Postharvest Biology and Technology 128 (2017) 121–129 Contents lists available at ScienceDirect Postharvest Biology and Technology journal homepage: www.elsevier.com/locate/postharvbio Spectrometric techniques for elemental profile analysis associated with bitter pit in apples a a a,b a Carlos Espinoza Zúñiga , Sanaz Jarolmasjed , Rajeev Sinha , Chongyuan Zhang , c,d c,d a,b, Lee Kalcsits , Amit Dhingra , Sindhuja Sankaran * a Department of Biological Systems Engineering, Washington State University, Pullman, WA, USA b Center for Precision and Automated Agricultural Systems, Department of Biological Systems Engineering, IAREC, Washington State University, Prosser, WA, USA c Department of Horticulture, Washington State University, Pullman, WA, USA d Tree Fruit Research and Extension Center, Washington State University, Wenatchee, WA, USA A R T I C L E I N F O A B S T R A C T Article history: ‘ ’ ‘ ’ ‘ ’ Received 6 December 2016 Bitter pit and healthy Honeycrisp , Golden Delicious , and Granny Smith apples were collected from Received in revised form 20 February 2017 three commercial orchards. Apples were scanned using Fourier transform infrared (FTIR) and X-ray Accepted 21 February 2017 fluorescence (XRF) spectrometers to associate the elemental profile with bitter pit occurrence in apples. Available online xxx The FTIR spectra were acquired from apple peel and flesh; while XRF spectra were acquired from the apple surface (peel). Destructive elemental analysis was also performed to estimate calcium, magnesium, Keywords: and potassium concentrations in the apples. There were significant differences between healthy and Apple disorder bitter pit affected apples in calcium, magnesium, and potassium concentrations, in addition to Support vector machine magnesium/calcium and potassium/calcium ratios (5% level of significance). Peak analysis of FTIR spectra FTIR spectrometry of prepared standards indicated the possible spectral regions associated with calcium content as 1150– XRF analyzer À1 1450 cm . Two different classification models (support vector machine, SVM and soft independent modeling of class analogy, SIMCA) were used to classify healthy and bitter pit affected apples using FTIR spectral signatures. FTIR spectra were able to predict bitter pit incidence in apples with higher classification accuracy using peel tissue (92%) than using flesh tissue with SVM model. The XRF technique could determine bitter pit incidence in apples and semi-quantitative analysis using XRF data was in agreement with the elemental analysis. FTIR and XRF spectrometric techniques are rapid methods that can be used for elemental profile analysis in apples. These techniques can serve as potential prediction tools for elemental profile analysis to detect bitter pit in apples. © 2017 Elsevier B.V. All rights reserved. 1. Introduction et al. (2010) also reported a high pectin methylesterase expression in cells of the calyx-end of bitter pit affected ‘Granny Smith’ apples. Bitter pit is a physiological disorder characterized by a localized Deesterification of pectins by pectin methylesterase in the cortical depression in the apple flesh. These depressions are more tissue decreased Ca in the apoplastic region of the cells, resulting in commonly located in the distal portion of the fruit (Amarante a higher membrane permeability and the collapse of the cells in et al., 2013). Although the specific reason associated with this bitter pit affected apples. However, Ca concentration by itself may disorder development is unknown, there have been several not be sufficient to explain bitter pit occurrence. In ‘Fuji’ apples, hypotheses presented to explain the occurrence of bitter pit. For Amarante et al. (2013) suggested that high magnesium (Mg)/Ca example, deficiency in the total calcium (Ca) concentration in ratio is a factor contributing to bitter pit occurrence based on distal bitter pit affected fruit has been reported (Ferguson and Watkins, apple peel sampling. de Freitas et al. (2010) also found that 1983, 1989; Amarante et al., 2006; Sharma et al., 2012). de Freitas [Mg + Potassium (K)]/Ca ratio was associated to bitter pit incidence in ‘Granny Smith’ apples. Miqueloto et al. (2011) studied the elemental concentrations related to bitter pit occurrence in apples and found the same relationship in ‘Fuji’ and ‘Catarina’ apples * Corresponding author at: Department of Biological Systems Engineering, affected by bitter pit. In addition, [K + Mg + Nitrogen (N)]/Ca ratio in Washington State University, Pullman, WA, USA. the peel and flesh of the same cultivars was also proportional to E-mail address: [email protected] (S. Sankaran). http://dx.doi.org/10.1016/j.postharvbio.2017.02.009 0925-5214/© 2017 Elsevier B.V. All rights reserved. 122 C.E. Zúñiga et al. / Postharvest Biology and Technology 128 (2017) 121–129 Table 1 bitter pit occurrences. It was proposed that the bitter pit Sample size from each location during first (2014) and second season (2015). occurrence may be induced by K and Mg competition with Ca for binding sites in the plasma membrane surface, resulting in the Season Location Cultivar Number of samples breakdown of the plasma membrane and probably the collapse of Healthy Bitter pit the cell (de Freitas et al., 2010; Miqueloto et al., 2011). 