Non-destructive detection of foreign contaminants in toast bread with near infrared spectroscopy and computer vision tec
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ORIGINAL PAPER
Non‑destructive detection of foreign contaminants in toast bread with near infrared spectroscopy and computer vision techniques Jifan Yin1,2 · Saima Hameed1,2 · Lijuan Xie1,2 · Yibin Ying1,2 Received: 9 April 2020 / Accepted: 26 August 2020 © Springer Science+Business Media, LLC, part of Springer Nature 2020
Abstract Non-destructive detection methods to identify food foreign contaminants meet the development of the food industry. Near infrared (NIR) spectroscopy and computer vision (CV) have been considerably intriguing due to the advantages of safety and rapidity. In this study, foreign contaminants, including metallic iron, polypropylene plastic, and hair in bread, were identified based on these two techniques. In NIR spectroscopy, the effectiveness of distance match and discriminant analysis methods combined with different spectral pretreatments was compared. It showed that the accuracy was 98%, 94%, 91%, and F-score was 0.97, 0.93, and 0.91 in the validation set for detecting foreign contaminants aforementioned when discriminant analysis combined with Savitzky-Golay smoothing was used. In CV, deep learning based on the modified U-net has applied to segment these contaminants on the surface of the bread; the accuracy of the test set was 95%, 93%, and 92%, respectively. Based on the results, it can be concluded that NIR spectroscopy and CV are both an operational way to detect foreign contaminants in bread. And these two techniques could be combined to apply in on-line detection further. Keywords Bread · Foreign contaminants · Near-infrared spectroscopy · Deep learning · U-net
Introduction Bread is one of the indispensable components of diets around the world, especially in Europe [1]. Its quality is of paramount importance during the process of bread production. Bread could be easily contaminated by foreign contaminants. Typical contaminants include metal, plastic, hair, etc., which pose a threat to human health as well as the company’s fame [2]. Equipment, facilities, and cleaning tools are the source of foreign metal contaminants in packaged food. The primary source of plastic is flour packaging bags. While hair is brought in by the workers. Besides, food safety attracts the public’s extensive attention, which is of vital importance for the food industry [3–5]. Therefore, it is * Lijuan Xie [email protected] 1
College of Biosystems Engineering and Food Science, Zhejiang University, 866 Yuhangtang Road, Hangzhou 310058, People’s Republic of China
Key Laboratory of On‑Site Processing Equipment for Agricultural Products, Ministry of Agriculture and Rural Affairs, Zhejiang University, Hangzhou 310058, People’s Republic of China
2
essential to find an effective method to detect foreign contaminants in bread. Manual sorting is still widely utilized in regions where labor rates remain low or industry is in small scale [6]. However, with the increment of the cost of labor and drawbacks to time-consuming with low efficiency and unverifiable accuracy, so automated techniques are needed. Cur
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