ارزیابی غیرمخرب کیفیت کیوی رقم ابوت با استفاده از بینی الکترونیکی

نوع مقاله : مقاله پژوهشی

نویسندگان

1 دانشجوی کارشناسی ارشد دانشکدۀ مهندسی و فناوری کشاورزی، دانشگاه تهران

2 استاد دانشکدۀ مهندسی و فناوری کشاورزی، دانشگاه تهران

3 استادیار گروه مهندسی مکانیک ماشین‏های کشاورزی، دانشکدۀ کشاورزی، دانشگاه شهرکرد

چکیده

امروزه روش‏های گوناگونی برای ارزیابی غیرمخرب کیفیت محصولات کشاورزی ابداع شده است و از آنها استفاده می‏شود. در تحقیق حاضر از سامانۀ بینی الکترونیکی به‏منظور ارزیابی غیرمخرب کیفیت کیوی رقم ابوت استفاده شده است. سامانۀ بینی الکترونیکی به کمک تکنیک شبکۀ عصبی مصنوعی و آنالیز مؤلفه‏های اصلی (PCA) قادر به طبقه‏بندی نارس، نیمه‌رسیده، رسیده، بیش از حد رسیده، و فساد کیوی رقم ابوت است. آنالیز مؤلفه‌های اصلی با دو مؤلفۀ  و ، 99 درصد از واریانس داده‏ها را پوشش داد و مراحل رسیدگی کیوی رقم ابوت را بدون تداخل طبقه‏بندی کرد. دقت طبقه‏بندی کل به کمک تکنیک شبکۀ عصبی مصنوعی 100 درصد محاسبه شد. کمترین و بیشترین میزان میانگین مربعات خطا به‌ترتیب در مرحلۀ نیمه‌رسیده 02523/0 و فساد کیوی رقم ابوت 00198/0 به‌دست آمد. همچنین در این تحقیق، سفتی به‌عنوان یکی از روش‏های مخرب ارزیابی رسیدگی کیفیت کیوی رقم ابوت اندازه‏گیری شد. نتایج آنالیز سفتی کیوی رقم ابوت نشان داد که بین سفتی مراحل پس از برداشت (نارس، نیمه‌رسیده، رسیده و بیش از حد رسیده) در سطح 5 درصد تفاوت معنی‏داری وجود دارد. پیش‏بینی سفتی کیوی رقم ابوت از روی بوی مراحل رسیدگی با استفاده از شبکۀ عصبی مصنوعی با ضریب 995/0  تعیین شد. سامانۀ بینی الکترونیکی مطالعه‌شده می‏تواند به‌عنوان ابزاری مطمئن برای پایش رسیدگی میوۀ کیوی در سردخانه‏ها استفاده شود.

کلیدواژه‌ها

موضوعات


عنوان مقاله [English]

Nondestructive quality evaluation of Abbot Kiwifruit using electronic nose

نویسندگان [English]

  • Amin Foroughi Rad 1
  • Seyed Saeed Mohtasebi 2
  • Mahdi Ghasemi 3
  • Mahmoud Omid 2
1
2
3
چکیده [English]

Currently, nondestructive quality evaluation of various agricultural products are being developed and used. In this study, application of an electronic nose system for the quality assessment of kiwifruit (Abbot variety) is used. Electronic nose system coupled with artificial neural network (ANN) and principal component analysis (PCA) techniques was able to classify unripe, half-ripe, ripe, over-ripe and spoiled kiwifruit cultivars successfully. The two main components & , contains about 99% of variance without overlapping. The success rate for ANN was found to be 100%. The minimum and maximum mean square error was obtained for the Half-ripe and spoiled samples as 0.02523 & 0.00198, respectively. In this paper, stiffness as a quality indicator for kiwi was measured and them predicted by electronic nose data. Analysis of the results showed that the kiwifruit firmness after harvest (unripe, half-ripe, ripe and overripe) has significant difference at 5%. Using artificial neural network, the firmness prediction of Abbot variety of kiwifruit through ripening stages aroma determined with the coefficient Electronic nose system can be considered as a reliable tool for the monitoring of kiwifruits in storage conditions.

کلیدواژه‌ها [English]

