Determination of Total Carotene and Vitamin C in Chili Powder (Capsicum annuum L.) Non-destructively Using Near-Infrared Spectroscopy

Nafis Khuriyati, Anggoro Cahyo Sukartiko, Moh Affan Fajar Falah, Ririn Nur Alfiani

Abstract


Chili (Capsicum annuum L.) is an important source of total carotene and vitamin C. Both substances are widely used in food processing materials, supporting a healthy immune system and medicine. However, destructive testing often obtains information about the substances, which damages the tested material and requires a relatively long analysis. Therefore, this research aims to develop calibration models of total carotene and vitamin C in chili powder for non-destructive testing using near-infrared spectroscopy. The samples consist of four groups of color, i.e., light green, dark green, red tinge, and red, with a total of 84 samples. Seventy percent of the sample was used for calibration, while the rest of the sample was used for validation. Spectra were measured using the NIRFlex N-500 instrument at a wavelength of 1000 nm to 2500 nm and analyzed with the partial least square (PLS) method using three spectral pre-treatments, which are multiplicative scatter correction (MSC), first derivative savitzky-golay, and de-trending. The accuracy and model reliability was determined by the coefficient of determination (R2) and the residual predictive deviation (RPD). The best calibration models were successfully obtained when the spectrum was processed using the first derivative savitzky-golay pre-treatment with 6 and 5 PLS factors for vitamin C and total carotene, respectively. Both models were accurate and can be potentially used for determining the total carotene and vitamin C in chili powder samples non-destructively.

Keywords


Chili powder; near-infrared spectroscopy; total carotene; vitamin C.

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References


Y. Guo, J. Bai, X. Duan, and J. Wang, “Accumulation characteristics of carotenoids and adaptive fruit color variation in ornamental pepper,†Sci. Hortic. (Amsterdam)., vol. 275, p. 109699, 2021, doi: 10.1016/j.scienta.2020.109699.

S. Sharma, V. Katoch, S. Kumar, and S. Chatterjee, “Functional relationship of vegetable colors and bioactive compounds: Implications in human health,†J. Nutr. Biochem., vol. 92, p. 108615, 2021, doi: 10.1016/j.jnutbio.2021.108615.

D. Giuffrida et al., “Evaluation of carotenoid and capsaicinoid contents in powder of red chili peppers during one year of storage,†Food Res. Int., vol. 65, pp. 163–170, 2014, doi: 10.1016/j.foodres.2014.06.019.

H. M. Berry, D. V. Rickett, C. J. Baxter, E. M. A. Enfissi, and P. D. Fraser, “Carotenoid biosynthesis and sequestration in red chilli pepper fruit and its impact on colour intensity traits,†J. Exp. Bot., vol. 70, no. 10, pp. 2637–2650, 2019, doi: 10.1093/jxb/erz086.

Z. Ye et al., “Effect of ripening and variety on the physiochemical quality and flavor of fermented Chinese chili pepper (Paojiao),†Food Chem., vol. 368, p. 130797, 2022, doi: 10.1016/j.foodchem.2021.130797.

P. Swapnil, M. Meena, S. K. Singh, U. P. Dhuldhaj, Harish, and A. Marwal, “Vital roles of carotenoids in plants and humans to deteriorate stress with its structure, biosynthesis, metabolic engineering and functional aspects,†Curr. Plant Biol., vol. 26, p. 100203, 2021, doi: 10.1016/j.cpb.2021.100203.

S. Zerres and W. Stahl, “Carotenoids in human skin,†Biochim. Biophys. Acta - Mol. Cell Biol. Lipids, vol. 1865, no. 11, p. 158588, 2020, doi: 10.1016/j.bbalip.2019.158588.

A. T. Richardson et al., “Discovery of a stable vitamin C glycoside in crab apples (Malus sylvestris),†Phytochemistry, vol. 173, p. 112297, 2020, doi: 10.1016/j.phytochem.2020.112297.

R. Lu, R. Van Beers, W. Saeys, C. Li, and H. Cen, “Measurement of optical properties of fruits and vegetables: A review,†Postharvest Biol. Technol., vol. 159, p. 111003, 2020, doi: 10.1016/j.postharvbio.2019.111003.

