Because of large variability in fruit maturity at harvest and subsequent eating quality, delivering consistency in fresh pear products remains a significant challenge to the industry (Kupferman et al., 2010). As such, a current research priority is to advance the understanding and assessment of maturity and quality indices to strengthen product homogeneity and consumer acceptance (Pacific Northwest Pear Research Committee, 2016). One such measure that has recently garnered interest in the tree fruit industry is that of DM—the sum of soluble (sugar) and insoluble (starch) carbohydrates, proteins, minerals, and other compounds (cell walls, organic acids, fibers, etc.) that accumulate in a fruit throughout its development on the tree from the metabolization of photosynthates (Suni et al., 2000). It is defined here as the ratio of fruit dry weight to fresh weight and expressed as a percentage.
DM can be distinguished from established maturity and quality indices such as color, firmness, and SSC in that it accounts for starch that later metabolizes to soluble sugars during the ripening process. Measured at or before harvest, it can be used to predict future internal quality following extended periods of cold storage as has been shown in apples (McGlone et al., 2003), kiwifruit (Crisosto et al., 2012), and mangoes (Subedi et al., 2007, 2010).
DM has also been suggested to be a reliable indicator of eating quality. In “Royal Gala” apple, Palmer et al. (2010) demonstrated significant increases in consumer liking, consumer acceptance, and purchase likelihood of high DM fruits compared with low DM fruits (DM in this case referred to as DM concentration and presented as g·kg−1). Similar relationships have since been characterized for sweet cherries and consumer acceptance (Escribano et al., 2017), avocados and consumer liking and purchase intent (Gamble et al., 2010), and kiwifruit and consumer acceptance, liking, and purchase intent (Crisosto et al., 2012; Harker et al., 2009; Jaeger et al., 2011).
Although a promising quality indictor, DM (and SSC) is traditionally a destructive measurement, requiring time and effort to be evaluated while resulting in loss of product, with both of these factors often promoting small sample sizes for testing. In the particular case of European pears (cv. Bartlett, d’Anjou, Bosc, etc.), which demonstrate erratic postharvest ripening patterns and temperature sensitivities during storage, the assessment of DM and SSC rapidly and nondestructively would greatly advance the ability to create a more homogenous product that can consistently satisfy the expectations of the consumer.
Near-IR (NIR, 700–2500 nm) spectroscopy, after decades of research and application, is now becoming equipped to measure these properties nondestructively with ever-increasing accuracy and efficiency. The NIR method is grounded in the principle that many compounds express unique absorbance spectra in the IR range, to which corresponding combination and overtone absorbance bands are found in the visible and NIR region. Measuring these spectra using various reflectance, interactance, or transmission-based methods, NIR instrumentation is able to predict the relative composition of compounds in a tissue that exhibit identifying absorbance characteristics in the NIR range (dos Santos et al., 2013; Nicolai et al., 2007; Wang et al., 2015).
Spectrochemical analysis of sugar (sucrose, glucose, and fructose) reveals numerous absorption bands in the shortwave NIR range related to the O–H and C–H groups of these molecules (Golic et al., 2003). Water is also easily detected in this range. Thus, tissues high in water and carbohydrate content such as thin-peel fruit are ideal candidates for assessment of DM and SSC using NIR spectroscopy (Saranwong and Kawano, 2007).
Although many studies have approached DM prediction using NIR, namely, in apple (McGlone et al., 2002a, 2003; Palmer et al., 2010; Travers et al., 2014a), kiwifruit (McGlone and Kawano, 1998; McGlone et al., 2002b, 2007; Osborne et al., 1996); and mango (Anderson et al., 2017), few have addressed pear (Travers et al., 2014b). To our knowledge, no other study has investigated DM and SSC prediction by NIR spectroscopy in d’Anjou or Bartlett pears, the two leading cultivars in North America. Here, we evaluated whether NIR spectroscopy could be used as a reliable predictor of postharvest DM and SSC in d’Anjou and Bartlett pears using a commercially available and hand-held NIR spectrometer.
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