Work

Prediction of Microbial Spoilage and Shelf-Life of Bakery Products Through Hyperspectral Imaging

Year: 2020

Type: article

Abstract: The shelf life of bakery products highly depends on the environment and it may get spoiled earlier than its expiry which results in food-borne diseases and may affect human health or may get wasted beforehand. The traditional spoilage detection methods are time-consuming and destructive in nature due to the time taken to get microbiological results. To the best of the author's knowledge, this work presents a novel method to automatically predict the microbial spoilage and detect its spatial location in baked items using Hyperspectral Imaging (HSI) range from $395-1000\,\,nm$ . A spectral preserve fusion technique has been proposed to spatially enhance the HSI images while preserving the spectral information. Furthermore, to automatically detect the spoilage, Principal Component Analysis (PCA) followed by K-means and SVM has been used. The proposed approach can detect the spoilage almost 24 hours before it started appearing or visible to a naked eye with 98.13% accuracy on test data. Furthermore, the trained model has been validated through external dataset and detected the spoilage almost a day before it started appearing visually.

Source: IEEE Access

DOI: 10.1109/access.2020.3026925

Authors: Zainab Saleem, Muhammad Hussain Khan, Muhammad Ahmad, Ahmed Sohaib, Hamail Ayaz, Manuel Mazzara

Institutions: Khwaja Fareed University of Engineering and Information Technology, National University of Computer and Emerging Sciences, Innopolis University

Language: English

Topic: Spectroscopy and Chemometric Analyses

Subfield: Analytical Chemistry

Field: Chemistry

Domain: Physical Sciences

SDGs: Zero hunger, Good health and well-being

Open Access status: gold

Estimated APC paid: $1,750