Wavelengths selection based on genetic algorithm (GA) and successive projections algorithms (SPA) combine with PLS regression for determination the soluble solids content in Nam-DokMai mangoes based on near infrared spectroscopy

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Kanvisit Maraphum
Artjima Ounkaew
Pornnapa Kasemsiri
Salim Hiziroglu
Jetsada Posom


The objective of this work was to search for an optimal wavelength selection for near infrared (NIR) spectroscopy for quality measurement of Nam-Dokmai mangoes. In this study, NIR spectroscopy has been applied to grading management systems for commercial mangoes export. Near infrared spectra were collected using a near infrared instrument incorporating a wavelength region of 860-1760 nm. Genetic algorithm (GA) and successive projections algorithms (SPA) was employed for selecting the spectra wavelengths. The selected wavelengths were also used to generate the prediction models via partial least square (PLS) regression. The optimal pretreatment was obtained from the second derivative. The model of full wavelengths rendered effective the best performance with r2 of 0.66-0.74, RMSEP of 0.72-0.80 °Brix and RPD equal to 1.8-2.0. The SPA-PLS resulted in values of r2, RMSEP and RPD were 0.43-0.70, 0.77-1.01°Brix and 1.4-1.9, respectively. Meanwhile, the result of GA-PLS performed efficiency with r2, RMSEP and RPD were 0.52-0.72, 0.74-0.96°Brix and 1.5-1.9, respectively. The outcome, the GA-PLS model (50 variables) is suitable for use in the measuring soluble solids content (SSC) in mangoes. This model could be used as screening purpose. It also was not different significantly when compared to the best model. Hence, authors suggested that the prediction model by GA-PLS with 50 variables can be effectively used for evaluating SSC in mangoes.


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Maraphum, K., Ounkaew, A., Kasemsiri, P., Hiziroglu, S., & Posom, J. (2021). Wavelengths selection based on genetic algorithm (GA) and successive projections algorithms (SPA) combine with PLS regression for determination the soluble solids content in Nam-DokMai mangoes based on near infrared spectroscopy. Engineering and Applied Science Research, 49(1), 119-126. Retrieved from https://ph01.tci-thaijo.org/index.php/easr/article/view/245217


Produce Report Partners. Mango Nam Dok Mai: A global favorite that must be tried, at least once in a lifetime [Internet]. 2020 [cited 2020 May 24]. Available from: https://www.producereport.com/article/mango-nam-dok-mai-global-favorite-must-be-tried-least-once-lifetime.

Tropical Green. Golden Namdokmai Mango [Internet]. 2021 [cited 2021 Jan 25]. Available from: https://www.tgfresh.com/product/thai-fresh-mango/.

Chomchalow N, Na Songkhla P. Thai mango export: a slow-but-sustainable development. AU J T. 2008;12(1):1-8.

Sombatpraiwan S, Tipyavimol T, Treeamnuk K. Factors related to ripening-stages of Nam Dok-mai mango after harvesting. J Thai Soc Agr Eng. 2012;18(1):52-8.

Williams PC. Implementation of near-infrared technology. In: Williams PC, Norris KH, editors. Near-infrared technology in the agricultural and food industries. Saint Paul: AACC Inc; 2001. p. 145-71.

Lammertyn J, Nicolai BM, De Smedt V, De Baerdemaeker J. Non-destructive measurement of acidity, soluble solids and firmness of Jonagold apples using NIR-spectroscopy. Trans ASAE. 1998;41(4):1089-94.

Maraphum K, Chuan-Udom S, Saengprachatanarug K, Wongpichet S, Posom J, Phuphaphud A, et al. Effect of waxy material and measurement position of a sugarcane stalk on the rapid determination of Pol value using a portable near infrared instrument. J Near Infrared Spectrosc. 2018;26(5):287-96.

Phuphaphud A, Saengprachatanarug K, Posom J, Maraphum K, Taira E. Prediction of the fibre content of sugarcane stalk by direct scanning using visible-shortwave near infrared spectroscopy. Vib Spectrosc. 2019;101:71-80.

Kawano S, Watanabe H, Iwamoto M. Determination of sugar content in intact peaches by near infrared spectroscopy with fiber optics in interactance mode. J JPN Soc Hortic Sci. 1992;61(2):445-51.

Kawano S, Fujiwara T, Iwamoto M. Nondestructive determination of sugar content in satsuma mandarin using near infrared (NIR) transmittance. J JPN Soc Hortic Sci. 1993;62(2):465-70.

Miyamoto K, Kitano Y. Non-destructive determination of sugar content in satsuma mandarin fruit by near infrared transmittance spectroscopy. J Near Infrared Spectrosc. 1995;3(4):227-37.

