A Review of Destructive and Non-destructive Methods ...

Food Bioprocess Technol DOI 10.1007/s11947-015-1568-y

REVIEW

A Review of Destructive and Non-destructive Methods for Determining Avocado Fruit Maturity Lembe Samukelo Magwaza 1

&

Samson Z. Tesfay 2

Received: 23 April 2015 / Accepted: 23 July 2015 # Springer Science+Business Media New York 2015

Abstract Optimum harvest maturity is one of the important factors determining the quality of avocado fruit. Currently, avocado harvest maturity is mostly determined using markers or indices such as mesocarp oil, dry matter, or moisture content, all quantified destructively using representative samples of a batch in a consignment. Although useful, destructive techniques are time-consuming and results reflect properties of specific produce evaluated. High variation in maturity stages affect postharvest quality and the rate of ripening within a consignment, causing logistical difficulties. Emerging analytical techniques have particular advantages in nondestructive detection of food quality and safety. In this paper, destructive and non-destructive analytical methods and instruments for determining maturity parameters of avocado fruit are discussed. This review also looks at the trends in applying emerging optical and imaging techniques to the analysis of avocado fruit maturity and quality, in particular, visible to near infrared spectroscopy, ultrasonic system, ultrasound imaging, hyperspectral imaging, magnetic resonance imaging, and fluorescence imaging. On the basis of the observed trends, the technical challenges and future prospects for commercial application of these non-destructive techniques for maturity determination of individual avocado fruit are presented.

* Lembe Samukelo Magwaza [email protected]; [email protected] 1

Department of Crop Science, School of Agricultural, Earth and Environmental Sciences, University of KwaZulu-Natal, Private Bag X01, Scottsville, Pietermaritzburg 3209, South Africa

2

Department of Horticultural Science, School of Agricultural, Earth and Environmental Sciences, University of KwaZulu-Natal, Private Bag X01, Scottsville, Pietermaritzburg 3209, South Africa

Keywords Mesocarp dry matter . Moisture content . Oil content . Near infrared spectroscopy . Nuclear magnetic resonance . Ultrasonic . Hyperspectral imaging

Introduction The avocado (Persea americana Mill.), botanically classified as a one-seeded berry (Fig. 1, Chanderbali et al. 2013), is one of the economically important fruit in subtropical and tropical horticulture, with an estimated annual production of more than 4.7 million tonnes (Quiñones-Islas et al. 2013; FAOSTAT 2015). The fruit has gained popularity because of increasing consumer awareness of the dietary value of avocado, which is largely monounsaturated fatty acid content and exceptional mineral, vitamin, and other beneficial anti-oxidant phytochemical contents (Villa-Rodríguez et al. 2011; Schaffer et al. 2013). As an oleaginous fruit, avocado is increasingly consumed, not only for its flavor but also for its high nutritional value and reported health benefits, including anti-cancer activity and prevention of cardiovascular diseases and diabetes (Ding et al. 2007, 2009). These health benefits are linked to low levels of cholesterol, high monounsaturated and polyunsaturated fats, and high antioxidant contents, contributed by several groups of phytochemicals and polyphenols in the fruit (Ding et al. 2007, 2009). An important determining factor of the external and internal eating quality of ripe avocado fruit quality is maturity level at harvest. The external quality of avocado fruit is evaluated using visual appearance. Eating quality, on the other hand, is mostly determined by flesh texture and flavor. Texture is mainly influenced by the stage of ripeness, which affects firmness and higher oil content that increases flesh creaminess and smoothness. Flavor, on the other hand, is influenced by cultivar and maturity (Hofman et al. 2013).

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Fig. 1 Intact and cut avocado fruit, a one-seeded berry, consisting of two morphologically distinct parts; the pericarp and the seed surrounded by the seed coat. The pericarp can be further differentiated into the outer layer known as exocarp (green peel or skin or rind), the middle fleshy

and edible layer referred to as the mesocarp which, generally, makes up the bulk of the pericarp and the hard inner layer called endocarp and the seed

Similar to other fruit, acceptable appearance, shape, size, firmness, flavor, and nutrient composition of avocado fruit are set at harvest (Fuchs et al. 1995). Therefore, accurate determination of avocado maturity stage is one of the aspects playing an important role in avocado postharvest quality. In an exportoriented avocado industry, harvesting fruit at optimum maturity is of utmost important because it provides market flexibility and ensures attainment of acceptable eating quality to the consumer (Kader 1999). In virtually all avocado cultivars, the picking period starts when the fruit has reached minimum maturity stage, which is determined by the ability to ripen without shriveling (Flitsanov et al. 2000). If harvested immature, the avocado fruit typically shrivels during storage, ripen abnormally, and has a watery taste and a poor, stringy, and rubbery texture (Pak et al. 2003; Gamble et al. 2010). Avocado fruit is among the few exceptional types of fruit that do not ripen while attached to a tree or plant. As a result, avocado are picked mature but unripe so that they withstand postharvest handling systems, especially when shipped over long distances. Avocado fruit can be harvested either at physiological maturity or at horticultural maturity, depending on the intended use of produce and shipping distances. Physiological maturity is defined as the stage of development when an avocado fruit will continue ontogeny even after it has been detached from the tree (Lee et al. 1983; Kader 1999). On the other hand, horticultural or Bcommercial^ maturity is defined as the developmental stage where the harvested avocado fruit possess the prerequisites for utilization by consumer and will undergo normal ripening and provide good eating quality (Wills et al. 2007). Commercial or horticultural maturity, however, is concerned with the time of harvest as related to a particular end-use that can be translated into market requirement which often bears little relation to physiological maturity (Wills et al. 2007). The price of fruit is usually high early in the picking season, encouraging the harvest of

immature fruit which does not ripen properly, but becomes watery, rubbery, flavorless, shriveled, and blackened. It is, therefore, important to allow avocado fruit to reach optimum maturity before harvesting and delivering to the market. A unique characteristic of avocado biology is the way it matures on the tree as the fruit does not ripen until harvested, a desired trait as it allows growers to delay harvesting if market prices are too low. As a result, avocado fruit may be left hanging on the tree for more than 12 months after reaching minimum harvest maturity (Woolf et al. 2004). However, an overmature fruit will have short shelf life and is more susceptible to postharvest physiological disorders and diseases, resulting to major economic losses (Flitsanov et al. 2000). Although the fruit remains attached to the tree for an extended period after reaching maturity, this may have a significant influence on the occurrence of rancidity during ripening and biennial bearing in some avocado cultivars (Whiley et al. 1992; 1996a, b). For instance, late harvesting of ‘Fuerte’ fruit with 30 % mesocarp dry matter content resulted in pronounced biennial bearing (Whiley ey al., 1996a, b). However, a study conducted by Kaiser and Wolstenholme (1994) in cool subtropical regions of South Africa showed that late harvesting had no effect on subsequent yields. This indicates that in cool subtropical regions, fruits can be stored on the tree and be harvested according to favorable marketing schedules. It is also difficult to determine avocado fruit maturity and to predict its ripening patterns, particularly due to its climacteric ripening pattern and physiology (Lee et al., 1983). Determining the commercial harvest time of avocado fruit is also difficult because, unlike many other fruit, avocado does not exhibit external visual and physical changes during maturation. Instead, maturation of an avocado fruit is characterized by a gradual decline of mesocarp moisture content (MC) and reciprocal increase of dry matter (DM) content (Clark et al. 2007;

