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ارزیابی غیرمخرب رسیدگی میوه انجیر با استفاده از طیفسنجی فروسرخ نزدیک موج کوتاه/مرئی | ||
| تحقیقات مهندسی صنایع غذایی | ||
| مقاله 8، دوره 21، شماره 1 - شماره پیاپی 72، مرداد 1401، صفحه 109-122 اصل مقاله (973.3 K) | ||
| نوع مقاله: مقاله پژوهشی | ||
| شناسه دیجیتال (DOI): 10.22092/fooder.2021.353402.1293 | ||
| نویسندگان | ||
| رضا صیاد حق شمار1؛ رضا محمدی گل* 2؛ بابک وکیل زاده کاجی3 | ||
| 1دانشجوی کارشناسی ارشد گروه مهندسی باغبانی، دانشکده کشاورزی، دانشگاه اراک، شهر اراک، ایران | ||
| 2گروه مهندسی باغبانی، دانشکده کشاورزی، دانشگاه اراک، اراک، ایران. | ||
| 3گروه مهندسی باغبانی، دانشکده کشاورزی، دانشگاه اراک، اراک، ایران | ||
| چکیده | ||
| در تحقیق حاضر، طیفسنجی بازتابی مرئی/ فروسرخ نزدیک موج کوتاه (Vis/SWNIR) در محدوده 950-425 نانومتر بهمنظور پیشبینی شاخص مزه و سفتی بافت میوه انجیر بررسی شد. علاوه بر این، عملکرد طبقهبندهای LDA و QDA در تفکیک انجیرهای رسیده، نیمهرسیده و نرسیده بر پایه ترکیب روشهای مختلف پیشپردازش طیفی بررسی گردید. تعداد 167 میوه انجیر پس از حذف دادههای پرت برای تدوین و اعتبارسنجی مدلها انتخاب شدند. از تکنیک تحلیل مؤلفههای اصلی برای استخراج مؤلفههای اصلی طیفها استفاده شد. عملکرد مدل حداقل مربعات جزئی (PLS) و روشهای رایج پیشپردازش دادههای طیفی با شاخصهای: انحراف پیشبینی باقیمانده (RPD)، ضریب همبستگی تخمین (rp) و ریشه میانگین مربعات خطای پیشبینی (RMSEP) ارزیابی شد. کارآمدی طبقهبندها به همراه روشهای پیشپردازش نیز با درصد دقت تفکیک درست دسته نمونههای آزمون اعتبارسنجی شد. بیشترین شاخص RPD در پیشبینی سفتی بافت و شاخص مزه به ترتیب برابر 1.79 (0.845=rp و 1.64= RMSEP) و0.89 (0.215=rp و 10.90= RMSEP) در پیشپردازش ترکیبی میانگینگیری متحرک (MA) و De-trending به دست آمد. دقت تفکیک درست طبقهبندهای LDA و QDA نیز برابر 93.33 درصد (بدون پیشپردازش طیفها) به دست آمد. | ||
| کلیدواژهها | ||
| رسیدگی انجیر؛ حداقل مربعات جزئی؛ طیفسنجی مرئی/ فروسرخ نزدیک موجکوتاه؛ طبقهبندی؛ غیرمخرب | ||
| عنوان مقاله [English] | ||
| Nondestructive Evaluation of Fig Fruit Maturity Using Visible / Short-Wave Near-Infrared Spectroscopy | ||
| نویسندگان [English] | ||
| Reza Saiad Haghshomar1؛ Reza Mohammadigol2؛ Babak Valizadehkaji3 | ||
| 1Department of Biosystem Engineering, Agriculture College, Arak University, Arak, Iran | ||
| 2Department of Biosystem Engineering, Agriculture College, Arak University, Arak, Iran | ||
| 3Assistant professor, Department of Biosystem Engineering, Agriculture College, Arak University, Arak, Iran. | ||
| چکیده [English] | ||
| In the present study, visible/shortwave near-infrared reflectance spectroscopy (Vis/SWNIR, 425–950 nm) was used to predict the taste index (SSC/TA) and flesh firmness of fig fruits. Besides, the efficiency of LDA and QDA classifiers in detecting ripe, semi-ripe, and unripe figs was studied based on a combination of pretreatment methods. A total of 167 fig trees were selected for the development and validation of the models. Principal component analysis (PCA) was employed to extract the principal components of the spectra. PLS performance and common spectral data pretreatment methods were evaluated using the residual prediction deviation (RPD), predictive correlation coefficient (rp), and root mean square error of prediction (RMSEP). Moreover, the efficiency of the classifiers and pretreatment methods was evaluated using the mean overall accuracy (%) of the testing samples. The highest mean value of RPDs based on the combined pretreatment method of MA + de-trending was 1.79 for flesh firmness (RMSEP = 1.64, rp = 0.845) and 0.89 for the taste index (RMSEP = 10.09, rp = 0.215).LDA and QDA classifiers had an overall accuracy of 93.33 percent (in no-pretreatment spectral data). | ||
| کلیدواژهها [English] | ||
| Fig ripeness, Partial least squares, Visible/shortwave near-infrared reflectance spectroscopy, Classification, Non-destructive evaluation | ||
| مراجع | ||
