**The Application of Near Infrared Spectroscopy for the Assessment of Avocado Quality Attributes**

Brett B. Wedding1,2, Carole Wright1, Steve Grauf1 and Ron D. White2 *1Rapid Assessment Unit, Department of Employment, Economic Development and Innovation, Queensland 2Rapid Assessment Unit, James Cook University, Queensland Australia* 

#### **1. Introduction**

210 Infrared Spectroscopy – Life and Biomedical Sciences

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Quality and safety evaluation of agricultural products has become an increasingly important consideration in market/commercial viability and systems for such evaluations are now demanded by customers, including distributors and retailers. Unfortunately, most horticultural products struggle with delivering adequate and consistent quality to the consumer. Removing inconsistencies and providing what the consumer expects is a key factor for retaining and expanding both domestic and international markets. Most commercial quality classification systems for fruit and vegetables are based on external features of the product, for example: shape, colour, size, weight and blemishes. However, the external appearance of most fruit is generally not an accurate guide to the internal or eating quality of the fruit. Internal quality of fruit is currently subjectively judged on attributes such as volatiles, firmness, and appearance. Destructive subjective measures such as internal flesh colour, or objective measures such as extraction of juice to measure sweetness (oBrix) or assessment of dry matter (DM) content are also used, although obviously not for every fruit – just a sample to represent the whole consignment.

For avocado fruit, external colour is not a maturity characteristic, and its smell is too weak and appears later in its maturity stage (Gaete-Garreton et al., 2005). Since maturity is a major component of avocado quality and palatability, it is important to harvest mature fruit, so as to ensure that fruit will ripen properly and have acceptable eating quality. Currently, commercial avocado maturity estimation is based on destructive assessment of the %DM, and sometimes percent oil, both of which are highly correlated with maturity (Clark et al., 2003; Mizrach & Flitsanov, 1999). Avocados Australia Limited (AAL (2008)) recommend a minimum maturity standard for its growers of 23 %DM (greater than 10% oil content) for the 'Hass' cultivar, although consumer studies indicate a preference for at least 25 %DM (Harker et al., 2007).

<sup>©</sup> Copyright - The State of Queensland, Department of Employment, Economic Development and Innovation, 2011. Published by InTech under Creative Commons Attribution 3.0 License.

The Application of Near Infrared Spectroscopy

vegetables (Clark et al., 1997; Clark et al., 2003).

to test every piece of product in an in-line setting.

across growing districts.

for the Assessment of Avocado Quality Attributes 213

the potential to measure the DM percentage in avocados (Chen et al., 1993; Kim et al., 1999), but the cost and challenges for in-line use in the sorting line means that it is not currently a commercially viable application for high volume, low value items such as fruit and

NIRS has been demonstrated to be an accurate, precise, rapid and non-invasive alternative to wet chemistry procedures for providing information about relative proportions of C-H, O-H and N-H bonds. Analysis of NIRS absorption spectra aids in the qualitative and quantitative determination of many constituents and properties of horticultural produce, including oil, water, protein, pH, acidity, firmness, and particularly soluble solids content or total soluble solids of fresh fruits (Abbott, 1999; Butz et al., 2005; Scotter, 1990). Of particular importance for the current study, NIRS has been used to estimate %DM in various horticultural products (Birth et al., 1985; Hartmann & Bijning-Pfaue, 1998; McGlone & Kawano, 1998; Sivakumar et al., 2006; Xiaobo et al., 2006) including avocados (Clark et al., 2003; Schmilovitch et al., 2001; Walsh et al., 2004). The technique requires minimal or no sample preparation, and avoids wastage and the need for reagents. Furthermore, it is multianalytical, allowing estimates of several characteristics simultaneously and has the potential

NIRS is a secondary method of determination and therefore must be calibrated against a primary reference method to develop a calibration model. However, to develop these predictive models requires many samples, many hours of work and many computer calculations to develop a statistical model which can be used to predict future samples (Davies, 2005). The validity of the calibration models for future predictions depends on how well the calibration set represents the composition of new samples. With horticultural products, the major challenge is to ensure that the calibration model is robust, that is, that the calibration model holds across growing seasons and potentially