Temma T, Hanamatsu K, Shinoki F. Development of a portable near infrared sugar-measuring instrument. J Near Infrared Spectrosc. 2002;10(1):77-83.

Taira E, Nakamura S, Hiyane R, Honda H, Ueno M. Development of a nondestructive measurement system for mango fruit using near infrared spectroscopy. Eng Appl Sci Res. 2017;44(3):189-92.

Jha SN, Chopra S, Kingsly ARP. Determination of sweetness of intact mango using visual spectral analysis. Biosyst Eng. 2005;91(2):157-61.

Pitak L, Sirisomboon P, Saengprachatanarug K, Wongpichet S, Posom J. Rapid elemental composition measurement of commercial pellets using line-scan hyperspectral imaging analysis. Energy. 2021;220:119698.

Liu D, Sun DW, Zeng XA. Recent advances in wavelength selection techniques for hyperspectral image processing in the food industry. Food Bioprocess Technol. 2014;7(2):307-23.

Posom J, Sirisomboon P. Evaluation of lower heating value and elemental composition of bamboo using near infrared spectroscopy. Energy. 2017;121:147-58.

Dardenne P. Some considerations about NIR spectroscopy: closing speech at NIR-2009. NIR News. 2010;21(1):8-14.

Araujo MCU, Bezerra STC, Galvao RKH, Yoneyama T, Chame HC, Visani V. The successive projections algorithm for variable selection in spectroscopic multicomponent analysis. Chemometr Intell Lab Syst. 2001;57(2):65-73.

Williams P, Norris K. Near-infrared technology in the agricultural and food industries. 2nd ed. Saint Paul: Amer Assn of Cereal Chemists; 1987.

Chang CW, Laird DA, Mausbach MJ, Hurburgh CR. Near-infrared reflectance spectroscopy-principal components regression analyses of soil properties. Soil Sci Soc Am J. 2001;65(2):480-90.

Wang J, Wang J, Chen Z, Han D. Development of multi-cultivar models for predicting the soluble solid content and firmness of European pear (Pyrus communis L.) using portable vis-NIR spectroscopy. Postharvest Biol Technol. 2017;129:143-51.

Ncama K, Opara UL, Tesfay S, Fawole OA, Magwaza LS. Application of Vis/NIR spectroscopy for predicting sweetness and fla- vour parameters of ‘Valencia’ orange (Citrus sinensis) and ‘Star Ruby’ grapefruit (Citrus x paradisi Macfad). J Food Eng. 2017;193:86-94.

Maraphum K, Saengprachatanarug K, Wongpichet S, Phuphaphud A, Posom J. In-field measurement of starch content of cassava tubers using handheld vis-near infrared spectroscopy implemented for breeding programmes. Comput Electron Agr. 2020;175(1):105607.

Maraphum K, Saengprachatanarug K, Aparatana K, Izumikawa Y, Taira E. Spatial mapping of brix and moisture content using hyperspectral imaging system in sugarcane stalk. J Near Infrared Spectrosc. 2020;28(4):167-74.

Osborne BG, Fearn T, Hindle PH. Practical NIR spectroscopy with applications in food and beverage analysis. UK: Addison-Wesley Longman Ltd; 1993.

Osborne BG, Fearn T. Near infrared spectroscopy in food analysis. London: Longman Scientific and Technical; 1986.

Workman J, Weyer L. Practical guide to interpretive near-infrared spec- troscopy. Boca Raton: CRC Press; 2007.

Posom J, Klaprachan J, Rattanasopa K, Sirisomboon P, Saengprachatanarug K, Wongpichet S. Predicting marian plum fruit quality without environmental condition impact by handheld visible-near-infrared spectroscopy. ACS Omega. 2020;5(43):27909-21.

Williams P. Near-infrared technology? Getting the best out of light edition: a short course in the practical implementation of near-infrared spectroscopy for the user. 2nd ed. Nanaimo: PDK Projects Inc; 2007.

Nagle M, Mahayothee B, Rungpichayapichet P, Janjai S, Muller J. Effect of irrigation on near-infrared (NIR) based prediction of mango maturity. Sci Hortic. 2010;125(4):771-4.

Sharma S, Sirisomboon P, Pornchaloempong P. Application of a Vis-NIR spectroscopic technique to measure the total soluble solids content of intact mangoes in motion on a belt conveyor. Hort J. 2020;89(5):545-52.

Rungpichayapichet P, Mahayothee B, Nagle M, Khuwijitjaru P, Muller J. Robust NIRS models for non-destructive prediction of postharvest fruit ripeness and quality in mango. Postharvest Biol Technol. 2016;111:31-40.