2014 Location 1 Honeycrisp 20 20 Bitter pit incidence leads to economic loss affecting apple Golden Delicious 20 20 producers. Therefore, early detection of bitter pit affected apples Granny Smith 10 10 would be helpful in improving production management practices Location 2 Honeycrisp 20 20 2015 Location 1 Golden Delicious 29 31 or economic decisions. The Fourier transform infrared (FTIR) Location 2 Honeycrisp 24 36 spectrometer determines the chemical composition of the tissues Golden Delicious 30 30 based on the absorption of infrared radiation. The absorption is Location 3 Honeycrisp 29 31 associated with the change of energy of specific molecules at a certain frequency (Perkins, 1986; Shiroma and Rodriguez-Saona, 2009; Oliveira et al., 2014). This technique has been used in food and agriculture research (Jaiswal et al., 2015; Al-Holy et al., 2015). Similarly, x-ray fluorescence (XRF) spectrometer determines the location 3 developed bitter pit. Table 1 describes the number of chemical composition of different materials (Craig et al., 2007; samples selected from each location. Peinado et al., 2010; Leopold et al., 2011; McLaren et al., 2012; Kalcsits, 2016). This method measures the energy of photons 2.2. XRF analysis dissipated by those materials after being radiated by an X-ray source (Peinado et al., 2010). These energy signatures are XRF measurements were performed at Washington State associated with specific elements. Therefore, FTIR and XRF University Tree Fruit Research and Extension Center, using an spectrometric techniques can be useful tools that could provide AXS Tracer 3 V portable handheld X-ray fluorometer (Bruker rapid elemental analysis in apples and predict bitter pit Elemental, Kennewick, Washington, USA). The device is equipped susceptibility in fruit. With this, the objective of this research with a Rhodium tube that serves as an X-ray source. The beam was to evaluate the FTIR and XRF spectrometric methods as a rapid 2 produced scans in an area of 0.5 cm with 0.1 cm depth. Each and precise technique to determine the elemental profile sample was scanned on three sides. After scanning the samples, the associated with bitter pit occurrence in different types of apples. data was processed using Artax software (Bruker Corporation, Billerica, Massachusetts, USA) and photon counts of K and Ca were 2. Materials and methods normalized against the photon counts for the rhodium X-ray tube. Statistical analysis was applied similar to the chemical analysis of 2.1. Fruit samples the fruit. More information about the equipment can be found in Kalcsits (2016). ‘Honeycrisp’, ‘Granny Smith’, and ‘Golden Delicious’ apples were harvested at commercial maturity. In 2014, apples were 2.3. FTIR analysis collected from two different orchard locations, Prescott (location 1) and Burbank (location 2), Washington, USA. In both field sites, FTIR spectral analysis was performed in the Analytical trees were eight years-old and grafted on M-9-T337 rootstock and Chemistry Laboratory, Washington State University, Pullman. For canopies planted with "V" trellis system. ‘Golden Delicious’ and sample analysis, three thin layers of peel and flesh were cut from ‘Granny Smith’ bitter pit samples were not found at location 2. In each apple and measured with an FTIR spectrometer (Shimadzu addition to this primary data, new samples were collected in 2015, IRPrestige, Kyoto, Japan). The tissue spectrum was acquired using for further validation of findings achieved using FTIR spectrometry. the device settings at Happ-Genzel apodization with 32 scans per À À In 2015, ‘Honeycrisp’ and ‘Golden Delicious’ apples (2 locations/ 1 1 sample, 4 cm resolution, and 600–4000 cm range. Sampling cultivar) were collected in three different orchard locations, rate was one sample per minute. Prescott (location 1), Burbank (location 2), and Quincy (location FTIR spectra from distilled water, calcium, magnesium, and 3), Washington, USA. In location 3, trees were 8 years old and potassium standards were also acquired to determine their grafted on M-26 rootstock and canopies planted in vertical axis spectral signatures and identify the elemental peaks. These system. More information on the orchard can be found in spectral signatures were compared with the healthy and bitter Jarolmasjed et al. (2016). pit affected apple spectra. ANOVA analysis was conducted using R The focus of this study was elemental profile analysis of bitter pit apples using multiple sensing techniques during storage. The reason that bitter pit samples from the field were utilized was that the bitter pit development in healthy appearing apples can be Table 2 inconsistent leading to unbalanced sample size (Nicolaï et al., Calcium, magnesium, and potassium concentrations of peel and flesh samples in À1 ‘ ’ fi 2006). Healthy and bitter pit affected samples were visually Honeycrisp apples. Results are expressed in mg kg of fresh fruit, coef cient of variation (CV) is expressed as percent.