Non-Destructive Detection Of Some Quality Characteristics Of Strawberry Fruit In The Ripening Stage Using Near Infrared Spectroscopy

Yıl 2024, Cilt: 11 Sayı: 1, 9 - 18, 28.01.2024

İlknur Yılmaz Mehmet Burak Büyükcan

https://doi.org/10.30910/turkjans.1349290

Öz

The products' internal and external quality characteristics were predicted using Fourier transform (FT-NIR) near-infrared spectroscopy technique in Albion cultivar (Fragaria ananassa) strawberry samples. Since the shelf life of strawberry fruits is short after harvest, their quality characteristics are an essential criterion, especially for exported products. Determining the quality characteristics of products using non-destructive measurement systems such as FT-NIR is less time-consuming and less costly than chemical or physical methods. Quality features are significant for exported products. Non-destructive spectroscopic measurements of strawberries were made using reflectance (780-2500 nm) and transmittance (800-1725 nm) techniques. Generally, high calibration and validation results were obtained for both measurement methods (Reflectance and Transmittance) in color properties. Hue prediction values on transmittance were predicted to have the best result in the measurement at R2=84.81 (RMSECV= 0.347) for validation, while R2=91.77 (RMSEE= 0.268) for calibration. In reflectance mode, it showed high predictive performance of a* value with the red color variable R2=82.19 (RMSECV= 5.81) for validation and R2=89.42 (RMSEE= 4.73) for calibration during the ripening period of the strawberry. On the other hand, the intrinsic properties' prediction performance remained lower than the color properties. The most successful prediction performance was found for soluble solids content (SSC) (R2=50.66; RMSECV= 0.951) in reflectance mode, while pH (R2=58.21; RMSECV= 0.0472) for transmittance mode. As can be seen from the results, using FT-NIR spectroscopy to predict color properties without damage during the ripening period of strawberry products was highly successful, while more restrictive results were obtained in predicting internal properties.

Anahtar Kelimeler

Strawberry, Near Infrared, Color, Non-destructive

Proje Numarası

FYL-2022-3997

Yakın Kızılötesi Spektroskopisi Kullanılarak Olgunlaşma Aşamasındaki Çilek Meyvesinin Bazı Kalite Özelliklerinin Hasarsız Tespiti

Öz

Yapılan çalışmada, Albion çeşidi (Fragaria ananassa) çilek örneklerinde Fourier dönüşümü (FT-NIR) yakın kızılötesi spektroskopi tekniği kullanılarak ürünlerin iç ve dış kalite özellikleri tahmin edilmesi amaçlanmaktadır. Çilek meyvelerinin raf ömürleri hasattan sonra kısa olmasından dolayı özellikle ihraç edilmekte olan ürünler için kalite özellikleri önemli bir kriterdir. FT-NIR gibi hasarsız ölçüm sistemleri kullanılarak ürünlerin kalite özelliklerinin belirlenmesi kimyasal ya da fiziksel metotlara göre daha az zaman alıcı ve daha az maliyetlidir. Özellikle ihracatı gerçekleştirilen ürünler için kalite özellikleri önem arz etmektedir. Çilek örneklerinin hasarsız spektroskopik ölçümleri yansıma (780-2500 nm) ve geçirgenlik (800-1725 nm) teknikleri kullanılarak yapılmıştır. Genel olarak renk özellikleri açısından her iki ölçüm yönteminde (Yansıma ve Geçirgenlik) yüksek kalibrasyon ve doğrulama sonuçları elde edilmiştir. Geçirgenliğe ilişkin renk tonu tahmin değerlerinin doğrulama için R2=84.81 (RMSECV= 0.347) ve kalibrasyon için R2=91.77 (RMSEE= 0.268) ile en iyi sonucu vereceği tahmin edilmiştir. Yansıma modunda, olgunlaşma sırasında doğrulama için kırmızı renk değişkeni R2=82.19 (RMSECV= 5.81) ve kalibrasyon için R2=89.42 (RMSEE= 4.73) ile a* değerinin yüksek tahmin performansı göstermiştir. Diğer taraftan, içsel özelliklerin tahmin performansı, renk özelliklerine göre daha düşük kalmıştır. En başarılı tahmin performansı yansıma modunda çözülebilir kuru madde oranı (R2=50.66; RMSECV= 0.951) için, geçirgenlik modunda ise pH (R2=58.21; RMSECV= 0.0472) için bulunmuştur. Sonuçlardan da anlaşılacağı üzere çilek ürünlerinin olgunlaşma döneminde renk özelliklerinin zarar görmeden tahmin edilmesinde FT-NIR spektroskopisi kullanılması oldukça başarılı olurken, iç özelliklerin tahmininde daha kısıtlayıcı sonuçlar elde edilmiştir.

