Fig. 1. The average relative reflectance spectra and standard deviation (SD) with resulting 2nd derivative spectral profle for tomato.
Fig. 2. Selection of feature wavelengths by VIP scores for (a) frmness and (b) SI of tomatoes. VIP, variable importance in projection; SI, sweetness index.
Fig. 3. Measured versus predicted (a) frmness and (b) SI estimated by PLS regression model using selected wavelengths. SI, sweetness index; a.u., arbitrary unit.
Fig. 3. Prediction maps showing the distribution of (a) frmness, and (b) SI in tomato samples.
Table 1. Statistics of quality parameters for tomato measured by standard methods.
Table 2. Results of PLS regression for frmness and SI with diferent preprocessing techniques.
Table 3. Results of PLS, and PLS-VIP based on Savitzky-Golay (S-G) 2nd derivative preprocessing spectra.
참고문헌
- Ahmed MR, Yasmin J, Lee WH, Mo C, Cho BK. 2017. Imaging technologies for nondestructive measurement of internal properties of agricultural products: A review. Journal of Biosystems Engineering 42:199-216.
- Andersen CM, Bro R. 2010. Variable selection in regression-a tutorial. Journal of Chemometrics 24:728-737. https://doi.org/10.1002/cem.1360
- Agbemavor WSK, Torgby-Tetteh W, Quartey EK, Nunoo J, Nunekpeku W, Owureku-Asare M, Agyei-Amponsah J, Apatey J. 2014. Physico-chemical evaluation of fruits from the fourth filial generation of some breeding lines of tomatoes. International Journal of Nutrition and Food Sciences 3:318-325. https://doi.org/10.11648/j.ijnfs.20140304.23
- Baiano A, Terracone C, Peri G, Romaniello R. 2012. Application of hyperspectral imaging for prediction of physico-chemical and sensory characteristics of table grapes. Computers and Electronics in Agriculture 87:142-151. https://doi.org/10.1016/j.compag.2012.06.002
- Barnes RJ, Dhanoa MS, Lister SJ. 1989. Standard normal variate transformation and de-trending of near-infrared diffuse reflectance spectra. Applied Spectroscopy 43:772-777. https://doi.org/10.1366/0003702894202201
- Buning-Pfaue H. 2003. Analysis of water in food by near infrared spectroscopy. Food Chemistry 82:107-115. https://doi.org/10.1016/S0308-8146(02)00583-6
- Camps C, Simone C, Gilli C. 2012. Assessment of tomato quality using portable NIR spectroscopy and PLSR with wavelengths selection. Acta Horticulture 936:437-442.
- Candolfi A, De Maesschalck R, Jouan-Rimbaud D, Hailey PA, Massart DL. 1999. The influence of data pre-processing in the pattern recognition of excipients near-infrared spectra. Journal of Pharmaceutical and Biomedical Analysis 21:115-132. https://doi.org/10.1016/S0731-7085(99)00125-9
- Dong W, Ni Y, Kokot S. 2013. A near-infrared reflectance spectroscopy method for direct analysis of several chemical components and properties of fruit, for example, chinese hawthorn. Journal of Agriculture and Food Chemistry 61:540-546. https://doi.org/10.1021/jf305272s
- ElMasry G, Wang N, ElSayed A, Ngadi M. 2007. Hyperspectral imaging for nondestructive determination of some quality attributes for strawberry. Journal of Food Engineering 81:98-107. https://doi.org/10.1016/j.jfoodeng.2006.10.016
- FAOSTAT (Food and Agriculture Organization Statistics). 2017. FAOSTAT [WWW Document]. Accessed in http://www.fao.org/faostat/en/#data/QC/visualize on 10 December 2017.
- Guo W, Zhao F, Dong J. 2016. Nondestructive measurement of soluble solids content of kiwifruits using near-infrared hyperspectral imaging. Food Analytical Methods 9:38-47. https://doi.org/10.1007/s12161-015-0165-z
- He Y, Zhang Y, Pereira AG, Gomez AH, Wang J. 2005. Nondestructive determination of tomato fruit quality characteristics using VIS/NIR spectroscopy technique. International Journal of Information Technology 11:97-108.