E. Albanell, M. Martínez, M. De Marchi, and C. L. Manuelian, “Prediction of bioactive compounds in barley by near-infrared reflectance spectroscopy (NIRS),†J. Food Compos. Anal., vol. 97, p. 103763, 2021, doi: 10.1016/j.jfca.2020.103763.

R. Sheng, W. Cheng, H. Li, S. Ali, A. A. Agyekum, and Q. Chen, “Model development for soluble solids and lycopene contents of cherry tomato at different temperatures using near-infrared spectroscopy,†Postharvest Biol. Technol., vol. 156, p. 110952, 2019, doi: 10.1016/j.postharvbio.2019.110952.

C. Li, H. Guo, B. Zong, P. He, F. Fan, and S. Gong, “Rapid and non-destructive discrimination of special-grade flat green tea using Near-infrared spectroscopy,†Spectrochim. Acta - Part A Mol. Biomol. Spectrosc., vol. 206, pp. 254–262, 2019, doi: 10.1016/j.saa.2018.07.085.

H. Lin et al., “Non-destructive detection of heavy metals in vegetable oil based on nano-chemoselective response dye combined with near-infrared spectroscopy,†Sensors Actuators, B Chem., vol. 335, no. February, p. 129716, 2021, doi: 10.1016/j.snb.2021.129716.

N. Khuriyati, A. C. Sukartiko, and R. N. Alfiani, “Non-destructive measurement of antioxidant activity and water content in chili powder (Capsicum annuum L.) using near-infrared spectroscopy,†Int. Food Res. J., vol. 29, no. 2, pp. 320–327, 2022.

F. W. Hong and K. S. Chia, “A review on recent near infrared spectroscopic measurement setups and their challenges,†Measurement, vol. 171, p. 108732, 2021, doi: 10.1016/j.measurement.2020.108732.

K. D. C. Perera, G. K. Weragoda, R. Haputhanthri, and S. K. Rodrigo, “Study of concentration dependent curcumin interaction with serum biomolecules using ATR-FTIR spectroscopy combined with Principal Component Analysis (PCA) and Partial Least Square Regression (PLS-R),†Vib. Spectrosc., vol. 116, p. 103288, 2021, doi: 10.1016/j.vibspec.2021.103288.

W. Tiaprasit and C. Sangpithukwong, Buchi NIRFlex N-500 Training Course. Bangkok: Buchi NIR Application Support, 2010.

H. Wang, X. Chu, P. Chen, J. Li, D. Liu, and Y. Xu, “Partial least squares regression residual extreme learning machine (PLSRR-ELM) calibration algorithm applied in fast determination of gasoline octane number with near-infrared spectroscopy,†Fuel, vol. 309, p. 122224, 2021, doi: 10.1016/j.fuel.2021.122224.

M. M. Oliveira, J. P. Cruz-Tirado, J. V. Roque, R. F. Teófilo, and D. F. Barbin, “Portable near-infrared spectroscopy for rapid authentication of adulterated paprika powder,†J. Food Compos. Anal., vol. 87, p. 103403, 2020, doi: 10.1016/j.jfca.2019.103403.

D. Musaddad et al., “The Potential of Chili Varieties as Powder Raw Material,†IOP Conf. Ser. Earth Environ. Sci., vol. 985, no. 1, 2022, doi: 10.1088/1755-1315/985/1/012044.

H. S. El-Mesery, H. Mao, and A. E. F. Abomohra, “Applications of non-destructive technologies for agricultural and food products quality inspection,†Sensors (Switzerland), vol. 19, no. 4, pp. 1–23, 2019, doi: 10.3390/s19040846.

B. T. Le, “Application of deep learning and near infrared spectroscopy in cereal analysis,†Vib. Spectrosc., vol. 106, p. 103009, 2020, doi: 10.1016/j.vibspec.2019.103009.

M. Zareef et al., “An Overview on the Applications of Typical Non-linear Algorithms Coupled With NIR Spectroscopy in Food Analysis,†Food Eng. Rev., vol. 12, no. 2, pp. 173–190, 2020, doi: 10.1007/s12393-020-09210-7.

N. Okubo and Y. Kurata, “Non-destructive classification analysis of green coffee beans by using near-infrared spectroscopy,†Foods, vol. 8, no. 2, 2019, doi: 10.3390/foods8020082.

