Hybrid method for accurate starch estimation in adulterated turmeric using Vis-NIR spectroscopy

References

• Aggarwal BB, Sundaram C, Malani N, Ichikawa H. 2007. Curcumin: the Indian solid gold. In: Aggarwal BB, Surh YJ, Shishodia S, editors. The molecular targets and therapeutic uses of curcumin in health and disease. Vol. 595. Springer US; p. 1–75. doi: 10.1007/978-0-387-46401-5_1.
   Google Scholar
• Amani M, Kakooei M, Moghimi A, Ghorbanian A, Ranjgar B, Mahdavi S, Davidson A, Fisette T, Rollin P, Brisco B, et al. 2020. Application of google earth engine cloud computing platform, sentinel imagery, and neural networks for crop mapping in Canada. Remote Sensing. 12(21):3561. doi: 10.3390/rs12213561.
   Web of Science ®Google Scholar
• Ashok V, Agrawal N, Durgbanshi A, Esteve-Romero J, Bose D. 2015. A novel micellar chromatographic procedure for the determination of metanil yellow in foodstuffs. Anal Methods. 7(21):9324–9330. doi: 10.1039/C5AY02377G.
   Web of Science ®Google Scholar
• Balasubrahmanyam N, Kumar KR, Anandaswamy B. 1979. Packaging and storage studies on ground turmeric (Curcuma longa L.) in flexible consumer packages. Indian Spices. 16(2):10–13. https://scholar.google.com/scholar_lookup?title=Packaging+and+storage+studies+on+ground+turmeric+%28Curcuma+longa%2C+L%29+in+flexible+consumer+packages+%5BIndia%5D.&author=Balasubrahmanyam+N.&publication_year=1979.
   Google Scholar
• Barbon Junior S, Mastelini SM, Barbon APAC, Barbin DF, Calvini R, Lopes JF, Ulrici A. 2020. Multi-target prediction of wheat flour quality parameters with near infrared spectroscopy. Inform Process Agric. 7(2):342–354. doi: 10.1016/j.inpa.2019.07.001.
   Google Scholar
• Beć K, Grabska J, Huck C. 2021. NIR spectroscopy of natural medicines supported by novel instrumentation and methods for data analysis and interpretation. J Pharm Biomed Anal. 193. doi: 10.1016/j.jpba.2020.113686.
   PubMed Web of Science ®Google Scholar
• Çetin N, Karaman K, Beyzi E, Sağlam C, Demirel B. 2021. Comparative evaluation of some quality characteristics of sunflower oilseeds (Helianthus annuus L.) through machine learning classifiers. Food Anal Methods. 14(8):1666–1681. doi: 10.1007/s12161-021-02002-7.
   Web of Science ®Google Scholar
• Chen L, Hu J, Zhang W, Zhang J, Guo P, Sun C. 2015. Simultaneous determination of nine banned azo dyes in foodstuffs and beverages by high-performance capillary electrophoresis. Food Anal Methods. 8(8):1903–1910. doi: 10.1007/s12161-014-0074-6.
   Web of Science ®Google Scholar
• Dhakal S, Chao K, Schmidt W, Qin J, Kim M, Chan D. 2016. Evaluation of turmeric powder adulterated with metanil yellow using FT-Raman and FT-IR spectroscopy. Foods. 5(4):36. doi: 10.3390/foods5020036.
   PubMedGoogle Scholar
• Dias MI, Sousa MJ, Alves RC, Ferreira ICFR. 2016. Exploring plant tissue culture to improve the production of phenolic compounds: a review. Ind Crops Prod. 82:9–22. doi: 10.1016/j.indcrop.2015.12.016.
   Web of Science ®Google Scholar
• Dixit S, Purshottam SK, Khanna SK, Das M. 2009. Surveillance of the quality of turmeric powders from city markets of India on the basis of curcumin content and the presence of extraneous colours. Food Addit Contam Part A. 26(9):1227–1231. doi: 10.1080/02652030903016586.
   Web of Science ®Google Scholar
• Fuh M. 2002. Determination of sulphonated azo dyes in food by ion-pair liquid chromatography with photodiode array and electrospray mass spectrometry detection. Talanta. 56(4):663–671. doi: 10.1016/S0039-9140(01)00625-7.
   PubMed Web of Science ®Google Scholar
• Galvin-King P, Haughey S, Elliott C. 2021. Garlic adulteration detection using NIR and FTIR spectroscopy and chemometrics. J Food Compos Anal. 96. doi: 10.1016/j.jfca.2020.103757.
   Web of Science ®Google Scholar
• Kar S, Tudu B, Jana A, Bandyopadhyay R. 2019. FT-NIR spectroscopy coupled with multivariate analysis for detection of starch adulteration in turmeric powder. Food Addit Contam Part A. 36(6):863–875. doi: 10.1080/19440049.2019.1600746.
