Contents lists available at ScienceDirect Postharvest Biology and Technology journal homepage: www.elsevier.com/locate/postharvbio Non-destructive identification and estimation of granulation in ‘Sai Num Pung’ tangerine fruit using near infrared spectroscopy and chemometrics Parichat Theanjumpol a,b , Kumpon Wongzeewasakun b,c , Nadthawat Muenmanee a,b , Sakunna Wongsaipun d , Chanida Krongchai d , Viboon Changrue b,c , Danai Boonyakiat b,e , Sila Kittiwachana d,f, ⁎ a Postharvest Technology Research Center, Faculty of Agriculture, Chiang Mai University, Chiang Mai, 50200, Thailand b Postharvest Technology Innovation Center, Office of the Higher Education Commission, Bangkok 10400, Thailand c Department of Mechanical Engineering, Faculty of Engineering, Chiang Mai University, Chiang Mai, 50200, Thailand d Department of Chemistry, Faculty of Science, Chiang Mai University, Chiang Mai, 50200, Thailand e Department of Plant and Soil Sciences, Faculty of Agriculture, Chiang Mai University, Chiang Mai, 50200, Thailand f Environmental Science Research Center (ESRC), Faculty of Science, Chiang Mai University, Chiang Mai, 50200, Thailand ARTICLE INFO Keywords: Citrus Granulation Near infrared Classification Postharvest quality ABSTRACT Granulation or ‘dry juice sac’ is a physiological disorder, which has a negative effect on the eating quality of citrus fruit. It is not easy to identify the fruit with dry juice sacs until the peel is removed. This research describes a quick and non-destructive method for detecting and estimating the occurrence of granulation in ‘Sai Num Pung’ tangerine based on the use of near infrared (NIR) spectroscopy and chemometric analysis. NIR spectra of 178 fruit samples were recorded after harvest and one day of storage at 25 °C. Moisture content (MC), soluble solids content (SSC) and titratable acidity (TA) were analyzed. The fruit were rated into five classes from A (no visible of granulation) up to E (most of the fruit body was opaque or the estimated percentage of granulation was more than 75%). Partial least squares (PLS) regression was used to investigate the relationship between the quality parameters and the occurrence of the granulation disorder. Classification models such as linear dis- criminant analysis, quadratic discriminant analysis, partial least squares-discriminant analysis, k nearest neighbor and supervised self-organizing map (SSOM) were used to identify the granulation classes. The pre- dictive results from PLS modelling revealed that the disorder could be related to lower MC, SSC and TA of the fruit. The results of this analysis supported the idea that spectroscopic measurement could be used to assess the incidence of granulation externally. In this research, SSOM, as a representative of non-linear classification, resulted in the best classification performance where the percentages of predictive ability, model stability and correctly classified (CC) were 93.7%, 95.3% and 94.0%, respectively, for the test samples generated by bootstrap method. The SSOM model was also tested with external validation samples which were the tangerine harvested in different season resulting in the CC of 78.4%. 1. Introduction ‘Sai Num Pung’ is a tangerine cultivar that is commonly cultivated in the mainland of Southeast Asia, in particular, Thailand (Ladaniya, 2008; Zhou et al., 2018). Due to its delicious flavor and sweetness, the cultivar is popular as either fresh fruit or juice. One of the important problems that directly affects citrus fruit quality is granulation or ‘dry juice sac’ disorder. Fruit with granulated juice sacs are prone to have lower soluble solids, total sugars and citric acid contents (Ritenour et al., 2004; Hofman, 2010). The cell walls of granulated fruit are hardened and thicker, resulting in lower extractable juice percentage, and the fruit, consequently, are usually tasteless. Tangerine fruit are prone to granulate during the early and late seasons of production, which poses a significant problem to both growers and buyers. The occurrence of the dry juice sacs is not visible from the outside. There- fore, it is not easy to separate fruit with the granulation from normal ones until the peel is removed. One goal of postharvest technology is to be able to test large num- bers of samples quickly and non-destructively. Ideally, the developed technology could be used in a packing line to non-destructively detect https://doi.org/10.1016/j.postharvbio.2019.03.009 Received 1 December 2018; Received in revised form 17 March 2019; Accepted 17 March 2019 ⁎ Corresponding author at: Department of Chemistry, Faculty of Science, Chiang Mai University, Chiang Mai, 50200, Thailand. E-mail address: silacmu@gmail.com (S. Kittiwachana). Postharvest Biology and Technology 153 (2019) 13–20 0925-5214/ © 2019 Elsevier B.V. All rights reserved. T