NIR as a tool to assess internal quality attributes of intact horticultural produce is well established in literature. In general however, the robustness of calibration models with respect to biological variability from different seasons has been neglected and therefore these calibration models may be optimistic with respect to prediction accuracies on future samples in practical applications, such as grading lines (Nicolaï et al., 2007). Nicolai et al. (2007) report that model prediction error in general may easily double when a calibration model is applied to a spectral data set of a different season or orchard. This lack of robustness often translates into bias (Golic & Walsh, 2006; Nicolaï et al., 2007). Robustness of calibration is consequently a critical issue (Nicolaï et al., 2007; Sánchez et al., 2003) and there has been recent work on fruit that considers the effect of different seasons (Peiris et al., 1998; Peirs et al., 2003; Miyanoto and Yoshinobu, 1995; Liu et al., 2005; Guthrie et al., 2005). These studies generally found that incorporating data from multiple growing seasons in the calibration model improved the predictive performance, compared with those calibration models developed using an individual season. Peiris et al. (1998) studied model robustness for the determination of soluble solids (SS) content of peaches and reported that a calibration developed on a population from three consecutive growing seasons had an improvement in prediction performance on a combined season validation set (standard error of prediction (SEP) of 0.94 - 1.26 %SS, and bias 0.17 - 0.38 %SS) over that developed from an individual season population (SEP of 0.90 - 1.36 %SS and bias 0.17

The inability to consistently guarantee internal fruit quality is an important commercial consideration of the Australian avocado industry (HAL & AAL, 2005). Retail and consumer surveys over the last 15+ years have shown that consumers are not always satisfied with avocado quality, mainly because of poor flesh quality that can not be determined until the fruit is cut (Embry, 2009; Gamble et al., 2008; Harker et al., 2007; Hofman & Ledger, 1999). The surveys show that only 30% of the Australian population eat avocados and they expect to discard one in every four pieces of fruit they purchase because of poor internal quality (Avocados Australia Limited & Primary Business Solutions, 2005). Other reasons contributing to reduced consumption include concerns over spoilage, convenience, price and limited availability (Harker, 2009). The surveys revealed that consumers select bruising as the major defect, followed by body and stem end rots (Harker, 2009). Bruising was found to be a more important barrier to purchasing than price (Harker, 2009).

Fruit quality reliability is a key factor impacting on supply chain efficiency and related profitability since repeat purchasing by consumers is significantly affected by a bad eating experience. For example, avocados with internal defects of 10% or more have a dramatic negative impact on the consumer repurchasing (Embry, 2009; Petty & Embry, 2011). Research has shown that if a consumer is dissatisfied with the quality of fruit purchased, then that consumer will not purchase that commodity for another 6 weeks (Embry, 2009). Australian avocado quality surveys have shown that increased levels of purchase can be achieved by improving overall quality. For example, there is potential to increase consumer purchasing by 9% by reducing the average level of damage or defects by 15% (Embry, 2009).

Australian avocado production is expanding rapidly and there are strong financial incentives to increase sales domestically and to export product to increase returns directly. Reliable export of avocados from Australia by sea freight requires long storage times, typically 2 - 3 weeks to Asia and 5 - 6 weeks to the European Union (Hofman & Marques, 2009). The biggest risk during transport is the development of rots and flesh disorders resulting in a poor quality product. The additional time and distance associated with most export markets results in longer times from harvest to consumption which increases the risks of quality loss before the consumer receives the fruit. The key factor for retaining and expanding both domestic and international markets is removing inconsistency and providing what the consumer expects, i.e., a consistent quality product with suitable DM content and fruit free of bruises and flesh disorders. A rapid and non-destructive system that can accurately and rapidly monitor internal quality attributes would allow the avocado industry to provide better, more consistent fruit eating quality to the consumer, and thus improve industry competitiveness and profitability.

