoli

Highlights

• Hyperspectral imaging using a number of devices has been applied to freeze-dried broccoli florets.

• Quantification and localisation of total glucosinolates in florets has been examined.

• The spectral region between 950 and 1650 nm was optimum.

• Glucosinolate compounds were mainly located in the external part of florets.

Abstract

The use of hyperspectral imaging to (a) quantify and (b) localise total glucosinolates in florets of a single broccoli species has been examined. Two different spectral regions (vis–NIR and NIR), a number of spectral pre-treatments and different mask development strategies were studied to develop the quantitative models. These models were then applied to freeze-dried slices of broccoli to identify regions within individual florets which were rich in glucosinolates. The procedure demonstrates potential for the quantitative screening and localisation of total glucosinolates in broccoli using the 950–1650 nm wavelength range. These compounds were mainly located in the external part of florets.

Introduction

Glucosinolates are a class of about 120 chemicals distributed in only 16 plant families. These compounds are well-known for their characteristic pungent smells and tastes which are typical of some Brassica vegetables such as cabbage, mustard, cress, cauliflower, broccoli, turnip, Brussel sprouts, radish and horseradish. Structurally, glucosinolates (β-thioglucoside-N-hydroxysulfates) are characterised by the presence of nitrogen and sulphur groups. Biosynthesis of glucosinolates is mainly carried out using glucose and amino acids such as methionine, alanine, leucine and valine (aliphatic glucosinolates) or tryptophan and phenylalanine (aromatic glucosinolates) (Crozier et al., 2006). Epidemiological studies have consistently reported a reduced incidence of a number of diseases in subjects consuming diets rich in these compounds although anti-nutritive effects of both glucosinolates and their hydrolysis products have also been reported (Crozier et al., 2006, Jeffery and Araya, 2009, Shahindi, 1990, Verkerk et al., 2009). Broccoli (Brassica oleracea L. var Italica) contains significant amounts of these potentially bioactive compounds (Vallejo et al., 2003, Wang et al., 2012a). This vegetable is an economically important crop in a number of countries which may act as a source not only of glucosinolates but also of vitamins, minerals and other beneficial phytochemicals (Jeffery et al., 2003, Wang et al., 2012a). It is therefore important to both characterise the content of bioactive compounds in broccoli and determine in what parts of the plant these bioactive compounds are accumulated. Our previous work has demonstrated the potential for near infrared spectroscopy to quantify total glucosinolates in freeze-dried powders with acceptable accuracy for screening purposes (Hernandez-Hierro et al., 2012). Information about the spatial distribution of the aforementioned compounds would also be useful but near infrared spectroscopy does not provide the capability to map the location of constituents. Hyperspectral imaging may hold the answer to this problem.

Hyperspectral imaging is an emerging technique for non-destructive food analysis which provides both spatial and spectral information about an object. Recorded images consist of many thousands of pixels in a two-dimensional array, with each pixel corresponding to a specific region on the surface of the sample; each pixel in a hyperspectral image therefore contains a spectrum of the sample at that specific position. Interrogation of these spectra makes possible the development of mathematical models to predict the chemical composition or functional class of a sample at each pixel. Reflectance imaging is the most common image acquisition mode and is usually carried out in either the visible-near infrared (vis–NIR; 400–1000 nm) or near infrared (NIR; 1000–1700 nm) spectral regions (Gowen et al., 2007). The use of multivariate chemometric methods is required to handle the large quantities of spectral data collected in each image and very many approaches are available for the development of regression models to predict constituent concentrations in a sample at pixel level.

The number of research applications of hyperspectral analysis has risen considerably in the food sector in the recent past (Burger and Gowen, 2011, Gowen et al., 2007, Lorente et al., 2012, McGoverin et al., 2010, Sun, 2010). Hyperspectral image analysis has been used to determine moisture, total soluble solids and pH in strawberries (ElMasry et al., 2007), firmness and soluble solids in apples (Mendoza et al., 2011, Wang et al., 2012b) anthocyanins in grape skins (Fernandes et al., 2011), chlorophyll distribution in cucumber leaves (Ji-Yong et al., 2012) and maturity stage of bananas (Rajkumar et al., 2012). Additionally, this analytical method has been used to determine moisture in dehydrated prawns (Wu et al., 2012) and some quality parameters of both lamb (Kamruzzaman et al., 2012) and pork (Barbin et al., 2012) meat. Results reported in these studies have indicated that hyperspectral imaging is able to predict a number of food components and quality parameters in a wide range of biological matrices.

The aim of this study was to evaluate the potential of hyperspectral imaging technology for the quantitative screening and localisation of total glucosinolates in freeze-dried broccoli. Since predictive models developed on freeze-dried powders by conventional NIR spectrometers may not be transferred directly to hyperspectral imaging datasets, a new predictive model must be generated using an actual hyperspectral imaging system on homogeneous, freeze-dried broccoli powders after which it may be applied to hyperspectral images of intact broccoli for localisation and quantitation of total glucosinolates. To our knowledge, this is the first time that this analytical tool has been applied to broccoli for these purposes.