  • Gas Sensors
  • electronic nose
  • Nondestructive evaluation
  • artificial neural network (ANN)
  • Principal Component Analysis (PCA)
Abbott, J. A. (1999). Quality measurement of fruits and
vegetables. Postharvest Biology and Technology,
15, 207-225.
Abedini, J. (2004). Post harvest physiology and
technology of Kiwifruit cool-storage & industrial.
Tehran: Danesh negar. (In Farsi).
Apetrei, C., Apetrei, I. M., Villanueva, S., de Saja, J. A .,
Gutierrez-Rosales, F ., & Rodriguez-Mendez,
M. L. (2010). Combination of an e-nose, an etongue
and an e-eye for the characterization of
olive oils with different degree of bitterness.
AnalyticaChimicaActa, 663, 91-97.
Barbri, N. E., Mirhisse, J., Ionescu, R., Bari, N. E.,
Correig, X., Bouchikhi, B., et al. (2009). An
electronic nose system based on a micromachined
gas sensor array to assess the freshness
of sardines. Sensors and Actuators B: Chemical,
141, 538-543.
Bhattacharrya, N., Bandyopadhyay, R., Bhuyan, M.,
Tudu, B., Ghosh, D. & Jana, A. (2008).
Electronic nose for black tea classification and
correlation of measurement with “Tea Taster”
marks. IEEE transactions on instrumentation and
measurement, 57, 1313-1321.
Concina, I., Falasconi, M., Gobbi, E., Bianchi, F.,
Musci, M., Mattarozzi, M., et al. (2009). Early
detection of microbial contamination in processed
tomatoes by electronic nose. Food Control, 20,
873-880.
Cozzolin, D.,Cynkar, W., Dambergs, R., & Smith, P.
(2010). Two- Dimensional Correlation analysis of
the effect of the effect of temperature on the
fingerprint of wines analyzed by mass
spectrometry electronic nose. Sensors and
Actuators B. 145. 628-634.
Di Natale, C., Macagnano, A., Martinelli, E., Paolesse,
R., Proietti, E.& D’ Amico, A. (2001). The
evaluation of quality of post-harvest oranges and
apples by means of an electronic nose. Sensors
and Actuators, B78, 26-31.
Dutta, R., Hines, E. L., Gardner, J. W., Udrea, D. D.
&Boilot, P. (2003). Non-destructive egg
freshness determination: an electronic nose base
approach. Measurement Science and Technology,
14, 190-198.
Foroughirad, A., Mohtasebi, S. S., Ghasemi-
Varnamkhasti, M. (2012). Nondestructive
evaluation of quality of food and agricultural
products using electronic nose equipped with
sensors MOS. National congress on food hygiene
& safety. Shiraz, Iran. (In Farsi).
Ghasemi-Varnamkhasti, M. (2011). Design,
development and implementation of a metal oxide
semiconductor (MOS) based machine olfaction
system and bioelectronics tongue to quality
change detection of beers coupled with pattern
recognition analysis techniques. Ph. D.
dissertation, University of Tehran. (In Farsi).
Ghasemi-Varnamkhasti, M., Mohtasebi, S. S., Siadat,
M., Lozano, J., Ahmadi, H., Razavi, S, H., &
Dicko, A. (2011). Aging fingerprint
characterization of beer using electronic nose.
Sensors and Actuators B, 159, 51-59.
Gomez, A. H., Hu, G., Wang, J. & Pereira, A. G.
(2006a). Evaluation of tomato maturity by
electronic nose. Computers and Electronics in
Agriculture, 54, 44-52.
Gomez, A. H., Wang, J., Hu, G., and Pereira, A. G.
(2006b). Electronic nose technique potential
monitoring mandarin maturity. Sensors and
Actuators B, 113, 347–353.
Guohua, H., Yuling, W., Dandan, Y., Wenwen, D.,
Linshan, Z., and Lvye, W. (2012). Study of
peach freshness predictive method based on
electronic nose. Food Control, 28, 25-32.
Hertog, M. L. A. T. M., Nicholson, S. E., and Jeffery,
P. B. (2004). The effect of modified atmospheres
on the rate of firmness change of Hayward
kiwifruit. Journal of the Postharvest Biology and
Technology, (31), 251-261.
Honh, H. K., Shin, H. W., Yun, D. Y., Kim, S. R.,
Kwon, C. H., Lee, K., and Moriizumi, T. (1996).
E-nose system with micro-gas sensor array.
Sensors of Actuators, B35–36, 338–341.
Korel, F., &Balaban, M. (2008). Electronic Nose
Technology in Food Analysis, 365-379.
Li, C., Schmidt, N. E., &Gitaitis, R. (2011). Detection
of onion postharvest diseases by analyses of
headspace volatiles using a gas sensor array and
GC-MS. Food Science and Technology, 44,
1019-1025.
Mirzaeemoghaddam, H. (2006). Investigation of some
mechanical properties of kiwifruit during storage.
Ph. D. dissertation, TarbiatModares University.
(In Farsi).
Soltanifiroz, M., Alimardani, R. &Omid, M. (2010).
Potential of using capacitor method in banana
ripeness detection. Iranian journal of biosystems
engineering, 1(42), 27-29. (In Farsi).
Persaud, K. C. and Dodd, G. H. (1982). Analysis of the
mammalian olfactory system using a model nose.
Nature, 299, 352–355.
Reinhard, H., Sager, F., &Zoller, O. (2008). Citrus
juice classification by SPME-GC-MS and
electronic nose measurements. Lwt-Food Science
and Technology, 41, 1906-1912.
Vestergaard, J., Martens, S. M., &Turkki, P. (2007).
Application of an electronic nose system for
prediction of sensory quality change s o f a meat
product (pizza topping) during storage. Lwt -
Food Science and Technology, 40, 1095-110 1.
Wang, B., Xu, S. Y., & Sun, D. W. (2010b).
Application of the electronic nose to the
identification of different milk flavorings. Food
Research International, 43,255-262.
White, A., Silva, H. N. D., Requejo-Tapia, C., Harker,
F. R. (2005). Evaluation of softening
characteristics of fruit 14 species of Actinidia.
Postharvest Biology and Technology, 35(2), 143-
151.
Zhang, H., Wang, J. & Sheng, Y. (2007). Predictions of
acidity, soluble solids and firmness of pear using
electronic nose technique. Journal of food
engineering, 86, 370-378.