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Alhamdan, A.M., Fickak, A. and Atia, A.R. 2019. Evaluation of sensory and texture profile analysis properties of stored Khalal Barhi dates nondestructively using Vis/NIR spectroscopy. Journal of Food Process Engineering. 42(6): e13215. Amuah, C.L., Teye, E., Lamptey, F.P., Nyandey, K., Opoku-Ansah, J. and Adueming, P.O.-W. 2019. Feasibility study of the use of handheld NIR spectrometer for simultaneous authentication and quantification of quality parameters in intact pineapple fruits. Journal of Spectroscopy. 1-9. Beghi, R., Buratti, S., Giovenzana, V., Benedetti, S. and Guidetti, R. 2017. Electronic nose and visible-near infrared spectroscopy in fruit and vegetable monitoring. Reviews in Analytical Chemistry. 36(4): 20160016. Callao, M.P. and Ruisánchez, I. 2018. An overview of multivariate qualitative methods for food fraud detection. Food Control. 86: 283-293. Cavaco, A.M., Pinto, P., Antunes, M.D., da Silva, J.M. and Guerra, R. 2009. ‘Rocha’pear firmness predicted by a Vis/NIR segmented model. Postharvest Biology and Technology. 51(3): 311-319. Cortés, V., Blasco, J., Aleixos, N., Cubero, S. and Talens, P. 2019. Monitoring strategies for quality control of agricultural products using visible and near-infrared spectroscopy: A review. Trends in Food Science & Technology. 85, 138-148. Forthofer, R.N., Lee, E. S. and Hernandez, M. 2006. Biostatistics: a guide to design, analysis and discovery. Elsevier Inc. https://doi.org/10.1016/C2009-0-03861-6 (pp. 349-386). Fu, X.-p., Ying, Y.-b., Zhou, Y., Xie, L.-j. and Xu, H.-r. 2009. Application of NIR spectroscopy for firmness evaluation of peaches. Journal of Zhejiang University SCIENCE B, 9(7): 552-557. Ghorbani, A., Hasanpoor, H. and Ercisli, S. 2018. Variation on biochemical, phytochemical and genetic diversity of fig (ficus carica) from East Azerbaijan province. Plant Environmental Physiology. 13(52): 16-28 (in Persian) Golic, M., Walsh, K. and Lawson, P. 2009. Short-wavelength near-infrared spectra of sucrose, glucose, and fructose with respect to sugar concentration and temperature. Applied spectroscopy. 57(2): 139-145. Guo, Y., Ni, Y. and Kokot, S. 2016. Evaluation of chemical components and properties of the jujube fruit using near infrared spectroscopy and chemometrics. Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy. 153, 79-86. Jamshidi, B. 2020. Ability of near-infrared spectroscopy for non-destructive detection of internal insect infestation in fruits: Meta-analysis of spectral ranges and optical measurement modes. Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy. 225, 117479. Jamshidi, B., Minaei, S., Mohajerani, E. and Ghassemian, H. 2012. Reflectance Vis/NIR spectroscopy for nondestructive taste characterization of Valencia oranges. Computers and Electronics in Agriculture. 85, 64-69. Kafle, G.K., Khot, L.R., Jarolmasjed, S., Yongsheng, S. and Lewis, K. 2016. Robustness of near infrared spectroscopy based spectral features for non-destructive bitter pit detection in honeycrisp apples. Postharvest Biology and Technology. 120, 188-192. Kaiyan, L., Chang, L., Huiping, S., Junhui, W. and Jie, C. 2020. Review on the Application of Machine Vision Algorithms in Fruit Grading Systems. Paper presented at the International Conference on Intelligent and Interactive Systems and Applications. Lin, H and Ying, Y. 2009. Theory and application of near infrared spectroscopy in assessment of fruit quality: a review. Sensing and Instrumentation for Food Quality and Safety. 3(2): 130-141. Magwaza, L.S., Opara, U.L., Nieuwoudt, H., Cronje, P.J., Saeys, W and Nicolaï, B. 2012. NIR spectroscopy applications for internal and external quality analysis of citrus fruit:a review. Food and Bioprocess Technology. 5(2): 425-444. Maniwara, P., Nakano, K., Ohashi, S., Boonyakiat, D., Seehanam, P., Theanjumpol, P. and Poonlarp, P. 2019. Evaluation of NIRS as non-destructive test to evaluate quality traits of purple passion fruit. Scientia Horticulturae. 257, 108712. Maraphum, K., Saengprachatanarug, K., Wongpichet, S., Phuphaphud, A. and Posom, J. 2020. In-field measurement of starch content of cassava tubers using handheld vis-near infrared spectroscopy implemented for breeding programmes. Computers and Electronics in Agriculture. 