   Web of Science ®Google Scholar
• Lanjewar MG, Morajkar PP, Parab J. 2022. Detection of tartrazine colored rice flour adulteration in turmeric from multi-spectral images on smartphone using convolutional neural network deployed on PaaS cloud. Multimed Tools Appl. 81(12):16537–16562. doi: 10.1007/s11042-022-12392-3.
   Web of Science ®Google Scholar
• Lanjewar MG, Panchbhai K, Charanarur P. 2023. Lung cancer detection from CT scans using modified DenseNet with feature selection methods and ML classifiers. Expert Syst Appl. 224. doi: 10.1016/j.eswa.2023.119961.
   PubMed Web of Science ®Google Scholar
• Lanjewar MG, Parate RK, Parab JS, Ahirwal MK, Londhe ND, Kumar A. 2022. Machine learning approach with data normalization technique for early stage detection of hypothyroidism. In: Artificial intelligence applications for health care; p. 91–108. doi: 10.1201/9781003241409-5.
   Google Scholar
• Lanjewar MG, Parate RK, Wakodikar R, Parab JS. 2023. Detection of starch in turmeric using machine learning methods. In: Kumar S, Sharma H, Balachandran K, Kim JH, Bansal JC, editors. Third congress on intelligent systems [Internet]. Vol. 613. Singapore: Springer Nature Singapore; p. 117–126. doi: 10.1007/978-981-19-9379-4_10.
   Google Scholar
• Liu H. 2014. r Value in pearson correlation. Teaching Statistics; [accessed 2023 Jul 1]. https://teachingstatistics.wordpress.com/2014/11/17/r-value-in-pearson-correlation/.
   Google Scholar
• Lohumi S, Lee S, Lee W-H, Kim MS, Mo C, Bae H, Cho B-K. 2014. Detection of starch adulteration in onion powder by FT-NIR and FT-IR spectroscopy. J Agric Food Chem. 62(38):9246–9251. doi: 10.1021/jf500574m.
   PubMed Web of Science ®Google Scholar
• Lopes JF, Ludwig L, Barbin DF, Grossmann MVE, Barbon S. 2019. Computer vision classification of barley flour based on spatial pyramid partition ensemble. Sensors. 19(13):2953. doi: 10.3390/s19132953.
   PubMed Web of Science ®Google Scholar
• Macêdo IYL, Machado FB, Ramos GS, Costa AGC, Batista RD, Filho ARG, Asquieri ER, Souza AR, Oliveira AE, Gil ES. 2021. Starch adulteration in turmeric samples through multivariate analysis with infrared spectroscopy. Food Chem. 340:127899. doi: 10.1016/j.foodchem.2020.127899.
   PubMed Web of Science ®Google Scholar
• Machine Learning Regression Evaluation Metrics. n.d. Engineering Education (EngEd) Program | Section; [accessed 2022 Dec 5]. https://www.section.io/engineering-education/machine-learning-regression-evaluation-metrics/.
   Google Scholar
• Measuring Particle Size Using Light Scattering. 2022. Chemistry LibreTexts [Internet]; [accessed 2023 Jul 1]. https://chem.libretexts.org/Bookshelves/Analytical_Chemistry/Instrumental_Analysis_(LibreTexts)/34%3A_Particle_Size_Determination/34.05%3A_Measuring_Particle_Size_Using_Light_Scattering.
   Google Scholar
• Ml | Extra Tree Classifier for Feature Selection. 2019. GeeksforGeeks. https://www.geeksforgeeks.org/ml-extra-tree-classifier-for-feature-selection/.
   Google Scholar
• Nie Z, Tremblay GF, Bélanger G, Berthiaume R, Castonguay Y, Bertrand A, Michaud R, Allard G, Han J. 2009. Near-infrared reflectance spectroscopy prediction of neutral detergent-soluble carbohydrates in timothy and alfalfa. J Dairy Sci. 92(4):1702–1711. doi: 10.3168/jds.2008-1599.
   PubMed Web of Science ®Google Scholar
• Parab J, Sequeira M, Lanjewar M, Pinto C, Naik G. 2021. Backpropagation neural network-based machine learning model for prediction of blood urea and glucose in CKD patients. IEEE J Transl Eng Health Med. 9:1–8. doi: 10.1109/JTEHM.2021.3079714.
   Google Scholar
• Peiris KHS, Wu X, Bean SR, Perez-Fajardo M, Hayes C, Yerka MK, Jagadish SVK, Ostmeyer T, Aramouni FM, Tesso T, et al. 2021. Near infrared spectroscopic evaluation of starch properties of diverse sorghum populations. Processes. 9(11):1942. doi: 10.3390/pr9111942.