- Huang Y, Lu R, Chen K. 2017. Nondestructive measurement of tomato postharvest quality using a multichannel hyperspectral imaging probe. In 2017 ASABE Annual International Meeting. American Society of Agricultural and Biological Engineers, Spokane, Washington, USA.
- Kamruzzaman M, ElMasry G, Sun D-W, Allen P. 2013. Non-destructive assessment of instrumental and sensory tenderness of lamb meat using NIR hyperspectral imaging. Food Chemistry 141:389-396. https://doi.org/10.1016/j.foodchem.2013.02.094
- Kamruzzaman M, ElMasry G, Sun D-W, Allen P. 2012a. Non-destructive prediction and visualization of chemical composition in lamb meat using NIR hyperspectral imaging and multivariate regression. Innovative Food Science and Emerging Technologies 16:218-226. https://doi.org/10.1016/j.ifset.2012.06.003
- Kamruzzaman M, ElMasry G, Sun D-W, Allen P. 2012b. Prediction of some quality attributes of lamb meat using near-infrared hyperspectral imaging and multivariate analysis. Analytica Chimica Acta 714:57-67. https://doi.org/10.1016/j.aca.2011.11.037
- Kandpal LM, Lee S, Kim MS, Bae H, Cho B-K. 2015. Short wave infrared (SWIR) hyperspectral imaging technique for examination of aflatoxin B1 (AFB1) on corn kernels. Food Control 51:171-176. https://doi.org/10.1016/j.foodcont.2014.11.020
- Kandpal LM, Lohumi S, Kim MS, Kang J-S, Cho B-K. 2016. Near-Infrared hyperspectral imaging system coupled with multivariate methods to predict viability and vigor in muskmelon seeds. Sensors Actuators B Chemical 229:534-544. https://doi.org/10.1016/j.snb.2016.02.015
- Kennard RW, Stone LA. 1969. Computer aided design of experiments. Technometrics 11:137-148. https://doi.org/10.1080/00401706.1969.10490666
- Kienzle S, Sruamsiri P, Carle R, Sirisakulwat S, Spreer W, Neidhart S. 2012. Harvest maturity detection for "Nam Dokmai" mango fruit (Mangifera indica L.) in consideration of long supply chains. Postharvest Biology and Technology 72:64-75. https://doi.org/10.1016/j.postharvbio.2012.04.011
- Lee H, Huy TQ, Park E, Bae HJ, Baek I, Kim MS, Mo C, Cho BK. 2017. Machine vision technique for rapid measurement of soybean seed vigor. Journal of Biosystems Engineering 42:227-233.
- Lee YJ, Kim KD, Lee HS, Shin BS. 2018. Vision-based potato detection and counting system for yield monitoring. Journal of Biosystems Engineering 43:103-109.
- Leiva-Valenzuela GA, Lu R, Aguilera JM. 2013. Prediction of firmness and soluble solids content of blueberries using hyperspectral reflectance imaging. Journal of Food Engineering 115:91-98. https://doi.org/10.1016/j.jfoodeng.2012.10.001
- Lim JG, Kim GY, Mo CY, Oh KM, Kim GS, Yoo HC, Ham HH, Kim YT, Kim SM, Kim MS. 2017. Rapid and nondestructive discrimination of Fusarium Asiaticum and Fusarium Graminearum in hulled barley (Hordeum vulgare L.) using near-infrared spectroscopy. Journal of Biosystems Engineering 42:301-313.