Anahtar Kelimeler

Çilek, Yakın Kızıl Ötesi, Renk, Hasarsız

Destekleyen Kurum

Çanakkale Onsekiz Mart Üniversitesi

Proje Numarası

FYL-2022-3997

Kaynakça

• Basak, J.K., Madhavi, B.G.K., Paudel, B., Kim, N.E., Kim, H.T., 2022. Prediction of Total Soluble Solids and pH of Strawberry Fruits Using RGB, HSV and HSL Colour Spaces and Machine Learning Models. Foods 11. https://doi.org/10.3390/foods11142086
• Berardinelli, A., Cevoli, C., Silaghi, F.A., Fabbri, A., Ragni, L., Giunchi, A., Bassi, D., 2010. FT-NIR spectroscopy for the quality characterization of apricots (Prunus Armeniaca L.). J Food Sci 75. https://doi.org/10.1111/j.1750-3841.2010.01741.x
• Buyukcan, M.B., Kavdir, I., 2017. Prediction of some internal quality parameters of apricot using FT-NIR spectroscopy. Journal of Food Measurement and Characterization 11, 651–659. https://doi.org/10.1007/s11694-016-9434-9
• Carlini, P., Massantini, R., Mencarelli, F., 2000. Vis-NIR measurement of soluble solids in cherry and apricot by PLS regression and wavelength selection. J Agric Food Chem 48, 5236–5242. https://doi.org/10.1021/jf000408f
• Gndodu, M.A., Gür, E., Eker, M., 2021. Comparison of aroma compounds and pomological characteristics of the fruits of “cv. Mondial gala” and local apple genotype “Gelin” cultivated in Çanakkale, Turkey. Journal of Tekirdag Agricultural Faculty 18, 10–20. https://doi.org/10.33462/jotaf.630009
• Guo, W., Fang, L., Liu, D., Wang, Z., 2015. Determination of soluble solids content and firmness of pears during ripening by using dielectric spectroscopy. Comput Electron Agric 117, 226–233. https://doi.org/10.1016/j.compag.2015.08.012
• Huang, Y., Lu, R., Chen, K., 2018. Assessment of tomato soluble solids content and pH by spatially-resolved and conventional Vis/NIR spectroscopy. J Food Eng 236, 19–28. https://doi.org/10.1016/j.jfoodeng.2018.05.008
• Kafkas, E., Koşar, M., Paydaş, S., Kafkas, S., Başer, K.H.C., 2007. Quality characteristics of strawberry genotypes at different maturation stages. Food Chem 100, 1229–1236. https://doi.org/10.1016/j.foodchem.2005.12.005
• Karlidag, H., Yildirim, E., Turan, M., 2009. Exogenous applications of salicylic acid affect quality and yield of strawberry grown under antifrost heated greenhouse conditions. Journal of Plant Nutrition and Soil Science 172, 270–276. https://doi.org/10.1002/jpln.200800058
• Kavdir, I., Buyukcan, M.B., Lu, R., Kocabiyik, H., Seker, M., 2009. Prediction of olive quality using FT-NIR spectroscopy in reflectance and transmittance modes. Biosyst Eng 103, 304–312. https://doi.org/10.1016/j.biosystemseng.2009.04.014
• Kumar, A., Joshi, R.C., Dutta, M.K., Jonak, M., Burget, R., 2021. Fruit-CNN: An Efficient Deep learning-based Fruit Classification and Quality Assessment for Precision Agriculture, in: International Congress on Ultra Modern Telecommunications and Control Systems and Workshops. IEEE Computer Society, pp. 60–65. https://doi.org/10.1109/ICUMT54235.2021.9631643