The development of automated technologies has enabled commercially feasible noninvasive methods for estimating internal quality attributes of agricultural products. These methods are generally based on one of the following properties: nuclear magnetic resonance (NMR) and magnetic resonance imaging (MRI), ultrasonics for vibrational characteristics, Xray and gamma ray transmission, electrical properties, firmness, density, optical reflectance and transmission. Today, emphasis is put on the development of non-destructive methods for real-time in-line applications. Although several non-invasive techniques exist for this (Abbott, 1999; Butz et al., 2005; Chen & Sun, 1991; Gaete-Garreton et al., 2005; Mizrach, 2000; Mizrach & Flitsanov, 1999), NMR and near infra-red spectroscopy (NIRS) are the leading candidates for the application to fruit and vegetables. NMR has been demonstrated to have

The inability to consistently guarantee internal fruit quality is an important commercial consideration of the Australian avocado industry (HAL & AAL, 2005). Retail and consumer surveys over the last 15+ years have shown that consumers are not always satisfied with avocado quality, mainly because of poor flesh quality that can not be determined until the fruit is cut (Embry, 2009; Gamble et al., 2008; Harker et al., 2007; Hofman & Ledger, 1999). The surveys show that only 30% of the Australian population eat avocados and they expect to discard one in every four pieces of fruit they purchase because of poor internal quality (Avocados Australia Limited & Primary Business Solutions, 2005). Other reasons contributing to reduced consumption include concerns over spoilage, convenience, price and limited availability (Harker, 2009). The surveys revealed that consumers select bruising as the major defect, followed by body and stem end rots (Harker, 2009). Bruising was found

Fruit quality reliability is a key factor impacting on supply chain efficiency and related profitability since repeat purchasing by consumers is significantly affected by a bad eating experience. For example, avocados with internal defects of 10% or more have a dramatic negative impact on the consumer repurchasing (Embry, 2009; Petty & Embry, 2011). Research has shown that if a consumer is dissatisfied with the quality of fruit purchased, then that consumer will not purchase that commodity for another 6 weeks (Embry, 2009). Australian avocado quality surveys have shown that increased levels of purchase can be achieved by improving overall quality. For example, there is potential to increase consumer purchasing by 9% by reducing the average level of damage or defects by 15% (Embry, 2009). Australian avocado production is expanding rapidly and there are strong financial incentives to increase sales domestically and to export product to increase returns directly. Reliable export of avocados from Australia by sea freight requires long storage times, typically 2 - 3 weeks to Asia and 5 - 6 weeks to the European Union (Hofman & Marques, 2009). The biggest risk during transport is the development of rots and flesh disorders resulting in a poor quality product. The additional time and distance associated with most export markets results in longer times from harvest to consumption which increases the risks of quality loss before the consumer receives the fruit. The key factor for retaining and expanding both domestic and international markets is removing inconsistency and providing what the consumer expects, i.e., a consistent quality product with suitable DM content and fruit free of bruises and flesh disorders. A rapid and non-destructive system that can accurately and rapidly monitor internal quality attributes would allow the avocado industry to provide better, more consistent fruit eating quality to the consumer, and thus

The development of automated technologies has enabled commercially feasible noninvasive methods for estimating internal quality attributes of agricultural products. These methods are generally based on one of the following properties: nuclear magnetic resonance (NMR) and magnetic resonance imaging (MRI), ultrasonics for vibrational characteristics, Xray and gamma ray transmission, electrical properties, firmness, density, optical reflectance and transmission. Today, emphasis is put on the development of non-destructive methods for real-time in-line applications. Although several non-invasive techniques exist for this (Abbott, 1999; Butz et al., 2005; Chen & Sun, 1991; Gaete-Garreton et al., 2005; Mizrach, 2000; Mizrach & Flitsanov, 1999), NMR and near infra-red spectroscopy (NIRS) are the leading candidates for the application to fruit and vegetables. NMR has been demonstrated to have

to be a more important barrier to purchasing than price (Harker, 2009).

improve industry competitiveness and profitability.

the potential to measure the DM percentage in avocados (Chen et al., 1993; Kim et al., 1999), but the cost and challenges for in-line use in the sorting line means that it is not currently a commercially viable application for high volume, low value items such as fruit and vegetables (Clark et al., 1997; Clark et al., 2003).