- Liu Y, Sun X, Ouyang A. 2010. Nondestructive measurement of soluble solid content of navel orange fruit by visible-NIR spectrometric technique with PLSR and PCA-BPNN. LWT - Food Science Technology 43:602-607. https://doi.org/10.1016/j.lwt.2009.10.008
- Lu R, Peng Y. 2006. Hyperspectral scattering for assessing peach fruit firmness. Biosystems Engineering 93:161-171. https://doi.org/10.1016/j.biosystemseng.2005.11.004
- Mendoza F, Lu R, Ariana D, Cen H, Bailey B. 2011. Integrated spectral and image analysis of hyperspectral scattering data for prediction of apple fruit firmness and soluble solids content. Postharvest Biology and Technology 62:149-160.
- Mo C, Lim J, Kwon SW, Lim DK, Kim MS, Kim G, Kang J, Kwon KD, Cho BK. 2017. Hyperspectral imaging and partial least square discriminant analysis for geographical origin discrimination of white rice. Journal of Biosystems Engineering 42:293-300.
- Nogales-Bueno J, Hernandez-Hierro JM, Rodriguez-Pulido FJ, Heredia FJ. 2014. Determination of technological maturity of grapes and total phenolic compounds of grape skins in red and white cultivars during ripening by near infrared hyperspectral image: A preliminary approach. Food Chemistry 152:586-591. https://doi.org/10.1016/j.foodchem.2013.12.030
- Noh HK, Lu R. 2007. Hyperspectral laser-induced fluorescence imaging for assessing apple fruit quality. Postharvest Biology and Technology 43:193-201. https://doi.org/10.1016/j.postharvbio.2006.09.006
- Penchaiya P, Bobelyn E, Verlinden BE, Nicolai BM, Saeys W. 2009. Non-destructive measurement of firmness and soluble solids content in bell pepper using NIR spectroscopy. Journal of Food Engineering 94:267-273. https://doi.org/10.1016/j.jfoodeng.2009.03.018
- Qin J, Kim MS, Chao K, Cho BK. 2017. Raman chemical imaging technology for food and agricultural application. Journal of Biosystems Engineering 42:170-189.
- Rahman A, Kandpal LM, Lohumi S, Kim MS, Lee H, Mo C, Cho B-K. 2017. Nondestructive estimation of moisture content, pH and soluble solid contents in intact tomatoes using hyperspectral imaging. Applied Science 7:109. https://doi.org/10.3390/app7010109
- Rahman A, Kondo N, Ogawa Y, Suzuki T, Kanamori K. 2016. Determination of K value for fish flesh with ultraviolet-visible spectroscopy and interval partial least squares (iPLS) regression method. Biosystems Engineering 141:12-18. https://doi.org/10.1016/j.biosystemseng.2015.10.004
- Rahman A, Kondo N, Ogawa Y, Suzuki T, Shirataki Y, Wakita Y. 2015. Prediction of K value for fish flesh based on ultraviolet-visible spectroscopy of fish eye fluid using partial least squares regression. Computers and Electronics in Agriculture 117:149-153. https://doi.org/10.1016/j.compag.2015.07.018
- Rajkumar P, Wang N, EImasry G, Raghavan GSVSV, Gariepy Y. 2012. Studies on banana fruit quality and maturity stages using hyperspectral imaging. Journal of Food Engineering 108:194-200. https://doi.org/10.1016/j.jfoodeng.2011.05.002
- Rungpichayapichet P, Nagle M, Yuwanbun P, Khuwijitjaru P, Mahayothee B, Muller J. 2017. Prediction mapping of physicochemical properties in mango by hyperspectral imaging. Biosystems Engineering 159:109-120. https://doi.org/10.1016/j.biosystemseng.2017.04.006
- Saad A, Jha SN, Jaiswal P, Srivastava N, Helyes L. 2016. Non-destructive quality monitoring of stored tomatoes using VIS-NIR spectroscopy. Engineering in Agriculture, Environment and Food 9:158-164. https://doi.org/10.1016/j.eaef.2015.10.004
- Schmilovitch Z, Ignat T, Alchanatis V, Gatker J, Ostrovsky V, Felfoldi J. 2014. Hyperspectral imaging of intact bell peppers. Biosystems Engineering 117:83-93. https://doi.org/10.1016/j.biosystemseng.2013.07.003
- Servakaranpalayam SS. 2006. Potential applications of hyperspectral imaging for the determination of total soluble solids, water content and firmness in mango. PhD dissertation, McGill University, Canada.