• Lewers, K.S., Newell, M.J., Park, E., Luo, Y., 2020. Consumer preference and physiochemical analyses of fresh strawberries from ten cultivars. International Journal of Fruit Science 20, 733–756. https://doi.org/10.1080/15538362.2020.1768617
• Li, B.J., Grierson, D., Shi, Y., Chen, K.S., 2022. Roles of abscisic acid in regulating ripening and quality of strawberry, a model non-climacteric fruit. Hortic Res. https://doi.org/10.1093/hr/uhac089
• Li, J., Huang, W., Zhao, C., Zhang, B., 2013. A comparative study for the quantitative determination of soluble solids content, pH and firmness of pears by Vis/NIR spectroscopy. J Food Eng 116, 324–332. https://doi.org/10.1016/j.jfoodeng.2012.11.007
• Liu, Q., Wei, K., Xiao, H., Tu, S., Sun, K., Sun, Y., Pan, L., Tu, K., 2019. Near-infrared hyperspectral imaging rapidly detects the decay of postharvest strawberry based on water-soluble sugar analysis. Food Anal Methods 12, 936–946. https://doi.org/10.1007/s12161-018-01430-2
• Mancini, M., Mazzoni, L., Qaderi, R., Leoni, E., Tonanni, V., Gagliardi, F., Capocasa, F., Toscano, G., Mezzetti, B., 2023. Prediction of Soluble Solids Content by Means of NIR Spectroscopy and Relation with Botrytis cinerea Tolerance in Strawberry Cultivars. Horticulturae 9, 91. https://doi.org/10.3390/horticulturae9010091
• Ménager, I., Jost, M., Aubert, C., 2004. Changes in Physicochemical Characteristics and Volatile Constituents of Strawberry (Cv. Cigaline) during Maturation. J Agric Food Chem 52, 1248–1254. https://doi.org/10.1021/jf0350919
• Moing, A., Renaud, C., Gaudillère, M., Raymond, P., Roudeillac, P., Denoyes-Rothan, B., 2001. Biochemical Changes during Fruit Development of Four Strawberry Cultivars, J. AMER. SOC. HORT. SCI.
• Nagle, M., Mahayothee, B., Rungpichayapichet, P., Janjai, S., Müller, J., 2010. Effect of irrigation on near-infrared (NIR) based prediction of mango maturity. Sci Hortic 125, 771–774. https://doi.org/10.1016/j.scienta.2010.04.044
• Nunes, M.C.N., Brecht, J.K., Morais, A.M.M.B., Sargent, S.A., 2006. Physicochemical changes during strawberry development in the field compared with those that occur in harvested fruit during storage. J Sci Food Agric 86, 180–190. https://doi.org/10.1002/jsfa.2314
• Olarewaju, O.O., Bertling, I., Magwaza, L.S., 2016. Non-destructive evaluation of avocado fruit maturity using near infrared spectroscopy and PLS regression models. Sci Hortic 199, 229–236. https://doi.org/10.1016/j.scienta.2015.12.047
• Özdemir, İ.S., Bureau, S., Öztürk, B., Seyhan, F., Aksoy, H., 2019. Effect of cultivar and season on the robustness of PLS models for soluble solid content prediction in apricots using FT-NIRS. J Food Sci Technol 56, 330–339. https://doi.org/10.1007/s13197-018-3493-3
• Park, J. Il, Lee, Y.K., Chung, W. Il, Lee, I.H., Choi, J.H., Lee, W.M., Ezura, H., Lee, S.P., Kim, I.J., 2006. Modification of sugar composition in strawberry fruit by antisense suppression of an ADP-glucose pyrophosphorylase. Molecular Breeding 17, 269–279. https://doi.org/10.1007/s11032-005-5682-9
