Prediction of some internal quality parameters of apricot using FT-NIR spectroscopy

被引:1
|
作者
M. Burak Buyukcan
Ismail Kavdir
机构
[1] Canakkale Onsekiz Mart University,Faculty of Agriculture, Department of Agricultural Machinery and Technologies Engineering
关键词
FT-NIR spectroscopy; Apricot; Firmness; SSC; Color;
D O I
暂无
中图分类号
学科分类号
摘要
The characteristics of internal quality attributes (firmness, soluble solids content and color values) of the Tokaloglu apricot cultivar (Prunus armeniaca L.) were predicted nondestructively using Fourier Transform-Near Infrared (FT-NIR) spectroscopy. Calibration methods were developed between the physical parameters, which were measured using standard methods, and the spectral measurements (in reflectance mode between 780 and 2500 nm) using Partial Least Squares method (PLS). Good correlations were obtained in calibration and validation procedures for Magness-Taylor (MT) maximum force, with a coefficient of determination (R2) of 0.82 (RMSEE = 4.45) in calibration and 0.80 (RMSECV = 4.68) in validation for multiple-harvest (MH) apricot group. The coefficient of determination (R2) for predicting MT slope was 0.79 (RMSEE = 0.83) in calibration and 0.77 (RMSECV = 0.88) in validation for the MH apricot group while it was 0.56 (RMSEE = 0.69) in calibration and 0.47 (RMSECV = 0.80) in validation for single-harvest (SH) apricot group. Good correlations were obtained for MT area with the coefficient of determination (R2) of 0.75 (RMSEE = 20.1) in calibration and R2 = 0.71 (RMSECV = 21) in validation for MH group. Good prediction values were obtained for soluble solids content for both applications (MH and SH) using FT-NIR spectroscopy: the best coefficient of determination was obtained for MH application with 0.77 (RMSEE = 1.45) in calibration and 0.75 (RMSECV = 1.51) in validation. Correlation values for prediction of chroma and hue were low for MH application, with R2 = 0.55 (RMSECV = 3.38) for chroma and with R2 = 0.16 (RMSECV = 0.49) for hue. The results showed that NIR spectroscopy has a good potential to predict internal quality of apricots non-destructively, however it has a limited ability to predict color features.
引用
收藏
页码:651 / 659
页数:8
相关论文
共 50 条
  • [41] Resolution and suppression of mechanical noise in FT-NIR spectroscopy
    Meyer, T
    Oelichmann, J
    Kellerhals, H
    TRAC-TRENDS IN ANALYTICAL CHEMISTRY, 2006, 25 (01) : 19 - 23
  • [42] Mealiness in 'Forelle' pears: relationship between TSS and prediction based on FT-NIR spectroscopy
    Muziri, T.
    Theron, K. I.
    Nieuwoudt, H. H.
    Schoeman, L.
    Crouch, E. M.
    VII INTERNATIONAL CONFERENCE ON MANAGING QUALITY IN CHAINS (MQUIC2017) AND II INTERNATIONAL SYMPOSIUM ON ORNAMENTALS IN ASSOCIATION WITH XIII INTERNATIONAL PROTEA RESEARCH SYMPOSIUM, 2018, 1201 : 339 - 345
  • [43] Evolution of Frying Oil Quality Using Fourier Transform Near-Infrared (FT-NIR) Spectroscopy
    Calero, Antonia M.
    Munoz, Estrella
    Perez-Marin, Dolores
    Riccioli, Cecilia
    Perez, Luis
    Garrido-Varo, Ana
    APPLIED SPECTROSCOPY, 2018, 72 (07) : 1001 - 1013
  • [44] FT-NIR SPECTROSCOPY OF SOME LONG-CHAIN FATTY-ACIDS AND ALCOHOLS
    OZAKI, Y
    LIU, YL
    CZARNECKI, MA
    NODA, I
    MACROMOLECULAR SYMPOSIA, 1995, 94 : 51 - 59
  • [45] Analysis of Drug Eluting Stent Coating Solutions Using FT-NIR Spectroscopy
    Bonenfant, Sacha
    Despagne, Frederic
    SPECTROSCOPY, 2009, : 13 - 13
  • [46] Estimation of quality of thermally modified beech wood with red heartwood by FT-NIR spectroscopy
    Todorovic, Nebojsa
    Popovic, Zdravko
    Milic, Goran
    WOOD SCIENCE AND TECHNOLOGY, 2015, 49 (03) : 527 - 549
  • [47] Classification of olives using FT-NIR spectroscopy, neural networks and statistical classifiers
    İsmail Kavdır
    M. Burak Büyükcan
    Ferhat Kurtulmuş
    Journal of Food Measurement and Characterization, 2018, 12 : 2493 - 2502
  • [48] Determination of viability of Retinispora (Hinoki cypress) seeds using FT-NIR spectroscopy
    Mukasa, Perez
    Wakholi, Collins
    Mo, Changyeun
    Oh, Mirae
    Joo, Hye-Joon
    Suh, Hyun Kwon
    Cho, Byoung-Kwan
    INFRARED PHYSICS & TECHNOLOGY, 2019, 98 : 62 - 68
  • [49] Classification of olives using FT-NIR spectroscopy, neural networks and statistical classifiers
    Kavdir, Ismail
    Buyukcan, M. Burak
    Kurtulmus, Ferhat
    JOURNAL OF FOOD MEASUREMENT AND CHARACTERIZATION, 2018, 12 (04) : 2493 - 2502
  • [50] Rapid detection of volatile compounds in apple wines using FT-NIR spectroscopy
    Ye, Mengqi
    Gao, Zhenpeng
    Li, Zhao
    Yuan, Yahong
    Yue, Tianli
    FOOD CHEMISTRY, 2016, 190 : 701 - 708