Non-destructive Approach to Detect Pesticides in Fruits and Vegetables using IoT Technology

Kanmani, R.; Maheswar, R.; Sureshkumar, A.; Mugilan, A.; Praveena, V.

doi:10.1109/iccci48352.2020.9104166

Cited by 7 publications

(1 citation statement)

References 27 publications

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“…In this study, we introduce a smart method using LinkSquare, which is a handheld spectrometer that was designed and built by ourselves and commercially available for sale to the public [ 7 , 8 , 9 , 10 , 11 , 12 ], to assess food freshness ( Figure 1 ). Samples were scanned with a VIS/NIR portable spectrometer and spectral data was analyzed using the Convolutional Neural Network (CNN)-based machine learning algorithm to evaluate the freshness of the objects [ 13 , 14 ].…”

Section: Introductionmentioning

confidence: 99%

Evaluation of Salmon, Tuna, and Beef Freshness Using a Portable Spectrometer

Moon

Kim²,

et al. 2020

Sensors

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There has been strong demand for the development of an accurate but simple method to assess the freshness of food. In this study, we demonstrated a system to determine food freshness by analyzing the spectral response from a portable visible/near-infrared (VIS/NIR) spectrometer using the Convolutional Neural Network (CNN)-based machine learning algorithm. Spectral response data from salmon, tuna, and beef incubated at 25 °C were obtained every minute for 30 h and then categorized into three states of “fresh”, “likely spoiled”, and “spoiled” based on time and pH. Using the obtained spectral data, a CNN-based machine learning algorithm was built to evaluate the freshness of experimental objects. In addition, a CNN-based machine learning algorithm with a shift-invariant feature can minimize the effect of the variation caused using multiple devices in a real environment. The accuracy of the obtained machine learning model based on the spectral data in predicting the freshness was approximately 85% for salmon, 88% for tuna, and 92% for beef. Therefore, our study demonstrates the practicality of a portable spectrometer in food freshness assessment.

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