• Rahman, M.M., Moniruzzaman, M., Ahmad, M.R., Sarker, B.C., Khurshid Alam, M., 2016. Maturity stages affect the postharvest quality and shelf-life of fruits of strawberry genotypes growing in subtropical regions. Journal of the Saudi Society of Agricultural Sciences 15, 28–37. https://doi.org/10.1016/j.jssas.2014.05.002
• Rodrigo, D., van Loey, A., Hendrickx, M., 2007. Combined thermal and high pressure colour degradation of tomato puree and strawberry juice. J Food Eng 79, 553–560. https://doi.org/10.1016/j.jfoodeng.2006.02.015
• Saad, A.G., Azam, M.M., Amer, B.M.A., 2022. Quality Analysis Prediction and Discriminating Strawberry Maturity with a Hand-held Vis–NIR Spectrometer. Food Anal Methods 15, 689–699. https://doi.org/10.1007/s12161-021-02166-2
• Sánchez, M.T., De La Haba, M.J., Benítez-López, M., Fernández-Novales, J., Garrido-Varo, A., Pérez-Marín, D., 2012. Non-destructive characterization and quality control of intact strawberries based on NIR spectral data. J Food Eng 110, 102–108. https://doi.org/10.1016/j.jfoodeng.2011.12.003
• Schmilovitch, ev, Mizrach, A., Hoffman, A., Egozi, H., Fuchs, Y., 2000. Determination of mango physiological indices by near-infrared spectrometry, Postharvest Biology and Technology.
• Seki, H., Ma, T., Murakami, H., Tsuchikawa, S., Inagaki, T., 2023. Visualization of Sugar Content Distribution of White Strawberry by Near-Infrared Hyperspectral Imaging. Foods 12. https://doi.org/10.3390/foods12050931
• Shen, F., Zhang, B., Cao, C., Jiang, X., 2018. On-line discrimination of storage shelf-life and prediction of post-harvest quality for strawberry fruit by visible and near infrared spectroscopy. J Food Process Eng 41. https://doi.org/10.1111/jfpe.12866
• Skrovankova, S., Sumczynski, D., Mlcek, J., Jurikova, T., Sochor, J., 2015. Bioactive compounds and antioxidant activity in different types of berries. Int J Mol Sci. https://doi.org/10.3390/ijms161024673
• Torres, I., Pérez-Marín, D., de la Haba, M.J., Sánchez, M.T., 2015. Fast and accurate quality assessment of Raf tomatoes using NIRS technology. Postharvest Biol Technol 107, 9–15. https://doi.org/10.1016/j.postharvbio.2015.04.004
• Weng, S., Yu, S., Dong, R., Pan, F., Liang, D., 2020. Nondestructive detection of storage time of strawberries using visible/near-infrared hyperspectral imaging. Int J Food Prop 23, 269–281. https://doi.org/10.1080/10942912.2020.1716793
• Williams, P., Norris, K., 1987. Near-infrared technology in the agricultural and food industries. American Association of Cereal Chemists, Inc., St. Paul, Minnesota.
• Włodarska, K., Szulc, J., Khmelinskii, I., Sikorska, E., 2019. Non-destructive determination of strawberry fruit and juice quality parameters using ultraviolet, visible, and near-infrared spectroscopy. J Sci Food Agric 99, 5953–5961. https://doi.org/10.1002/jsfa.9870