Forecast of Apple Internal Quality Indices at Harvest and During Storage by VIS-NIR Spectroscopy

被引:0
|
作者
Timea Ignat
Susan Lurie
Juliana Nyasordzi
Viacheslav Ostrovsky
Haim Egozi
Aharon Hoffman
Haya Friedman
Asya Weksler
Ze’ev Schmilovitch
机构
[1] Agricultural Research Organization,Institute of Agricultural Engineering, Volcani Center
[2] Agricultural Research Organization,Department of Postharvest Science, Volcani Center
来源
Food and Bioprocess Technology | 2014年 / 7卷
关键词
Soluble solid content; Ripening; Titratable acidity; Firmness; Starch; Nondestructive analysis;
D O I
暂无
中图分类号
学科分类号
摘要
Harvest date is generally established by monitoring small batches of fruit prior to harvest for changes in maturity parameters such as firmness, starch index, and total soluble solid content using destructive methods. Substituting this method with nondestructive visible-near infrared (VIS-NIR) spectrophotometers could save manpower hours and improve accuracy for harvesting for prolonged storage. Six hundred apples (Malusdomestica Borkh.) from three different orchards for each of three cultivars, “Granny Smith,” “Pink Lady,” and “Starking” were used to build calibration and validation models in different spectral regions. Two instruments, VIS-NIR (340–1,014 nm) and short-wavelength near-infrared (SWIR) (850–1,888 nm) spectrophotometers, measured the apples at harvest both in a static position and on a moving cell conveyer, and these measurements were used to predict total soluble solid (TSS) content, titratable acidity (TA), and firmness at harvest and after 2, 4, and 6 months of 0 °C storage. Starch was also predicted at harvest. The best R2 values were for TSS and starch (0.86 to 0.91) while TA and firmness predictions were less precise (0.53 to 0.78). The findings of the study indicate that the method offers potential for nondestructive prediction of ripeness and quality parameters of different cultivars of apples originating from different orchards. Moreover, the method enables forecasting of apple internal composition changes during storage based on the spectral signature at the time of harvest. Application of the results of this study could serve as a basis for the development of an automatic system for forecasting of apple internal composition change and of a sorting system.
引用
收藏
页码:2951 / 2961
页数:10
相关论文
共 50 条
  • [31] Experimental setup for fast VIS-NIR spectroscopy measurements in deflagrations
    Dabos, Marie
    Lecysyn, Nicolas
    Tran, Khanh-Hung
    Ranc-Darbord, Isabelle
    Genetier, Marc
    Baudin, Gerard
    Serio, Bruno
    AUTOMATED VISUAL INSPECTION AND MACHINE VISION IV, 2021, 11787
  • [32] Improving apple fruit quality predictions by effective correction of vis-NIR laser diffuse reflecting images
    Qing Zhao-shen
    Ji Bao-ping
    Shi Bo-lin
    Zhu Da-zhou
    Tu Zhen-hua
    Zude Manuela
    SPECTROSCOPY AND SPECTRAL ANALYSIS, 2008, 28 (06) : 1273 - 1277
  • [33] Potential of vis-NIR spectroscopy to monitor the silica precipitation reaction
    Rey-Bayle, Maud
    Bendoula, Ryad
    Henrot, Serge
    Lamiri, Kilani
    Baco-Antoniali, Franck
    Caillol, Noemie
    Gobrecht, Alexia
    Roger, Jean-Michel
    ANALYTICAL AND BIOANALYTICAL CHEMISTRY, 2017, 409 (03) : 785 - 796
  • [34] Vis-NIR spectroscopy for the on-site prediction of wood properties
    Kobori, Hikaru
    Kojima, Miho
    Yamamoto, Hiroyuki
    Sasaki, Yasutoshi
    Yamaji, Fabio Minoru
    Tsuchikawa, Satoru
    FORESTRY CHRONICLE, 2013, 89 (05): : 631 - 638
  • [35] Fuji apple storage time rapid determination method using Vis/NIR spectroscopy
    Liu, Fuqi
    Tang, Xuxiang
    BIOENGINEERED, 2015, 6 (03) : 166 - 169
  • [36] Predicting the Quality of Tangerines Using the GCNN-LSTM-AT Network Based on Vis-NIR Spectroscopy
    Wu, Yiran
    Zhu, Xinhua
    Huang, Qiangsheng
    Zhang, Yuan
    Evans, Julian
    He, Sailing
    APPLIED SCIENCES-BASEL, 2023, 13 (14):
  • [37] A ready-to-use portable VIS-NIR spectroscopy device to assess superior EVOO quality
    Violino, Simona
    Taiti, Cosimo
    Ortenzi, Luciano
    Marone, Elettra
    Pallottino, Federico
    Costa, Corrado
    EUROPEAN FOOD RESEARCH AND TECHNOLOGY, 2022, 248 (04) : 1011 - 1019
  • [38] DETERMINATION OF QUALITY AND RIPENING STAGES OF 'PACOVAN' BANANAS USING VIS-NIR SPECTROSCOPY AND MACHINE LEARNING
    Ferreira, Iara J. S.
    Almeida, Sarah L. F. de O.
    Neto, Acacio Figueiredo
    Costa, Daniel dos Santos
    ENGENHARIA AGRICOLA, 2022, 42
  • [39] Non-Destructive Quality Evaluation of 80 Tomato Varieties Using Vis-NIR Spectroscopy
    Duckena, Lilija
    Alksnis, Reinis
    Erdberga, Ieva
    Alsina, Ina
    Dubova, Laila
    Duma, Mara
    FOODS, 2023, 12 (10)
  • [40] Rapid assessment of soil water repellency indices using Vis-NIR spectroscopy and pedo-transfer functions
    Davari, Masoud
    Fahmideh, Soheyla
    Mosaddeghi, Mohammad Reza
    GEODERMA, 2022, 406