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Parodi et al. 2007). Mesocarp oil content has also been shown to increase steadily with fruit development (Hofman et al. 2002). As mesocarp oil content increases, water content decreases by the same amount, so that the total percentage of oil and water content remains constant during life of any avocado fruit (Ozdemir and Topuz 2004). Hence, mesocarp oil, MC, and DM contents are used commercially as indicators of avocado maturity in different countries. Although these methods are commercially useful, they are inherently destructive, time-consuming, and cost-ineffective and require precise sample preparation (Arpaia et al. 2001). Furthermore, these destructive measurements usually involve few representative samples on which the maturity of fruit batch is based (Blakey et al. 2009; Magwaza et al. 2012). The disadvantage of these methods apart from their labor-intensive and destructive nature is that growers stand the risk of delivering poor-quality fruit to the market due to fruit quality variation within a batch. Poor quality fruit resulting from immature fruit in a batch may lead to uneven ripening and consumer dissatisfaction affecting future purchase of the fruit (Kruger and Lemmer 2007; Kruger and Magwaza 2012). Intra-varietal differences in fruit composition as well as difficulties of controlling and forecasting avocado fruit ripening pattern have been recently reported as major causes of concern among consumers and importers (Donetti and Terry 2014). For the avocado industry to improve its competiveness and maintain consumer confidence in the product purchased, the industry should supply fruit of consistent quality with predictable and even ripening. There is, therefore, a need to develop objective, fast, and non-destructive techniques that can be used to accurately determine maturity of individual fruit. Several attempts have been made, in recent years, to develop reliable non-destructive technologies for determining qualityrelated parameters in avocado, such as firmness, mesocarp DM, MC, and oil content. For instance, Chen et al. (1993) and Kim et al. (1999) showed that nuclear magnetic resonance (NMR) has a potential to estimate mesocarp oil content of avocado fruit. Mizrach and Flitsanov (1995a, b) showed that DM and oil content were correlated with ultrasonic parameters of avocado fruit, indicating the potential of the ultrasonic system for non-destructive prediction of avocado maturity and ripening patterns. Other non-destructive techniques for quantifying avocado fruit maturity parameters include ultrasound (Flitsanov et al. 2000; Mizrach 2008) and visible to near infrared (Vis/NIR) spectroscopy (Vis/NIRS, Clark et al. 2003; Wedding et al. 2013). This aim of this paper is to review current destructive methods and instruments for determining maturity indices of avocado fruit. This review also looks at the trends in applying emerging optical and imaging techniques for non-destructive analysis of avocado fruit maturity and quality, in particular NMR, Vis/NIRS, hyperspectral imaging, and ultrasonic systems.

Indices for Determining Maturity In order to establish reliable maturity indices for deciding when to harvest a fruit, some measurable quality attributes must change during fruit development and maturation (Woolf et al. 2003). A classic example of such change is gradual increase in soluble solid content and decline of titratable acids in citrus fruit during growth and development (Marsh et al. 1999). In avocado, mesocarp oil content and dry matter gradually increase during fruit growth and these are significantly correlated with maturity and eating quality (Lee et al. 1983). However, the level of increase of these parameters is highly dependent upon the cultivar being evaluated. Therefore, mesocarp oil content or related attributes such as dry matter and moisture contents are accepted worldwide as reliable maturity parameters on which time to harvest can be decided (Woolf et al. 2003; Mizrach 2008). Oil Content The nutritional value of avocado fruit is uniquely characterized by the high oil content mainly represented by unsaturated fatty acids (Donetti and Terry 2014). Depending on the variety and growth conditions, the oil content of fresh mesocarp tissue of the avocado fruit ranges from a minimum of 8 to 30 % and does not change with time after harvest (Lee et al. 1983; Quiñones-Islas et al. 2013). The oil fraction can be up to about 70 % of the mesocarp dry matter, although variations in oil content and composition have been observed due to growing regions, cultivar, harvest time, and spatially within the fruit (Gómez-López 1999; Landahl et al. 2009). Generally, oil fraction is mainly composed of monounsaturated oleic acid (50 to 60 % of the fatty acid content), saturated palmitic acid (15 to 20 %), unsaturated palmitoleic (6 to10%), polyunsaturated linoleic (11 to 15 %), and linolenic acid (± 1 %) (Donetti and Terry 2014). Variations in oil content and composition have been observed across harvest time, with avocado fruit harvested late in season having higher oil content (Villa-Rodríguez et al. 2011). For example, the oil content of ‘Hass’ avocado respectively increased from 14.36 to 17.77 % while that of ‘Fuete’ increased from 11.02 to 19.57 % from November to January (Ozdemir and Topuz, 2004). From the same study, it was observed that oleic acid was the only fatty acid which increased continuously from November to January in both varieties, with percentages ranging from 59.3 to 73.0 % in the ‘Fuerte’ and from 47.2 to 59.5 % in the ‘Hass’. Palmitic acid, on the other hand generally decreased consistently from 22.4 to 12.0 % for ‘Fuerte’ and from 23.3 to 16.8 % for ‘Hass’ during the same period. These results collectively showed mesocarp oil content and composition as reliable indices for measuring avocado maturity. However, compositional changes in lateharvest fruit are less consistent than those during early

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harvest, hence more difficult to have a reliable maturity standard (Hofman et al. 2013). In many avocado-producing countries, oil content is used as an indicator of fruit maturity and, thus, commonly defines the optimum harvest period. Cultivar-cultivar variation in mesocarp oil content have been reported to range from 5 to >30 % (Woolf et al. 2004). It is therefore important to keep in mind that minimum maturity standards for one cultivar are not applicable to other cultivars. For instance, in Hawaii, the minimum oil content standard for ‘Hawaii Fancy’ and ‘Hawaii No. 1’ cultivars is 12 %. However, ‘Sharwil’ fruit may contain an oil content significantly higher than 12 %, and fruit harvested at this level may be immature, with a watery taste and rubbery texture (HDOA 1986). Although oil content is the oldest and considered the most reliable indicator of fruit maturity and eating quality (Kaiser and Wolstenholme 1994), high cost of analysis and difficulty of measurements resulted in a search for alternative maturity parameters. Studies by Chen et al. (2009) and Carvalho et al. (2014) showed significant positive correlations (R value of up to 0.99) between avocado oil content and dry matter content during maturation. In general, the oil content of avocado fruit is about 11 units less than mesocarp DM content. For example, the average dry matter content of fruit with 20 % oil content is 31 % (Hofman et al. 2013). Due to the strong correlation between dry matter and oil content, Australia, Chile, Israel, and USA use percentage of dry matter content as an indirect measure to determine oil content and hence maturity for different cultivars (Woolf et al. 2004; Chen et al. 2009). Although oil content has been shown to be highly correlated to flavor and eating quality, fruit-to-fruit variability in levels and varietal differences in accumulation rate (Lee 1981a, b) make it an imperfect threshold for maturity. Further, analysis of dry matter is far simpler to perform than mesocarp oil content; hence, DM is now the standard maturity measurement used throughout most of the world (Obenland et al. 2012). Dry Matter and Moisture Contents Dry matter increases during fruit development, mainly due to the increase in oil. The dry matter content in the mesocarp of fresh avocado fruit is quite variable, ranging from low (35 %) in fruit that are potentially suitable for processing (Clark et al. 2007). According to Gamble et al. (2010), avocados with a range of DM levels from 20 to 40 % are considered minimally mature (Table 1) and very mature, respectively. Currently, no suitable method exists for segregating these categories in harvested lines. Most avocado-producing countries set minimum maturity standards to prevent immature fruit from entering the market. Many of these countries adopt the Californian minimum dry matter

