Low cost smart phone diagnostics for food using paper-based colorimetric sensor arrays

Chen, Yu; Fu, Guoqing; Zilberman, Yoram; Ruan, Wenjie; Ameri, Shideh Kabiri; Zhang, Yu Shrike; Miller, Eric L.; Sonkusale, Sameer

doi:10.1016/j.foodcont.2017.07.003

Cited by 116 publications

(64 citation statements)

References 34 publications

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“…Porphyrins and metalloporphyrins are nearly ideal for the detection of metal-ligating vapors, because of their open coordination sites for axial ligation, their large spectral shifts upon ligand binding, and their intense coloration. Common pH indicator dyes change color in response to changes in the proton (Brønsted) acidity or basicity of their environment [28].Besides, Nile red has been widely studied for sensing the low-concentration VOC benefits due to its solvatochromic properties for changing optical characteristics and its exceptional stability with regard to temperature and watervapor [33,34].…”

Section: E-nose Based On Colorimetric Sensorsmentioning

confidence: 99%

Detection of Beef Adulterated with Pork Using a Low-Cost Electronic Nose Based on Colorimetric Sensors

Han

Huang

Aheto

et al. 2020

Foods

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The present study was aimed at developing a low-cost but rapid technique for qualitative and quantitative detection of beef adulterated with pork. An electronic nose based on colorimetric sensors was proposed. The fresh beef rib steaks and streaky pork were purchased and used from the local agricultural market in Suzhou, China. The minced beef was mixed with pork ranging at levels from 0%~100% by weight at increments of 20%. Protein, fat, and ash content were measured for validation of the differences between the pure beef and pork used in basic chemical compositions. Fisher linear discriminant analysis (Fisher LDA) and extreme learning machine (ELM) were utilized comparatively for identification of the ground pure beef, beef–pork mixtures, and pure pork. Back propagation-artificial neural network (BP-ANN) models were built for prediction of the adulteration levels. Results revealed that the ELM model built was superior to the Fisher LDA model with higher identification rates of 91.27% and 87.5% in the training and prediction sets respectively. Regarding the adulteration level prediction, the correlation coefficient and the root mean square error were 0.85 and 0.147 respectively in the prediction set of the BP-ANN model built. This suggests, from all the results, that the low-cost electronic nose based on colorimetric sensors coupled with chemometrics has a great potential in rapid detection of beef adulterated with pork.

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Section: E-nose Based On Colorimetric Sensorsmentioning

confidence: 99%

Detection of Beef Adulterated with Pork Using a Low-Cost Electronic Nose Based on Colorimetric Sensors

Han

Huang

Aheto

et al. 2020

Foods

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“…The model for discriminating the different Pseudomonas species was built by back‐propagation artificial neural network, and the results revealed that fusion‐based model has superior discrimination capability compared with that based on the 2 techniques individually. Recently, Chen et al repurposed the food's barcode as a colorimetric sensor array in order to monitor the condition of packaged food and has the potential to indicate the end‐of‐life of the perishable food products. A smart phone camera was integrated to observe the color changes from the sensor barcode for estimating the ageing and quality of food products quantitatively.…”

Section: Sensors Used In Meat Industrymentioning

confidence: 99%

An overview of smart packaging technologies for monitoring safety and quality of meat and meat products

et al. 2018

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The food packaging sector has experienced much development since its inception. In the past few decades, innovations in packaging sector have led to the development of smart packaging (SP) systems that carve a niche in a highly competitive food industry. SP systems have great potential for improving the shelf‐life, and safety of food products apart from their basic roles of protecting the products against unwanted biological, chemical, and physical damage and keeping them clean. Indicators and sensors, SP components, are used for real‐time monitoring of meat quality and subsequently inform the retailers and consumers about the freshness, microbiological, temperature, and shelf life status of the products. Barcodes and radio‐frequency identification tags are employed in meat packaging for real‐time information about the authenticity, and traceability of the products in the supply chain. Recently, innovations in SP technologies resulted in fast, sensitive, and effective detection, sensing, and record keeping of freshness, microbiological, and shelf life status of meat and meat products. The SP system shows promise for extensive utilization in the meat industry in response to the consumer appreciation for safe, and quality meat products, as well as their waste reduction notions. This paper gives an updated overview of ongoing scientific research, and recent technological advances that offer the perspectives of developing smart meat packaging systems that are capable of monitoring the physical, microbial, and chemical changes of the package contents from producer to the point of sale and even beyond, and remediating potential adverse reactions.

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“…The biosensor systems are also developed to test for chemicals that indicate spoilage. However, regardless of the high accuracy of these systems, they are neither cost effective nor user friendly for consumers to directly monitor the food quality at the point of consumption [ 157 ].…”

Section: Application In Food Zoology and Botanymentioning

confidence: 99%

“…As an alternative to direct readout of the information from the sensor with laboratory equipment or smartphones, colorimetric sensing array arranged in a barcode/QR code can be processed by a mobile phone camera [ 157 ]. In this sensor, a low-cost paper substrate is impregnated with cross-reactive vapor sensitive dyes encapsulated in resin microbeads ( Figure 4 a).…”

Section: Application In Food Zoology and Botanymentioning

confidence: 99%

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Real Time Analysis of Bioanalytes in Healthcare, Food, Zoology and Botany

Wang

Ramnarayanan

Cheng

2017

Sensors

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The growing demand for real time analysis of bioanalytes has spurred development in the field of wearable technology to offer non-invasive data collection at a low cost. The manufacturing processes for creating these sensing systems vary significantly by the material used, the type of sensors needed and the subject of study as well. The methods predominantly involve stretchable electronic sensors to monitor targets and transmit data mainly through flexible wires or short-range wireless communication devices. Capable of conformal contact, the application of wearable technology goes beyond the healthcare to fields of food, zoology and botany. With a brief review of wearable technology and its applications to various fields, we believe this mini review would be of interest to the reader in broad fields of materials, sensor development and areas where wearable sensors can provide data that are not available elsewhere.

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Low cost smart phone diagnostics for food using paper-based colorimetric sensor arrays

Cited by 116 publications

References 34 publications

Detection of Beef Adulterated with Pork Using a Low-Cost Electronic Nose Based on Colorimetric Sensors

Detection of Beef Adulterated with Pork Using a Low-Cost Electronic Nose Based on Colorimetric Sensors

An overview of smart packaging technologies for monitoring safety and quality of meat and meat products

Real Time Analysis of Bioanalytes in Healthcare, Food, Zoology and Botany

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