G. B. Cagampang and F. M. Rodriguez, Methods of Analysis For Screening Crops of Appropriate Qualities. Los Banos, Philiphiness: Analytical Services Laboratory, Institute of Plant Breeding, University of The Philippines at Los Banos, 1980.

J. F. H. Jr, M. C. Howard, and C. Nitzl, “Assessing measurement model quality in PLS-SEM using confirmatory composite analysis,†J. Bus. Res., vol. 109, pp. 101–110, 2020, doi: 10.1016/j.jbusres.2019.11.069.

M. Sarstedt, C. M. Ringle, and J. F. Hair, Partial Least Squares Structural Equation Modeling. Switzerland: Springer International Publishing, 2021. doi: 10.1007/978-3-319-05542-8_15-2.

F. D. Anggraeni, N. Khuriyati, M. A. F. Falah, H. Nishina, K. Takayama, and N. Takahashi, “Non-destructive measurement of lycopene content in high soluble solids stored tomato (Solanum Lycopersicum Mill. cv Rinka 409),†Int. J. Adv. Sci. Eng. Inf. Technol., vol. 10, no. 6, pp. 256–2574, 2020, doi: 10.18517/ijaseit.10.6.9478.

R. Hayati, Z. Zulfahrizal, and A. A. Munawar, “Robust prediction performance of inner quality attributes in intact cocoa beans using near infrared spectroscopy and multivariate analysis,†Heliyon, vol. 7, no. 2, p. e06286, 2021, doi: 10.1016/j.heliyon.2021.e06286.

R. N. M. J. Páscoa, P. A. L. S. Porto, A. L. Cerdeira, and J. A. Lopes, “The application of near infrared spectroscopy to wine analysis: An innovative approach using lyophilization to remove water bands interference,†Talanta, vol. 214, p. 120852, 2020, doi: 10.1016/j.talanta.2020.120852.

M. A. Calegari, B. B. Ayres, L. M. dos Santos Tonial, S. M. de Alencar, and T. L. C. Oldoni, “Fourier transform near infrared spectroscopy as a tool for predicting antioxidant activity of propolis,†J. King Saud Univ. - Sci., vol. 32, no. 1, pp. 784–790, 2019, doi: 10.1016/j.jksus.2019.02.006.

O. O. Olarewaju et al., “Model development for non-destructive determination of rind biochemical properties of ‘Marsh’ grapefruit using visible to near-infrared spectroscopy and chemometrics,†Spectrochim. Acta - Part A Mol. Biomol. Spectrosc., vol. 209, pp. 62–69, 2019, doi: 10.1016/j.saa.2018.10.027.

X. Jun-fang, L. Xiao-yu, L. Pei-wu, M. Qian, and D. Xiao-xia, “Application of Wavelet Transform in the Prediction of Navel Orange Vitamin C Content by Near-Infrared Spectroscopy,†Agric. Sci. China, vol. 6, no. 9, pp. 1067–1073, 2007, doi: 10.1016/S1671-2927(07)60148-5.

P. Sodata and J. Peerapattana, “Application of near infrared spectroscopy with chemometrics for qualitative and quantitative dental caries assessment,†Vib. Spectrosc., vol. 111, p. 103170, 2020, doi: 10.1016/j.vibspec.2020.103170.

P. Sampaio, A. Soares, A. Castanho, A. S. Almeida, J. Oliveira, and C. Brites, “Dataset of Near-infrared spectroscopy measurement for amylose determination using PLS algorithms,†Data Br., vol. 15, pp. 389–396, 2017, doi: 10.1016/j.dib.2017.09.077.

L. R. Costa, G. H. D. Tonoli, F. R. Milagres, and P. R. G. Hein, “Artificial neural network and partial least square regressions for rapid estimation of cellulose pulp dryness based on near infrared spectroscopic data,†Carbohydr. Polym., vol. 224, p. 115186, 2019, doi: 10.1016/j.carbpol.2019.115186.

J. Jiang et al., “Non-destructive quality assessment of chili peppers using near-infrared hyperspectral imaging combined with multivariate analysis,†Postharvest Biol. Technol., vol. 146, pp. 147–154, 2018, doi: 10.1016/j.postharvbio.2018.09.003.




DOI: http://dx.doi.org/10.18517/ijaseit.13.1.16800

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