Table 1 Minimum percent dry matter content (%) that various avocado varieties must reach before they can be commercially harvested and sold to the public (Ranney et al. 1992; Avocado certification program 2014) Varieties

Dry matter (%)

Anaheim, Benik, Bonita, Carlsbad, Dickinson, Edranol, Elsie, Itzamna, MacArthur, Nabal, Ryan, Queen, Thille Bacon

20.8

17.7

Clifton, Covacado, Duke, Henry, Jalna, Leucadia, Santana, Select, Teague, 287 Fuerte

18.7

Gwen Hass

24.2 20.8

Jim

19.3

Pinkerton Reed

21.6 18.7

Rincon Susan

20.4 18.4

Zutano

18.7

19.0

standard of 20.8 % for ‘Hass’. In an attempt to reduce physiological disorders on fruit to be stored for extended period, some countries adopted a higher minimum DM standard of 25 % (Pak et al. 2003). Minimum dry matter content ranges from 17 to 25 % (Table 1), depending on cultivar (19.0 % for ‘Fuerte’, 20.8 % for ‘Hass’, and 24.2 % for ‘Gwen’) and the country (21 % for Australia, 21.6–22.8 % for USA, and 23.0 % for Mexico, South America, and South Africa for ‘Hass’ avocado) (Kassim et al. 2013; Carvalho et al. 2014). In California, the minimum percentage of dry matter at harvest for the major cultivars are ‘Bacon’ (17.7 %), ‘Fuerte’ (19.0 %), ‘Gwen’ (24.2 %), ‘Hass’ (20.8 %), ‘Pinkerton’ (21.6 %), ‘Reed’ (18.7 %), and ‘Zutano’ (18.7 %) (Hofman et al. 2002). Arpaia et al. (2003a, b) evaluated a relationship between dry weight, as a measure of fruit maturity, and consumer acceptability of ‘Hass’ avocado fruit. Their results showed that as dry matter increases, so does the acceptability of the fruit. Similar results were obtained by Gamble et al. (2010), where consumers showed a progressive increase in liking and intent to buy avocados as the DM increased from 20 to 38 %. An average of 26 % mesocarp DM content was required to achieve 70–89 % intention to purchase. However, a study by Chen et al. (2009) showed that late season fruit had higher dry matter and oil contents and had a shorter shelf life than early and midseason fruit, but fruit flavor and texture did not change throughout the season. The minimum maturity standard recommended by the Australian industry to its growers and packhouses to achieve best tasting fruit was updated from 21 to 23 % DM (greater than 10 % oil content) for ‘Hass’ avocados (Avocados Australia Limited 2008). However, the standard for

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‘Shepard’ avocado fruit remained at 21 % DM. Updated minimum maturity standard for ‘Hass’ was based on consumer research showing that consumer acceptance of the quality of avocados which declined from approximately 95 to 70 % if the DM was below 23 % and that up to 70 % of consumers would prefer 26 % DM avocados to 22 % DM avocados. In general, consumer studies indicate a preference for at least 25 % dry matter (Harker et al. 2007). In South Africa, a minimum required oil level (fresh mass based) is 9.8 %, whereas the minimum DM level at which avocados are accepted is 23 % in the case of ‘Hass’ and 20 % with regard to all the other cultivars (Kruger et al. 1995). Moisture content is one of the most important indices evaluated in foods, especially fruits. It is a good indicator of their economic value because it reflects solid contents and serves to assess its perishability (Vinha et al. 2013). In avocado fruit, mesocarp MC is considered an important factor in avocado ripening and eating quality (Bower et al. 2007; Blakey et al. 2009). As a result of the relationship with eating quality, MC is the preferred indicator of maturity in South Africa (Hofman et al. 2002). The recommended moisture content for acceptable eating quality is in the range of 69 to 75 % depending on the cultivar (Mans et al. 1995). Export of early season ‘Fuerte’ commences once the moisture content has reached 78 to 80 %, which is equivalent to oil content of 9 to 11 % (Dodd et al. 2010; Kassim et al. 2013). For ‘Pinkerton’ cultivar exported from South Africa, the moisture content must be between 80 and 73 % (Kruger et al. 2004; Snijder et al. 2002, 2003). A relationship between oil, DM, and MC was shown in a study by Parodi et al. (2007), who showed a positive correlation between oil content and dry matter content of pulp in avocado fruit (r = 0.96). These authors also showed that oil and pulp moisture content had an inverse correlation (r = −0.96). This indicated that the maturity parameters can be used interchangeabley. The discussion above shows that fruit DM (or its complement MC) has a major impact on fruit ripening and eating quality. It also shows that fruit maturity is often quantified in terms of mesocarp DM content as an indirect measure for oil content. The use of dry matter as a harvest index is based on a minimum eating quality and has been found not to be useful in determining later stages of maturity (Hofman et al. 2000). However, as avocado fruit continues to accumulate oil while on the tree, the measurement of dry matter does give some measure of change in the fruit during the season. Furthermore, by their physiological nature, the oil and DM percentages of avocado fruit increase observed during growth and development does not change after harvest (Degani et al. 1986). Therefore, DM content is not a reliable index for physiological changes associated with postharvest fruit ripening. This is also supported by Hofman et al. (2000), who reported that for late harvested ‘Hass’ avocado fruit, set maturity indices, mesocarp MC, DM, and oil content are not reliable.

Fruit Color as an Indicator Harvest Maturity As mentioned earlier (in the introduction section), avocado fruit does not give obvious indication of maturity as it does not ripen as long as it remains attached to a tree. Although the fruit does not exhibit obvious external visual and physical changes during maturation, the skin color of some cultivars changes from green to light green with maturity. Reddish streaks may also appear at the stem end of certain deep green-skinned cultivars such as ‘Fuerte’ when the fruit becomes mature. The area of the stem nearest the fruit changes from a green to brown or black color when the fruit is mature and ready for harvest (Sotto 2000). An internal fruit characteristic indicative of harvest maturity is the color of the seed coat. Internally, the seed coat of mature avocado fruit typically turns brown when the fruit is sufficiently mature for harvest (Blumenfeld and Gazit 1971; Lee 1981a). In addition, very mature ‘Hass’ avocado fruit have been reported to develop darker color not associated with ripening while still hanging on tree (Hofman and Jobin-Décor 1999; Cox et al. 2004). This indicated that skin color can therefore be used as an indicator of maturity stage for late-hanging ‘Hass’ avocado fruit. Furthermore, dark-skinned avocados such as ‘Hass’ and ‘Gem’ change color from green to purplish-black during ripening (Guerrero and Benavides 2014). The fact that exocarp turns purplish-black as these avocado varieties ripen postharvest has made skin color change an indicator of ripeness. Firmness as a Measure of Maturity and Ripeness Fruit firmness is one of the reliable and universally accepted methods for assessing avocado fruit maturity and ripeness. Earlier studies established that firmness correlates well with maturity measured by other parameters such as oil content and eating quality (Köhne et al. 1998; White et al. 1999). Firmness readings of avocado fruit change gradually as the avocado fruit matures or ripens and therefore are indicative of both its maturity and postharvest ripening. Firmness as a maturity parameter declines slowly as the fruit matures. When used as a measure of postharvest ripening stage, firmness initially declines at a moderate rate; then, the rate of decline increases and falls close to zero at full ripening. Avocado consumers are capable of distinguishing a soft ready-to-eat avocado fruit from an unripened one but cannot distinguish between firmness of fruit at different stages of maturity. Therefore, there was a need for a reliable method, supported by appropriate sensors, for the non-destructive measurement of avocado fruit firmness for maturity and ripeness. The demand for consistent supply of high-quality avocado, especially in ripe and ready outlets, has spurred the need to develop a reliable, rapid, non-destructive, non-invasive technique for measuring fruit ripeness. Several techniques for detection and measurement of fruit avocado maturity and ripeness