NIRS has been demonstrated to be an accurate, precise, rapid and non-invasive alternative to wet chemistry procedures for providing information about relative proportions of C-H, O-H and N-H bonds. Analysis of NIRS absorption spectra aids in the qualitative and quantitative determination of many constituents and properties of horticultural produce, including oil, water, protein, pH, acidity, firmness, and particularly soluble solids content or total soluble solids of fresh fruits (Abbott, 1999; Butz et al., 2005; Scotter, 1990). Of particular importance for the current study, NIRS has been used to estimate %DM in various horticultural products (Birth et al., 1985; Hartmann & Bijning-Pfaue, 1998; McGlone & Kawano, 1998; Sivakumar et al., 2006; Xiaobo et al., 2006) including avocados (Clark et al., 2003; Schmilovitch et al., 2001; Walsh et al., 2004). The technique requires minimal or no sample preparation, and avoids wastage and the need for reagents. Furthermore, it is multianalytical, allowing estimates of several characteristics simultaneously and has the potential to test every piece of product in an in-line setting.

NIRS is a secondary method of determination and therefore must be calibrated against a primary reference method to develop a calibration model. However, to develop these predictive models requires many samples, many hours of work and many computer calculations to develop a statistical model which can be used to predict future samples (Davies, 2005). The validity of the calibration models for future predictions depends on how well the calibration set represents the composition of new samples. With horticultural products, the major challenge is to ensure that the calibration model is robust, that is, that the calibration model holds across growing seasons and potentially across growing districts.

NIR as a tool to assess internal quality attributes of intact horticultural produce is well established in literature. In general however, the robustness of calibration models with respect to biological variability from different seasons has been neglected and therefore these calibration models may be optimistic with respect to prediction accuracies on future samples in practical applications, such as grading lines (Nicolaï et al., 2007). Nicolai et al. (2007) report that model prediction error in general may easily double when a calibration model is applied to a spectral data set of a different season or orchard. This lack of robustness often translates into bias (Golic & Walsh, 2006; Nicolaï et al., 2007). Robustness of calibration is consequently a critical issue (Nicolaï et al., 2007; Sánchez et al., 2003) and there has been recent work on fruit that considers the effect of different seasons (Peiris et al., 1998; Peirs et al., 2003; Miyanoto and Yoshinobu, 1995; Liu et al., 2005; Guthrie et al., 2005). These studies generally found that incorporating data from multiple growing seasons in the calibration model improved the predictive performance, compared with those calibration models developed using an individual season. Peiris et al. (1998) studied model robustness for the determination of soluble solids (SS) content of peaches and reported that a calibration developed on a population from three consecutive growing seasons had an improvement in prediction performance on a combined season validation set (standard error of prediction (SEP) of 0.94 - 1.26 %SS, and bias 0.17 - 0.38 %SS) over that developed from an individual season population (SEP of 0.90 - 1.36 %SS and bias 0.17

The Application of Near Infrared Spectroscopy

**2.1.1 Fruit for dry matter model development** 

**2.1.2 Fruit for impact and rot model development** 

**2. Materials and methods 2.1 Avocado fruit samples** 

the hard green stage of ripeness.

stage of ripeness.

**2.2 NIR data collection 2.2.1 Dry matter NIR data** 

for the Assessment of Avocado Quality Attributes 215

'Hass' avocado fruit were obtained over the 2006, 2007 and 2008 growing seasons (Harvest months: May to November) from two commercial farms in the major production districts of Bundaberg, South East Queensland (Latitude: 24° 52' South, Longitude: 152° 21' East) and Childers, South East Queensland (Latitude: 25° 14' South, Longitude: 152° 16' East). Avocado fruit were harvested at three maturity stages through each season, corresponding to early, mid and late season harvests over the three growing seasons. This allowed for sufficient variability in the %DM range and other seasonal factors to be included in the calibration procedure. A minimum of 100 fruit were collected at each harvest giving a total of a minimum of 900 individual fruit for each growing region. All fruit were harvested at

'Hass' avocado fruit were obtained over the 2008 growing season from two farms in Queensland, Australia. The first farm is located near Ravenshoe on the Atherton Tablelands in North Queensland (Latitude: 17° 38' South, Longitude: 145° 29' East) and the second farm is located in the major production district of Toowoomba, South East Queensland (Latitude: 27° 33' South, Longitude: 151° 58' East). Fruit from Ravenshoe were used for the impact assessment trials (n = 102), while Toowoomba fruit (n = 125) were used for rot susceptibility (shelf life) trials. All fruit were harvested at the hard green

