A Step Towards Miniaturized Milk Adulteration Detection System: Smartphone-Based Accurate pH Sensing Using Electrospun Halochromic Nanofibers
- 14 Downloads
Development of an economical miniaturized platform for monitoring inherent biophysical properties of milk is imperative for tamper-proof milk adulteration detection. Towards this, herein, we demonstrate synthesis and evaluation of a paper-based scalable pH sensor derived from electrospun halochromic nanofibers. The sensor manifests into three unique color-signatures corresponding to pure (6.6 ≤ pH ≤ 6.9), acidic (pH < 6.6), and basic (pH > 6.9) milk samples, enabling a colorimetric detection mechanism. In a practical prototype, color transitions on the sensor strips are captured using smartphone camera and subsequently assigned to one of the three pH ranges using an image-based classifier. Specifically, we implemented three well-known machine learning algorithms and compared their classification performances. For a standard training-to-test ratio of 80:20, support vector machines achieved nearly perfect classification with average accuracy of 99.71%.
KeywordspH sensor Nanofibers Milk adulteration detection Electrospinning Support vector machines Monte Carlo cross validation
A part of the reported work (characterization) was carried out at the Nanofabrication facility, Indian Institute of Technology Bombay under the Indian Nanoelectronics Users Program which is sponsored by Department of Electronics and Information Technology (DeitY), Ministry of Communications and Information Technology, the Government of India. Manne Shanmukh Reddy would like to thank the Ministry of Electronics and Information Technology (MeitY), the Government of India, for providing fellowship grant under Visvesvaraya PhD Scheme for Electronics and IT.
Compliance with Ethical Standards
Conflict of Interest
Suryasnata Tripathy declares that he has no conflict of interest. Manne Shanmukh Reddy declares that he has no conflict of interest. Siva Rama Krishna Vanjari declares that he has no conflict of interest. Soumya Jana declares that he has no conflict of interest. Shiv Govind Singh declares that he has no conflict of interest.
This article does not contain any studies with human participants or animals performed by any of the authors.
Informed consent is not applicable in this study.
- Ahmad SA, Ahmed M, Qadir MA, Shafiq MI, Batool N, Nosheen N, Ahmad M, Mahmood RK, Khokhar ZU (2016) Quantitation and risk assessment of chemical adulterants in milk using UHPLC coupled to photodiode array and differential refractive index detectors. Food Anal Methods 9:3367–3376CrossRefGoogle Scholar
- Bishop CM (2006) Pattern recognition and machine learning. Springer, BerlinGoogle Scholar
- Cortes C, Vapnik V (1995) Support-vector networks. Mach Learn 20:273–297Google Scholar
- Lu M, Shiau Y, Wong J, Lin R, Kravis H, Blackmon T, Pakzad T, Jen T, Cheng A, Chang J, Ong E, Sarfaraz N, Wang NS (2013) Milk spoilage: methods and practices of detecting milk quality. Food Nutr Sci 4:113–123Google Scholar
- Macek K (2008) Pareto principle in datamining: an above-average fencing algorithm. Acta Polytechnica 48:55–59Google Scholar
- Tang T, Zulkafli M (2013) Electronic tongue for fresh milk assessment- a revisit of using pH as indicator. IEEE International conference on circuits and systems, Kuala Lumpur, pp 167–171Google Scholar
- Tripathy S, Deep K, Agarwal A, Vanjari SRK, Singh SG (2016) Facile, low-cost, halochromic platform using electrospun nanofibers for milk adulteration detection. International conference on emerging electronics, Indian Institute of Technology Bombay, IndiaGoogle Scholar