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status have been suggested in the literature and used commercially. A critical step in the ripening and pre-packing chain is the grading of fruit into firmness categories using techniques such as gentle hand squeezing of the fruit, firmometer (Swarts 1981), puncture tests using Effegi probes (Arpaia et al. 1987) and conical probes (Meir et al. 1995), Hall’s Avoscan (Kruger et al. 2000), Sinclair non-destructive firmness tester (Valero et al. 2007), Aweta or Autoline acoustic firmness sensor, and Aweta impact method firmness tester (Woolf et al. 2013). The Firmometer originally developed in South Africa measures by means of a contact that is pressed onto an avocado by a 300-g weight for duration of 10 s (Swarts 1981). The scale ranges from 0 (hard) to 120 (soft). The handheld densimeter on the other hand is a non-destructive instrument that measures firmness by means of a small metal ball pressed onto the fruit and a reading of 100 (hard) to 0 shore (soft) is recorded (Köhne et al. 1998). A study by White et al. (1999) showed that the Firmometer had the closest relationship with hand firmness test (R2 = 0.93) across the entire firmness range. The authors also reported the use of a 200-g weight rather than a 300-g weight on the Firmometer to allow greater measurement sensitivity of softer fruit.

this point, the flesh begins to accumulate oil and this may explain why cessation of sugar accumulation. These results and those reported by Tesfay et al. (2012a, b) suggest the potential of using C7 sugars and total soluble sugars as a method for quantifying maturity of avocado fruit. However, considering that sugar accumulation stops after reaching minimum maturity, the concentration of this chemical parameter cannot be used commercially to quantify maturity beyond minimum maturity stage of 20.8 % DM. In addition, analysis of individual sugar concentrations requires time-consuming sample preparation and analysis using high-performance liquid tomography, hence not practical in commercial setups. The industry demand for a simple analytical method to determine sugar content has led to the proposal to use total soluble solids (TSS) as a measure of fruit sweetness and maturity (Özdemir et al. 2009). These authors hypothesized that TSS would accumulate as the fruit grows; however, the result from their study showed no consistent trend in how TSS changes over the growing period. Although several studies in the literature have reported TSS of as a quality parameter (Özdemir et al. 2009; Vinha et al. 2013), it is not a reliable maturity index for avocado fruit.

Sugar Concentrations

Analytical Methods for Measuring Maturity Sugar content is the most important quality parameter used to indicate sweetness of many horticultural products, in laboratories for research and by industry to determine marketing standards (Magwaza and Opara 2015). Non-structural sugars (sucrose, glucose, and fructose) and sugar alcohols (e.g., sorbitol and manitol) constitute the majority (approximately 85 %) of total soluble solids in many fruits (Magwaza and Opara 2015). In avocado, the disaccharide sucrose, and its component hexoses, fructose, glucose, and seven carbon (C7) sugars, D-mannoheptulose and its reduced form polyol, and perseitol are dominant sugars constituting at least 98 % of the fruit total soluble sugars (Liu et al. 1999a, b; Cowan 2004). The C7 sugars have been found to comprise more than half of avocado fruit total soluble sugars, with the balance being accounted for by the more common hexose sugars, glucose, and fructose (Liu et al. 1999b). As a result, avocado fruit maturity has recently been related to the concentration of mesocarp C7 sugars. Liu et al. (1999b) showed that as the avocado fruit grew in size, the flesh tissue accumulated proportionally higher levels of total soluble solids coinciding with the increase in dry weight. The authors also showed that the C7 sugars, D-mannoheptulose and perseitol, were the major nonstructural carbohydrates present in fruit tissues of ‘Hass’ avocado and as such appeared to play an essential role in fruit growth. Total soluble sugars was a major biomass component for the young fruit during the early rapid development stage and stopped accumulating in fruit tissues when fruit reached minimum maturity stage (20.8 % DM for ‘Hass’ avocado). At

Quality and maturity evaluation of horticultural products has become an increasingly important consideration in market or commercial viability, and systems for such evaluations are now demanded by customers, including distributors and retailers (Wedding et al. 2011a, b). Assessment of avocado fruit quality is a challenging task because quality is a combination of different attributes. Some of these attributes are visual appearance, texture, nutritional value, dry matter, oil content, and flavor. Most of these fruit quality parameters can be determined either by humans in a taste panel exercise, destructive physico-chemical analysis, or non-destructive methods (Kader et al. 2002). In most commercial avocado operations, quality determination is done mechanically; hence, the method to distinguish quality between fruit samples is based on one or a few single attributes, such as size, texture, and maturity. This way of differentiation between samples draws final conclusion on the produce quality based on limited information. Over the years, many destructive and non-destructive methods and associated instruments have been developed to measure quality and quality-related attributes of avocado fruit. Destructive Methods Method for Quantifying oil Content Different researchers in the literature report results for oil determined using differed analytical methods and extraction

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solvents (Table 2). The most accurate method adopted for oil determination was to dry the mesocarp and then to employ the solvent extraction method to measure the oil content. In this method, the oil is extracted from the known mass of dry mesocarp sample using petroleum ether or hexane as a solvent. After which, the oil percentage is calculated using Eq. 1, described by Carvalho et al. (2014). Oil content ðw=wÞ ¼

drymatter ð%Þ x oil weight ðgÞ dry pulp weight ðgÞ

ð1Þ

Petroleum ether or hexane extraction of dried tissue in a Soxhlet extractor is the standard method for analyzing oil content, but this method is too slow to be generally useful to the avocado industry (Carvalho et al. 2014). According to Lewis et al. (1978), using petroleum ether for an extraction time of 4 hours yielded an oil content of 74–75 %. However, hexane has become the solvent of choice for solvent extraction because of high stability of the solvent, low evaporation loss, low corrosiveness, little greasing residue, and better odor and flavor of the extracted product (Meyer and Terry 2008). These authors compared Soxhlet extraction method with hexane extraction method. They reported that an average oil yield using the Soxhlet technique was significantly higher than that obtained by homogenization with hexane. A possible explanation for these differences may be that Soxhlet extracted in a more exhaustive manner than with hexane, therefore recovering more non-targeted compounds other than triglycerides such as gums, waxes, and non-saponifiable material (viz. sterols, pigments, and hydrocarbons), resulting in a higher overall oil value (Meyer and Terry 2008). Solvent extraction has several drawbacks, including high capital equipment cost, operational expenditure, and concerns for environmental pollution. In commercial operations, mesocarp oil content of avocado fruit is determined by means of shorter refractometric which involves Halowax oil (monochloronaphthalene) as a solvent Table 2