   Web of Science ®Google Scholar
• Pelliccia D. 2018. Two scatter correction techniques for NIR spectroscopy in Python. https://nirpyresearch.com/two-scatter-correction-techniques-nir-spectroscopy-python/.
   Google Scholar
• Principal Component Analysis is a Powerful Instrument in Occupational Hygiene Inquiries. 2004. The annals of occupational hygiene [Internet]; [accessed 2023 Jul 7]. doi: 10.1093/annhyg/meh075.
   Google Scholar
• Ranjan R, Kumar N, Kiranmayee AH, Panchariya PC. 2021. Application of handheld NIR spectroscopy for detection of adulteration in turmeric powder. 2021 7th International Conference on Advanced Computing and Communication Systems (ICACCS) [Internet]. Coimbatore: IEEE; p. 1238–1241. doi: 10.1109/ICACCS51430.2021.9441790.
   Google Scholar
• Richardson PIC, Muhamadali H, Ellis DI, Goodacre R. 2019. Rapid quantification of the adulteration of fresh coconut water by dilution and sugars using Raman spectroscopy and chemometrics. Food Chem. 272:157–164. doi: 10.1016/j.foodchem.2018.08.038.
   PubMed Web of Science ®Google Scholar
• Rinnan Å, Berg FVD, Engelsen SB. 2009. Review of the most common pre-processing techniques for near-infrared spectra. TrAC Trends Anal Chem. 28(10):1201–1222. doi: 10.1016/j.trac.2009.07.007.
   Web of Science ®Google Scholar
• Shah R. 2017. Identification and estimation of non-permitted food colours (metanil yellow and aniline dyes) in turmeric powder by rapid color test and thin layer chromatography. WJPPS. 6:2034–2045. doi: 10.20959/wjpps20178-9867.
   Google Scholar
• Shannon M, Lafeuille J-L, Frégière-Salomon A, Lefevre S, Galvin-King P, Haughey SA, Burns DT, Shen X, Kapil A, McGrath TF, et al. 2022. The detection and determination of adulterants in turmeric using Fourier-transform infrared (FTIR) spectroscopy coupled to chemometric analysis and micro-FTIR imaging. Food Control. 139:109093. doi: 10.1016/j.foodcont.2022.109093.
   Web of Science ®Google Scholar
• Shmueli B. 2019. Matthews correlation coefficient is the best classification metric you’ve never heard of. https://towardsdatascience.com/the-best-classification-metric-youve-never-heard-of-the-matthews-correlation-coefficient-3bf50a2f3e9a
   Google Scholar
• Specimen Optical Path Length Variations. 2023. Nikon’s MicroscopyU [Internet]. https://www.microscopyu.com/tutorials/specimen-optical-path-length-variations.
   Google Scholar
• Stacking in Machine Learning. 2019. GeeksforGeeks [Internet]; [accessed 2023 Jul 1]. https://www.geeksforgeeks.org/stacking-in-machine-learning/.
   Google Scholar
• Tateo F, Bononi M. 2004. Fast determination of Sudan I by HPLC/APCI-MS in hot chilli, spices, and oven-baked foods. J Agric Food Chem. 52(4):655–658. doi: 10.1021/jf030721s.
   PubMed Web of Science ®Google Scholar
• Thangavel K, Dhivya K. 2019. Determination of curcumin, starch and moisture content in turmeric by Fourier transform near infrared spectroscopy (FT-NIR). Eng Agric Environ Food. 12(2):264–269. doi: 10.1016/j.eaef.2019.02.003.
   Google Scholar
• Williams P, Norris KH, American Association of Cereal Chemists, editors. 2001. Near-infrared technology: in the agricultural and food industries. 2nd ed. St. Paul, MN: American Association of Cereal Chemists.
   Google Scholar
• Zeaiter M, Rutledge D. 2009. Preprocessing methods. In: Comprehensive chemometrics. The Netherlands: Elsevier; p. 121–231. doi: 10.1016/B978-044452701-1.00074-0.
   Google Scholar
• Zhao S, Yin J, Zhang J, Ding X, Wu Y, Shao B. 2012. Determination of 23 dyes in chili powder and paste by high-performance liquid chromatography–electrospray ionization tandem mass spectrometry. Food Anal Methods. 5(5):1018–1026. doi: 10.1007/s12161-011-9337-7.
   Web of Science ®Google Scholar
• Zhong J, Qin X. 2016. Rapid quantitative analysis of corn starch adulteration in konjac glucomannan by chemometrics-assisted FT-NIR spectroscopy. Food Anal Methods. 9(1):61–67. doi: 10.1007/s12161-015-0176-9.
   Web of Science ®Google Scholar