The spectra of whole, intact avocado fruit were collected using a commercially available Matrix-F, FT-NIR spectrophotometer (Bruker Optics, Ettlingen, Germany; operating software: OPUS™ version 5.1 - 6.5) in the 830 – 2500 nm range. Spectra were obtained in diffuse reflectance mode, using a standard 4 x 20 watt tungsten light source fibre-coupled emission head fitted to the spectrometer. The external emission head was placed directly above the avocado fruit (0o configuration). A light reducing box with a 60 mm diameter cut out window was used to hold the fruit, so that the fruit skin was directly exposed to the focal point of the emission head. A path-length of approximately 170 mm from the external emission head light source to the surface of the fruit provided a spectral scan diameter on the avocado of approximately 50 mm. In obtaining each sample spectrum, 32 scans at a resolution of 8 cm-1 were collected and averaged. Due to the large variability in the %DM within a fruit (Schroeder, 1985; Wedding et al., 2010; Woolf et al., 2003) two NIR spectra were collected from each fruit, one spectra from each opposing side midway from the peduncle and base (i.e., equatorial region). A white spectralon standard was used as the optical reference standard for the system prior to the collection of each set of sample spectra. Fruit spectra were acquired after sample temperature equilibration in an air-conditioned

laboratory at approximately 22 - 24 C, and within two days of harvest.


There have been limited investigations of avocado maturity based on %DM using NIRS. Schmilovitch et al. (2001) used a dispersive NIR spectrophotometer in reflectance mode to assess the 'Ettinger' and 'Fuerte' cultivars (both relatively thin-skinned) in the range 1200 - 2400 nm. Preliminary results identified standard errors of prediction for both 'Ettinger' and 'Fuerte' as 0.9 and 1.3%, respectively, over a 14 – 24 %DM range. Clark et al. (2003) investigated the use of a fixed polychromatic/diode array (PDA) spectrophotometer for estimating %DM in whole New Zealand 'Hass' avocado fruit using both reflectance and interactance modes. They concluded that interactance mode was a better predictor of %DM compared with reflectance. Reflectance models required high numbers (12 to 20) of latent variables (LV), indicating the models struggled against spectral noise and so required incorporation of many small spectral features to improve accuracy. Clark et al. (2003) reported interactance validation statistics of R2 (coefficient of determination) prediction >0.83, and root mean square error of prediction (RMSEP) <1.8 %DM, over a range of 20 – 45 %DM, while the corresponding reflectance results were <0.75 and >1.9 %DM, respectively. Walsh et al. (2004), using a fixed PDA spectrophotometer (Ziess MMS1/NIR-enhanced spectrometer, Germany) in the 300 - 1100 nm range, reported calibration results of r (correlation coefficient) = 0.89, root mean square error of crossvalidation (RMSECV) = 1.14, with a standard deviation ratio (SDR = standard deviation of the data set divided by the RMSECV or RMSEP) = 2.2, for %DM of avocado fruit of unspecified cultivar. The SDR statistic is the measurement of the ability of an NIRS model to predict a constituent and enables comparison of model performance across populations with different standard deviations (Baillères et al., 2002; Golic & Walsh, 2006). The higher the SDR statistic the greater the power of the model to predict the chemical composition accurately (Cozzolino et al., 2004). SDR values between 2.0 and 2.4 for 'difficult' applications, such as high moisture materials including fruit and vegetables are regarded as adequate for rough screening; a value between 2.5 and 2.9 are regarded as adequate for screening; a value between 3.0 and 3.4 is regarded as satisfactory for quality control; a value between 3.4 and 4.0 is regarded as very good for process control; values above 4.1 are excellent for any application (Nicolaï et al., 2007; Schimleck et al., 2003; Williams, 2008).

This study assessed the potential of FT-NIR diffuse reflectance spectroscopy as an objective non-invasive method for determining internal quality attributes of whole 'Hass' avocado fruit. These include: (a) to predict maturity and thereby eating quality based on %DM; (b) to predict the risk of developing internal rot disorders (i.e., rot susceptibility) as an indication of shelf-life; (c) to detect bruises. The study also demonstrates the importance of the calibration model development process to incorporate seasonal and geographical variability to ensure model robustness.