(Lee 1981a, b). Refractive index technique for extracting and measuring oil content is described in detail by Lee (1981a, b) and later by Gaete-Garretón et al. (2005). Although the method is useful, it has some serious limitations, including the inconsistent refractive index of Halowax oil, temperaturedependent readings, difficulty in reading the small scale, many procedural transfers, and expensive equipment make the procedure unsatisfactory for most growers (Lee 1981a). Furthermore, Halowax oil (chloronaphthalene) is a suspected carcinogen and will not be available in the future. While Soxhlet extraction is accurate, it is time-consuming and requires operation at very high temperatures (Meyer and Terry 2008). The refractive index method, on the other hand, although accepted as an international standard, its accuracy is questionable. A statistical analysis of the regression correlation between the standard methods of Soxhlet and refractive index performed by Kosenthal et al. (1985) showed a correlation coefficient of 0.90, indicating that accuracies of these methods are not the same. The inaccuracy of the refractive index was confirmed by Barry et al. (1983) who showed that oil content measured non-destructively using NMR was closely related to Soxhlet extracted oil (r = 0.98) than that measured by the refractive index method (r = 0.85). Limitations and the destructive nature of refractive index and Soxhlet extraction methods for determining avocado oil content demonstrate the need for the development of a reliable and non-destructive technique for measuring maturity index. Methods for Quantifying dry Matter and Moisture Contents Dry matter or moisture content is the easiest and relatively accurate maturity test for avocado fruit maturity. Quick reference tables of how different researchers in various parts of the world determined DM and MC content are provided in Tables 3 and 4, respectively. DM or MC content at which harvest takes place will depend on variety and the intended market. Maturity standard of an orchard or consignment is

A summary of different analytical method for determining mesocarp oil content (%) as a maturity parameter of various avocado varieties

Cultivar

Method of oil determination

Oil content (%)

Reference

Baco, Fuerte, Hass, Pinkerton, Zutano

Soxhlet extraction

7.5–14.5 %

Lee and Young (1983); Lee et al. (1983)

Fuerte Fuerte Fuerte Hass Hass Hass

Refractive index technique Soxhlet extraction using hexane Petroleum ether extraction

10.5–18.8 % 12.0–21.0 % 14.4–20.2 % 11.0–19.6 % 15.3–19.0 % 18.4–20.4 %

Mizrach and Flitsanov (1999); Mizrach (2000) Kaiser and Wolstenholme (1994) Ozdemir and Topuz (2004)

Hexane extraction Soxhlet extraction using hexane

Meyer and Terry (2010) Villa-Rodríguez et al. (2011)

Hass

Hexane extraction

42.8–71.3 %

Landahl et al. (2009)

Hass

Soxhlet using petroleum ether

0.0–25.0 %

Carvalho et al. (2014)

Sharwil

Petroleum ether extraction

18.0–28.0 %

Chen et al. (2009)

Food Bioprocess Technol Table 3

A summary of different drying methods used to determine dry matter content as a maturity parameter of various avocado varieties

Cultivar

Drying method

Drying temp.

Drying time

Levels (%)

Reference

Baco, Fuerte, Hass, Pinkerton, Zutano

Microwave oven

High power

Constant mass (±15 min)

15–26 %

Lee and Young (1983); Lee et al. (1983)

Fuerte

Forced air oven

105 °C

3h

25–36 %

Mizrach and Flitsanov (1999); Mizrach (2000)

Fuerte Fuerte, Gem, Hass, Gwen, Lamb, Pinkerton, Reed Hass

Oven Microwave oven

70 °C 50 % power of 1000 watt 65 °C

Constant mass 40 min

24–30 % 22–34 %

Ozdemir and Topuz (2004) Arpaia et al. (2001)

20–45 %

Clark et al. (2003, 2007)

Forced air oven

Hass

Freeze-dried

55 °C

Until constant mass (24–48 h) 7 days

21–33 %

Donetti and Terry (2014)

Hass

Freeze-dried

55 °C

7 days

23–39 %

Landahl et al. (2009)

Hass

Oven

65 °C

2–3 days

22–26 %

Gamble et al. (2010)

Hass

Oven

90 °C

24 h

NS

Pedreschi et al. (2014)

Hass

Freeze-dried

55 °C

7 days

NS

Meyer and Terry (2010)

Hass

Oven

70 °C

Until constant mass

32–37 %

Villa-Rodríguez et al. (2011)

Hass

Microwave oven

NS

Until constant mass

30–37 %

Wang et al. (2012)

Hass

Fan-forced oven

60–65 °C

Until constant mass (±72 h)

14–40 %

Wedding et al. (2011a, b, 2013)

Hass

Oven

60 °C

Until constant mass

10–40 %

Carvalho et al. (2014)

Sharwil

Oven

70 °C

Until constant mass (48 h)

29–38 %

Chen et al. (2009)

NS not specified

usually based on an average DM or MC over a sample of at least 10 fruits (Arpaia et al. 2001). DM and MC are measured by taking a known weight of avocado flesh tissue and drying it to a point where no further weight loss occurs with DM and MC calculated using Eqs. 2 and 3 (Arpaia et al. 2001), respectively. The test can be done using a conventional oven, domestic food dehydrator, freeze drying, or a microwave (although this can be less reliable because of the risk of burning the samples). Due to time-saving advantages, most pack houses use a microwave oven-based drying method (Dodd et al. 2010). When using an oven, DM is usually determined according to Lee et al. (1983), where fresh samples taken from the fruit tissue are weighed in a petri dish and dried in an oven at 60 °C for 2–3 days (or until constant mass is reached) and reweighed. Mizrach and Flitsanov (1999) and Mizrach (2000) reduced the time for drying avocado samples from 3 days to 3 h by operating the Table 4

oven at 105 °C (refer to Table 3). However, it should be noted that at temperatures higher than 70 °C, there is a risk of burning the sample and oil, resulting in incorrect DM content. To reduce this effect, the oven for drying avocado fruit should be operated at a maximum of 70 °C. According to Sippel et al. (1995), freeze drying and conventional oven drying gave a very good correlation (R2 = 0.96) enabling the former to be reliably used as an alternative for future analysis of DM and MC. Freeze drying can be considered as a reliable method because there is no risk of burning oil or samples during drying. A new fast technique and an efficient tool based on microwave oven drying was evaluated and described by Arpaia et al. (2001). The microwave oven is a relatively faster method for the drying avocado samples, with drying time ranging between 15 and 40 min, depending on the microwave settings and model. This method allowed dry matter testing

A summary of different drying methods used to determine moisture content as a maturity parameter of avocado fruit

Cultivar

Drying method

Drying temp.