175, 105607. Matteoli, S., Diani, M., Massai, R., Corsini, G. and Remorini, D. 2015. A spectroscopy-based approach for automated nondestructive maturity grading of peach fruits. IEEE Sensors Journal. 15(10): 5455-5464. Mouazen, A., Kuang, B., De Baerdemaeker, J. and Ramon, H. 2010. Comparison among principal component, partial least squares and back propagation neural network analyses for accuracy of measurement of selected soil properties with visible and near infrared spectroscopy. Geoderma. 158(1-2): 23-31. Munera, S., Besada, C., Aleixos, N., Talens, P., Salvador, A., Sun, D.-W., Cubero, S. and Blasco, J. 2017. Non-destructive assessment of the internal quality of intact persimmon using colour and VIS/NIR hyperspectral imaging. LWT. 77, 241-248. Nicolai, B.M., Beullens, K., Bobelyn, E., Peirs, A., Saeys, W., Theron, K.I. and Lammertyn, J. 2007. Nondestructive measurement of fruit and vegetable quality by means of NIR spectroscopy: A review. Postharvest Biology and Technology. 46(2): 99-118. Pourdarbani, R., Sabzi, S., Kalantari, D. and Arribas, J. I. 2020. Non-destructive visible and short-wave near-infrared spectroscopic data estimation of various physicochemical properties of Fuji apple (Malus pumila) fruits at different maturation stages. Chemometrics and Intelligent Laboratory Systems. 206, 104147. Rossel, R.V., McGlynn, R. and McBratney, A. 2006. Determining the composition of mineral-organic mixes using UV–vis–NIR diffuse reflectance spectroscopy. Geoderma. 137(1-2): 70-82. Shafiee, S. and Minaei, S. 2018. Combined data mining/NIR spectroscopy for purity assessment of lime juice. Infrared Physics & Technology. 91, 193-199. Szuvandzsiev, P., Helyes, L., Lugasi, A., Szántó, C., Baranowski, P. and Pék, Z. 2014. Estimation of antioxidant components of tomato using VIS-NIR reflectance data by handheld portable spectrometer. International Agrophysics. 28(4):521-527. Teye, E., Amuah, C.L., McGrath, T. and Elliott, C. 2019. Innovative and rapid analysis for rice authenticity using hand-held NIR spectrometry and chemometrics. Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy. 217, 147-154. Theanjumpol, P., Wongzeewasakun, K., Muenmanee, N., Wongsaipun, S., Krongchai, C., Changrue, V., Boonyakiat, D. and Kittiwachana, S. 2019. Non-destructive identification and estimation of granulation in ‘Sai Num Pung’tangerine fruit using near infrared spectroscopy and chemometrics. Postharvest Biology and Technology. 153, 13-20. Tiecher, T., Moura-Bueno, J.M., Caner, L., Minella, J.P., Evrard, O., Ramon, R., Naibo, G., Barros, C.A., Silva, Y.J. and Amorim, F.F. 2021. Improving the quantification of sediment source contributions using different mathematical models and spectral preprocessing techniques for individual or combined spectra of ultraviolet–visible, near-and middle-infrared spectroscopy. Geoderma. 384, 114815. Torkashvand, A.M., Ahmadi, A. and Nikravesh, N.L. 2017. Prediction of kiwifruit firmness using fruit mineral nutrient concentration by artificial neural network (ANN) and multiple linear regressions (MLR). Journal of Integrative Agriculture. 16(7): 1634-1644. Tuai, P., Tinjauan, S., Jusoh, N.A.M., Ding, P. and Yeat, C.S. 2020. Extending post-harvest quality of fresh fig (Ficus carica L.) fruit through manipulation of pre-and post-harvest practices: A review. Sains Malaysiana. 49(3): 553-560. Wang, H., Peng, J., Xie, C., Bao, Y. and He,Y. 2015. Fruit quality evaluation using spectroscopy technology: A review. Sensors. 15(5): 11889-11927. Wanitchang, P., Terdwongworakul, A., Wanitchang, J. and Nakawajana, N. 2011. Non-destructive maturity classification of mango based on physical, mechanical and optical properties. Journal of Food Engineering. 105(3): 477-484. Yan, H. and Siesler, H. W. 2018. Identification of textiles by handheld near infrared spectroscopy: Protecting customers against product counterfeiting. Journal of Near Infrared Spectroscopy. 26(5): 311-321. | ||
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