- Steidle Neto AJ, Lopes DC, Pinto FAC, Zolnier S. 2017. Vis/NIR spectroscopy and chemometrics for non-destructive estimation of water and chlorophyll status in sunflower leaves. Biosystems Engineering 155:124-133. https://doi.org/10.1016/j.biosystemseng.2016.12.008
- Su W-H, Sun D-W. 2016. Comparative assessment of feature-wavelength eligibility for measurement of water binding capacity and specific gravity of tuber using diverse spectral indices stemmed from hyperspectral images. Computers and Electronics in Agriculture 130:69-82. https://doi.org/10.1016/j.compag.2016.09.015
- Tallada JG, Nagata M, Kobayashi T. 2006. Non-destructive estimation of firmness of strawberries (Fragaria x ananassa Duch.) using NIR hyperspectral imaging. Environmental Control in Biology 44:245-255. https://doi.org/10.2525/ecb.44.245
- Toledo-Martin EM, Garcia-Garcia MC, Font R, Moreno-Rojas JM, Gomez P, Salinas-Navarro M, Del Rio-Celestino M. 2016. Application of visible/near-infrared reflectance spectroscopy for predicting internal and external quality in pepper. Journal of the Science of Food and Agriculture 96:3114-3125. https://doi.org/10.1002/jsfa.7488
- Wang S, Huang M, Zhu Q. 2012. Model fusion for prediction of apple firmness using hyperspectral scattering image. Computers and Electronics in Agriculture 80:1-7. https://doi.org/10.1016/j.compag.2011.10.008
- Williams P, Norris KH. 2001. Near-infrared technology in the agricultural and food industries, 2nd ed. American Association of Cereal Chemists, MN, USA.
- Wold S, Sjostrom M, Eriksson L. 2001. PLS-regression: A basic tool of chemometrics. Chemometrics and Intelligent Laboratory Systems 58:109-130. https://doi.org/10.1016/S0169-7439(01)00155-1
- Zhu H, Chu B, Fan Y, Tao X, Yin W, He Y. 2017. Hyperspectral imaging for predicting the internal quality of kiwifruits based on variable selection algorithms and chemometric models. Scientific Reports 7:7845. https://doi.org/10.1038/s41598-017-08509-6
- Zhu N, Lin M, Nie Y, Wu D, Chen K. 2016. Study on the quantitative measurement of firmness distribution maps at the pixel level inside peach pulp. Computers and Electronics in Agriculture 130:48-56. https://doi.org/10.1016/j.compag.2016.09.018
- Zhu Q, Huang M, Zhao X, Wang S. 2013. Wavelength selection of hyperspectral scattering image using new semi-supervised affinity propagation for prediction of firmness and soluble solid content in apples. Food Analitical Methods 6:334-342. https://doi.org/10.1007/s12161-012-9442-2
피인용 문헌
- Raman spectroscopic analysis to detect olive oil mixtures in argan oil vol.46, pp.1, 2018, https://doi.org/10.7744/kjoas.20190008
- Physicochemical Quality Changes in Tomatoes during Delayed Cooling and Storage in a Controlled Chamber vol.10, pp.6, 2018, https://doi.org/10.3390/agriculture10060196
- Detection of E.coli biofilms with hyperspectral imaging and machine learning techniques vol.47, pp.3, 2018, https://doi.org/10.7744/kjoas.20200052
- Online Application of a Hyperspectral Imaging System for the Sorting of Adulterated Almonds vol.10, pp.18, 2020, https://doi.org/10.3390/app10186569
- Intact macadamia nut quality assessment using near-infrared spectroscopy and multivariate analysis vol.102, pp.None, 2021, https://doi.org/10.1016/j.jfca.2021.104033