Drying time

Levels (%)

Reference

Fuerte and Hass seeds

Oven

75 °C

Until constant mass (48 to 72 h)

60–80 %

Kalala et al. (2005)

Hass

Freeze-dried

NS

7 days

63–72 %

Blakey et al. (2009)

Hass Hass and Fuerte

Forced air oven Forced air oven

70 °C 70 °C

Until constant mass Until constant mass

78–65 % 65–71 %

Kruger and Magwaza (2012) Kruger et al. (2008)

Pinkerton

Forced air oven

70 °C

Until constant mass

88–72 %

Magwaza et al. (2009)

NS not specified

Food Bioprocess Technol

to be standardized in terms of the sampling method, the sampling location within the fruit, and the quantity of flesh to be used. Arpaia et al. (2001) conducted another study to compare positional effects on dry matter using a longitudinal wedge of fruit tissue as compared to an equatorial sample and reported that both samples provided similar results. However, the core sampling method is the most preferable one as it allows for more rapid fruit sampling and is safer and less cumbersome since it requires minimal use of sharp implements. In addition, the samples obtained from the coring method can be better used for dry matter determination, fruit nutrient analysis, and other purposes. CA  100 BA ðB  AÞ  ðC  AÞ  100 Moisture content ð%Þ ¼ BA

Dry matter ð%Þ ¼

ð2Þ ð3Þ

where A is mass of petri dish, B is total mass of fresh sample and petri dish, and C is total mass of dry sample and petri dish. Non-destructive Methods For the determination of fruit quality attributes related to avocado fruit maturity and postharvest quality, conventional destructive analytical methods are generally adopted. However, conventional methods and techniques are sometimes expensive, laborious, and invasive, and some are possible only in laboratories since specific instruments are required for these purposes. One of the major difficulties with fruit maturity studies is the strong heterogeneity in fruit physiological parameters, which culminates in uneven ripening of harvested avocado fruit following storage. Moreover, complicated sample preprocessing is usually required and causes difficulty for real-time and on-line monitoring in commercial packing houses. The increasing consumer awareness of quality standards are leading to a strong drive for improved and consistent supply of quality fruit and vegetables. To satisfy this continually increasing consumer demand for quality produce, the postharvest sector of the fresh fruit industry is dynamic (Aleixos et al. 2002). The past two decades saw a large number of instrumental sensors for real-time and non-destructive testing of fruit quality parameters. Development of these automated technologies has enabled commercially feasible non-invasive methods for estimating internal quality attributes of horticultural products. Non-destructive techniques detect fruit quality parameters due to differences in optical characteristics, sound, and density. Several non-destructive methods for maturity evaluation of avocado fruit have been developed, including NMR (Chen et al. 1993), vibration energy (Peleg et al. 1990), acoustic sensors (Galili et al. 1998), Vis/NIRS (Schmilovitch et al. 2001; Clark et al. 2003), and ultrasonic excitation (Mizrach et al. 1996;

Flitsanov et al. 2000). Although several non-invasive techniques have been developed and tested, NMR and NIRS are leading candidates for the application to fruit and vegetables. Vis/NIRS Vis/NIRS with suitable chemometric analysis has been used and shown to be a precise, rapid, and non-destructive alternative to destructive methods for providing non-visible information about comparative proportions of C–H, O–H, and N–H bonds (Wang and Paliwal 2007; Antonucci et al. 2011; Magwaza et al. 2012; 2013a; 2014a). Avocado fruit quality parameters such as DM, moisture, and oil content are based on organic molecules which contain C–H, O–H, C–O, and C–C bonds; hence, it is possible to use NIRS methods to quantify these parameters. For example, Schmilovitch et al. (2001) used a dispersive NIR spectrophotometer in reflectance mode to determine mesocarp dry matter content of ‘Ettinger’ and ‘Fuerte’ avocado cultivars in the range 1200–2400 nm. Table 5 presents an overview of research conducted in the recent years focusing on the quality prediction of avocado fruit using Vis/NIRS. As presented in Table 5, Vis/NIR spectroscopy has been used successfully to predict avocado fruit maturity parameters such as dry matter and moisture content. Different ranges of spectral wavelength have been used in calibration models to predict avocado fruit quality parameters. From statistical data (considerably higher R2 and lower error of regression) of each case cited in Table 5, it can be concluded that Vis/NIRS is a suitable non-destructive instrument for quantifying avocado fruit maturity parameters. Although significant amount of research has been conducted on the use of Vis/NIRS and associated statistical regressions, no research is available reporting the use of this system for online prediction of avocado maturity standards and quality. Nevertheless, the high prediction accuracy for developed regression models encourages the continuation of this research field to be used for on-line maturity and quality prediction. Predictive results by (Schmilovitch et al. 2001) identified root mean square errors of prediction (RMSEP) for both ‘Ettinger’ and ‘Fuerte’ to be 0.9 and 1.3 %, respectively, over a 14–24 % DM range. A study by Clark et al. (2003) evaluated the use of NIRS and partial least squares regression chemometric methods to determine dry matter content of intact ‘Hass’ avocado fruit and reported significantly accurate prediction results (R2 = 0.88 and RMSEP = 1.8 %) over a range of 20–45 % DM. Using visible to short wave near infrared spectroscopy (300–1100 nm), Walsh et al. (2004) reported prediction results with correlation coefficient (r) of 0.89 and root mean square error of cross validation (RMSECV) of 1.14 %. Blakey et al. (2009) evaluated dry matter content and went further to determine water content and related these maturity parameters to ripening patterns. The preliminary results from this group indicated that NIRS could be used to separate

Food Bioprocess Technol Table 5

An overview of applications of visible to near infrared spectroscopy (Vis/NIRS) to measure maturity parameters in avocado fruit

Cultivar

Measured parameter

Spectrophotometer

Wavelength range

Hass

DM

Polychromatic/diode array spectrometer (Zeiss MMS1-NIR, Germany)

300–1140 nm R2 = 0.88 1.80 %

Hass

MC

Hass

DM

Accuracy

RMSEP Reference

R2 = 0.75 2.60 % NIRS6500 spectrophotometer (Foss NIRSystems, 400–2500 nm R2 = 0.92 NS Silver Spring, USA) Benchtop, Matrix-F, FT-NIR spectrophotometer 830–2500 nm R2 = 0.89 NS (Bruker Optics, Ettlingen, Germany)

NIRS6500 spectrophotometer (Foss NIRSystems, R2 = 0.85 2.49 % Silver Spring, USA) Hass MC 400–2500 nm R2 = 0.84 2.38 % Oil content R2 = 0.58 5.44 % Unspecified cultivar DM Ziess MMS1/NIR-enhanced spectrometer, 300–1100 nm R = 0.89 1.14 % Germany

Clark et al. (2003)

Blakey et al. (2009) Wedding et al. (2011a, b, c, 2013)

DM

Olarewaju (2015) Walsh et al. (2004)

DM mesocarp dry matter content, MC mesocarp moisture content, NS not specified

avocado fruit into broad groups with respect to DM, MC, and potential to ripen under commercial conditions. Wedding et al. (2010) evaluated the potential of Fourier transform NIR (F-TNIR) spectroscopy for non-destructive estimation of DM of intact avocado fruit and reported promising prediction statistics (R2p = 0.76 and RMSEP of 1.53 %) in the DM range between 19.4–34.2 %. In later studies, Wedding et al. (2011b) reported better prediction statistics for DM with (R2p ) of 0.93 and RMSEP of 1.48 %. Because of spectral differences due to the biological variability of samples from different orchards and season, Wedding et al. (2011a, c and 2013) tested model robustness and reported that models accurately predicted DM of samples from different locations and seasons. In addition, mid Infrared (MIR) spectroscopy in combination with multivariate statistical techniques (chemometrics) has also been successfully applied for quality control and adulterant detection of avocado fruit (Quiñones-Islas et al. 2013). Although, NIR and MIR spectroscopy have been tested to predict quality parameters of avocado fruit, there have been limited or no investigations using NIRS to determine avocado maturity based on oil content, which is considered the most reliable parameter related to avocado eating quality (Lee et al. 1983). These studies collectively indicate the potential of NIRS to assess maturity-related quality attributes of intact avocado fruit is well established in literature. However, very few studies addressed robustness of calibration models between seasons and across orchards locations, which is a critical issue and this active area of research (Wedding et al. 2013; Magwaza et al. 2014b, c). Hyperspectral Imaging Hyperspectral imaging system is a recent addition to techniques used to investigate changes in fruit quality parameters. This technique is used to gather spectral characteristics of fruit

and has shown to have good ability to correlate with changes in quality parameters. The principles of hyperspectral and multispectral imaging techniques have been discussed in several reviews (Maftoonazad and Ramaswamy 2006; Gowen et al., 2007; Cubero et al. 2011). Both these image analysis techniques have been used to assess quality parameters of fruit and vegetables. Measuring ripeness of tomato (Hahn 2002) defects of apples (Mehl et al. 2002), and sugar contents in melons (Tsuta et al. 2002; Sugiyama and Tsuta 2010)) are some examples of the application of hyperspectral observation in food systems. In avocado fruit, Girod et al. (2008) evaluated ‘Hass’ avocado maturity using hyperspectral imaging and reported prediction accuracy (R2) of 0.96 and RMSEP of 1.35 % DM (Table 6). In a later study, Maftoonazad et al. (2011) used hyperspectral imaging to model quality changes during storage at different temperatures, in which multilayer artificial neural networks (ANN) were used to develop models. These authors reported that reflectance hyperspectral imaging combined with ANN models accurately predicted avocado quality-related physiological parameters such as respiration rate, firmness, color change, and weight loss with R2— values of up to 0.97. Despite the wide use and success of hyperspectral and multispectral imaging applications in other fruits and vegetables, according to our knowledge, this is the only study conducted to apply this technique to test avocado fruit quality parameters. Image Processing and Analysis The fact that avocado exocarp turns purplish-black as darkskinned varieties mature while still hanging and ripen has made skin color change an indicator of maturity and ripeness stage for late-hanging fruit. Color measurements are mostly performed by humans. However, this classification is subjective and takes time that could be used by farm personnel to

Food Bioprocess Technol Table 6

A summary of applications of hyperspectral imaging to quantify maturity and ripening related parameters of avocado fruit

Cultivar

Measured parameter

Data acquisition mode

Wavelength range

Accuracy

Reference

Hass

Respiration rate Firmness Total color difference Weight loss Dry matter content

Reflectance

350–2500 nm

Maftoonazad et al. (2011)

Reflectance

400–1000 nm

R2 R2 R2 R2 R2

Hass

perform other activities to achieve higher quality standards. Thus, an automated non-destructive system for classifying avocados would result in better utilization of resources and improve efficiency of packing houses. For this reason, image processing for evaluating avocado maturity and ripening has been proposed by Arzate-Vázquez et al. (2011) and Guerrero and Benavides (2014). For instance, Arzate-Vázquez et al. (2011) undertook a study to evaluate the ripening process of ‘Hass’ avocados during storage by nondestructive image processing method. The study followed the changes in image features during ripening by applying a computer vision system, extracting color and textural parameters. The accuracy of 87.90 % was obtained when image processing algorithm for selecting avocados was compared with the test set classified by expert panelists. A later study by Guerrero and Benavides (2014) compared image processing algorithm with the opinion of an expert humans, from the set of images obtained for ‘Hass’ avocados at different stages of maturity, viz. green, mature, and very mature. Based on the results, these authors reported an accuracy of 82.22 % in identifying green, mature, and very mature avocados. Image processing have been reported to have more advantages than other noninvasive techniques because it is cheap and easily adaptable to obtain measurements in a packing line, has high accuracy and good correlation with visual human inspection, and is very versatile because it allows to obtain a widespread number of features from a simple digital image (Wu and Sun 2013). Therefore, image processing is a valuable tool for supporting automated processes regarding non-destructive maturity assessment and classification of avocado fruit. Nuclear Magnetic Resonance Imaging NMR has been demonstrated to have the potential for the estimation of oil and dry matter content in avocados (Chen et al. 1993; Kim et al. 1999). Barry et al. (1983) showed a significant correlation (r = 0.98) between NMR-predicted oil content and that was measured using Soxhlet extraction method. Their study also showed that oil content measured by NMR was more closely related to Soxhlet extracted oil (r = 0.98) than that determined using refractive index method (r = 0.85). Chen et al. (1993, 1996) investigated the feasibility of using NMR imaging, spectroscopy, and relaxometry as

= = = = =

0.85 0.94 0.99 0.98 0.96

Girod et al. (2008)

indices of maturity. Although useful correlations were established between the signal intensities of the oil and water peaks in the frequency spectrum, it is unlikely that these peaks could be resolved at the low proton frequencies applicable to on-line situations. Useful correlations were also found between the water transverse and longitudinal relaxation times and percentage dry weight. In a study, Marigheto et al. (2005) developed a low-field NMR protocol suitable for on-line measurement of oil content in intact avocado. Although NMR has shown potential to measure oil content, the cost and challenges for in-line use in the sorting line means it is not currently a commercially viable application for high-volume, low-value items such as fruits and vegetables (Clark et al. 1997; 2003). Future designs of NMR equipment should consider the potential for in-line maturity testing in the packing line. Ultrasonic Measurement System Ultrasonic measurement system is one of the recent addition to the list of acoustical technologies and methods used for fruit quality evaluation during various stages of pre- and postharvest processes (Mizrach 2000). A quick reference table for application of ultrasonic methods to measure avocado fruit maturity is provided in Table 7. In a study by Mizrach and Flitsanov (1999), it was found that the dry material content of the avocado, measured destructively on the seventh day, correlated quite closely with the ultrasonic attenuation measured in the fruit on the same day (R = 0.813). In a later study, Mizrach (2000) developed a non-destructive ultrasonic measurement system for the assessment of some transmission parameters which might have quantitative relations with avocado fruit maturity and other quality-related attributes. This method has been used in several studies to measure ultrasonic wave attenuation of avocado fruit to assess fruit properties and to relate them to shelf life and maturity parameters (Mizrach and Flitsanov 1999; Mizrach et al. 1999; Mizrach 2000, 2008). For instance, Mizrach et al. (1999) performed a study to follow the changes in avocado fruit non-destructively during maturation on the tree. During this study, a nonlinear regression procedure was performed to relate variations in ultrasound attenuation and DM to growth time. An exponential expression was selected as the curve of best fit between

Food Bioprocess Technol Table 7

An overview of different applications of ultrasonic techniques to determine maturity parameters of avocado fruit

Cultivar

Measured Frequency Attenuation coefficient parameter (kHz) (dB/mm)

Velocity range Equation Accuracy Reference (m/s) type

Ettinger

DM

50

2.5–5.0 (increase with time) 200–400

Parabolic R = 0.99

Mizrach et al. (1999)

Fuerte

DM

50

2.5–5.0 (increase with time) 200–400

Parabolic R = 0.99

Mizrach et al. (1999)

2

Fuerte

DM

50

2.5–5.0 (increase with time) 200–400

Linear

R = 0.66 Mizrach and Flitsanov (1999)

Fuerte Fuerte

DM MC

50 NS

2.5–5.0 (increase with time) 300–400 NS 160–360

Linear Linear

R = 0.81 r = 0.84

NS

NS

Linear

R2 = 0.98 Gaete-Garreton et al. (2005)

Unspecified cultivar Firmness

NS

Mizrach and Flitsanov (1995a, b) Self et al. (1994)

DM mesocarp dry matter content, MC mesocarp moisture content, NS not specified, R2 coefficient of determination, R or r correlation coefficient

ultrasonic attenuation and mesocarp DM content of ‘Ettinger’ fruit during growth. These studies suggested that DM content in avocado mesocarp could be evaluated by ultrasonic attenuation measurement during fruit growth and that the harvest time could be determined thereby.

Prospects for Future Research The importance of quality in the horticultural industry has grown steadily in the past few years, due to increasing market saturation and competitive pressure from globalization of markets. As a result, the quality of supplied food products has become the most important decisive differentiating features in global customer-supplier relationship (Lehnert et al. 2014; Schütz et al. 2014). In avocado fruit, quality is evaluated by size, estimated oil content (or dry matter), absence of defects, and firmness (OECD 2004; Landahl et al. 2009). However, these quality-related parameters are measured separately using different techniques. Most of the previous investigations of non-destructive technologies for evaluating avocado fruit maturity status have focused on assessing specific quality attributes and do not integrate quantitative assessment of external and internal quality attributes in one system. Trends in analytical chemistry are towards simple and less time-consuming analytical methods. Therefore, objective for future research should also include development of non-destructive methods for integrated non-destructive measurements of external and internal composition of avocado fruit maturity and quality attributes. Although non-destructive technologies for measuring avocado maturity parameters, such as Vis/NIRS, have been significantly evaluated, there is still a need for continued development of models for predicting DM, MC, oil content, and fatty acid composition. One of the challenges hindering commercial applications of Vis/NIRS technology is the inherent variability in avocado fruit and the lack of clarity over the parameter to test. Although researchers have applied different Vis/NIRS analytical frameworks and chemometrics to improve prediction, it is conceivable that the choice of these

parameters could affect both model accuracy and robustness and this deserves attention for future research. Due to multiple wavelengths required for accurate results and optimal penetration depth, Vis/NIRS has not reached the speed of scanning required in a packing line which could be up to 10 fruits per second (Magwaza et al. 2012). Furthermore, the roughness of some avocado fruit cultivars such as ‘Hass’ may cause wavelength scatter and specular reflection before penetration, reducing accuracy. Previous studies (Wedding et al., 2013) showed that validating using large sample size improves accuracy. Therefore, future model calibration and validation should have large sample size. The main goal of non-destructive technology research in avocado fruit is to develop most reliable and non-invasive analytical methods. To reach this goal, various techniques have been proposed and some of them are still under investigation, such as hyperspectral imaging and image processing. As mentioned earlier, hyperspectral imaging and associated image analysis technology can provide more details and more precise determination of avocado maturity. High predictive performance of calibration models of hyperspectral imaging and image analysis demonstrated high-level accuracy. However, before these techniques can be successfully implemented on a commercial sorting line, validation across avocado varieties and different seasons is advisable. Flotation of fruits and vegetables in water has been demonstrated by the industry to be a feasible alternative for segregating fresh produce based on internal quality parameters such as DM (or MC) and soluble solids content. This method has used in experimental setups on fruit including feijoa, grape, mango, and kiwifruit (Jacobi et al. 1995; Clark et al. 2005; 2007). The citrus and potato industry represents known examples of commercial application of density grading (Clark et al. 2007; Londhe et al. 2013). The potential of extending density grading to other crops is appealing, especially for avocado where there may be benefit of reducing the variability within harvested produce to improve storage life and reduce analytical time for determining maturity based on DM or MC. However, unlike other fruits,

Food Bioprocess Technol

sorting avocado using floatation method poses some problems. The obvious one is that avocado fruit contains seeds of variable sizes with specific gravity greater than that of water while mesocarp, also of variable sizes, containing oil-storage cells. Avocado oil in the pulp has a specific gravity of approximately 920 kg m−3 (Bora et al. 2001). Therefore, flesh density technically should decrease with increasing oil content during growth and maturation, showing a greater tendency to float. According to Clark et al. (2007), the seed and flesh represent opposing buoyancy forces of differing magnitude making it difficult to predict the success of a segregation technique based on flotation, explaining why flotation has not been implemented in commercial operations. Besides measuring maturity towards the end of the season with the intent to identify industry-specified levels, future research should focus on rheological characteristics during fruit growth to develop reliable maturity standards. Although the industry has developed maturity indices for different avocadoproducing regions of the world, climate change over the years may have influenced effectiveness of maturity parameters. This points out that avocado maturity indices and instruments for quantifying these parameters are constantly evolving and so should our approach for analyzing these parameters. With a view to adapting orchards to climate change, moving into the future, there might be a need to re-evaluate its impact on the performance of various currently used avocado maturity parameters. There has been increased scientific awareness of the influence of preharvest factors on the rate of fruit maturity, postharvest quality, and health benefits associated with consuming the fruit (Donetti and Terry 2014). However, due to the limited number of studies comparing performance of analytical for determining maturity properties of avocado fruit across different growing conditions, future research should focus on this. In addition, there is still a lack of literature linking avocado maturity with bioactive compounds. The assessment of such biochemical compounds and correlation with fruit maturity constitute the principal framework for future research which could lead to reliable avocado maturity parameters.

Conclusions Although research in the early years of the twentieth century demonstrated that eating quality of avocado fruit improved over the season as the oil content increases, measuring oil content is time-consuming and difficult. Due to the correlation between oil and dry matter content or moisture content, mesocarp DM and MC are internationally accepted and used commercially as indirect, faster, and low-technology alternatives for assessing avocado oil content and maturity for different cultivars. Although the initial adoption of dry matter content was a great simplification of the refractometric method

utilized for oil determination, current methods for determining dry matter are still somewhat cumbersome, time-consuming, and require repetitive fruit cutting and use of sharp implements. The need for non-destructive analytical methods is obvious, and considerable amount of research has been conducted to develop method for non-invasive determination of avocado maturity parameters. Despite the considerable amount of research reporting feasibility of non-destructive methods for detecting maturity of avocado fruit in recent years, very few of these techniques have been applied in commercial operations. Although several techniques for nondestructive detection and prediction of avocado fruit maturity parameters have been developed, Vis/NIRS is evidently the most advanced with regard to instrumentation, applications, accessories, and chemometric software packages. Vis/NIRS is rapid and the equipment costs are lower than NMR, making it a commercially feasible alternative for maturity determination. However, Vis/NIRS is not as advanced with avocados as it with other fruit industries such as apples, peaches, and pears where spectrophotometers have been implemented in packing houses. This review of literature showed that the changes in dry matter, moisture, and oil content during avocado fruit growth correlate well with ultrasonic attenuation measurements. This relationship indicates that ultrasonic instruments may become usable in non-destructive determination of avocado fruit dry matter, moisture, and oil contents and the precise determination of the harvest time.

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